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Publications 2022 - Journal papers (166)

  1. LIFEx-texture: Ketcherside T, Shi C, Chen Q, et al. Evaluation of repeatability and reproducibility of radiomic features produced by the fan-beam kV-CT on a novel ring gantry-based PET/CT linear accelerator. Med Phys. 2023;50:3719–3725. https://doi.org/10.1002/mp.16399
  2. LIFEx-texture: Liu JJ, Wang YZ, Chen N, Wang QN, Liu L, Li Y, Lei L, Wu Y. Hypothesis generation: Quantitative research to levator ani muscle injury based on MRI texture analysis. J Obstet Gynaecol Res. 2022 Dec;48(12):3269-3278. https://doi.org/10.1111/jog.15440. Epub 2022 Sep 27. PMID: 36167929.
  3. LIFEx-texture: Altay C, Başara Akın I, Özgül AH, Adıyaman SC, Yener AS, Seçil M. Machine learning analysis of adrenal lesions: Preliminary study evaluating texture analysis in the differentiation of adrenal lesions. Diagn Interv Radiol. DOI: 10.5152/dir.2022.21266 (doi)
  4. LIFEx-texture: Annovazzi, A.; Ferraresi, V.; Covello, R.; Ascione, A.; Vari, S.; Petrongari, M.G.; Baldi, J.; Biagini, R.; Sciuto, R. Prognostic Value of Pre-Treatment [18F]FDG PET/CT Texture Analysis in Undifferentiated Soft-Tissue Sarcoma. J. Clin. Med. 2023, 12, 279. https://doi.org/10.3390/jcm12010279 (doi)
  5. Vuijk, F.A.; Kleiburg, F.; Noortman, W.A.; Heijmen, L.; Feshtali Shahbazi, S.; van Velden, F.H.P.; Baart, V.M.; Bhairosingh, S.S.; Windhorst, B.D.; Hawinkels, L.J.A.C.; et al. Prostate-Specific Membrane Antigen Targeted Pet/CT Imaging in Patients with Colon, Gastric and Pancreatic Cancer. Cancers 2022, 14, 6209. https://doi.org/10.3390/cancers14246209 (doi)
  6. LIFEx-texture: Hinzpeter, R.; Kulanthaivelu, R.; Kohan, A.; Avery, L.; Pham, N.-A.; Ortega, C.; Metser, U.; Haider, M.; Veit-Haibach, P. CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling. Cancers 2022, 14, 6224. https://doi.org/10.3390/cancers14246224 (doi)
  7. LIFEx-texture: Peter McAnena, Brian M. Moloney, Robert Browne, Niamh O’Halloran, Leon Walsh, Sinead Walsh, Declan Sheppard, Karl J. Sweeney, Michael J. Kerin and Aoife J. Lowery. A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer. McAnena et al. BMC Medical Imaging. (2022) 22:225 https://doi.org/10.1186/s12880-022-00956-6 (doi)
  8. LIFEx-texture: Wu FZ, Wu YJ, Tang EK. An integrated nomogram combined semantic-radiomic features to predict invasive pulmonary adenocarcinomas in subjects with persistent subsolid nodules. Quant Imaging Med Surg 2022. doi:10.21037/qims-22-308 (doi)
  9. LIFEx-MTV: Winkelmann, M., Blumenberg, V., Rejeski, K. et al. Prognostic value of the International Metabolic Prognostic Index for lymphoma patients receiving chimeric antigen receptor T-cell therapy. Eur J Nucl Med Mol Imaging (2022). https://doi.org/10.1007/s00259-022-06075-2 (doi)
  10. LIFEx-texture: Assadi M, Manafi-Farid R, Jafari E, Keshavarz A, Divband G, Moradi MM, Adinehpour Z, Samimi R, Dadgar H, Jokar N, Mayer B and Prasad V (2022) Predictive and prognostic potential of pretreatment 68Ga-PSMA PET tumor heterogeneity index in patients with metastatic castration-resistant prostate cancer treated with 177Lu-PSMA. Front. Oncol. 12:1066926. doi: 10.3389/fonc.2022.1066926 (doi)
