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Publications 2024 - Journal papers (185)

(135)

  1. LIFEx-texture: Liping Yang, Hongchao Ding, Xing Gao, Yuchao Xu, Shichuan Xu and Kezheng Wang. Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics. Yang et al. BMC Cancer (2024) 24:1316 https://doi.org/10.1186/s12885-024-13031-w
  2. LIFEx-texture: Daniel Stocker, Stefanie Hectors, Brett Marinelli, Guillermo Carbonell, Octavia Bane, Miriam Hulkower, Paul Kennedy, Weiping Ma, Sara Lewis, Edward Kim, Pei Wang, Bachir Taouli. Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI‑based machine learning approach. Abdominal Radiology, accepted: 17 September 2024
    https://doi.org/10.1007/s00261-024-04606-z
  3. LIFEx-texture: Kallos-Balogh P, Vas NF, Toth Z, Szakall S, Szabo P, Garai I, et al. (2024) Multicentric study on the reproducibility and robustness of PET-based radiomics features with a realistic activity painting phantom. PLoS ONE 19(10): e0309540. https://doi.org/10.1371/journal.pone.0309540
  4. LIFEx-texture: Barioni, E.D.; Lopes, S.L.P.d.C.; Silvestre, P.R.; Yasuda, C.L.; Costa, A.L.F. Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions. J. Imaging 2024, 10, 263. https://doi.org/10.3390/jimaging10110263
  5. LIFEx-texture: Gelardi, F.; Cavinato, L.; De Sanctis, R.; Ninatti, G.; Tiberio, P.; Rodari, M.; Zambelli, A.; Santoro, A.; Fernandes, B.; Chiti, A.; et al. The Predictive Role of Radiomics in Breast Cancer Patients Imaged by [18F]FDG PET: Preliminary Results from a Prospective Cohort. Diagnostics 2024, 14, 2312. https://doi.org/10.3390/diagnostics14202312
  6. LIFEx-texture: Michel Destine and Alain Seret. Quantitative assessment of kidney split function and mean transit time in healthy patients using dynamic 18 F‑FDG PET/MRI studies with denoising and deconvolution methods making use of Legendre polynomials. Destine and Seret EJNMMI Reports (2024) 8:33. https://doi.org/10.1186/s41824‑024‑00221‑9
  7. LIFEx-texture: Soleymani Y, Valibeiglou Z, Fazel Ghaziani M, Jahanshahi A, Khezerloo D. Radiomics reproducibility in computed tomography through changes of ROI size, resolution, and hounsfield unit: A phantom study. Radiography (Lond). 2024 Oct 17;30(6):1629-1636. https://doi.org/10.1016/j.radi.2024.10.003. Epub ahead of print. PMID: 39423630.
  8. LIFEx-texture: Rajgor AD, Kui C, McQueen A, Cowley J, Gillespie C, Mill A, Rushton S, Obara B, Bigirumurame T, Kallas K, O'Hara J, Aboagye E, Hamilton DW. Computed tomography-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: a pilot study. J Laryngol Otol. 2024 Jun;138(6):685-691. https://doi.org/10.1017/S0022215123002372. Epub 2023 Dec 14. PMID: 38095096; PMCID: PMC11096831.
  9. LIFEx-texture: Mahmoud M, Lin KH, Lee RC, Liu CA. Assessment of Y-90 Radioembolization Treatment Response for Hepatocellular Carcinoma Cases Using MRI Radiomics. Mol Imaging Radionucl Ther. 2024 Oct 7;33(3):156-166. https://doi.org/10.4274/mirt.galenos.2024.59365. PMID: 39373149.
  10. LIFEx-texture: Ran CQ, Su Y, Li J, Wu K, Liu ZL, Yang Y, Zhang MX, Yuan G, Yu XF, He WT. Epicardial adipose tissue volume highly correlates with left ventricular diastolic dysfunction in endogenous Cushing's syndrome. Ann Med. 2024 Dec;56(1):2387302. https://doi.org/10.1080/07853890.2024.2387302. Epub 2024 Aug 5. PMID: 39101236; PMCID: PMC11302473.
  11. LIFEx-texture: Mahmoud M, Lin K, Lee R, Liu C. Treatment Response for Hepatocellular Carcinoma Cases Using MRI Radiomics. Mol Imaging Radionucl Ther. 2024 Oct;33(3):156-166. https://doi.org/10.4274/mirt.galenos.2024.59365
  12. LIFEx-texture: Crimì, F., Turatto, F., D’Alessandro, C. et al. Texture analysis can predict response to etoposide-doxorubicin-cisplatin in patients with adrenocortical carcinoma. J Endocrinol Invest (2024). https://doi.org/10.1007/s40618-024-02476-2
  13. LIFEx-main: Zhang, X., Xiang, Z., Wang, F. et al. Feasibility of shortening scan duration of 18F-FDG myocardial metabolism imaging using a total-body PET/CT scanner. EJNMMI Phys 11, 83 (2024). https://doi.org/10.1186/s40658-024-00689-1
  14. LIFEx-MTV: Hong, Sp., Lee, S.M., Yoo, I.D. et al. Clinical value of SUVpeak-to-tumor centroid distance on FDG PET/CT for predicting neoadjuvant chemotherapy response in patients with breast cancer. Cancer Imaging 24, 136 (2024). https://doi.org/10.1186/s40644-024-00787-4
  15. LIFEx-MTV: Seban, RD., Champion, L., De Moura, A. et al. Pre-treatment [18F]FDG PET/CT biomarkers for the prediction of antibody-drug conjugates efficacy in metastatic breast cancer. Eur J Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s00259-024-06929-x
  16. LIFEx-MTV: F. Kleiburg, L.F. de Geus-Oei, S.A.C. Luelmo, R. Spijkerman, J.J. Goeman, F.A.J. Toonen, F. Smit, T. van der Hulle, L. Heijmen, PSMA PET/CT for treatment response evaluation at predefined time points is superior to PSA response for predicting survival in metastatic castration-resistant prostate cancer patients, European Journal of Radiology (2024), doi: https://doi.org/10.1016/j.ejrad.2024.111774
  17. LIFEx-texture: Ogün BülBül, Demet Nak, Sibel Göksel. Prediction of lesion-based treatment response after two cycles of Lu-177 PSMA treatment in metastatic castration-resistant prostate cancer using machine learning. Urol Int 1–12. https://doi.org/10.1159/000541628
  18. LIFEx-texture: Li, Jiatong; Cui, Nan; Wang, Yanmei; Li, Wei; Jiang, Zhiyun; Liu, Wei; Guo, Chenxu; Wang, Kezheng. Prediction of preoperative lymph-vascular space invasion and survival outcomes of cervical squamous cell carcinoma by utilizing 18F-FDG PET/CT imaging at early stage. Nuclear Medicine Communications ():10.1097/MNM.0000000000001909, October 02, 2024. https://doi.org/10.1097/MNM.0000000000001909
  19. LIFEx-texture: Yang, F., Wang, C., Shen, J. et al. End-to-end [18F]PSMA-1007 PET/CT radiomics-based pipeline for predicting ISUP grade group in prostate cancer. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04601-4
  20. LIFEx-texture: Zhang, Y., Huang, W., Jiao, H. et al. PET radiomics in lung cancer: advances and translational challenges. EJNMMI Phys 11, 81 (2024). https://doi.org/10.1186/s40658-024-00685-5
  21. LIFEx-texture: Pizzuto, D.A., Guerreri, M., Zamboglou, C. et al. The clinical predictive value of radiomic features from [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET in patients with prostate cancer: a preliminary comparative study. Clin Transl Imaging (2024). https://doi.org/10.1007/s40336-024-00659-8
  22. LIFEx-texture: Toshinari Horie, Motohiro Fujiwara, Yuma Waseda, Hajime Tanaka, Soichiro Yoshida and Yasuhisa Fujii. Radiomics analysis using non-contrast computed tomography for predicting high-dependency unit admission in patients with acute pyelonephritis. International Journal of Urology 2024. http://doi.org/10.1111/iju.15591
  23. LIFEx-texture: Khangembam B C, Jaleel J, Roy A, et al. (September 16, 2024) A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning. Cureus 16(9): e69532. https://doi.org/10.7759/cureus.69532
  24. LIFEx-texture: Wang, N., Dai, M., Jing, F., Liu, Y., Zhao, Y., Zhang, Z., … Zhao, X. (2024). Value of 18F-FDG PET/CT-based radiomics features for differentiating primary lung cancer and solitary lung metastasis in patients with colorectal adenocarcinoma. International Journal of Radiation Biology, 1–9. https://doi.org/10.1080/09553002.2024.2404465
  25. LIFEx-texture: Li, C., Lu, X., Zhang, F. et al. Neuroblastoma with high ASPM reveals pronounced heterogeneity and poor prognosis. BMC Cancer 24, 1151 (2024). https://doi.org/10.1186/s12885-024-12912-4
  26. LIFEx-MTV: Ali Abdulhasan Kadhim, Peyman Sheikhzadeh, Mehrshad Abbasi, Saeed Afshar, Nasim Vahidfar, Shirin Asidkar, Mehrnoosh Karimipourfard, Zahra Valibeiglou, Mohammad Reza. A Investigating Patient-Specific Absorbed Dose Assessment for Copper-64 PET/CT. Frontiers in Biomedical Technologies. Vol. 12, No. 4. https://fbt.tums.ac.ir/index.php/fbt/article/download/1058/436
  27. LIFEx-texture: Nakajo, M., Hirahara, D., Jinguji, M. et al. Applying deep learning-based ensemble model to [18F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases. Jpn J Radiol (2024). https://doi.org/10.1007/s11604-024-01649-6
  28. LIFEx-texture: Lee, J.W.; Won, Y.K.; Ahn, H.; Lee, J.E.; Han, S.W.; Kim, S.Y.; Jo, I.Y.; Lee, S.M. Peritumoral Adipose Tissue Features Derived from [18F]fluoro-2-deoxy-2-D-glucose Positron Emission Tomography/ Computed Tomography as Predictors for Response to Neoadjuvant Chemotherapy in Breast Cancer Patients. J. Pers. Med. 2024, 14, 952. https://doi.org/10.3390/jpm14090952
