Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.
Cornford P, van den Bergh RCN, Briers E, Van den Broeck T, Brunckhorst O, Darraugh J, Eberli D, De Meerleer G, De Santis M, Farolfi A, et al. EAU-EANM-ESTRO-ESUR-ISUP-SIOG guidelines on prostate Cancer-2024 update. Part I: screening, diagnosis, and local treatment with curative intent. Eur Urol. 2024;86(2):148–63. https://doi.org/10.1016/j.eururo.2024.03.027.
Kunju LP, Daignault S, Wei JT, Shah RB. Multiple prostate cancer cores with different Gleason grades submitted in the same specimen container without specific site designation: should each core be assigned an individual Gleason score? Hum Pathol. 2009;40(4):558–64. https://doi.org/10.1016/j.humpath.2008.07.020
Papp L, Spielvogel CP, Grubmüller B, Grahovac M, Krajnc D, Ecsedi B, Sareshgi RAM, Mohamad D, Hamboeck M, Rausch I, et al. Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [(68)Ga]Ga-PSMA-11 PET/MRI. Eur J Nucl Med Mol Imaging. 2021;48(6):1795–805. https://doi.org/10.1007/s00259-020-05140-y.
Moazemi S, Erle A, Khurshid Z, Lütje S, Muders M, Essler M, Schultz T, Bundschuh RA. Decision-support for treatment with (177)Lu-PSMA: machine learning predicts response with high accuracy based on PSMA-PET/CT and clinical parameters. Ann Transl Med. 2021;9(9):818. https://doi.org/10.21037/atm-20-6446.
Article PubMed PubMed Central Google Scholar
Awenat S, Piccardo A, Carvoeiras P, Signore G, Giovanella L, Prior JO, Treglia G. Diagnostic role of (18)F-PSMA-1007 PET/CT in prostate cancer staging: a systematic review. Diagnostics (Basel Switzerland). 2021;11(3). https://doi.org/10.3390/diagnostics11030552.
Foley RW, Redman SL, Graham RN, Loughborough WW, Little D. Fluorine-18 labelled prostate-specific membrane antigen (PSMA)-1007 positron-emission tomography-computed tomography: normal patterns, pearls, and pitfalls. Clin Radiol. 2020;75(12):903–13. https://doi.org/10.1016/j.crad.2020.06.031.
Ghezzo S, Bezzi C, Presotto L, Mapelli P, Bettinardi V, Savi A, Neri I, Preza E, Samanes Gajate AM, De Cobelli F, et al. State of the Art of radiomic analysis in the clinical management of prostate cancer: A systematic review. Crit Rev Oncol Hematol. 2022;169:103544. https://doi.org/10.1016/j.critrevonc.2021.103544.
Bezzi C, Mapelli P, Presotto L, Neri I, Scifo P, Savi A, Bettinardi V, Partelli S, Gianolli L, Falconi M, et al. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging. 2021;48(12):4002–15. https://doi.org/10.1007/s00259-021-05338-8.
Tunali I, Gillies RJ, Schabath MB. Application of radiomics and artificial intelligence for lung cancer precision medicine. Cold Spring Harb Perspect Med. 2021;11(8). https://doi.org/10.1101/cshperspect.a039537.
Lohmann P, Galldiks N, Kocher M, Heinzel A, Filss CP, Stegmayr C, Mottaghy FM, Fink GR, Jon Shah N, Langen KJ. Radiomics in neuro-oncology: basics, workflow, and applications. Methods. 2021;188:112–21. https://doi.org/10.1016/j.ymeth.2020.06.003.
Solari EL, Gafita A, Schachoff S, Bogdanović B, Villagrán Asiares A, Amiel T, Hui W, Rauscher I, Visvikis D, Maurer T, et al. The added value of PSMA PET/MR radiomics for prostate cancer staging. Eur J Nucl Med Mol Imaging. 2022;49(2):527–38. https://doi.org/10.1007/s00259-021-05430-z.