  11. LIFEx-texture: De Robertis, R.; Tomaiuolo, L.; Pasquazzo, F.; Geraci, L.; Malleo, G.; Salvia, R.; D’Onofrio, M. Correlation between ADC Histogram-Derived Metrics and the Time to Metastases in Resectable Pancreatic Adenocarcinoma. Cancers 2022, 14, 6050. https://doi.org/ 10.3390/cancers14246050 (doi)
  12. LIFEx-texture: Stoehr F, Kloeckner R, Pinto dos Santos D, Schnier M, Müller L, Mähringer-Kunz A, Dratsch T, Schotten S, Weinmann A, Galle PR, Mittler J, Düber C, Hahn F. Radiomics-Based Prediction of Future Portal Vein Tumor Infiltration in Patients with HCC—A Proof-of-Concept Study. Cancers. 2022; 14(24):6036. https://doi.org/10.3390/cancers14246036 (doi)
  13. LIFEx-texture: Yu K, Ying J, Zhao T, Lei L, Zhong L, Hu J, Zhou JW, Huang C, Zhang X. Prediction model for knee osteoarthritis using magnetic resonance–based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative. Quant Imaging Med Surg 2023;13(1):352-369. doi: 10.21037/qims-22-368 (doi)
  14. LIFEx-texture: Gong H, Tang B, Li T, Li J, Tang L, Ding C. The added prognostic values of baseline PET dissemination parameter in patients with angioimmunoblastic T-cell lymphoma. eJHaem. 2022;1–11 (doi)
  15. LIFEx-texture: Alencar NRG, Machado MAD, Mourato FA, Oliveira ML, Moraes TF, Mattos Junior LAR, Chang TC, Azevedo CRAS and Brandão SCS (2022) Exploratory analysis of radiomic as prognostic biomarkers in 18F-FDG PET/CT scan in uterine cervical cancer. Front. Med. 9:1046551. doi: 10.3389/fmed.2022.1046551 (doi)
  16. LIFEx-main: Ahrari, S.; Zaragori, T.; Bros, M.; Oster, J.; Imbert, L.; Verger, A. Implementing the Point Spread Function Deconvolution for Better Molecular Characterization of Newly Diagnosed Gliomas: A Dynamic 18F-FDOPA PET Radiomics Study. Cancers 2022, 14, 5765. https://doi.org/10.3390/cancers14235765 (doi)
  17. LIFEx-texture: Xue, Xq., Yu, WJ., Shao, XL. et al. Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer. Abdom Radiol (2022) https://doi.org/10.1007/s00261-022-03738-4 (doi)
  18. LIFEx-main: Leslie Guzene, Arnaud Beddok, Christophe Nioche, Romain Modzelewski, Cedric Loiseau, Julia Salleron, Juliette Thariat. Assessing inter-observer variability in the delineation of structures in radiation oncology: A systematic review. International Journal of Radiation Oncology*Biology*Physics, 2022, ISSN 0360-3016 (doi)
  19. LIFEx-texture: Zhang, T.; Xiang, Y.; Wang, H.; Yun, H.; Liu, Y.; Wang, X.; Zhang, H. Radiomics Combined with Multiple Machine Learning Algorithms in Differentiating Pancreatic Ductal  Adenocarcinoma from Pancreatic Neuroendocrine Tumor: More Hands Produce a Stronger Flame.  J. Clin. Med. 2022, 11, 6789. https://doi.org/10.3390/jcm11226789 (doi)
  20. LIFEx-texture: Berchiolli, R.; Torri, L.; Bertagna, G.; Canovaro, F.; Zanca, R.; Bartoli, F.; Mocellin, D.M.; Ferrari, M.; Erba, P.A.; Troisi, N. [18 F]-Fludeoxyglucose Positron Emission Tomography/Computed Tomography with Radiomics Analysis in Patients Undergoing Aortic In-Situ Reconstruction with Cryopreserved Allografts. Diagnostics 2022, 12, 2831. https://doi.org/10.3390/diagnostics12112831 (doi)
  21. LIFEx-texture: Chen, NB., Xiong, M., Zhou, R. et al. CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment. Radiat Oncol 17, 184 (2022). https://doi.org/10.1186/s13014-022-02136-w (doi)