  29. LIFEx-texture: Fenglian Jing, Xinchao Zhang, Yunuan Liu, & al. Baseline 18F-FDG PET Radiomics Predicting Therapeutic Efficacy of Diffuse Large B-Cell Lymphoma after R-CHOP (-Like) Therapy. Cancer Biotherapy and Radiopharmaceuticals. 4 September 2024. https://doi.org/10.1089/cbr.2024.0115
  30. LIFEx-texture: Chen, Yu-Hung; Lue, Kun-Han; Chu, Sung-Chao; Lin, Chih-Bin; Liu, Shu-Hsin. The value of 18F-fluorodeoxyglucose positron emission tomography-based radiomics in non-small cell lung cancer. Tzu Chi Medical Journal ():10.4103/tcmj.tcmj_124_24, September 03, 2024. | https://doi.org/10.4103/tcmj.tcmj_124_24
  31. LIFEx-texture: Fereshteh Yousefirizi, Annudesh Liyanage, Ivan S. Klyuzhin, Arman Rahmim. From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing. Journal of Medical Imaging and Radiation Sciences, Volume 55, Issue 4, 2024, 101745, ISSN 1939-8654, https://doi.org/10.1016/j.jmir.2024.101745
  32. LIFEx-texture: C. Masson-Grehaigne, M. Lafon, J. Palussiere et al., Single- and multi-site radiomics may improve overall survival prediction for patients with metastatic lung adenocarcinoma, Diagnostic and Interventional  Imaging (2024), https://doi.org/10.1016/j.diii.2024.07.005 Diagnostic and Interventional Imaging 000 (2024) 1−14
  33. LIFEx-texture: Toniolo, A.; Agostini, E.; Ceccato, F.; Tizianel, I.; Cabrelle, G.; Lupi, A.; Pepe, A.; Campi, C.; Quaia, E.; Crimì, F. Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation? Curr. Oncol. 2024, 31, 4917–4926. https://doi.org/10.3390/curroncol31090364
  34. LIFEx-texture: Seyed Ali Mirshahvalad, Adriano B. Dias, Sangeet Ghai, Claudia Ortega, Nathan Perlis, Alejandro Berlin, Lisa Avery, Theodorus van der Kwast, Ur Metser, Patrick Veit-Haibach. Value of Dynamic Contrast-Enhanced MRI for Grade Group Prediction in Prostate Cancer: A Radiomics Pilot Study, Academic Radiology, 2024, ISSN 1076-6332. https://doi.org/10.1016/j.acra.2024.08.004
  35. LIFEx-texture: Yusuke Kawashima, Aya Hagimoto, Hiroshi Abe, Masaaki Miyakoshi, Yoshihiro Kawabata, Hiroko Indo, Tatsuro Tanaka. Using texture analysis of ultrasonography images of neck lymph nodes to differentiate metastasis to non-metastasis in oral maxillary gingival squamous cell carcinoma. Journal of Oral and Maxillofacial Surgery, Medicine, and Pathology, 2024. ISSN 2212-5558. https://doi.org/10.1016/j.ajoms.2024.07.013
  36. LIFEx-texture: Kote, Rutuja; Ravina, Mudalsha; Goyal, Harish; Mohanty, Debajyoti; Gupta, Rakesh; Shukla, Arvind Kumar; Reddy, Moulish; Prasanth, Pratheek N. Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients. Nuclear Medicine Communications. https://doi.org/10.1097/MNM.0000000000001885, August 08, 2024
  37. LIFEx-texture: Seda Gülbahar Ateş, Bedriye Büşra Demirel, Esra Kekilli, Erdem Öztürk, Gülin Uçmak. Primary tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET/CT for the prediction of biochemical recurrence in prostate cancer. Revista Española de Medicina Nuclear e Imagen Molecular (English Edition), 2024, 500032, ISSN 2253-8089, https://doi.org/10.1016/j.remnie.2024.500032
  38. LIFEx-texture: Jing, F., Zhang, X., Liu, Y. et al. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma with extranodal involvement. Clin Transl Oncol (2024). https://doi.org/10.1007/s12094-024-03633-y
  39. LIFEx-texture: Zhi, H., Xiang, Y., Chen, C. et al. Development and validation of a machine learning-based 18F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival. Cancer Imaging 24, 99 (2024). https://doi.org/10.1186/s40644-024-00741-4
  40. LIFEx-MTV: Ronald Boellaard, Irène Buvat, Christophe Nioche, Luca Ceriani, Anne-Ségolène Cottereau, Luca Guerra, Rodney J. Hicks, Salim Kanoun, Carsten Kobe, Annika Loft, Heiko Schöder, Annibale Versari, Conrad-Amadeus Voltin, Gerben J.C. Zwezerijnen, Josée M. Zijlstra, N. George Mikhaeel, Andrea Gallamini, Tarec C. El-Galaly, Christine Hanoun, Stephane Chauvie, Romain Ricci, Emanuele Zucca, Michel Meignan, Sally F. Barrington. International Benchmark for Total Metabolic Tumor Volume Measurement in Baseline 18F-FDG PET/CT of Lymphoma Patients: A Milestone Toward Clinical Implementation. Journal of Nuclear Medicine Aug 2024, jnumed.124.267789; https://doi.org/10.2967/jnumed.124.267789
  41. LIFEx-MTV: Madeleine J Karpinski, Johannes Hüsing, Kevin Claassen & al. Combining PSMA-PET and PROMISE to re-define disease stage and risk in patients with prostate cancer: a multicentre retrospective study. The lancet Oncology :July 29, 2024 https://doi.org/10.1016/S1470-2045(24)00326-7
  42. LIFEx-texture: Pellegrino, S., Origlia, D., Di Donna, E. et al. Coefficient of variation and texture analysis of 18F-FDG PET/CT images for the prediction of outcome in patients with multiple myeloma. Ann Hematol (2024). https://doi.org/10.1007/s00277-024-05905-7
  43. LIFEx-MTV: Cui, S., Xin, W., Wang, F. et al. Metabolic tumour area: a novel prognostic indicator based on 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma in the R-CHOP era. BMC Cancer 24, 895 (2024). https://doi.org/10.1186/s12885-024-12668-x
  44. LIFEx-MTV: Lukas Muller, Daniel Bender, Simon J. Gairing & al. Amount of ascites impacts survival in patients with hepatocellular carcinoma undergoing transarterial chemoembolization advocating for volumetric assessment. Scientific Reports | (2024) 14:16550, https://doi.org/10.1038/s41598-024-67312-2
  45. LIFEx-texture: N. Agüloğlu, A. Aksu, D.S. Unat, Ö. Selim Unat. The value of PET/CT radiomic texture analysis of primary mass and mediastinal lymph node on survival in patients with non-small cell lung cancer. Revista Española de Medicina Nuclear e Imagen Molecular (English Edition), 2024, 500027, ISSN 2253-8089, https://doi.org/10.1016/j.remnie.2024.500027
  46. LIFEx-texture: Lafon, M., Cousin, S., Alamé, M. et al. Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01163-1
  47. LIFEx-texture: Luca Viganò, Valentina Zanuso, Francesco Fiz, Luca Cerri, Maria Elena Laino, Angela Ammirabile, Elisa Maria Ragaini, Samuele Viganò, Luigi Maria Terracciano, Marco Francone, Francesca Ieva, Luca Di Tommaso, Lorenza Rimassa. CT-based radiogenomics of intrahepatic cholangiocarcinoma. Digestive and Liver Disease, 2024, ISSN 1590-8658, https://doi.org/10.1016/j.dld.2024.06.033
  48. LIFEx-texture: Masson-Grehaigne, C.; Lafon, M.; Palussière, J.; Leroy, L.; Bonhomme, B.; Jambon, E.; Italiano, A.; Cousin, S.; Crombé, A. Enhancing Immunotherapy Response Prediction in Metastatic Lung Adenocarcinoma: Leveraging Shallow and Deep Learning with CT-Based Radiomics across Single and Multiple Tumor Sites. Cancers 2024, 16, 2491. https://doi.org/10.3390/cancers16132491
  49. LIFEx-MTV: Cui, S., Xin, W., Wang, F. et al. Metabolic tumour area: a novel prognostic indicator based on 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma in the R-CHOP era.BMC Cancer 24, 895 (2024). https://doi.org/10.1186/s12885-024-12668-x
  50. LIFEx-texture: Fiz F, Ragaini EM, Sirchia S, Masala C, Viganò S, Francone M, Cavinato L, Lanzarone E, Ammirabile A, Viganò L. Radiomic Gradient in Peritumoural Tissue of Liver Metastases: A Biomarker for Clinical Practice? Analysing Density, Entropy, and Uniformity Variations with Distance from the Tumour. Diagnostics. 2024; 14(14):1552. https://doi.org/10.3390/diagnostics14141552
  51. LIFEx-texture: Ricarda Hinzpeter, Roshini Kulanthaivelu, Andres Kohan, & al. Predictive [18F]-FDG PET/CT-Based Radiogenomics Modelling of Driver Gene Mutations in Non-small Cell Lung Cancer. Academic Radiology, July 13, 2024, https://doi.org/10.1016/j.acra.2024.06.038
  52. LIFEx-MTV: Lasnon, C., Morel, A., Aide, N. et al. Baseline and early 18F-FDG PET/CT evaluations as predictors of progression-free survival in metastatic breast cancer patients treated with targeted anti-CDK therapy. Cancer Imaging 24, 90 (2024). https://doi.org/10.1186/s40644-024-00727-2
  53. LIFEx-texture: Role of FDG-PET Radiomics in the Diagnosis of Cardiovascular Inflammation: A Narrative Review. Journal of Clinical & Diagnostic Research, 2024, Vol 18, Issue 6, p1. https://doi.org/10.7860/JCDR/2024/70573.19571
  54. LIFEx-texture: Pellegrino, S., Origlia, D., Di Donna, E. et al. Coefficient of variation and texture analysis of 18F-FDG PET/CT images for the prediction of outcome in patients with multiple myeloma.Ann Hematol (2024). https://doi.org/10.1007/s00277-024-05905-7
