1. LIFEx-texture: van Eijnatten EJM, Camps G, Guerville M, Fogliano V, Hettinga K, Smeets PAM. Milk coagulation and gastric emptying in women experiencing gastrointestinal symptoms after ingestion of cow's milk. Neurogastroenterology & Motility. 2024;36:e14696. https://doi.org/10.1111/nmo.14696
  2. LIFEx-MTV: Voltin, CA., Paccagnella, A., Winkelmann, M. et al. Multicenter development of a PET-based risk assessment tool for product-specific outcome prediction in large B-cell lymphoma patients undergoing CAR T-cell therapy. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06554-0
  3. LIFEx-texture: E. Babu, Ravi Krishna, Dathu Anushka, Medharimetla Lokesh, Boda Laila, Begari Mohan. Customized 3D CNN Model-based Lung Cancer Classification from Chest X-ray Images. IJARST. Volume 13, Issue 12, Dec 2023 ISSN 2457-0362, Page 268. https://www.ijarst.in/public/uploads/paper/516491702544047.pdf
  4. LIFEx-MTV: Alexander Dierks, Alexander Gäble, Andreas Rinscheid, Georgine Wienand, Christian H. Pfob, MalteKircher, Johanna S. Enke, Tilman Janzen, Marianne Patt, Martin Trepel, Dorothea Weckermann, Ralph A. Bundschuh, Constantin Lapa. First Safety and Efficacy Data with the Radiohybrid 177Lu-rhPSMA-10.1 for the Treatment of Metastatic Prostate Cancer. Journal of Nuclear Medicine Dec 2023, jnumed.123.266741; https://doi.org/10.2967/jnumed.123.266741
  5. LIFEx-Main: Pitarch, G., Rotstein Habarnau, Y., Chirico, R. et al. Exploring the applicability of a lesion segmentation method on [18F]fluorothymidine PET/CT images in diffuse large B-cell lymphoma. European J Hybrid Imaging 7, 28 (2023). https://doi.org/10.1186/s41824-023-00184-3
  6. LIFEx-MTV: Voltin, CA., Paccagnella, A., Winkelmann, M. et al. Multicenter development of a PET-based risk assessment tool for product-specific outcome prediction in large B-cell lymphoma patients undergoing CAR T-cell therapy. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06554-0
  7. LIFEx-texture: Hsiao C-C, Peng C-H, Wu F-Z, Cheng D-C. Impact of Voxel Normalization on a Machine Learning-Based Method: A Study on Pulmonary Nodule Malignancy Diagnosis Using Low-Dose Computed Tomography (LDCT). Diagnostics. 2023; 13(24):3690. https://doi.org/10.3390/diagnostics13243690
  8. LIFEx-TMTV: Jafari, E., Zarei, A., Dadgar, H. et al. A convolutional neural network–based system for fully automatic segmentation of whole-body [68Ga]Ga-PSMA PET images in prostate cancer. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06555-z
  9. LIFEx-texture: Zhang Jianyua, Zhao Xinming, Zhao Yan, Zhang Jingmian, Zhang Zhaoqi. Prediction of epidermal growth factor receptor mutation subtypes in patients with non-small cell lung cancer by 18F-FDG PET/CT radiomic ; (6): 480-485, 2023. Chinese Journal of Nuclear Medicine and Molecular Imaging. https://doi.org/ 10.3760/cma.j.cn321828-20220109-00008
  10. LIFEx-texture: Zhao, W., Ozawa, Y., Hara, M. et al. Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes. Jpn J Radiol (2023). https://doi.org/10.1007/s11604-023-01512-0
  11. LIFEx-texture: Philip Whybra, Alex Zwanenburg, Vincent Andrearczyk, Roger Schaer, Aditya P Apte, et al.. The Image Biomarker Standardization Initiative: Standardized convolutional filters for quantitative radiomics Authors and affiliations. 2023. https://hal.science/hal-04305625
  12. LIFEx-MTV: Kibrom B Girum, Anne-Ségolène Cottereau, Laetitia Vercellino, Louis Rebaud, Jérôme Clerc, et al.. Tumor location relative to the spleen is a prognostic factor in lymphoma patients: a demonstration from the REMARC trial. Journal of Nuclear Medicine, In press. https://hal.science/hal-04305558
  13. LIFEx-MTV: Zhaoting Cheng, Sijuan Zou, Jianyuan Zhou, Shuang Song, Yuankai Zhu, Jun Zhao, Xiaohua Zhu; Prognostic Value of Somatostatin Receptor-Derived Volumetric Parameters from a Hybrid Standardized Uptake Value Thresholding Method in Patients with 68Ga-DOTATATE-Avid Stage IV Neuroendocrine Neoplasms: A Preliminary Study. Neuroendocrinology 2023 ; https://doi.org/10.1159/000530771
  14. LIFEx-texture: Aksu, A., Küçüker, K.A., Solmaz, Ş. et al. A different perspective on PET/CT before treatment in patients with Hodgkin lymphoma: importance of volumetric and dissemination parameters. Ann Hematol (2023). https://doi.org/10.1007/s00277-023-05547-1
  15. LIFEx-MTV: Aksu, A., Küçüker, K.A., Solmaz, Ş. et al. A different perspective on PET/CT before treatment in patients with Hodgkin lymphoma: importance of volumetric and dissemination parameters. Ann Hematol (2023). https://doi.org/10.1007/s00277-023-05547-1
  16. LIFEx-MTV: Dang, J., Peng, X., Wu, P. et al. Predictive value of Dmax and %ΔSUVmax of 18F-FDG PET/CT for the prognosis of patients with diffuse large B-cell lymphoma. BMC Med Imaging 23, 173 (2023). https://doi.org/10.1186/s12880-023-01138-8
  17. LIFEx-texture: Liu, J., Tang, M., Zhu, D. et al. The remodeling of metabolic brain pattern in patients with extracranial diffuse large B-cell lymphoma. EJNMMI Res 13, 94 (2023). https://doi.org/10.1186/s13550-023-01046-6
  18. LIFEx-texture: Frood, R., Mercer, J., Brown, P. et al. Training and external validation of pre-treatment FDG PET-CT-based models for outcome prediction in anal squamous cell carcinoma. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10340-9
  19. LIFEx-texture: Lara Cavinato, Michela Carlotta Massi, Martina Sollini, Margarita Kirienko &
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  20. LIFEx-texture: Magdalena Belyanova, Martin Krupev. Texture analysis of adenomatous and metastatic adrenal lesions on native and contrast-enhanced computed tomography. Comptes rendus de l’Academie bulgare des Sciences. Tome 76, No 10, 2023. https://doig.org/10.7546/CRABS.2023.10.16
  21. LIFEx-MTV: Jing, F., Liu, Y., Zhao, X. et al. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma. EJNMMI Res 13, 92 (2023). https://doi.org/10.1186/s13550-023-01047-5
  22. LIFEx-main: Berbís, M.Á., Godino, F.P., Rodríguez-Comas, J. et al. Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application. Abdom Radiol (2023). https://doi.org/10.1007/s00261-023-04071-0