  22. LIFEx-texture: Li J, Zhang B, Ge S, Deng S, Hu C and Sang S (2022) Prognostic value of 18F-FDG PET/CT radiomic model based on primary tumor in patients with non-small cell lung cancer: A large single-center cohort study. Front. Oncol. 12:1047905. doi: 10.3389/fonc.2022.1047905 (doi)
  23. LIFEx-texture: Rozenblum, L., Zaragori, T., Tran, S. et al. Differentiating high-grade glioma progression from treatment-related changes with dynamic [18F]FDOPA PET: a multicentric study. Eur Radiol (2022)(doi)
  24. LIFEx-texture: Analisi radiomica di tumori cerebrali studiati con 18F-DOPA PET/CT= Radiomics analysis of brain tumor studied with 18F-DOPA PET/CT - Thesis - AM Torre - 2022 (link)
  25. LIFEx-texture: Gruzdev I.S., Karmazanovsky G.G., Lapteva M.G., Zamyatina K.A., Tikhonova V.S., Kondratyev E.V., Struchkov V.Yu., Glotov A.V., Proskuryakov I.S., Podluzhny D.V., Revishvili A.Sh. Texture and CT-features in differentiation of hypervascular pancreatic neuroendocrine tumors from renal cell carcinoma metastases: diagnostic model. Medical Visualization. 2022. https://doi.org/10.24835/1607-0763-1247 (doi)
  26. LIFEx-texture: Meng Dai, Na Wang, Xinming Zhao, Jianyuan Zhang, Zhaoqi Zhang, Jingmian Zhang, Jianfang Wang, Yujing Hu, Yunuan Liu, Xiujuan Zhao and Xiaolin Chen. Value of Presurgical 18F-FDG PET/CT Radiomics for Predicting Mediastinal Lymph Node Metastasis in Patients with Lung Adenocarcinoma. CANCER BIOTHERAPY AND ADIOPHARMACEUTICALS Volume 00, Number 00, 2022 - Mary Ann Liebert, Inc. - DOI: 10.1089/cbr.2022.0038 (doi)
  27. LIFEx-texture: Moataz A.S. Soliman, Linda C. Kelahan, Michael Magnetta, Hatice Savas, Rishi Agrawal, Ryan J. Avery, Pascale Aouad, Benjamin Liu, Yue Xue, Young K. Chae, Riad Salem, Al B. Benson, Vahid Yaghmai and Yuri S. Velichko. A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials. JCO Clinical Cancer Informatics. September 21, 2022 (doi)
  28. LIFEx-texture: : Hannequin P, Decroisette C, Kermanach P, Berardi G, Bourbonne V. FDG PET and CT radiomics in diagnosis and prognosis of non-small-cell lung cancer. Transl Lung Cancer Res 2022;11(10):2051-2063. doi: 10.21037/tlcr-22- 158 (doi)
  29. LIFEx-texture: M. Hatt · A. K. Krizsan  · A. Rahmim  · T. J. Bradshaw  · P. F. Costa  · A. Forgacs  · R. Seifert · A. Zwanenburg  · I. El Naqa · P. E. Kinahan · F. Tixier  · A. K. Jha · D. Visvikis. Joint EANM/SNMMI guideline on radiomics in nuclear medicine. European Journal of Nuclear Medicine and Molecular Imaging (doi)
  30. LIFEx-texture: Qian LD, Feng LJ, Zhang SX, Liu J, Ren JL, Liu L, Zhang H, Yang J. 18F-FDG PET/CT imaging of pediatric peripheral neuroblastic tumor: a combined model to predict the International Neuroblastoma Pathology Classification. Quant Imaging Med Surg 2022. (doi)
  31. LIFEx-texture: Kulanthaivelu R, Kohan A, Hinzpeter R, Liu ZA, Hope A, Huang SH, Waldron J, O’Sullivan B, Ortega C, Metser U and Veit-Haibach P (2022) Prognostic value of PET/CT and MR-based baseline radiomics among patients with non-metastatic nasopharyngeal carcinoma. Front. Oncol. 12:952763 (doi)
  32. LIFEx-texture: Hinzpeter, R.; Mirshahvalad, S.A.; Kulanthaivelu, R.; Ortega, C.; Metser, U.; Liu, Z.A.; Elimova, E.; Wong, R.; Yeung, J.; Jang, R.W.: Veit-Haibach, P. Prognostic Value of [18F]-FDG PET/CT Radiomics Combined with Sarcopenia Status among Patients with Advanced Gastroesophageal Cancer. Cancers 2022, 14, 5314 (doi)