  55. LIFEx-texture: Daniel Mannina, Ameya Kulkarni, Christian B. van der Pol, Reem Al Mazroui, Peri Abdullah, Sayali Joshi, Abdullah Alabousi. Utilization of Texture Analysis in Differentiating Benign and Malignant Breast Masses: Comparison of Grayscale Ultrasound, Shear Wave Elastography, and Radiomic Features. Journal of Breast Imaging, 2024, Vol. XX, No. XX, 1–7. https:/doi.org/10.1093/jbi/wbae037
  56. LIFEx-texture: Müller, L., Bender, D., Gairing, S.J. et al. Amount of ascites impacts survival in patients with hepatocellular carcinoma undergoing transarterial chemoembolization advocating for volumetric assessment. Sci Rep 14, 16550 (2024). https://doi.org/10.1038/s41598-024-67312-2
  57. LIFEx-texture: Zuo, R., Liu, S., Li, W. et al. Clinical value of 68Ga-pentixafor PET/CT in patients with primary aldosteronism and bilateral lesions: preliminary results of a single-centre study. EJNMMI Res 14, 61 (2024). https://doi.org/10.1186/s13550-024-01125-2
  58. LIFEx-texture: Alessandro Stefano. Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research. Computers in Biology and Medicine 179 (2024) 108827. https://doi.org/10.1016/j.compbiomed.2024.108827
  59. LIFEx-texture: Julia J.M. Roelofs, Elise J.M. van Eijnatten, Patteela Prathumars, Joris de Jong, Ron Wehrens, Diederik Esser, Anja E.M. Janssen, Paul A.M. Smeets. Gastric emptying and nutrient absorption of pea protein products differing in heat treatment and texture: A randomized in vivo crossover trial and in vitro digestion study. Food Hydrocolloids, Volume 149, 2024, 109596, ISSN 0268-005X, https://doi.org/10.1016/j.foodhyd.2023.109596
  60. LIFEx-texture: Elise J. M. van Eijnatten, Julia J. M. Roelofs, Guido Camps, Thom Huppertz, Tim T. Lambers and Paul A. M. Smeets. Gastric coagulation and postprandial amino acid absorption of milk is affected by mineral composition: a randomized crossover trial - Food & Function 2024, 15, 3098-3107 https://doi.org/10.1039/D3FO04063A
  61. LIFEx-texture: Arnaud Beddok, Fanny Orlhac, Valentin Calugaru, Laurence Champion, Catherine Ala Eddine, et al.. [18F]-FDG PET and MRI radiomic signatures to predict the risk and the location of tumor recurrence after re-irradiation in head and neck cancer. European Journal of Nuclear Medicine and Molecular Imaging, 2022, Online ahead of print. https://doi.org/10.1007/s00259-022-06000-7
  62. LIFEx-texture: Nicolas Captier, Marvin Lerousseau, Fanny Orlhac, Narinée Hovhannisyan-Baghdasarian, Marie Luporsi, Erwin Woff, Sarah Lagha, Paulette Salamoun Feghali, Christine Lonjou, Clément Beaulaton, Hélène Salmon, Thomas Walter, Irène Buvat, Nicolas Girard, Emmanuel Barillot. Integration of clinical, pathological, radiological, and transcriptomic data improves the prediction of first-line immunotherapy outcome in metastatic non-small cell lung cancer. medRxiv. June 2024. https://doi.org/10.1101/2024.06.27.24309583
  63. LIFEx-texture: Kawashima Y, Abe H, Hagimoto A, Miyakoshi M, Kawabata Y, Indo H, et al. Texture analysis of ultrasonography to differentiate metastatic from nonmetastatic cervical lymph nodes in mandibular gingival squamous cell carcinoma. Oral Sci Int. 2024. https://doi.org/10.1002/osi2.1260
  64. LIFEx-texture: Dong S, Fu A, Liu J. Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging using machine learning. Quant Imaging Med Surg 2024. https://doi.org/10.21037/qims-24-100
  65. LIFEx-texture: Nicolas Captier, Fanny Orlhac, Narinee Hovhannisyan-Baghdasarian, Marie Luporsi, Nicolas Girard, and Irene Buvat. RadShap: An Explanation Tool for Highlighting the Contributions of Multiple Regions of Interest to the Prediction of Radiomic Models. Journal of Nuclear Medicine, published on June 21, 2024, https://doi.org/10.2967/jnumed.124.267434
  66. LIFEx-texture: M. U. Khan, F. Bianconi, M. L. Fravolini and B. Palumbo, "Sensitivity of radiomics features to region volume: A CT phantom study," 2024 International Conference on Control, Automation and Diagnosis (ICCAD), Paris, France, 2024, pp. 1-5, https://doi.org/10.1109/ICCAD60883.2024.10553720
  67. LIFEx-texture: Bian, S., Hong, W., Su, X. et al. A dynamic online nomogram predicting prostate cancer short-term prognosis based on 18F-PSMA-1007 PET/CT of periprostatic adipose tissue: a multicenter study. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04421-6
  68. LIFEx-texture: Martinelli, E., Ciardiello, D., Martini, G. et al. Radiomic Parameters for the Evaluation of Response to Treatment in Metastatic Colorectal Cancer Patients with Liver Metastasis: Findings from the CAVE-GOIM mCRC Phase 2 Trial. Clin Drug Investig (2024). https://doi.org/10.1007/s40261-024-01372-0
  69. LIFEx-main: Kunichika, H.; Minamiguchi, K.; Tachiiri, T.; Shimizu, K.; Taiji, R.; Yamada, A.; Nakano, R.; Irizato, M.; Yamauchi, S.; Marugami, A.; et al. Prediction of Efficacy for Atezolizumab/Bevacizumab in Unresectable Hepatocellular Carcinoma with Hepatobiliary-Phase Gadolinium Ethoxybenzyl-Diethylenetriaminepentaacetic Acid MRI. Cancers 2024, 16, 2275. https://doi.org/10.3390/cancers16122275
  70. LIFEx-main: Zhang, D., Zheng, B., Xu, L. et al. A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer. Insights Imaging 15, 150 (2024). https://doi.org/10.1186/s13244-024-01733-5
  71. Bortolotto, C., Pinto, A., Brero, F. et al. CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency. Eur Radiol Exp 8, 71 (2024). https://doi.org/10.1186/s41747-024-00468-8
  72. LIFEx-texture: Alanezi, S.T.; Kra´sny, M.J.; Kleefeld, C.; Colgan, N. Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models with Tuned Hyperparameters. Cancers 2024, 16, 2163. https://doi.org/10.3390/cancers16112163
  73. LIFEx-texture: Aouadi S, Torfeh T, Bouhali O, Yoganathan SA, Paloor S, Chandramouli S, Hammoud R, Al-Hammadi N. Prediction of cervix cancer stage and grade from diffusion weighted imaging using EfficientNet. Biomed Phys Eng Express. 2024 Jun 10;10(4). https://doi.org/10.1088/2057-1976/ad5207. PMID: 38815562
  74. LIFEx-texture: Zinsz, A., Ahrari, S., Becker, J. et al. Amino-acid PET as a prognostic tool after post Stupp protocol temozolomide therapy in high-grade glioma patients. J Neurooncol (2024). https://doi.org/10.1007/s11060-024-04722-2
  75. LIFEx-texture: Crombé, A., Lucchesi, C., Bertolo, F. et al. Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis. npj Precis. Onc. 8, 129 (2024). https://doi.org/10.1038/s41698-024-00616-8
  76. LIFEx-main: Tang EK, Wu YJ, Chen CS, Wu FZ. Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time. Quant Imaging Med Surg 2024;14(6):3983-3996. https://doi.org/10.21037/qims-23-1759
  77. LIFEx-texture: Panagiotidis, Emmanouil; Andreou, Sotiria; Paschali, Anna; Angeioplasti, Kyra; Vlontzou, Evaggelia; Kalathas, Theodore; Pipintakou, Angeliki; Fothiadaki, Athina; Makridou, Anna; Chatzimarkou, Michael; Papanastasiou, Emmanouil; Datseris, Ioannis; Chatzipavlidou, Vasiliki. Towards improved diagnosis: radiomics and quantitative biomarkers in 18F-PSMA-1007 and 18F-fluorocholine PET/CT for prostate cancer recurrence. Nuclear Medicine Communications: June 03, 2024. https://doi.org/10.1097/MNM.0000000000001867
  78. LIFEx-texture: Mona Elhaj, Ahmad Joman Alghamdi, Hamid Osman, Majd Alnefaie, Taef Althomali, Maha Aljuaid, Mrooj Alharthi, Renad Alamri, Ahlam Ali Y. Asiri, Mohamed Alkhader Mohamed Hamad, Hanan Elnour, Amel F. Alzain, Hajar Al Asmari, Mayeen Uddin Khandaker, Mustafa Z. Mahmoud. Analyzing pancreatic characteristics in diabetic patients: A texture-based CT investigation with volume assessment. Journal of Radiation Research and Applied Sciences,
    Volume 17, Issue 3, 2024, 100967, ISSN 1687-8507, https://doi.org/10.1016/j.jrras.2024.100967
  79. LIFEx-texture: Fiz, F., Rossi, N., Langella, S. et al. Radiomics of Intrahepatic Cholangiocarcinoma and Peritumoral Tissue Predicts Postoperative Survival: Development of a CT-Based Clinical-Radiomic Model. Ann Surg Oncol (2024). https://doi.org/10.1245/s10434-024-15457-9
  80. LIFEx-main: Gao, J., Zhou, J., Liu, C. et al. Outcome prediction of SSTR-RADS-3A and SSTR-RADS-3B lesions in patients with neuroendocrine tumors based on 68Ga-DOTATATE PET/MR. J Cancer Res Clin Oncol 150, 272 (2024). https://doi.org/10.1007/s00432-024-05776-5
  81. LIFEx-main: Linjun Ju, Wenbo Li, Rui Zuo, Zheng Chen, Yue Li, Yuyue Feng, Yuting Xiang, Hua Pang. Deep Learning Features and Metabolic Tumor Volume Based on PET/CT to Construct Risk Stratification in Non-small Cell Lung Cancer. Academic Radiology, 2024, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2024.04.036