  23. LIFEx-texture: Bülbül, H.M., Burakgazi, G. & Kesimal, U. Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer. Jpn J Radiol (2023). https://doi.org/10.1007/s11604-023-01502-2
  24. LIFEx-MTV: Jing, F., Liu, Y., Zhao, X. et al. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma. EJNMMI Res 13, 92 (2023). https://doi.org/10.1186/s13550-023-01047-5
  25. LIFEx-texture: van Eijnatten EJM, Camps G,Guerville M, Fogliano V, Hettinga K, Smeets PAM. Milkcoagulation and gastric emptying in women experiencinggastrointestinal symptoms after ingestion of cow's milk. Neurogastroenterology & Motility. 2023;00:e14696. https://doi.org/10.1111/nmo.14696
  26. LIFEx-texture: Yoon, H.; Choi, W.H.; Joo, M.W.; Ha, S.; Chung, Y.-A. SPECT/CT Radiomics for Differentiating between Enchondroma and Grade I Chondrosarcoma. Tomography 2023, 9, 1868–1875. https://doi.org/10.3390/tomography9050148
  27. LIFEx-main: Xiyao Lei, Zhuo Cao, Yibo Wu, Jie Lin, Zhenhua Zhang, Juebin Jin, Yao Ai, Ji Zhang, Dexi Du, Zhifeng Tian, Congying Xie, Weiwei Yin and Xiance Jin. Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics. Insights into Imaging. (2023) 14:174 https://doi.org/10.1186/s13244-023-01528-0
  28. LIFEx-texture: Taleie, H., Hajianfar, G., Sabouri, M. et al. Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms. J Digit Imaging (2023). https://doi.org/10.1007/s10278-023-00891-0
  29. LIFEx-texture: Marchal, E., Palard-Novello, X., Lhomme, F. et al. Baseline [18F]FDG PET features are associated with survival and toxicity in patients treated with CAR T cells for large B cell lymphoma. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06427-6
  30. LIFEx-texture: Ninatti, G., Pini, C., Bono, B.C. et al. The prognostic power of [11C]methionine PET in IDH-wildtype diffuse gliomas with lower-grade histological features: venturing beyond WHO classification. J Neurooncol (2023). https://doi.org/10.1007/s11060-023-04438-9
  31. LIFEx-Main: Duwe G, Müller L, Ruckes C, Fischer ND, Frey LJ, Börner JH, Rölz N, Haack M, Sparwasser P, Jorg T, et al. Change in Splenic Volume as a Surrogate Marker for Immunotherapy Response in Patients with Advanced Urothelial and Renal Cell Carcinoma—Evaluation of a Novel Approach of Fully Automated Artificial Intelligence Based Splenic Segmentation. Biomedicines. 2023; 11(9):2482. https://doi.org/10.3390/biomedicines11092482 
  32. LIFEx-QualityControl: Koffi N’guessan Placide Gabin Allangba, Annick Kouame Koutouan, Alessia Giuliano, Zié Traoré, Antonio Traino. Partial Volume Effect (PVE) Correction in Single Photon Emission Computed Tomography (SPECT) Imaging. Radiation Science and Technology. Vol. 9, No. 3, 2023, pp. 26-35. https://doi.org/10.11648/j.rst.20230903.11
  33. LIFEx-texture: Duwe, G.; Müller, L.; Ruckes, C.; Fischer, N.D.; Frey, L.J.; Börner, J.H.; Rölz, N.; Haack, M.; Sparwasser, P.; Jorg, T.; et al. Change in Splenic Volume as a Surrogate Marker for Immunotherapy Response in Patients with Advanced Urothelial and Renal Cell Carcinoma—Evaluation of a Novel Approach of Fully Automated Artificial Intelligence Based Splenic Segmentation. Biomedicines 2023, 11, 2482. https://doi.org/10.3390/biomedicines11092482
  34. LIFEx-texture: Xian He, Zhi Chen, Yutao Gao, Wanjing Wang, Meng You. Reproducibility and location-stability of radiomic features derived from Cone-Beam Computed Tomography: a phantom study. The British Institute of Radiology. https://doi.org/10.1259/dmfr.20230180
  35. LIFEx-MTV: van Heek L, Weindler J, Gorniak C, et al. Prognostic value of baseline metabolic tumor volume (MTV) for forecasting chemotherapy outcome in early-stage unfavorable Hodgkin lymphoma: Data from the phase III HD17 trial. Eur J Haematol. 2023;1‐7. https://doi.org/10.1111/ejh.14093
  36. LIFEx-texture: Nakamori, A., Tsuyoshi, H., Tsujikawa, T. et al. Evaluation of calcification distribution by CT-based textural analysis for discrimination of immature teratoma. J Ovarian Res 16, 179 (2023). https://doi.org/10.1186/s13048-023-01268-1
  37. LIFEx-MTV: Nalan Alan-Selcuk, Gamze Beydagi, Emre Demirci, Meltem Ocak, Serkan Celik, Bala B. Oven, Turkay Toklu, Ipek Karaaslan, Kaan Akcay, Omer Sonmez and Levent Kabasakal. Clinical Experience with [225Ac]Ac-PSMA Treatment in Patients with [177Lu]Lu-PSMA–Refractory Metastatic Castration-Resistant Prostate Cancer. Journal of Nuclear Medicine, published on August 24, 2023, http://doi.org/10.2967/jnumed.123.265546
  38. LIFEx-texture: Chilaca-Rosas, M.-F.; Contreras-Aguilar, M.-T.; Garcia-Lezama, M.; Salazar-Calderon, D.-R.; Vargas-Del-Angel, R.-G.; Moreno-Jimenez, S.; Piña-Sanchez, P.; Trejo-Rosales, R.-R.; Delgado-Martinez, F.-A.; Roldan-Valadez, E. Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation. Diagnostics 2023, 13, 2669. https://doi.org/10.3390/diagnostics13162669
  39. Hajri, R.; Nicod-Lalonde, M.; Hottinger, A.F.; Prior, J.O.; Dunet, V. Prediction of Glioma Grade and IDH Status Using 18F-FET PET/CT Dynamic and Multiparametric Texture Analysis. Diagnostics 2023, 13, 2604. https://doi.org/10.3390/diagnostics13152604
  40. LIFEx-texture: Li, J., Cui, N., Jiang, Z. et al. Differentiating thymic epithelial tumors from mediastinal lymphomas: preoperative nomograms based on PET/CT radiomic features to minimize unnecessary anterior mediastinal surgery. J Cancer Res Clin Oncol (2023). https://doi.org/10.1007/s00432-023-05054-w
  41. LIFEx-texture: Samimi, R., Shiri, I., Ahmadyar, Y. et al. Radiomics predictive modeling from dual-time-point FDG PET Ki parametric maps: application to chemotherapy response in lymphoma. EJNMMI Res 13, 70 (2023). https://doi.org/10.1186/s13550-023-01022-0
  42. LIFEx-texture: Balma, M.; Laudicella, R.; Gallio, E.; Gusella, S.; Lorenzon, L.; Peano, S.; Costa, R.P.; Rampado, O.; Farsad, M.; Evangelista, L.; et al. Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs). Life 2023, 13, 1647. https://doi.org/10.3390/life13081647
  43. LIFEx-texture: Lee, H.; Moon, S.H.; Hong, J.Y.; Lee, J.; Hyun, S.H. A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer. Cancers 2023, 15, 3841. https://doi.org/10.3390/cancers15153841