  33. LIFEx-texture: Sun R, Lerousseau M, Briend-­Diop J, et al. Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-­PD1 immunotherapy. Journal for ImmunoTherapy of Cancer 2022;10:e004867 (doi)
  34. LIFEx-texture: Adrián A. Negreros‑Osuna, Diego A. Ramírez‑Mendoza, Claudio Casas‑Murillo, Abraham Guerra‑Cepeda, David Hernández‑Barajas, Guillermo Elizondo‑Riojas. Clinical‑radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors. ONCOLOGY LETTERS 24: 446, 2022 (doi)
  35. LIFEx-texture: Roy, S.; Meena, T.; Lim, S.-J. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine. Diagnostics 2022, 12, 2549 (doi)
  36. LIFEx-texture: Negreros‑Osuna, A. A., Ramírez‑Mendoza, D. A., Casas‑Murillo, C., Guerra‑Cepeda, A., Hernández‑Barajas, D., Elizondo‑Riojas, G."Clinical‑radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors". Oncology Letters 24.6 (2022): 446 (doi)
  37. LIFEx-texture: Feng, C.; Zhou, Z.; Huang, Q.; Meng, X.; Li, Z.; Wang, Y. Radiomics Nomogram Based on High-b-Value Diffusion-Weighted Imaging for Distinguishing the Grade of Bladder Cancer. Life 2022, 12, 1510 (doi)
  38. LIFEx-texture: Ling liang, Haiyan zhang, Haike Lei, Hong Zhou,Yongzhong Wu, and Jiang shen. Diagnosis of Benign and Malignant PulmonaryGround-Glass Nodules Using ComputedTomography Radiomics Parameters. Technology in Cancer Research & Treatment. October 19, 2022 (doi)
  39. LIFEx-texture: 18F-FDG PET maximum intensity projections and artificial intelligence: a win-win combination to easily measure prognostic biomarkers in DLBCL patients. Kibrom Berihu Girum, Louis Rebaud, Anne-Ségolène Cottereau, Michel Meignan, Jérôme Clerc, Laetitia Vercellino, Olivier Casasnovas, Franck Morschhauser, Catherine Thieblemont, Irène Buvat. Journal of Nuclear Medicine Jun 2022, jnumed.121.263501(doi)
  40. LIFEx-texture: Yi J, Lei X, Zhang L, et al. The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer. Technology in Cancer Research & Treatment. 2022;21 (doi)
  41. LIFEx-texture: Ahn, H.; Song, G.J.; Jang, S.-H.; Son, M.W.; Lee, H.J.; Lee, M.-S.; Lee, J.-H.; Oh, M.-H.; Jeong, G.C.; Yun, J.H.; et al. Predicting the Recurrence of Gastric Cancer Using the Textural Features of Perigastric Adipose Tissue on [18F]FDG PET/CT. Int. J. Mol. Sci. 2022, 23, 11985 (doi)
  42. LIFEx-texture: Ahn, H.; Song, G.J.; Jang, S.-H.; Son, M.W.; Lee, H.J.; Lee, M.-S.; Lee, J.-H.; Oh, M.-H.; Jeong, G.C.; Yun, J.H.; et al. Predicting the Recurrence of Gastric Cancer Using the Textural Features of Perigastric Adipose Tissue on [18F]FDG PET/CT. Int. J. Mol. Sci. 2022, 23, 11985 (doi)
  43. LIFEx-texture: Teng Y, Chen C, Zhang Y, Xu J. The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma. Transl Cancer Res 2022 (doi)
  44. LIFEx-texture: Tran, V.T.; Tu, S.-J.; Tseng, J.-R. 68Ga-PSMA-11 PET/CT Features Extracted from Different Radiomic Zones Predict Response to Androgen Deprivation Therapy in Patients with Advanced Prostate Cancer. Cancers 2022, 14, 4838 (doi)
  45. LIFEx-MTV: Durmo R, Donati B, Rebaud L, etal. Prognostic value of lesion dissemination in doxorubicin, bleomycin, vinblastine, and dacarbazine‐treated, interimPET‐negative classical Hodgkin Lymphoma patients: A radio‐genomic study. Hematol Oncol.  2022;40(4):645‐657 (doi)