  82. LIFEx-texture: Villagran M, Driban JB, Lu B, MacKay JW, McAlindon TE, Harkey MS. Radiomic features of the medial meniscus predicts incident destabilizing meniscal tears: data from the osteoarthritis initiative. J Orthop Res. 2024;1‐8. https://doi.org/10.1002/jor.25851
  83. LIFEx-texture: Yao Ai, Xiaoyang Zhu, Yu Zhang, Wenlong Li, Heng Li, Zeshuo Zhao, Jicheng Zhang, Boda Ning, Chenyu Li, Qiao Zheng, Ji Zhang, Juebin Jin, Yiran Li, Congying Xie, Xiance Jin. MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer. Radiotherapy and Oncology, Volume 197, 2024, 110328, ISSN 0167-8140, https://doi.org/10.1016/j.radonc.2024.110328
  84. LIFEx-texture: Hinzpeter, R.; Mirshahvalad, S.A.; Murad, V.; Avery, L.; Kulanthaivelu, R.; Kohan, A.; Ortega, C.; Elimova, E.; Yeung, J.; Hope, A.; et al. The [18F]F-FDG PET/CT Radiomics Classifier of Histologic Subtypes and Anatomical Disease Origins across Various Malignancies: A Proof-of-Principle Study. Cancers 2024, 16, 1873. https://doi.org/10.3390/cancers16101873
  85. LIFEx-texture: Pepponi, M., Berti, V., Fasciglione, E. et al. [68Ga]DOTATOC PET-derived radiomics to predict genetic background of head and neck paragangliomas: a pilot investigation. Eur J Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s00259-024-06735-5
  86. Yu, Yu1; Zhu, Jing2; Sang, Shibiao1; Yang, Yi3; Zhang, Bin1; Deng, Shengming1,4. Application of 18F-FDG PET/CT imaging radiomics in the differential diagnosis of single-nodule pulmonary metastases and second primary lung cancer in patients with colorectal cancer. Journal of Cancer Research and Therapeutics 20(2):p 599-607, April 2024. https://doi.org/10.4103/jcrt.jcrt_1674_23
  87. LIFEx-main: Laudicella, R., Comelli, A., Schwyzer, M. et al. PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI. Radiol med (2024). https://doi.org/10.1007/s11547-024-01820-z
  88. LIFEx-texture: Yesh Datar, Sarah A.M. Cuddy, Gavin Ovsak, Gerard T. Giblin, Mathew S. Maurer, Frederick L. Ruberg, Rima Arnaout, Sharmila Dorbala. Myocardial Texture Analysis of Echocardiograms in Cardiac Transthyretin Amyloidosis, Journal of the American Society of Echocardiography, 2024, ISSN 0894-7317, https://doi.org/10.1016/j.echo.2024.02.005
  89. LIFEx-MTV: Tricarico P, Chardin D, Martin N, et al. Total metabolic tumor volume on 18 F-FDG PET/CT is a game-changer for patients with metastatic lung cancer treated with immunotherapy. Journal for ImmunoTherapy of Cancer 2024;12:e007628. https://doi.org/10.1136/jitc-2023-007628
  90. LIFEx-texture: Troiano, G., Rapani, A., Fanelli, F., Berton, F., Caroprese, M., Lombardi, T., Zhurakivska, K., & Stacchi, C. (2024). Inter and intra-operator reliability of Lekholm and Zarb classification and proposal of a novel radiomic data-driven clustering for qualitative assessment of edentulous alveolar ridges. Clinical Oral Implants Research, 00, 1–10. https://doi.org/10.1111/clr.14271
  91. LIFEx-main: Zinsz, A., Pouget, C., Rech, F. et al. The role of [18 F]FDOPA PET as an adjunct to conventional MRI in the diagnosis of aggressive glial lesions. Eur J Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s00259-024-06720-y
  92. LIFEx-texture: Qian, L., Zhou, Z., Li, S., Liu, J., Zhang, S., Ren, J., Wang, W., & Yang, J. (2024). 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging of pediatric neuroblastoma: a multi-omics parameters method to predict MYCN copy number category. Quantitative Imaging In Medicine And Surgery, 14(4), 3131-3145. https://doi.org/10.21037/qims-23-494
  93. LIFEx-texture: Hathaway, Q.A., Abdeen, Y., Conte, J. et al. Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer. Int J Cardiovasc Imaging (2024). https://doi.org/10.1007/s10554-024-03101-2
  94. LIFEx-texture: Lee, J.W., Ahn, H., Yoo, I.D. et al. Relationship of FDG PET/CT imaging features with tumor immune microenvironment and prognosis in colorectal cancer: a retrospective study. Cancer Imaging 24, 53 (2024). https://doi.org/10.1186/s40644-024-00698-4
  95. LIFEx-texture: Laskov V, Rothbauer D, Malikova H (2024) Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan. PLoS ONE 19(4): e0301978. https://doi.org/10.1371/journal.pone.0301978
  96. LIFEx-Texure: UEDA, Cristina Emiko; DIAS, Laís Flausino; CARNEIRO, Camila de Godoi; SAPIENZA, Marcelo Tatit; BUCHPIGUEL, Carlos Alberto; DUARTE, Paulo Schiavom. Correlation of 18F-sodium fluoride uptake and radiodensity in extraosseous metastases of medullary thyroid carcinoma. Arch. Endocrinol. Metab., v. 68, e230152, Apr. 2024. https://doi.org/10.20945/2359-4292-2023-0152
  97. LIFEx-texture: Guillaume Declaux, Baudouin Denis de Senneville, Hervé Trillaud, Paulette Bioulac-Sage, Charles Balabaud, Jean-Frédéric Blanc, Laurent Facq, Nora Frulio. Assessment of a multivariable model using MRI-radiomics, age and sex for the classification of hepatocellular adenoma subtypes, Research in Diagnostic and Interventional Imaging, Volume 10, 2024, 100046, ISSN 2772-6525, https://doi.org/10.1016/j.redii.2024.100046
  98. LIFEx-texture: Bülbül, H.M., Burakgazi, G., Kesimal, U. et al. Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening radiomics in bowel wall thickening. Jpn J Radiol (2024). https://doi.org/10.1007/s11604-024-01558-8
  99. LIFEx-texture: Simone Famularo et al., European Journal of Surgical Oncology, https://doi.org/10.1016/j.ejso.2024.108274
  100. LIFEx-texture: Khateri, M., Babapour Mofrad, F., Geramifar, P. et al. Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade. Phys Eng Sci Med (2024). https://doi.org/10.1007/s13246-024-01402-3
  101. LIFEx-texture: Lanzarin-Minero AM, Reyes-Gonzalez JP, Fajardo-Fregoso BF. Predictores radiómicos F18-FDG PET/CT en la respuesta patológicacompleta a la quimioterapia neoadyuvante en pacientes con cáncer de mama. Anales de Radiología México. 2022;21(4):225-237. https://webcir.org/revistavirtual/3_2024/pdf/mexicoAnales/1_anales_en.pdf
  102. LIFEx-texture: Russo L et al., Radiomics for clinical decision support in radiation oncology, Clinical Oncology, https://doi.org/10.1016/ j.clon.2024.03.003
  103. LIFEx-texture: Nakajo, M., Hirahara, D., Jinguji, M. et al. Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis. Jpn J Radiol (2024). https://doi.org/10.1007/s11604-024-01546-y
  104. LIFEx-texture: Bai, J., He, M., Gao, E. et al. High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10686-8
  105. LIFEx-texture: Seong-O Shim, Lal Hussain, Wajid Aziz, Abdulrahman A. Alshdadi, Abdulrahman Alzahrani, Abdulfattah Omar. Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach. International journal of Imaging systems and technology. Volume34, Issue2, March 2024, e23059. https://doi.org/10.1002/ima.23059
  106. LIFEx-texture: Takeyama, N., Sasaki, Y., Ueda, Y. et al. Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study. Jpn J Radiol (2024). https://doi.org/10.1007/s11604-024-01545-z
  107. LIFEx-texture: Yang, T., Feng, J., Yao, R. et al. CT-based pancreatic radiomics predicts secondary loss of response to infliximab in biologically naive patients with Crohn’s disease. Insights Imaging 15, 69 (2024). https://doi.org/10.1186/s13244-024-01637-4
  108. LIFEx-texture: Norikane, T.; Ishimura, M.; Mitamura, K.; Yamamoto, Y.; Arai-Okuda, H.; Manabe, Y.; Murao, M.; Morita, R.; Obata, T.; Tanaka, K.; et al. Texture Features of 18F-Fluorodeoxyglucose Positron Emission Tomography for Predicting Programmed Death-Ligand-1 Levels in Non-Small Cell Lung Cancer. J. Clin. Med. 2024, 13, 1625. https://doi.org/10.3390/jcm13061625
  109. LIFEx-texture: Graillon, T., Salgues, B., Horowitz, T. et al. Peptide radionuclide radiation therapy with Lutathera in multirecurrent nonanaplastic meningiomas: antitumoral activity study by growth rate analysis. J Neurooncol (2024). https://doi.org/10.1007/s11060-024-04622-5
  110. LIFEx-texture: Hovhannisyan-Baghdasarian N. , Luporsi M., Captier N., Nioche C , Cuplov V , Woff E, Hegarat N. , Livartowski A. , Girard N., Buvat I and Orlhac F. Promising Candidate Prognostic Biomarkers in [18F]FDGPET Images: Evaluation in Independent Cohorts ofNon–Small Cell Lung Cancer Patients. J Nucl Med 2024; 00:1–8. http://doi.org/10.2967/jnumed.123.266331
  111. LIFEx-texture: Abenavoli EM, Linguanti F, Anichini M, Miele V, Mungai F, Palazzo M, Nassi L, Puccini B, Romano I, Sordi B, Sciagrà R, Simontacchi G, Vannucchi AM, Berti V. Texture analysis of 18F-FDG PET/CT and CECT: Prediction of refractoriness of Hodgkin lymphoma with mediastinal bulk involvement. Hematol Oncol. 2024 Mar;42(2):e3261. http://doi.org/10.1002/hon.3261 PMID: 38454623