  44. LIFEx-texture: Shuilin Zhao, Jing Wang, Chentao Jin, Xiang Zhang, Chenxi Xue, Rui Zhou, Yan Zhong, Yuwei Liu, Xuexin He, Youyou Zhou, Caiyun Xu, Lixia Zhang, Wenbin Qian, Hong Zhang, Xiaohui Zhang, and Mei Tian. Stacking Ensemble Learning–Based [18F]FDG PET Radiomics for Outcome Prediction in Diffuse Large B-Cell Lymphoma Journal of Nuclear Medicine, published on July 27, 2023 as doi: https://doi.org/10.2967/jnumed.122.265244
  45. LIFEx-texture: Jia T, Lv Q, Cai X, Ge S, Sang S, Zhang B, Yu C and Deng S (2023) Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer. Front. Oncol. 13:1210125. doi: https://doi.org/10.3389/fonc.2023.1210125
  46. LIFEx-texture: Pellegrino, S.; Fonti, R.; Hakkak Moghadam Torbati, A.; Bologna, R.; Morra, R.; Damiano, V.; Matano, E.; De Placido, S.; Del Vecchio, S. Heterogeneity of Glycolytic Phenotype Determined by 18F-FDG PET/CT Using Coefficient of Variation in Patients with Advanced Non-Small Cell Lung Cancer. Diagnostics 2023, 13, 2448. https://doi.org/10.3390/diagnostics13142448
  47. LIFEx-texture: Jia, T., Lv, Q., Zhang, B. et al. Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics. BMC Med Imaging 23, 93 (2023). https://doi.org/10.1186/s12880-023-01052-z
  48. LIFEx-texture: Yaltırık Bilgin E, Ünal Ö, Törenek Ş, et al. (July 16, 2023) Computerized Tomography Texture Analysis in the Differential Diagnosis of Intracranial Epidermoid and Arachnoid Cysts. Cureus 15(7): e41945. DOI https://doi.org/10.7759/cureus.41945
  49. LIFEx-texture: Amirhossein Sanaat, Hossein Shooli, Andrew Stephen Böhringer, Maryam Sadeghi, Isaac Shiri, Yazdan Salimi, Nathalie Ginovart, Valentina Garibotto, Hossein Arabi, Habib Zaidi. A cycle‑consistent adversarial network for brain PET partial volume correction without prior anatomical information. European Journal of Nuclear Medicine and Molecular Imaging (2023) 50:1881–1896. https://doi.org/10.1007/s00259-023-06152-0
  50. LIFEx-texture: Hanekamp, B.A., Viktil, E., Slørdahl, K.S. et al. Magnetic resonance imaging of anal cancer: tumor characteristics and early prediction of treatment outcome. Strahlenther Onkol (2023). https://doi.org/10.1007/s00066-023-02114-5
  51. LIFEx-texture: Sheen, H., Shin, HB., Kim, H. et al. Application of error classification model using indices based on dose distribution for characteristics evaluation of multileaf collimator position errors. Sci Rep 13, 11027 (2023). https://doi.org/10.1038/s41598-023-35570-1
  52. LIFEx-texture: Park, YJ., Park, Y.S., Kim, S.T. et al. A Machine Learning Approach Using [18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor. Mol Imaging Biol (2023). https://doi.org/10.1007/s11307-023-01832-7
  53. LIFEx-texture: Zhou, Y., Zhang, B., Han, J. et al. Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells. J Cancer Res Clin Oncol (2023). https://doi.org/10.1007/s00432-023-05038-w
  54. LIFEx-MTV: Peng X, Yu S, Kou Y, Dang J, Wu P, Yao Y, Shen J, Liu Y, Wang X, Cheng Z. Prediction nomogram based on 18F-FDG PET/CT and clinical parameters for patients with diffuse large B-cell lymphoma. Ann Hematol. 2023 Jul 3. doi: 10.1007/s00277-023-05336-w. Epub ahead of print. PMID: 37400729
  55. LIFEx-Viewer: Zhong, H., Huang, D., Wu, J. et al. 18F‑FDG PET/CT based radiomics features improve prediction of prognosis: multiple machine learning algorithms and multimodality applications for multiple myeloma. BMC Med Imaging 23, 87 (2023). https://doi.org/10.1186/s12880-023-01033-2
  56. LIFEx-texture: Fournier, C.; Leguillette, C.; Leblanc, E.; Le Deley, M.-C.; Carnot, A.; Pasquier, D.; Escande, A.; Taieb, S.; Ceugnart, L.; Lebellec, L. Diagnostic Value of the Texture Analysis Parameters of Retroperitoneal Residual Masses on Computed Tomographic Scan after Chemotherapy in Non-Seminomatous Germ Cell Tumors. Cancers 2023, 15, 2997. https://doi.org/10.3390/cancers15112997
  57. LIFEx-texture: Weiyue Tan, Yi Zhang, Jie Wang, Zhonghang Zheng, Ligang Xing, Xiaorong Sun, FDG PET/CT Tumor Dissemination Characteristic Predicts the Outcome of First-Line Systemic Therapy in Non-small Cell Lung Cancer, Academic Radiology, 2023, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2023.03.027
  58. LIFEx-texture: Kawaji, K., Nakajo, M., Shinden, Y. et al. Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer. Mol Imaging Biol (2023). https://doi.org/10.1007/s11307-023-01823-8
  59. LIFEx-texture: Mustafa Orhan Nalbant, Ozkan Oner, Ozlem Akinci, Elif Hocaoglu, Ercan Inci, Analysis of Pancreatobiliary and Intestinal Type Periampullary Carcinomas Using Volumetric Apparent Diffusion Coefficient Histograms, Academic Radiology, 2023, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2023.04.031
  60. LIFEx-texture: Partial tumor irradiation plus pembrolizumab in treating large advanced solid tumor metastases. Mark C. Korpics, Benjamin E. Onderdonk, Rebekah E. Dadey, Jared H. Hara, Lilit Karapetyan, Yuanyuan Zha, Theodore G. Karrison, Adam C. Olson, Gini F. Fleming, Ralph R. Weichselbaum, Riyue Bao, Steven J. Chmura and Jason J. Luke. J Clin Invest. 2023;133(10):e162260. https://doi.org/10.1172/JCI162260
  61. LIFEx-texture: Abdollahi Hamid, Dehesh Tania, Abdalvand Neda, Rahmim Arman. Radiomics and dosiomics-based prediction of radiotherapy-induced xerostomia in head and neck cancer patients. International Journal of Radiation Biology. 2023/05/12. https://doi.org/10.1080/09553002.2023.2214206
  62. LIFEx-content: Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah, Hugo J. W. L. Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Spyridon Bakas, Roelof J. Beukinga, Ronald Boellaard, Marta Bogowicz, Luca Boldrini, Irène Buvat, Gary J. R. Cook, Christos Davatzikos, Adrien Depeursinge, Marie-Charlotte Desseroit, Nicola Dinapoli, Cuong Viet Dinh, Sebastian Echegaray, Issam El Naqa, Andriy Y. Fedorov, Roberto Gatta, Robert J. Gillies, Vicky Goh, Michael Götz, Matthias Guckenberger, Sung Min Ha, Mathieu Hatt, Fabian Isensee, Philippe Lambin, Stefan Leger, Ralph T.H. Leijenaar, Jacopo Lenkowicz, Fiona Lippert, Are Losnegård, Klaus H. Maier-Hein, Olivier Morin, Henning Müller, Sandy Napel, Christophe Nioche, Fanny Orlhac, Sarthak Pati, Elisabeth A.G. Pfaehler, Arman Rahmim, Arvind U.K. Rao, Jonas Scherer, Muhammad Musib Siddique, Nanna M. Sijtsema, Jairo Socarras Fernandez, Emiliano Spezi, Roel J.H.M. Steenbakkers, Stephanie Tanadini-Lang, Daniela Thorwarth, Esther G.C. Troost, Taman Upadhaya, Vincenzo Valentini, Lisanne V. van Dijk, Joost van Griethuysen, Floris H.P. van Velden, Philip Whybra, Christian Richter, Steffen Löck. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. RadiologyVol. 295, No. 2.https://doi.org/10.1148/radiol.2020191145