  46. LIFEx-texture: Botta, F.; Ferrari, M.; Raimondi, S.; Corso, F.; Lo Presti, G.; Mazzara, S.; Airò Farulla, L.S.; Radice, T.; Vanazzi, A.; Derenzini, E.; et al. The Impact of Segmentation Method and Target Lesion Selection on Radiomic Analysis of 18 F-FDG PET Images in Diffuse Large B-Cell Lymphoma. Appl. Sci. 2022, 12, 9678 (doi)
  47. LIFEx-texture:  Saveria Mazzara, Laura Travaini, Francesca Botta, Chiara Granata, Giovanna Motta, Federica Melle, Stefano Fiori, Valentina Tabanelli, Anna Vanazzi, Safaa Ramadan, Tommaso Radice, Sara Raimondi, Giuliana Lo Presti, Mahila E. Ferrari, Barbara Alicja, Jereczek-Fossa, Corrado Tarella, Francesco Ceci, Stefano Pileri, Enrico Derenzini. Gene expression profiling and FDG-PET radiomics uncover radiometabolic signatures associated with outcome in DLBCL American Society of Hematology. ADV-2022-007825R1 (link)
  48. LIFEx-texture: Huang Y-M, Wang T-E, Chen M-J, Lin C-C, Chang C-W, Tai H-C, Hsu S-M and Chen Y-J (2022) Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy. Front. Oncol. 12:906498. (doi)
  49. LIFEx-texture: Anne-Leen, D., Machaba, S., Alex, M. et al. Principal component analysis of texture features derived from FDG PET images of melanoma lesions. EJNMMI Phys 9, 64 (2022) (doi)
  50. LIFEx-texture: Hannequin P, Decroisette C, Kermanach P, Berardi G, Bourbonne V. FDG PET and CT radiomics in diagnosis and prognosis of non-small-cell lung cancer. Transl Lung Cancer Res 2022 (doi)
  51. LIFEx-viewer: Rahimpour, M., Saint Martin, MJ., Frouin, F. et al. Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI. Eur Radiol (2022) (doi)
  52. LIFEx-texture: De Robertis, R., Geraci, L., Tomaiuolo, L. et al. Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis. Radiol med (2022) (doi)
  53. LIFEx-texture: Mori N, Mugikura S, Endo T, Endo H, Oguma Y, Li L, Ito A, Watanabe M, Kanamori M, Tominaga T, Takase K. Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area. Neuroradiology. 2022 Aug 31. doi: 10.1007/s00234-022-03045-1. Epub ahead of print. PMID: 36044063.
  54. LIFEx-texture: Crimì, F.; Zanon, C.; Cabrelle, G.; Luong, K.D.; Albertoni, L.; Bao, Q.R.; Borsetto, M.; Baratella, E.; Capelli, G.; Spolverato, G.; et al. Contrast-Enhanced CT Texture Analysis in Colon Cancer: Correlation with Genetic Markers. Tomography 2022, 8, 2193–2201. (doi)
  55. LIFEx-texture: Toshihiro Nakao, Mitsuo Shimada, Kozo Yoshikawa, Takuya Tokunaga, Masaaki Nishi, Hideya Kashihara, Chie Takasu, Yuma Wada and Toshiaki Yoshimoto. Computed tomography texture analysis for the prediction of lateral pelvic lymph node metastasis of rectal cancer. World Journal of Surgical Oncology (2022) 20:281 (doi)
  56. LIFEx-texture: Sun R, Henry T, Laville A, et al. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? Journal for ImmunoTherapy of Cancer 2022;10:e004848 (doi)
  57. LIFEx-texture: Oliver Barachini, Michaela Schaer, Siroos Mirzaei, Klaus Hergan, Shahin Zandieh. Evaluation of MRI-based radiomic features in heart morphologic variations as a consequence of autoimmune thyroid disorders. Medicine (2022) 101:34 (doi)
  58. LIFEx-texture: Yunus, M.M.; Sabarudin, A.; Karim, M.K.A.; Nohuddin, P.N.E.; Zainal, I.A.; Shamsul, M.S.M.; Yusof, A.K.M. Reproducibility and Repeatability of Coronary Computed Tomography Angiography (CCTA) Image Segmentation in Detecting Atherosclerosis: A Radiomics Study. Diagnostics 2022, 12, 2007 (doi)