  112. LIFEx-texture: Graillon, T., Salgues, B., Horowitz, T. et al. Peptide radionuclide radiation therapy with Lutathera in multirecurrent nonanaplastic meningiomas: antitumoral activity study by growth rate analysis. J Neurooncol (2024). https://doi.org/10.1007/s11060-024-04622-5
  113. LIFEx-texture: V. Navyasree, M. Meghana, N. Vaishnavi, N. Bhargavi, P. Mounika. Customized 3D CNN Model-based Lung Cancer Classification from Chest X-ray Images. International Journal For Advanced Research In Science & Technology. Volume 13, Issue 12, Dec 2023 ISSN 2457-0362. p849. https://www.ijarst.in/public/uploads/paper/397961708781960.pdf
  114. LIFEx-texture: Karabay N, Odaman H, Vahaplar A, Kizmazoglu C, Kalemci O. MRI-based Texture Analysis in Differentiation of Benign and Malignant Vertebral Compression Fractures. Current Medical Imaging. 2024 Feb. https://doi.org/10.2174/0115734056290762240209071656. PMID: 38415478.
  115. LIFEx-texture: A. Kohan, R. Hinzpeter, R. Kulanthaivelu, SA Mirshahvalad, L. Avery, M. Tsao, Q. Li, C. Ortega, U. Metser, A. Hope, P. Veit-Haibach, Contrast Enhanced CT Radiogenomics in a Retrospective NSCLC Cohort: Models, Attempted Validation of a Published Model and the Relevance of the Clinical Context, Academic Radiology, 2024, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2024.01.031
  116. LIFEx-texture: Hakkak Moghadam Torbati A, Pellegrino S, Fonti R, Morra R, De Placido S, Del Vecchio S. Machine Learning and Texture Analysis of [18F]FDG PET/CT Images for the Prediction of Distant Metastases in Non-Small-Cell Lung Cancer Patients. Biomedicines. 2024; 12(3):472. https://doi.org/10.3390/biomedicines12030472
  117. LIFEx-texture: Fan X, Zhang H, Wang Z, et al. Diagnosing postoperative lymph node metastasis in thyroid cancer with multimodal radiomics and clinical features. DIGITAL HEALTH. 2024;10. https://doi.org/10.1177/20552076241233244
  118. LIFEx-texture: Fukushima, Yasuhiroa; Suzuki, Keisukeb; Kim, Maib; Gu, Wenchaoc,d; Yokoo, Satoshib; Tsushima, Yoshitod. Evaluation of bone marrow invasion on the machine learning of 18F-FDG PET texture analysis in lower gingival squamous cell carcinoma. Nuclear Medicine Communications ():10.1097/MNM.0000000000001826, February 19, 2024. https://doi.org/10.1097/MNM.0000000000001826
  119. LIFEx-texture: Albano, D., Calabrò, A., Talin, A. et al. 2-[18]F FDG PET/CT dissemination features in adult burkitt lymphoma Are predictive of outcome. Ann Hematol (2024). https://doi.org/10.1007/s00277-024-05672-5
  120. LIFEx-main: Hongyue Zhao, Yexin Su, Yan Wang, Zhehao Lyu, Peng Xu, Wenchao Gu, Lin Tian and Peng Fu. Using tumor habitat-derived radiomic analysis during pretreatment 18 F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer. Zhao et al. Cancer Imaging (2024) 24:2. https://doi.org/10.1186/s40644-024-00670-2
  121. LIFEx-texture: Palomino-Fernandez D, Milara E, Galiana A, Sanchez-Ortiz M, Seiffert AP, Jiménez-Almonacid J, Gomez-Grande A, Ruiz-Solis S, Ruiz-Alonso A, Gomez EJ, et al. Textural and Conventional Pretherapeutic [18F]FDG PET/CT Parameters for Survival Outcome Prediction in Stage III and IV Oropharyngeal Cancer Patients. Applied Sciences. 2024; 14(4):1454. https://doi.org/10.3390/app14041454
  122. LIFEx-Main: Ahrari, S., Zaragori, T., Zinsz, A. et al. Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma. Sci Rep 14, 3256 (2024). https://doi.org/10.1038/s41598-024-53693-x
  123. LIFEx-texture: The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Philip Whybra, Alex Zwanenburg, Vincent Andrearczyk, Roger Schaer, Aditya P. Apte, Alexandre Ayotte, Bhakti Baheti, Spyridon Bakas, Andrea Bettinelli, Ronald Boellaard, Luca Boldrini, Irène Buvat, Gary J. R. Cook, Florian Dietsche, Nicola Dinapoli, Hubert S. Gabrys, Vicky Goh, Matthias Guckenberger, Mathieu Hatt, Mahdi Hosseinzadeh, Aditi Iyer, Jacopo Lenkowicz, Mahdi A. L. Loutfi, Steffen Löck, Francesca Marturano, Olivier Morin, Christophe Nioche, Fanny Orlhac, Sarthak Pati, Arman Rahmim, Seyed Masoud Rezaeijo, Christopher G. Rookyard, Mohammad R. Salmanpour, Andreas Schindele, Isaac Shiri, Emiliano Spezi, Stephanie Tanadini-Lang, Florent Tixier, Taman Upadhaya, Vincenzo Valentini, Joost J. M. van Griethuysen, Fereshteh Yousefirizi, Habib Zaidi, Henning Müller, Martin Vallières, and Adrien Depeursinge. Radiology 2024 310:2 https://doi.org/10.1148/radiol.231319
  124. LIFEx-texture: Wang, Menglua; Peng, Mengyea; Yang, Xinyuea; Zhang, Yinga; Wu, Tingtinga; Wang, Zeyub; Wang, Kezhenga. Preoperative prediction of microsatellite instability status: development and validation of a pan-cancer PET/CT-based radiomics model. Nuclear Medicine Communications, February 05, 2024. https://doi.org/10.1097/MNM.0000000000001816
  125. LIFEx-texture: Hajri R, Nicod-Lalonde M, Hottinger AF, Prior JO, Dunet V. Prediction of Glioma Grade and IDH Status Using 18F-FET PET/CT Dynamic and Multiparametric Texture Analysis. Diagnostics (Basel). 2023 Aug 5;13(15):2604. doi: https://doi.org/10.3390/diagnostics13152604. PMID: 37568967; PMCID: PMC10417545.
  126. LIFEx-Main: Ha S, O JH, Park C, Boo SH, Yoo IR, Moon HW, Chi DY, Lee JY. Dosimetric Analysis of a Phase I Study of PSMA-Targeting Radiopharmaceutical Therapy With [177Lu]Ludotadipep in Patients With Metastatic Castration-Resistant Prostate Cancer. Korean J Radiol. 2024 Feb;25(2):179-188. https://doi.org/10.3348/kjr.2023.0656
  127. LIFEx-Main: Albano, D.; Calabrò, A.; Dondi, F.; Bertagna, F. 2-[18F]-FDG PET/CT Semiquantitative and Radiomics Predictive Parameters of Richter’s Transformation in CLL Patients. Medicina 2024, 60, 203. https://doi.org/10.3390/medicina60020203
  128. LIFEx-texture: Xiaojing Jiang, Tianyue Li, Jianfang Wang, Zhaoqi Zhang, Xiaolin Chen, Jingmian Zhang, and Xinming Zhao. Noninvasive Assessment of HER2 Expression Status in Gastric Cancer Using 18F-FDG Positron Emission Tomography/Computed Tomography-Based Radiomics: A Pilot Study. Cancer Biotherapy and Radiopharmaceuticals. https://doi.org/10.1089/cbr.2023.0162
  129. LIFEx-Main: Pellegrino, S.; Fonti, R.; Vallone, C.; Morra, R.; Matano, E.; De Placido, S.; Del Vecchio, S. Coefficient of Variation in Metastatic Lymph Nodes Determined by 18F-FDG PET/CT in Patients with Advanced NSCLC: Combination with Coefficient of Variation in Primary Tumors. Cancers 2024, 16, 279. https://doi.org/10.3390/cancers16020279
  130. LIFEx-texture: Kumar, R., Ramachandran, A., Mittal, B.R. et al. Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer on 68 Ga PSMA PET/CT Delayed Imaging by Analyzing Radiomic Features. Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s13139-023-00832-3
  131. LIFEx-texture: Martin, A.; Marcelin, C.; Petitpierre, F.; Jambon, E.; Maaloum, R.; Grenier, N.; Le Bras, Y.; Crombé, A. Clinical, Technical, and MRI Features Associated with Patients’ Outcome at 3 Months and 2 Years following Prostate Artery Embolization: Is There an Added Value of Radiomics? J. Pers. Med. 2024, 14, 67. https://doi.org/10.3390/jpm14010067
  132. LIFEx-texture: Saleh T. Alanezi, Waleed M. Almutairi, Michelle Cronin, Oliviero Gobbo, Shane M. O’Mara, Declan Sheppard, William T. O’Connor, Michael D. Gilchrist, Christoph KleefeldNiall Colgan. Whole-brain traumatic controlled cortical impact to the left frontal lobe: Magnetic resonance image-based texture analysis. Journal of Neuropathology & Experimental Neurology, 2024, 1–13. https://doi.org/10.1093/jnen/nlad110
  133. LIFEx-texture: Alanezi ST, Almutairi WM, Cronin M, Gobbo O, O'Mara SM, Sheppard D, O'Connor WT, Gilchrist MD, Kleefeld C, Colgan N. Whole-brain traumatic controlled cortical impact to the left frontal lobe: Magnetic resonance image-based texture analysis. J Neuropathol Exp Neurol. 2024 Jan 2:nlad110. https://doi.org/10.1093/jnen/nlad110. Epub ahead of print. PMID: 38164986
  134. LIFEx-texture: van Staalduinen EK, Matthews R, Khan A, Punn I, Cattell RF, Li H, Franceschi A, Samara GJ, Czerwonka L, Bangiyev L, et al. Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone. Diagnostics. 2024; 14(1):71. https://doi.org/10.3390/diagnostics14010071
  135. LIFEx-texture: Leszczyński W, Kazimierczak W, Lemanowicz A, Serafin Z. Texture analysis of chest X-ray images for the diagnosis of COVID-19 pneumonia. Pol J Radiol. 2024 Jan 25;89:e49-e53. https://doi.org/10.5114/pjr.2024.134818. PMID: 38371891; PMCID: PMC10867972.