  63. LIFEx-texture: Yusuke Kawashima DDS, PhD , Masaaki Miyakoshi DDS, PhD, Yoshihiro Kawabata DDS, PhD , Hiroko Indo DDS, PhD , Efficacy of texture analysis of ultrasono-graphic images in the differentiation of metastatic and non-metastatic cervical lymph nodes in patients with squamous cell carcinoma of the tongue, Oral Surg Oral Med Oral Pathol Oral Radiol (2023), doi: https://doi.org/10.1016/j.oooo.2023.04.012
  64. LIFEx-Main: Boursier, C., Zaragori, T., Bros, M. et al. Semi-automated segmentation methods of SSTR PET for dosimetry prediction in refractory meningioma patients treated by SSTR-targeted peptide receptor radionuclide therapy. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-09697-8
  65. LIFEx-texture: Ishimura, M., Norikane, T., Mitamura, K. et al. FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma. Sci Rep 13, 6742 (2023). https://doi.org/10.1038/s41598-023-34061-7
  66. LIFEx-texture: Ishimura, M., Norikane, T., Mitamura, K. et al. FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma. Sci Rep 13, 6742 (2023). https://doi.org/10.1038/s41598-023-34061-7
  67. LIFEx-texture: Vural Topuz Ö, Aksu A, Yılmaz Özgüven MB. Una perspectiva diferente sobre la radiómica PET con 18F-FDG en pacientes con cáncer colorrectal; la relación entre el análisis intra y peritumoral y los hallazgos patológicos. Rev Esp Med Nucl Imagen Mol. 2023. https://doi.org/10.1016/j.remn.2023.04.002
  68. LIFEx-texture: Crombé A, Palussière J, Catena V, Cazayus M, Fonck M, Béchade D, et al. Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?. Br J Radiol (2023) 10.1259/bjr.20201371.
  69. LIFEx-texture: Alessandra Zorz, Andrea D'Alessio, Federica Guida, Rehema Masaka Ramadan, Elisa Richetta, Lea Cuppari, Riccardo Pellerito, Gian Mauro Sacchetti, Marco Brambilla, Marta Paiusco, Michele Stasi, Roberta Matheoud, Impact of patient’s habitus on image quality and quantitative metrics in 18F-FDG PET/CT images, Physica Medica, Volume 109, 2023, 102584, ISSN 1120-1797, https://doi.org/10.1016/j.ejmp.2023.102584.
  70. LIFEx-texture: Vural O, Aydos U, Okur A, Pinarli FG, Atay LÖ. Prognostic Values of Primary Tumor Textural Heterogeneity and Blood Biomarkers in High-risk Neuroblastoma. J Pediatr Hematol Oncol. 2023 Mar 16. doi: 10.1097/MPH.0000000000002662. Epub ahead of print. PMID: 37027243.
  71. LIFEx-texture: Laino ME, Fiz F, Morandini P, et al. A virtual biopsy of liver parenchyma to predict the outcome of liver resection. Updates in Surgery. April 2023:1-13. doi:10.1007/s13304-023-01495-7
  72. LIFEx-texture: Ortega, C.; Eshet, Y.; Prica, A.; Anconina, R.; Johnson, S.; Constantini, D.; Keshavarzi, S.; Kulanthaivelu, R.; Metser, U.; Veit-Haibach, P. Combination of FDG PET/CT Radiomics and Clinical Parameters for Outcome Prediction in Patients with Hodgkin’s Lymphoma. Cancers 2023, 15, 2056. https://doi.org/10.3390/ cancers15072056
  73. LIFEx-texture: Eric Po-Yu Huang, Huey-Shyan Lin, Yi-Chun Chen, Yi-He Li, Yi-Luan Huang, Yu-Jeng Ju, Hsien-Chung Y, Gregory A. Kicska and Ming-Ting Wu Lower attenuation and higher kurtosis of coronary artery calcification associated with vulnerable plaque – an agatston score propensity-matched CT radiomics study Huang et al. BMC Cardiovascular Disorders (2023) 23:158. https://doi.org/10.1186/s12872-023-03162-6
  74. LIFEx-texture: Maki Amano, Katsuhiro Sano,Shohei Fujita, Naoyuki Takei, Akihiko Wada, Kanako Sato, Junko Kikuta, Yoshiki Kuwatsuru, Rina Tachibana, Towa Sekine, Yoshiya Horimoto, and Shigeki Aoki. Feasibility of Quantitative MRI Using 3D-QALAS for Discriminating Immunohistochemical Status in Invasive Ductal Carcinoma of the Breast. J. MAGN. RESON. IMAGING 2023. https://doi.org/10.1002/jmri.28683
  75. LIFEx-texture: Nagara Tamaki, Kenji Hirata, Tomoya Kotani, Yoshitomo Nakai, Shigenori Matsushima, Kei Yamada. Four‑dimensional quantitative analysis using FDG‑PET in clinical oncology. Japanese Journal of Radiology. https://doi.org/10.1007/s11604-023-01411-4
  76. LIFEx-texture: Fukai S, Daisaki H, Ishiyama M, et al. Reproducibility of the principal component analysis (PCA)-based data-driven respiratory gating on texture features in non‑small cell lung cancer patients with 18 F‑FDG PET/CT. J Appl Clin Med Phys. 2023;e13967. https://doi.org/10.1002/acm2.13967
  77. LIFEx-texture: Abenavoli, E.M.; Barbetti, M.; Linguanti, F.; Mungai, F.; Nassi, L.; Puccini, B.; Romano, I.; Sordi, B.; Santi, R.; Passeri, A.; et al. Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques. Cancers 2023, 15, 1931. https://doi.org/10.3390/cancers15071931
  78. LIFEx-texture: Cepeda, S.; Luppino, L.T.; Pérez-Núñez, A.; Solheim, O.; García-García, S.; Velasco-Casares, M.; Karlberg, A.; Eikenes, L.; Sarabia, R.; Arrese, I.; et al. Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers 2023, 15, 1894. https://doi.org/10.3390/cancers15061894
  79. LIFEx-texture: Watanabe, M., Ashida, R., Miyakoshi, C. et al. Prognostic analysis of curatively resected pancreatic cancer using harmonized positron emission tomography radiomic features. European J Hybrid Imaging 7, 5 (2023). https://doi.org/10.1186/s41824-023-00163-8
  80. LIFEx-texture: Dondi, F.; Gatta, R.; Albano, D.; Bellini, P.; Camoni, L.; Treglia, G.; Bertagna, F. Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [18 F]FDG PET/CT: A Comparison between Two PET/CT Scanners. J. Clin. Med. 2023, 12, 255. https://doi.org/10.3390/jcm12010255