  59. LIFEx-texture: Tikhonova, V.S., Karmazanovsky, G.G., Kondratyev, E.V. et al. Radiomics model–based algorithm for preoperative prediction of pancreatic ductal adenocarcinoma grade. Eur Radiol (2022) (doi)
  60. LIFEx-texture: Pasini, G.; Bini, F.; Russo, G.; Comelli, A.; Marinozzi, F.; Stefano, A. matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model. J. Imaging 2022, 8, 221 (doi)
  61. LIFEx-texture: Ahn, H.; Song, G.J.; Jang, S.-H.; Lee, H.J.; Lee, M.-S.; Lee, J.-H.; Oh, M.-H.; Jeong, G.C.; Lee, S.M.; Lee, J.W. Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers. Cancers 2022, 14, 3936 (doi)
  62. LIFEx-texture: Jinling Yi, Xiyao Lei, Lei Zhang, Qiao Zheng, Juebin Jin, Congying Xie, Xiance Jin and Yao Ai. The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer. Technology in Cancer Research & Treatment Volume 21: 1-11 2022 (doi)
  63. LIFEx-texture: Laudicella, R.; Comelli, A.; Liberini, V.; Vento, A.; Stefano, A.; Spataro, A.; Crocè, L.; Baldari, S.; Bambaci, M.; Deandreis, D.; et al. [ 68Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [177Lu]DOTATOC PRRT: The “Theragnomics” Concept. Cancers 2022, 14, 984 (doi)
  64. LIFEx-texture: Xue X-q, Yu W-J, Shi X, Shao X-L and Wang Y-T (2022) 18F-FDG PET/CTbased radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer. Front. Oncol. 12:911168 (doi)
  65. LIFEx-texture: Fiz F, Bottoni G, Bini F, et al. Prognostic value of texture analysis of the primary tumour in high-risk neuroblastoma: An 18F-DOPA PET study. Pediatr Blood Cancer. 2022; e29910 (doi)
  66. LIFEx-texture: Dopamine dysfunction in depression: Takehiro Tamura, Genichi Sugihara, Kyoji Okita, Yohei Mukai, Hiroshi Matsuda, Hiroki Shiwaku, Shunsuke Takagi, Hiromitsu Daisaki, Ukihide Tateishi and Hidehiko Takahashi. Application of texture analysis to dopamine transporter single-photon emission computed tomography imaging. Translational Psychiatry (2022) 12:309 (doi)
  67. LIFEx-MTV: Francesca Tutino, Giulia Puccini, Flavia Linguanti, Benedetta Puccini, Luigi Rigacci, Sofya Kovalchuk, Roberto Sciagrà and Valentina Berti.  Baseline metabolic tumor volume calculation using different SUV thresholding methods. 0143-3636 Copyright © 2020 Wolters Kluwer Health (doi)
  68. LIFEx-texture: Anai, K., Hayashida, Y., Ueda, I. et al. The effect of CT texture-based analysis using machine learning approaches on radiologists' performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma. Jpn J Radiol (2022) (doi)
  69. LIFEx-texture: Seong-O Shim, Monagi H. Alkinani, Lal Hussain, Wajid Aziz. Feature Ranking Importance from Multimodal Radiomic Texture Features using Machine Learning Paradigm: A Biomarker to Predict the Lung Cancer. Big Data Research 29 (2022) 100331 (doi)
  70. LIFEx-texture-review: Dhivya Venkatesan, Ajay Elangovan, Harysh Winster, Md Younus Pasha, Kripa Susan Abraham, Satheeshkumar J, Sivaprakash P, Ayyadurai Niraikulam, Abilash Valsala Gopalakrishnan, Arul Narayanasamy, Balachandar Vellingiri. Diagnostic and therapeutic approach of artificial intelligence in neuro-oncological diseases. Biosensors and Bioelectronics: X, volume 11, sept 2022, 100188 (doi)