 

Thesis (3):

  1. LIFEx-texture: Louis Rebaud. Whole-body / total-body biomarkers in PET imaging. https://theses.hal.science/tel-04618815
  2. LIFEx-texture: Evaluation of texture analysis capabilities computed tomographic images in complex diagnostics of hepatocellular cancer. National Medical Center Vidshnevsky, Russian Federation. Dissertation. 2023. (link)
  3. LIFEx-texture: Dominik Steube. Deep Learning Ansätze zur automatischen Klassifikation und Segmentierung von PET/CT Daten. Universität Ulm. https://doi.org/10.18725/OPARU-53062

 

Conference (8) :

  1. LIFEx-texture: Francesco Bianconi, Mario L. Fravolini, Elena Caltana. Muhammad U. Khan1,2 Barbara Palumbo. Classification of lung nodules on CT via pseudo-colour images and deep features from pre-trained convolutional networks. CCIW 2024, Milan, 25–27 Sep. 2024 https://www.bianconif.net/stuff/CCIW-2024-bianconi.pdf
  2. LIFEx-texture: A. Kordonis, K. Niapou, S. Paisiou, M.-E. Tomazinaki, A. Karaiskou, N. Bertsekas, P. Rondogianni, A. Samartzis. Comparison of PET Textural Metrics in Different Platforms based on Phantom Studies. 2nd Panhellenic congress of medical physics. oct 2024, Eugenides foundation https://pcmp2024.medical-physics.eu/wp-content/uploads/2024/10/P_3_6.pdf
  3. LIFEx-texture: Sharma, N., Balogova, S., Noskovicova, L., Montravers, F., Talbot, JN., Trentin, E. (2024). Automatic Interpretation of F-Fluorocholine PET/CT Findings in Patients with Primary Hyperparathyroidism: A Novel Dataset with Benchmarks. In: Suen, C.Y., Krzyzak, A., Ravanelli, M., Trentin, E., Subakan, C., Nobile, N. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2024. Lecture Notes in Computer Science(), vol 15154. Springer, Cham. https://doi.org/10.1007/978-3-031-71602-7_7
  4. LIFEx-texture: 925P External validation of the CD8 radiomics signature as a prognostic marker in recurrent or metastatic head and neck cancer treated with nivolumab. Adrien, L. et al. Annals of Oncology, Volume 35, S646 - S647
  5. LIFEx-texture: Kuznetsov A.I. Development of a prognostic model for diagnosis of prostate cancer based on radiomics of biparametric magnetic resonance imaging apparent diffusion coefficient maps and stacking of machine learning algorithms // Digital Diagnostics. - 2024. - Vol. 5. - N. 1S. - P. 80-82. https://doi.org/10.17816/DD626145
  6. LIFEx-texture: Prediction of adrenal masses nature through texture analysis and deep learning: Preliminary results from ENS@T RADIO-AI multicentric study. Lorenzo Tucci, Giulio Vara, Valentina Morelli, Edelmiro Luis Menendez Torre, Ulrich Dischinger, Athina Markou, Massimo Terzolo, Ariadni Spyroglou, Chiara Parazzoli, Aresta Carmen, Iacopo Chiodini, Diego Rivas, Alba Gutiérrez, Wiebke Schlötelburg, Krystallenia Alexandraki, Soraya Puglisi, Ilaria Improta, Antonio De Leo, Saverio Selva, Laura Alberici, Andrea De Giglio, Maria Abbondanza Pantaleo, Caterina Balacchi, Cristina Mosconi, Valentina Vicennati, Uberto Pagotto & Guido Di Dalmazi. Endocrine Abstracts (2024) 99 OC11.3, https://doi.org/10.1530/endoabs.99.OC11.3
  7. LIFEx-texture: Lorenzo Tucci, Antonio De Leo, Giulio Vara, Kimberly Coscia, Saverio Selva, Claudio Ricci, Laura Alberici, Caterina Balacchi, Donatella Santini, Valentina Vicennati, Uberto Pagotto, Cristina Mosconi, Giovanni Tallini & Guido Di Dalmazi. Radiomics for immunohistochemistry prediction in pheochromocytoma: a pilot study. Endocrine Abstracts (2024) 99 EP326, https//doi.org/10.1530/endoabs.99.EP326
  8. LIFEx-texture: Philip, M., Watts, J., Welch, A., McKiddie, F., Nath, M. XGBoost classifier-based survival prediction in head and neck cancer patients using pre-treatment PET images. 27th Conference on Medical Image Understanding and Analysis 2023. Foresterhill, Aberdeen, Scotland p192. https://www.pure.ed.ac.uk/ws/portalfiles/portal/409666338/9782832512319_1_.PDF