  81. LIFEx-texture: Lara Cavinato, Noemi Gozzi, Martina Sollini, Margarita Kirienko, Carmelo Carlo-Stella, Chiara Rusconi, Arturo Chiti, Francesca Ieva, Explainable domain transfer of distant supervised cancer subtyping model via imaging-based rules extraction, Artificial Intelligence in Medicine, Volume 138, 2023, 102522, ISSN 0933-3657, https://doi.org/10.1016/j.artmed.2023.102522
  82. LIFEx-texture: Awais, Muhammad; Khan, Shahmeer; Wasay, Mohammad; Azeemuddin, Muhammad; Shoukat, Ayesha; and Khan, Hafsa (2022) "Mr Textural Features (Radiomics) For Predicting Response to Treatment in Patients with Intracranial Tuberculoma: A Retrospective Cross-Sectional Study," Pakistan Journal of Neurological Sciences (PJNS): Vol. 17: Iss. 3, Article 9. https://ecommons.aku.edu/pjns/vol17/iss3/9
  83. LIFEx-texture: Mazzara S, Travaini L, Botta F, Granata C, Motta G, Melle F, Fiori S, Tabanelli V, Vanazzi A, Ramadan S, Radice T, Raimondi S, Lo Presti G, Ferrari M.E, Jereczek-Fossa B.A, TarellaC, Ceci F, Pileri S, Derenzini E. Gene expression profiling and FDG-PET radiomics uncover radiometabolic signatures associated with outcome in DLBCL. Blood Adv (2023) 7 (4): 630–643. http://dx.doi.org/10.1182/bloodadvances.2022007825
  84. LIFEx-MTV: Chan KC, Perucho JAU, Subramaniam RM, Lee EYP. Utility of pre-treatment 18F-fluorodeoxyglucose PET radiomic analysis in assessing nodal involvement in cervical cancer. Nucl Med Commun. 2023 Feb 27:e001672. doi: 10.1097/MNM.0000000000001672. Epub ahead of print. PMID: 36826394. http://dx.doi.org/10.1097/MNM.0000000000001672
  85. LIFEx-texture: Costa, G., Cavinato, L., Fiz, F. et al. Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible. J Digit Imaging (2023). https://doi.org/10.1007/s10278-023-00799-9
  86. LIFEx-texture: Troiano G, Fanelli F, Rapani A, et al. Can radiomic features extracted from intra-oral radiographs predict physiological bone remodeling around dental implants: A hypothesis-generating study. Journal of Clinical Periodontology. 2023 Feb. DOI: 10.1111/jcpe.13797. PMID: 36843362. https://doi.org/10.1111/jcpe.13797
  87. LIFEx-texture: Chilaca-Rosas M-F, Garcia-Lezama M, Moreno-Jimenez S, Roldan-Valadez E. Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation. Diagnostics. 2023; 13(5):849. https://doi.org/10.3390/diagnostics13050849
  88. LIFEx-texture: Colelli G, Barzaghi L, Paoletti M, Monforte M, Bergsland N, Manco G, Deligianni X, Santini F, Ricci E, Tasca G, Mira A, Figini S and Pichiecchio A (2023) Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease. Front. Neurol. 14:1105276. doi: 10.3389/fneur.2023.1105276
  89. LIFEx-texture: Basso Dias, A., Mirshahvalad, S.A., Ortega, C. et al. The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06136-0
  90. LIFEx-texture: Amirhossein Sanaat, Hossein Shooli, Andrew Stephen Böhringer, Maryam Sadeghi, Isaac Shiri, Yazdan Salimi, Nathalie Ginovart, Valentina Garibotto, Hossein Arabi, Habib Zaidi. A cycle‑consistent adversarial network for brain PET partial volume correction without prior anatomical information. European Journal of Nuclear Medicine and Molecular Imaging. 20 Feb 2023. https://doi.org/10.1007/s00259-023-06152-0
  91. LIFEx-texture: Na Wang, Meng Dai, Yan Zhao, Zhaoqi Zhang, Jianfang Wang, Jingmian Zhang, Yingchen Wang, Yunuan Liu, Fenglian Jing, Xinming Zhao. Value of pre-treatment 18F-FDG PET/CT radiomics in predicting the prognosis of stage III-IV colorectal cancer. European Journal of Radiology Open 10 (2023) 100480. https://doi.org/10.1016/j.ejro.2023.100480
  92. LIFEx-viewer: Jing Gao, Si Xu, Huijun Ju, Yu Pan and Yifan Zhang. The potential application of MR‑derived ADCmin values from 68Ga‑DOTATATE and 18F‑FDG dual tracer PET/MR as replacements for FDG PET in assessment of grade and stage of pancreatic neuroendocrine tumors. Gao et al. EJNMMI Research. https://doi.org/10.1186/s13550-023-00960-z
  93. LIFEx-texture: Agüloğlu N, Aksu A, Unat DS. Machine learning approach using 18F-FDG PET-based radiomics in differentiation of lung adenocarcinoma with bronchoalveolar distribution and infection. Nucl Med Commun. 2023 Feb 9. doi: 10.1097/MNM.0000000000001667. Epub ahead of print. PMID: 36756766
  94. LIFEx-texture: Crimì, F.; Agostini, E.; Toniolo, A.; Torresan, F.; Iacobone, M.; Tizianel, I.; Scaroni, C.; Quaia, E.; Campi, C.; Ceccato, F. CT Texture Analysis of Adrenal Pheochromocytomas: A Pilot Study. Curr. Oncol. 2023, 30, 2169–2177. https://doi.org/10.3390/curroncol30020167
  95. LIFEx-texture: N. Stogiannos, H. Bougias, E. Georgiadou, S. Leandrou, P. Papavasileiou. Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study. Radiography 29 (2023) 355e361. https://doi.org/10.1016/j.radi.2023.01.010
  96. LIFEx-texture: Nicosia, L.; Pesapane, F.; Bozzini, A.C.; Latronico, A.; Rotili, A.; Ferrari, F.; Signorelli, G.; Raimondi, S.; Vignati, S.; Gaeta, A.; et al. Prediction of the Malignancy of a Breast Lesion Detected on Breast Ultrasound: Radiomics Applied to Clinical Practice. Cancers 2023, 15, 964. https://doi.org/10.3390/cancers15030964
  97. LIFEx-texture: Ju, H.M.; Kim, B.-C.; Lim, I.; Byun, B.H.; Woo, S.-K. Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes. Int. J. Mol. Sci. 2023, 24, 2794. https://doi.org/10.3390/ijms24032794
  98. LIFEx-texture: Wang, X., Dai, Y., Lin, H. et al. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-09412-7
  99. LIFEx-texture: Cavinato, L.; Sollini, M.; Ragni, A.; Bartoli, F.; Zanca, R.; Pasqualetti, F.; Marciano, A.; Ieva, F.; Erba, P.A. Radiomics-Based Inter-Lesion Relation Network to Describe [18 F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer. Cancers 2023, 15, 823. https://doi.org/10.3390/cancers15030823
  100. LIFEx-texture: Seda Gülbaha Ates, Gülay Bilir Dilek, Gülin Ucmak. Primary tumor heterogeneity on pretreatment 18F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy. 2253-8089/© 2023 Sociedad Espanola de Medicina Nuclear e Imagen Molecular. https://doi.org/10.1016/j.remnie.2023.01.001
  101. LIFEx-CalciumQuantitation: Nardone, V.; Reginelli, A.; De Marco, G.; Natale, G.; Patanè, V.; De Chiara, M.; Buono, M.; Russo, G.M.; Monti, R.; Balestrucci, G.; Salvarezza, M.; Di Guida, G.; D’Ippolito, E.; Sangiovanni, A.; Grassi, R.; D’Onofrio, I.; Belfiore, M.P.; Cimmino, G.; Della Corte, C.M.; Vicidomini, G.; Fiorelli, A.; Gambardella, A.; Morgillo, F.; Cappabianca, S. Role of Cardiac Biomarkers in Non-Small Cell Lung Cancer Patients. Diagnostics 2023, 13, 400. https://doi.org/10.3390/diagnostics13030400