  71. LIFEx-texture-review: Sun R, Henry T, Laville A, et al. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?
     Journal for ImmunoTherapy of Cancer 2022;10:e004848 (doi)
  72. LIFEx-texture: Valladares, A, Beyer, T, Papp, L, Salomon, E, Rausch, I. A multi-modality physical phantom for mimicking tumor heterogeneity patterns in PET/CT and PET/MRI. Med Phys. 2022; 1- 11 (doi)
  73. LIFEx-texture: Bianconi, F.; Palumbo, I.; Fravolini, M.L.; Rondini, M.; Minestrini, M.; Pascoletti, G.; Nuvoli, S.; Spanu, A.; Scialpi, M.; Aristei, C.; et al. Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans. Sensors 2022, 22, 5044 (doi)
  74. LIFEx-texture: Nakajo M, Kawaji K, Nagano H, Jinguji M, Mukai A, Kawabata H, Tani A, Hirahara D, Yamashita M, Yoshiura T. The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer. Mol Imaging Biol. 2022 Jul 21. (doi)
  75. LIFEx-texture: Yunus, M.M.; Mohamed Yusof, A.K.; Ab Rahman, M.Z.; Koh, X.J.; Sabarudin, A.; Nohuddin, P.N.E.; Ng, K.H.; Kechik, M.M.A.; Karim, M.K.A. Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA). Diagnostics 2022, 12, 1660 (doi)
  76. LIFEx-texture: Zhang R, Hohenforst-Schmidt W, Steppert C, Sziklavari Z, Schmidkonz C, Atzinger A, Kuwert T, Klink T, Sterlacci W, Hartmann A, Vieth M, Förster S. Standardized 18F-FDG PET/CT radiomic features provide information on PD-L1 expression status in treatment-naïve patients with non-small cell lung cancer. Nuklearmedizin. 2022 Jun 29. English. doi: 10.1055/a-1816-6950. Epub ahead of print. PMID: 35768005. (doi)
  77. LIFEx-texture: Reza Jahangir, Alireza Kamali-Asl and Hossein Arabi. Deep Learning-Based Attenuation and Scatter Correction of Brain 18F-FDG PET Images in the Image Domain. arXiv preprint arXiv:2206.14673, 2022 (link)
  78. LIFEx-texture: Zhang R, Hohenforst-Schmidt W, Steppert C, Sziklavari Z, Schmidkonz C, Atzinger A, Kuwert T, Klink T, Sterlacci W, Hartmann A, Vieth M, Förster S. Standardized 18F-FDG PET/CT radiomic features provide information on PD-L1 expression status in treatment-naïve patients with non-small cell lung cancer. Nuklearmedizin. 2022 Jun 29. English (doi)
  79. LIFEx-texture: Soliter Pulmoner Nodüllerin Sınıflandırılmasında 18F-FDG PET/BT Radyomik Özelliklerine Dayalı Makine Öğrenme Modellerinin Tanısal Performansı. Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules. Mol Imaging Radionucl Ther 2022;31:82-88 (doi)
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Review (4):

  1. LIFEx-texture: Mirestean C.C., Iancu R.I. Iancu D.T. Delta-radiomics Entropy Based on Tumor Heterogeneity Concept-Response Predictor to Irradiation for Unresectable/recurrent Glioblastoma. Modern Medicine | 2022, Vol. 29, No. 4. (link)
  2. Hung, K.F.; Ai, Q.Y.H.; Wong, L.M.; Yeung, A.W.K.; Li, D.T.S.; Leung, Y.Y. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics 2023, 13, 110. https://doi.org/ 10.3390/diagnostics13010110 (doi)
  3. LIFEx-texture: Sotiris Raptis, Christos Ilioudis, Vasiliki Softa and Kiki Theodorou. Artificial Intelligence in Predicting Treatment Response in Non-Small-Cell Lung Cancer (NSCLC) BioMedical Journal of Scientific & Technical Research, Dec 2022 DOI: 10.26717/BJSTR.2022.47.007497 (doi)
  4.  LIFEx-texture:Li, S., Zhou, B. A review of radiomics and genomics applications in cancers: the way towards precision medicine. Radiat Oncol 17, 217 (2022). https://doi.org/10.1186/s13014-022-02192-2 (doi)

 

Thesis (1):

  1. LIFEx-texture: Negreros Osuna A.A. Análisis de la textura tomográfica en tumores renales en etapa avanzada como biomarcador para la predicción de respuesta al tratamiento sistémico con inhibidores de la tirosina quinasa. Doctor en medicina, Nov 2022. http://eprints.uanl.mx/24772/1/1080328720.pdf