 

Review (14):

  1. LIFEx-texture: Víctor M. Oyervides-Juárez, Alder E. Perales-Mendoza, Sofía N. Sánchez-Morales, Marianela Madrazo-Morales, Mayela Z. Gutiérrez-Guajardo*, and Oscar Vidal-Gutiérrez. The innovation of mediastinal staging in lung cancer with artificial intelligence. Medicina Universitaria,  2024;26(3):86-91 https://doi.org/10.24875/RMU.24000007
  2. LIFEx-texture: Zhang, Y., Huang, W., Jiao, H. et al. PET radiomics in lung cancer: advances and translational challenges. EJNMMI Phys 11, 81 (2024). https://doi.org/10.1186/s40658-024-00685-5
  3. LIFEx-texture: Aouadi, Souha, et al. ‘Review of Cervix Cancer Classification Using Radiomics on Diffusion-Weighted Imaging’. Biomedical Engineering, IntechOpen, 31 July 2024. Crossref, https://doi.org/10.5772/intechopen.107497
  4. LIFEx-texture: Dong, D. et al. (2024). Radiomics and Multiomics Research. In: Liu, S. (eds) Artificial Intelligence in Medical Imaging in China. Springer, Singapore. https://doi.org/10.1007/978-981-99-8441-1_4
  5. LIFEx-texture: Amrane, K., Meur, C.L., Thuillier, P. et al. Review on radiomic analysis in 18F-fluorodeoxyglucose positron emission tomography for prediction of melanoma outcomes. Cancer Imaging 24, 87 (2024). https://doi.org/10.1186/s40644-024-00732-5
  6. LIFEx-texture: Zhaoshuo Diao, Huiyan Jiang. A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features. Computers in Biology and Medicine, 2024, 108461, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2024.108461
  7. LIFEx-main: Varlamova, E.V.; Butakova, M.A.; Semyonova, V.V.; Soldatov, S.A.; Poltavskiy, A.V.; Kit, O.I.; Soldatov, A.V. Machine Learning Meets Cancer. Cancers 2024, 16, 1100. https://doi.org/10.3390/cancers16061100
  8. LIFEx-texture: Tapper, W.; Carneiro, G.; Mikropoulos, C.; Thomas, S.A.; Evans, P.M.; Boussios, S. The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer. J. Pers. Med. 2024, 14, 287. https://doi.org/ 10.3390/jpm14030287
  9. LIFEx-texture: Anghel, C.; Grasu, M.C.; Anghel, D.A.; Rusu-Munteanu, G.-I.; Dumitru, R.L.; Lupescu, I.G. Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images. Diagnostics 2024, 14, 438. https://doi.org/10.3390/diagnostics14040438
  10. LIFEx-texture: Shiva Singh, Bahram Mohajer, Shane A. Wells, Tushar Garg, Kate Hanneman, Takashi Takahashi, Omran AlDandan, Morgan P. McBee, Anugayathri Jawahar. Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research, Academic Radiology,
    2024, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2024.01.024
  11. LIFEx-texture: Ballal et al. (2023). A systematic review of the management and implications of radiation-induced lymphopenia and the predictive rate of radiomic-based approaches in lung cancer Multidiscip. Rev. (2023) 6:e2023ss008, Supplementary Issue: Medical (AlliedCon 2023). https://doi.org/10.31893/multirev.2023ss008
  12. LIFEx-texture: Akin, O.; Lema-Dopico, A.; Paudyal, R.; Konar, A.S.; Chenevert, T.L.; Malyarenko, D.; Hadjiiski, L.; Al-Ahmadie, H.; Goh, A.C.; Bochner, B.; et al. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers 2023, 15, 5468. https://doi.org/10.3390/ cancers15225468
  13. LIFEx-texture: Shiva Singh, Bahram Mohajer, Shane A. Wells, Tushar Garg, Kate Hanneman, Takashi Takahashi, Omran AlDandan, Morgan P. McBee, Anugayathri Jawahar. Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research,
    2024, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2024.01.024
  14. LIFEx-texture: Liu, J.; Cundy, T.P.; Woon, D.T.S.; Lawrentschuk, N. A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans. Cancers 2024, 16, 486. https://doi.org/10.3390/cancers16030486

 

Supplement (16):