  102. LIFEx-texture: Mendes, B.; Domingues, I.; Dias, F.; Santos, J. Cone Beam Computed Tomography Radiomics for Prostate Cancer: Favourable vs. Unfavourable Prognosis Prediction. Appl. Sci. 2023, 13, 1378. https://doi.org/10.3390/app13031378
  103. LIFEx-texture: Degtiarova G, Garefa C, Boehm R, CianconeD, Sepulcri D, Gebhard C, Giannopoulos Aju, Pazhenkottil P, Kaufmann P.A. and Buechel R.R Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of concept study using 13N-ammonia positron emission tomography. J Nucl Cardiol 1071-3581. http://dx.doi.org/10.1007/s12350-022-03179-y
  104. 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
  105. LIFEx-texture: Yang, M., Li, X., Cai, C. et al. [18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10503-8
  106. LIFEx-texture: Awais, M., Khan, N., Khan, A.K. et al. CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study. Abdom Radiol (2023). https://doi.org/10.1007/s00261-023-04103-9
  107. LIFEx-texture: Daria Kifjak, Maximilian Hochmair, Daniel Sobotka, Alexander R. Haug, Raphael Ambros, Florian Prayer, Benedikt H. Heidinger, Sebastian Roehrich, Ruxandra-Iulia Milos, Wolfgang Wadsak, Thorsten Fuereder, Dagmar Krenbek, Andreas Fazekas, Michael Meilinger, Marius E. Mayerhoefer, Georg Langs, Christian Herold, Helmut Prosch, Lucian Beer, Metabolic tumor volume and sites of organ involvement predict outcome in NSCLC immune-checkpoint inhibitor therapy. European Journal of Radiology, Volume 170, 2024, 111198, ISSN 0720-048X, https://doi.org/10.1016/j.ejrad.2023.111198
  108. LIFEx-texture: Müller, L., Tibyampansha, D., Mildenberger, P. et al. Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.BMC Med Imaging 23, 187 (2023). https://doi.org/10.1186/s12880-023-01142-y
  109. LIFEx-texture: She, J., Huang, H., Ye, Z. et al. Automatic biometry of fetal brain MRIs using deep and machine learning techniques. Sci Rep 13, 17860 (2023). https://doi.org/10.1038/s41598-023-43867-4
  110. LIFEx-texture: Akıncı Ö, Türkoğlu F, Nalbant MO, İnci E. Differentiating Renal Cell Carcinoma and Minimal Fat Angiomyolipoma with Volumetric MRI Histogram Analysis. Med J Bakirkoy 2023;19:256-262. https://doi.org/10.4274/BMJ.galenos.2023.2023.3-19
  111. Toffoli T, Saut O, Etchegaray C, Jambon E, Le Bras Y, Grenier N, Marcelin C. Differentiation of Small Clear Renal Cell Carcinoma and Oncocytoma through Magnetic Resonance Imaging-Based Radiomics Analysis: Toward the End of Percutaneous Biopsy. Journal of Personalized Medicine. 2023; 13(10):1444. https://doi.org/10.3390/jpm13101444 
  112. Lu J, Jiang N, Zhang Y and Li D (2023) A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma. Front. Oncol. 13:979437 https://doi.org/10.3389/fonc.2023.979437
  113. LIFEx-texture: Hasan, A.M., Al-Waely, N.K.N., Aljobouri, H.K., Jalab, H.A., Ibrahim, R.W., Meziane, F., Molecular Subtypes Classification of Breast Cancer in DCE-MRI Using Deep Features, Expert Systems with Applications (2023), doi: https://doi.org/10.1016/j.eswa.2023.121371
  114. Niyoteka, S., Seban, RD., Rouhi, R. et al. A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced cancers. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06320-2
  115. LIFEx-texture: Li, J., Du, J., Li, Y. et al. A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy. BMC Gastroenterol 23, 274 (2023). https://doi.org/10.1186/s12876-023-02902-4
  116. LIFEx-texture: Hermet P, Delache B, Herate C, Wolf E, Kivi G, Juronen E, et al. (2023) Broadly neutralizing humanized SARS-CoV-2 antibody binds to a conserved epitope on Spike and provides antiviral protection through inhalation-based delivery in non-human primates. PLoS Pathog 19(8): e1011532. https://doi.org/10.1371/journal.ppat.1011532
  117. Chen, J., Xu, K., Li, C. et al. [68Ga]Ga-FAPI-04 PET/CT in the evaluation of epithelial ovarian cancer: comparison with [18F]F-FDG PET/CT. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06369-z
  118. LIFEx-texture: Ma, H., Zhang, D., Wang, Y. et al. Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI. BMC Psychiatry 23, 466 (2023). https://doi.org/10.1186/s12888-023-04966-8
  119. LIFEx-texture: Ma, H., Zhang, D., Wang, Y. et al. Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI. BMC Psychiatry 23, 466 (2023). https://doi.org/10.1186/s12888-023-04966-8
  120. Šedienė, S.; Kulakienė, I.; Urbonavičius, B.G.; Korobeinikova, E.; Rudžianskas, V.; Povilonis, P.A.; Jaselskė, E.; Adlienė, D.; Juozaitytė, E. Development of a Model Based on Delta-Radiomic Features for the Optimization of Head and Neck Squamous Cell Carcinoma Patient Treatment. Medicina 2023, 59, 1173. https://doi.org/10.3390/medicina59061173
  121. LIFEx-texture: Malet J, Ancel J, Moubtakir A, Papathanassiou D, Deslée G, Dewolf M. Assessment of the Association between Entropy in PET/CT and Response to Anti-PD-1/PD-L1 Monotherapy in Stage III or IV NSCLC. Life. 2023; 13(4):1051. https://doi.org/10.3390/life13041051
  122. LIFEx-texture: Vani Rajasekar, M.P. Vaishnnave, S. Premkumar, Velliangiri Sarveshwaran, V. Rangaraaj, Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques, Results in Engineering, Volume 18, 2023, 101111, ISSN 2590-1230, https://doi.org/10.1016/j.rineng.2023.101111
  123. LIFEx-texture: Fu-Zong Wu, Yun-Ju Wu, Chi-Shen Chen, En-Kuei Tang. Prediction of Interval Growth of Lung Adenocarcinomas Manifesting as Persistent Subsolid Nodules ≤3 cm Based on Radiomic Features, Academic Radiology, 2023, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2023.02.033
  124. LIFEx-MTV: Elahmadawy MA, Ashraf A, Moustafa H, Kotb M, Abd El-Gaid S. Prognostic value of initial [18F]FDG PET/computed tomography volumetric and texture analysis-based parameters in patients with head and neck squamous cell carcinoma. Nucl Med Commun. 2023 Apr 10. doi: 10.1097/MNM.0000000000001695. Epub ahead of print. PMID: 37038954.