  1. LIFEx-main: S Soares Brandao, A G S M Saura Martins, R J C A M Cavalcanti Amorim Martins, J M D R S Duarte Ribeiro Sobrinho, M M C B De Moraes Chaves Becker, R O B De Oliveira Buril, V O M De Oliveira Menezes, F A M Alves Mourato, Nearly perfect reproducibility degree of computed tomography in the evaluation of subcutaneous, visceral, and epicardial adipose volumes and radiodensities in lymphoma patients, European Heart Journal - Cardiovascular Imaging, Volume 25, Issue Supplement_1, July 2024, jeae142.015, https://doi.org/10.1093/ehjci/jeae142.015
  2. LIFEx-main: S Soares Brandao, R J C A M Cavalcanti Amorim Martins, A G S M Saura Martins, J M D R S Duarte Ribeiro Sobrinho, M M C B De Moraes Chaves Becker, R O B De Oliveira Buril, V O M De Oliveira Menezes, F A M Alves Mourato, Comparative analysis of volume and distribution of body fat in patients with lymphoma before and after chemotherapy, European Heart Journal - Cardiovascular Imaging, Volume 25, Issue Supplement_1, July 2024, jeae142.014, https://doi.org/10.1093/ehjci/jeae142.014
  3. LIFEx-texture: Auriac Julie, Mathilde Droguet, Lalith Kumar Shiyam Sundar, Romain-David Seban, Marie Luporsi, Manuel Pires, Christophe Nioche, Thomas Beyer, François-Clément Bidard, Irene Buvat, Fanny Orlhac. Prognostic stratification of metastatic triple-negative breast cancer patients using PET-radiomic features from malignant and tumor-free regions. Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241952; http://jnm.snmjournals.org/content/65/supplement_2/241952.abstract
  4. LIFEx-MTV: Fanny Orlhac, Narinée Hovhannisyan Baghdasarian, Hornella Fokem-Fosso, Marie Luporsi, HubertTissot, Christophe Nioche, Alain Livartowski, Paulette Salamoun-Feghali, Nadia Hegarat, NicolasGirard, Irene Buvat. Quantification of lesion dissemination (Dmax) in [18F]FDG-PET/CT imaging: a prognostic factor complementary to Total Metabolic Tumor Volume (TMTV) for advanced non-small cell lung cancer patients. Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241937; http://jnm.snmjournals.org/content/65/supplement_2/241927.abstract
  5. LIFEx-main: Auriac Julie, Lalith Kumar Shiyam Sundar, Romain-David Seban, Marie Luporsi, Christophe Nioche, Thomas Beyer, Irene Buvat, Fanny Orlhac. MOOSE vs TotalSegmentator: Comparison of feature values of segmented anatomical regions in [18F]FDG PET/CT images Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241948; http://jnm.snmjournals.org/content/65/supplement_2/241927.abstract
  6. LIFEx-MTV: Mathilde Droguet, Lalith Kumar Shiyam Sundar, Manuel Pires, Narinée Hovhannisyan Baghdasarian, Nicolas Captier, Marie Luporsi, Erwin Woff, Christophe Nioche, Frédérique Frouin, Thomas Beyer, Nicolas Girard, Irene Buvat, Fanny Orlhac. Automated segmentation of lesions in [18F]FDG PET/CT images of lung cancer patients: external evaluation of an AI-driven lesion segmentation tool (LION). Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241927; http://jnm.snmjournals.org/content/65/supplement_2/241927.abstract
  7. LIFEx-texture: Victor Comte, Hornella Fokem-Fosso, Olivier Humbert, Narinée Hovhannisyan Baghdasarian, NicolasCaptier, Marie Luporsi, Erwin Woff, Christophe Nioche, Nicolas Girard, Irene Buvat, Fanny Orlhac. Development and external validation of a PET-radiomic model to predict overall survival in advanced NSCLC patients treated by immunotherapy. Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241256; ; http://jnm.snmjournals.org/content/65/supplement_2/241256.abstract
  8. LIFEx-texture: Dwivedi Pooja, Jha Ashish, Choudhury Sayak, Barage Sagar and RANGARAJAN, VENKATESH. Exploring the impact of feature selection methods and classification algorithms on the predictive performance of PET radiomic ML models in lung cancer ; Journal of Nuclear Medicine, J Nucl Med, 24133, 24133, 65, supplement 2, 2024/06/01; http://jnm.snmjournals.org/content/65/supplement_2/24133.abstract
  9. LIFEx-texture:  Monica Yadav, Jeeyeon Lee, Haseok Kim, Seyoung Lee, Taegyu Um, Salie Lee, Trie Arni Djunadi, Liam IL Young Chung, Jisang Yu, DarrenRodrigues, Nicolo Gennaro, Leeseul Kim, Yuchan Kim, Myungwoo Nam, Ilene Hong, Jessica Jang, Amy Cho, Grace Kang, Yury Velichko, and Young Kwang Chae. Harmonization radiomics model to predict immune checkpoint inhibitor-related pneumonitis (CIP) in patients with non-small cell lung cancer (NSCLC). Meeting Abstract: 2024 ASCO Annual Meeting I. Journal of Clinical Oncology. Volume 42, Number 16_suppl. https://ascopubs.org/doi/abs/10.1200/JCO.2024.42.16_suppl.12142
  10. LIFEx-texture: Koki Enomoto, Soichiro Yoshida, Haruto Izumi, Sho Uehara, Yoh Matsuoka, Kohei Yamamoto, Daisuke Hirahara, Tatsunori Saho, Eichi Takaya, Shohei Fukuda, Yuma Waseda, Hajime Tanaka, Kenichi Ohashi and Yasuhisa Fujii. Are the differences in MRI findings between CRIBRIFORM and NON-CRIBRIFORM Cancer? An analysis using radiomics and delta-radiomics. The Journal of urology. Vol. 211, No. 5S, Supplement, Saturday, May 4, 2024; e443.https://doi.org/10.1097/01.JU.0001009448.41537.64.09
  11. LIFEx-texture: M Winkelmann, V Blumenberg, K Rejeski, V Bücklein, C Schmidt, F Dekorsy, P Bartenstein, J Ricke, M Subklewe, W Kunz. Charakterisierung des International Metabolic Prognostic Index (IMPI) und seiner Komponenten im Rahmen der CAR-T-Zell-Behandlung von Lymphomen. Rofo 2024; 196(S 01): S51. https://doi.org/10.1055/s-0044-1781616
  12. LIFEx-texture: Abstracts - 23rd FHNO Conference, 2023. Journal of Head & Neck Physicians and Surgeons 12(Suppl 2):p S1-S115, April 2024. | DOI: 10.4103/2347-8128.243190
  13. LIFEx-texture: Seyoung Lee, Kai Zhang, Jeeyeon Lee, Peter Haseok Kim, Amogh Hiremath, Salie Lee, Monica Yadav, Maria J. Chuchuca, Taegyu Um, Myungwoo Nam, Liam Il-Young Chung, Hye Sung Kim, Jisang Yu, Trie Arni Djunadi, Leeseul Kim, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Grace Kang, Jessica Jang, Amy Cho, Soowon Lee, Cecilia Nam, Timothy Hong, Yuri S. Velichko, Anant Madabhushi, Nathaniel Braman, Young Kwang Chae. Accelerated and precise tumor segmentation in NSCLC: A comparative analysis of automated ClickSeg and manual annotation for radiomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2595. https://doi.org/10.1158/1538-7445.AM2024-2595
  14. LIFEx-texture: Monica Yadav, Jeeyeon Lee, Peter Haseok Kim, Seyoung Lee, Taegyu Um, Salie Lee, Maria Jose Chuchuca, Trie Arni Djunadi, Liam Il-Young Chung, Jisang Yu, Darren Rodrigues, Nicolo Gennaro, Leeseul Kim, Myungwoo Nam, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Jessica Jang, Grace Kang, Amy Cho, Soowon Lee, Timothy Hong, Cecilia Nam, Yury S Velichko, Young Kwang Chae. Harmonization radiomics models to predict tumor response in non-small cell lung cancer (NSCLC) patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7530. https://doi.org/10.1158/1538-7445.AM2024-7530
  15. LIFEx-texture: Monica Yadav, Jeeyeon Lee, Peter Haseok Kim, Seyoung Lee, Taegyu Um, Salie Lee, Maria Jose Chuchuca, Trie Arni Djunadi, Liam Il-Young Chung, Jisang Yu, Darren Rodrigues, Nicolo Gennaro, Leeseul Kim, Myungwoo Nam, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Jessica Jang, Grace Kang, Amy Cho, Soowon Lee, Timothy Hong, Cecilia Nam, Yury S Velichko, Young Kwang Chae. Harmonization radiomics model to predict immune checkpoint inhibitor-related pneumonitis (CIP) in non small cell lung cancer (NSCLC) in patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7529. https://doi.org/10.1158/1538-7445.AM2024-7529
  16. LIFEx-texture: Seyoung Lee, Amogh Hiremath, Jeeyeon Lee, Peter Haseok Kim, Kai Zhang, Salie Lee, Monica Yadav, Maria J. Chuchuca, Taegyu Um, Myungwoo Nam, Liam Il-Young Chung, Hye Sung Kim, Jisang Yu, Trie Arni Djunadi, Leeseul Kim, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Grace Kang, Jessica Jang, Amy Cho, Soowon Lee, Cecilia Nam, Timothy Hong, Yuri S. Velichko, Vamsidhar Velcheti, Anant Madabhushi, Nathaniel Braman, Young Kwang Chae. AI-powered radiomics model predicts immune checkpoint inhibitor-related pneumonitis (CIP) in advanced NSCLC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2594. https://doi.org/10.1158/1538-7445.AM2024-2594

Others (9):

  1. LIFEx-texture: Lian, A. et al. Correlation between Changes in Primary Tumor Radiomic Features and Response during Definitive Radiation for Lung Cancer. International Journal of Radiation Oncology, Biology, Physics, Volume 120, Issue 2, e155 - e156 (link)
  2. LIFEx-texture: The value of machine learning models based on 18F-FDG PET/CT radiomics for predicting the degree of tumour differentiation in patients with non-small cell lung cancer YU Jun, LI Yang, YANG Xue, BI Xiaofeng, REN Dongdong, REN Chunling, HUANG Lei. Department of Nuclear Medicine, Ningbo Mingzhou. Hospital, Ningbo 315100, China. R734.2; R445.6 DOI: 10.3969/j.issn.2095-9400.2024.09.003 ;
  3. LIFEx-MTV: S. Gülbahar Ateş, B.B. Demirel, E. Kekilli, E. Öztürk, G. Uçmak. Heterogeneidad del tumor primario en la PET/TC con [68Ga]Ga-PSMA previa al tratamiento para la predicción de la recurrencia bioquímica en el cáncer de próstata. Revista Española de Medicina Nuclear e Imagen Molecular, 2024, 500032, ISSN 2253-654X, https://doi.org/10.1016/j.remn.2024.500032
  4. LIFEx-texture: N. Agüloğlu, A. Aksu, D.S. Unat, Ö. Selim Unat. Valor del análisis de la textura radiómica de la masa primaria y el ganglio linfático mediastínico de la PET/TC en la supervivencia de pacientes con cáncer de pulmón de célula no pequeña. Revista Española de Medicina Nuclear e Imagen Molecular. 2024, 500027, ISSN 2253-654X, https://doi.org/10.1016/j.remn.2024.500027
  5. LIFEx-texture: Shiyuan Liu. Artificial Intelligence in Medical Imaging in China. Springer Singapore, 978-981-99-8441-1, 02 August 2024 https://doi.org/10.1007/978-981-99-8441-1
  6. LIFEx-texture: Yesh Datar, Sarah A.M. Cuddy, Gavin Ovsak, Gerard T. Giblin, BCh, Mathew S. Maurer, Frederick L. Ruberg, Rima Arnaout, Sharmila Dorbala. Myocardial Texture Analysis of Echocardiograms in Cardiac Transthyretin Amyloidosis. Brief Research Communication| Volume 37, ISSUE 5, P570-573, May 2024. https://doi.org/10.1016/j.echo.2024.02.005
  7. LIFEx-MTV: Jiang Chong, Teng Yue, Ding Chongyang. Survival prognosis analysis of diffuse large B-cell lymphoma patients using tumor distribution patterns and metabolic tumor volume prediction with 18F-FDG PET[J]. International Journal of Radiation Medicine and Nuclear Medicine, 2024, 48(0): 1-8. https://doi.org/10.3760/cma.j.cn121381-202306031-00412
  8. LIFEx-texture: Contreras Aguilar, M. T., Salazar Calderon, D. R., Moreno Jimenez, S., & Chilaca Rosas, M. F. (2024). Determination of volumetry and compacity with a radiomics platform of high-grade CNS gliomas treated with radiotherapy. Archivos De Neurociencias, 29(S1). Retrieved from https://archivosdeneurociencias.org/index.php/ADN/article/view/522
  9. LIFEx-texture: Khromova S.V., Karmazanovsky G.G., Karelskaya N.A., Gruzdev I.S. The texture analysis of computed tomography studies in clear cell renal cell carcinoma: reproducibility of 2D and 3D segmentation. Almanac of clinical medicine. ISSN 2587-9294. Vol 51, No 8 (2023) https://doi.org/10.18786/2072-0505-2024-52-007