  125. LIFEx-texture: Amrane, K., Thuillier, P., Bourhis, D. et al. Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer. Sci Rep 13, 5789 (2023). https://doi.org/10.1038/s41598-023-31587-8
  126. LIFEx-texture: Ari Lee, Gun-Chan Park, Eunae Sandra Cho, Yoon Joo Choi, Kug Jin Jeon, Sang Sun Han, Chena Lee. Radiomics-based sialadenitis staging in contrast-enhanced computed tomography and ultrasonography: A preliminary rat model study, Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 2023, ISSN 2212-4403, https://doi.org/10.1016/j.oooo.2023.04.005
  127. LIFEx-texture: Özgül, H.A., Akin, I.B., Mutlu, U. et al. Diagnostic value of machine learning-based computed tomography texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton. Skeletal Radiol (2023). https://doi.org/10.1007/s00256-023-04333-4
  128. LIFEx-texture: Xie Y, Teng Y, Jiang C, Ding C, Zhou Z. Prognostic value of 18F-FDG lesion dissemination features in patients with peripheral T-cell lymphoma (PTCL). Jpn J Radiol. 2023 Feb 8. doi: 10.1007/s11604-023-01398-y. Epub ahead of print. PMID: 36752954.
  129. LIFEx-texture: Bhatt, M., Shende, P. Advancement in Machine Learning: A Strategic Lookout from Cancer Identification to Treatment. Arch Computat Methods Eng (2023). https://doi.org/10.1007/s11831-023-09886-0
  130. LIFEx-texture: Zhao, X., Zhao, Y., Zhang, J. et al. Predicting PD-L1 expression status in patients with non-small cell lung cancer using [18F]FDG PET/CT radiomics. EJNMMI Res 13, 4 (2023). https://doi.org/10.1186/s13550-023-00956-9
  131. LIFEx-texture: Yutao Yang, Hao Chen, Min Ji, Jianzhang Wu, Xiaoshan Chen, Fenglin Liu, Shengxiang Rao, A new radiomics approach combining the tumor and peri-tumor regions to predict lymph node metastasis and prognosis in gastric cancer, Gastroenterology Report, Volume 11, 2023, goac080, https://doi.org/10.1093/gastro/goac080


Thesis (6):

  1. LIFEx-texture: Emre Uysal. Nazofarenks karsinomunda tedavi oncesi cekilen kontrastli manyetik rezonans goruntulemeden erken tedavi yaniti ongorulebilir mi? Thesis · October 2023. https://doi.org/10.13140/RG.2.2.22343.47524
  2. LIFEx-texture: SANAAT, Amirhossein. Strategies for improvement of PET instrumentation performance and imaging methodology. 2023. https://doi.org/10.13097/archive-ouverte/unige:171571
  3. LIFEx-texture: Giulia Colelli, Artificial Intelligence, Mathematical Modeling and Magnetic Resonance Imaging for Precision Medicine in Neurology and Neuroradiology, Università degli Studi di Pavia, Università della Svizzera italiana, 2022 (link)
  4. LIFEx-texture: Hamza CHEGRAOUI. Machine learning for genomics and imaging data integration applied to neuro-oncology. Paris-Saclay, le 23 mars 2023 (link)
  5. LIFEx-texture: Federico Loi. Radiogenomic features of CNS tumors and MiRNAs correlation phenotypes analysis. Jan 2020. Università degli Studi di Cagliari. (link)
  6. LIFEx-texture: Artificial intelligence in molecular imaging: from machine to deep learning. Riccardo Laudicella. https://iris.unime.it/retrieve/e234d14a-b211-415e-8bef-0640248279de/Tesi.pdf


Poster (1):

  1. LIFEx-texture: Giulia Martini, Valerio Nardone, Davide Ciardiello, Marco De Chiara, Teresa Troiani, Luca D'ambrosio, Stefania Napolitano, Claudia Cardone, Chiara Cremolini, Filippo Pietrantonio, Evaristo Maiello, Antonio Avallone, Salvatore Cappabianca, Fortunato Ciardiello, Alfonso Reginelli, and Erika Martinelli. Journal of Clinical Oncology 2023 41:4_suppl, 241-241(link)


Article not in English (1):

  1. LIFEx-texture: SG Ates, GB Dilek, G Uçmak. Heterogeneidad del tumor primario en la18F-FDG PET/TC pretratamiento para predecir el pronóstico en pacientes con cáncer de recto sometidos a cirugía tras. Revista Española de Medicina Nuclear e Imagen …, 2023, ISSN 2253-654X. https://doi.org/10.1016/j.remn.2023.01.001


Review (30):

  1. LIFEx-texture: Xue Yang, Kexin Huang, Dewei Yang, Weiling Zhao, and Xiaobo Zho. Biomedical Big Data Technologies, Applications, andChallenges for Precision Medicine: A Review. 2023 2300163 . Global Challenges published. http://doi/org/10.1002/gch2.202300163
  2. LIFEx-texture: Hugo C. Temperley, Niall J. O’Sullivan, Caitlin Waters, Alison Corr, Brian J. Mehigan, Grainne O’Kane, Paul McCormick, Charles Gillham, Emanuele Rausa, John O. Larkin, James F. Meaney, Ian Brennan,and Michael E. Kelly. Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review. The American Surgeon 2023, Vol. 0(0) 1–10. http://doi.org/10.1177/00031348231216494
  3. LIFEx-texture: Akin O, Lema-Dopico A, Paudyal R, Konar AS, Chenevert TL, Malyarenko D, Hadjiiski L, Al-Ahmadie H, Goh AC, Bochner B, et al. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers. 2023; 15(22):5468. https://doi.org/10.3390/cancers15225468
  4. LIFEx-texture: Filippi, L., Ferrari, C., Nuvoli, S. et al. Pet-radiomics in lymphoma and multiple myeloma: update of current literature. Clin Transl Imaging (2023). https://doi.org/10.1007/s40336-023-00604-1
  5. LIFEx-texture: Xinyi Chen, Xiang Liu, Yuke Wu, Zhenglei Wang, Shuo Hong Wang. Research related to the diagnosis of prostate cancer based on machine learning medical images: a review. International Journal of Medical Informatics. 2023, 105279, ISSN 1386-5056, https://doi.org/10.1016/j.ijmedinf.2023.105279
  6. LIFEx-texture: Yaru Feng1,2, Jing Gong1,2, Tingdan Hu1,2, Zonglin Liu1,2, Yiqun Sun1,2, Tong Tong. Radiomics for predicting survival in patients with locally advanced rectal cancer: a systematic review and meta-analysis. Quant Imaging Med Surg 2023. https://dx.doi.org/10.21037/qims-23-69
  7. LIFEx-texture: L. Tong et al., "Integrating Multi-omics Data with EHR for Precision Medicine Using Advanced Artificial Intelligence," in IEEE Reviews in Biomedical Engineering, https://doi.org/10.1109/RBME.2023.3324264
  8. LIFEx-texture: Zhang, W.; Guo, Y.; Jin, Q. Radiomics and Its Feature Selection: A Review. Symmetry 2023, 15, 1834. https://doi.org/10.3390/sym15101834
  9. LIFEx-texture: Hirata, K., Kamagata, K., Ueda, D. et al. From FDG and beyond: the evolving potential of nuclear medicine. Ann Nucl Med (2023). https://doi.org/10.1007/s12149-023-01865-6
  10. LIFEx-texture: Michail E. Klontzas, Salvatore Claudio Fanni, Emanuele Neri. Introduction to Artificial Intelligence. Springer Nature, 15 sept. 2023 - 165 pages (link)
  11. IFEx-texture: Stamoulou, E. et al. (2023). Using Commercial and Open-Source Tools for Artificial Intelligence: A Case Demonstration on a Complete Radiomics Pipeline. In: Klontzas, M.E., Fanni, S.C., Neri, E. (eds) Introduction to Artificial Intelligence. Imaging Informatics for Healthcare Professionals. Springer, Cham. https://doi.org/10.1007/978-3-031-25928-9_2
  12. LIFEx-texture: Burak Kocak, Sabahattin Yuzkan, Samet Mutlu, Elif Bulut, Irem Kavukoglu, Publications poorly report the essential RadiOmics ParametERs (PROPER): a meta-research on quality of reporting, European Journal of Radiology, 2023, 111088, ISSN 0720-048X, https://doi.org/10.1016/j.ejrad.2023.111088
  13. LIFEx-texture: Xiaorong Wu, Andreas Polychronis. Application of radiomics and artificial intelligence in lung cancer immunotherapy: a guide and hurdles from clinical trials. Wu et al. J Cancer Metastasis Treat 2023;9:29 https://doi.org.10.20517/2394-4722.2023.10
  14. LIFEx-texture: Tabassum, M.; Suman, A.A.; Suero Molina, E.; Pan, E.; Di Ieva, A.; Liu, S. Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers 2023, 15, 3845. https://doi.org/10.3390/cancers15153845
  15. LIFEx-texture: Liu, Z., Duan, T., Zhang, Y. et al. Radiogenomics: a key component of precision cancer medicine. Br J Cancer (2023). https://doi.org/10.1038/s41416-023-02317-8
  16. LIFEx-texture: Albalkhi, I., Bhatia, A., Lösch, N. et al. Current state of radiomics in pediatric neuro-oncology practice: a systematic review. Pediatr Radiol (2023). https://doi.org/10.1007/s00247-023-05679-6
  17. LIFEx-texture: Jahanshahi, A., Soleymani, Y., Fazel Ghaziani, M. et al. Radiomics reproducibility challenge in computed tomography imaging as a nuisance to clinical generalization: a mini-review. Egypt J Radiol Nucl Med 54, 83 (2023). https://doi.org/10.1186/s43055-023-01029-6
  18. LIFEx-texture: Carole Koechli, Daniel R. Zwahlen, Philippe Schucht, Paul Windisch, Radiomics and Machine Learning for Predicting the Consistency of Benign Tumors of the Central Nervous System: A Systematic Review, European Journal of Radiology, 2023, 110866, ISSN 0720-048X, https://doi.org/10.1016/j.ejrad.2023.110866
  19. LIFEx-texture: Jahanshahi, A., Soleymani, Y., Fazel Ghaziani, M. et al. Radiomics reproducibility challenge in computed tomography imaging as a nuisance to clinical generalization: a mini-review. Egypt J Radiol Nucl Med 54, 83 (2023). https://doi.org/10.1186/s43055-023-01029-6
  20. Albano, D.; Treglia, G.; Dondi, F.; Calabrò, A.; Rizzo, A.; Annunziata, S.; Guerra, L.; Morbelli, S.; Tucci, A.; Bertagna, F. 18F-FDG PET/CT Maximum Tumor Dissemination (Dmax) in Lymphoma: A New Prognostic Factor? Cancers 2023,15,2494. https://doi.org/ 10.3390/cancers15092494
  21. LIFEx-texture: Ekmekcioglu, O., Terry, S.Y.A., Morbelli, S. et al. Superfluous, controversial and luxury issues in nuclear medicine. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06228-x
  22. LIFEx-texture: Malcolm, J.A., Tacey, M., Gibbs, P. et al. Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-09653-6
  23. LIFEx-texture: Matteo Ferro & al. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review.,Ther Adv Urol 2023, Vol. 15: 1–26 https://doi.org/10.1177/17562872231164803
  24. LIFEx-texture: E Pfaehler, I Zhovannik, L Wei, R Boellaard, A Dekker. The state of Reproducibility and Repeatability in Radiomics. Pitfalls of Image Biomarke (link)
  25. LIFEx-texture: Abler, D., Schaer, R., Oreiller, V. et al. QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research. Eur Radiol Exp 7, 16 (2023). https://doi.org/10.1186/s41747-023-00326-z
  26. LIFEx-texture: Mirestean, C.C.; Iancu, R.I.; Iancu, D.P.T. Simultaneous Integrated Boost (SIB) vs. Sequential Boost in Head and Neck Cancer (HNC) Radiotherapy: A Radiomics-Based Decision Proof of Concept. J. Clin. Med.2023,12,2413. https:// doi.org/10.3390/jcm12062413
  27. LIFEx-texture: Oliveira C, Oliveira F, Vaz SC, Marques HP, Cardoso F. Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer. Br J Radiol (2023) https://doi.org/10.1259/bjr.20220655
  28. LIFEx-texture: Zhou Huijie, Luo Qian, Wu Wanchun, Li Na, Yang Chunli, Zou Liqun. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front. Immunol., 01 March 2023, Sec. Cancer Immunity and Immunotherapy, Volume 14 - 2023 | https://doi.org/10.3389/fimmu.2023.1088874
  29. LIFEx-texture: Abou Karam, G.; Malhotra, A. PET/CT May Assist in Avoiding Pointless Thyroidectomy in Indeterminate Thyroid Nodules: A Narrative Review. Cancers 2023, 15, 1547. https://doi.org/10.3390/cancers15051547
  30. LIFEx-texture: Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma. Eren M., Veziroglu, Faraz Farhadi, Navid Hasani, Moozhan Nikpanah, Mark Roschewski, Ronald M. Summers and Babak Saboury. Semin Nucl Med 00:1-23. https://doi.org/10.1053/j.semnuclmed.2022.11.003