Ghezzo S, Mapelli P, Bezzi C, Samanes Gajate AM, Brembilla G, Gotuzzo I, Russo T, Preza E, Cucchiara V, Ahmed N, et al. Role of [(68)Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer. Eur J Nucl Med Mol Imaging. 2023;50(8):2548–60. https://doi.org/10.1007/s00259-023-06187-3.
Hamm CA, Baumgärtner GL, Biessmann F, Beetz NL, Hartenstein A, Savic LJ, Froböse K, Dräger F, Schallenberg S, Rudolph M, et al. Interactive explainable deep learning model informs prostate cancer diagnosis at MRI. Radiology. 2023;307(4):e222276. https://doi.org/10.1148/radiol.222276.
Esteva A, Feng J, van der Wal D, Huang SC, Simko JP, DeVries S, Chen E, Schaeffer EM, Morgan TM, Sun Y, et al. Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. NPJ Digit Med. 2022;5(1):71. https://doi.org/10.1038/s41746-022-00613-w.
Article PubMed PubMed Central Google Scholar
Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288(1):62–81. https://doi.org/10.1111/joim.13030.
Singh R, Lanchantin J, Sekhon A, Qi Y. Attend and predict: Understanding gene regulation by selective attention on chromatin. Adv Neural Inf Process Syst. 2017;30:6785–95.
PubMed PubMed Central Google Scholar
Rodríguez-Pérez R, Bajorath J. Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values. J Med Chem. 2020;63(16):8761–77. https://doi.org/10.1021/acs.jmedchem.9b01101.
Giraud P, Giraud P, Nicolas E, Boisselier P, Alfonsi M, Rives M, Bardet E, Calugaru V, Noel G, Chajon E, et al. Interpretable machine learning model for locoregional relapse prediction in oropharyngeal cancers. Cancers (Basel). 2020;13(1). https://doi.org/10.3390/cancers13010057.
Fan Z, Jiang J, Xiao C, Chen Y, Xia Q, Wang J, Fang M, Wu Z, Chen F. Construction and validation of prognostic models in critically ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach. J Transl Med. 2023;21(1):406. https://doi.org/10.1186/s12967-023-04205-4.
Article PubMed PubMed Central Google Scholar
Zwanenburg A, Vallières M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, et al. The image biomarker standardization initiative: standardized quantitative radiomics for High-Throughput image-based phenotyping. Radiology. 2020;295(2):328–38. https://doi.org/10.1148/radiol.2020191145.
Spohn SKB, Kramer M, Kiefer S, Bronsert P, Sigle A, Schultze-Seemann W, Jilg CA, Sprave T, Ceci L, Fassbender TF, et al. Comparison of manual and Semi-Automatic [(18)F]PSMA-1007 PET based contouring techniques for intraprostatic tumor delineation in patients with primary prostate cancer and validation with histopathology as standard of reference. Front Oncol. 2020;10:600690. https://doi.org/10.3389/fonc.2020.600690.
Article PubMed PubMed Central Google Scholar
Zhang YF, Zhou C, Guo S, Wang C, Yang J, Yang ZJ, Wang R, Zhang X, Zhou FH. Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer. J Cancer Res Clin Oncol. 2024;150(2):78. https://doi.org/10.1007/s00432-023-05574-5.
Article PubMed PubMed Central Google Scholar
Yang Y, Cheng J, Peng Z, Yi L, Lin Z, He A, Jin M, Cui C, Liu Y, Zhong Q, et al. Development and validation of Contrast-Enhanced CT-Based deep transfer learning and combined Clinical-Radiomics model to discriminate thymomas and thymic cysts: A multicenter study. Acad Radiol. 2024;31(4):1615–28. https://doi.org/10.1016/j.acra.2023.10.018.
Zhu JY, He HL, Lin ZM, Zhao JQ, Jiang XC, Liang ZH, Huang XP, Bao HW, Huang PT, Chen F. Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: from static images to CEUS video analysis. Front Oncol. 2022;12:951973. https://doi.org/10.3389/fonc.2022.951973.
Article PubMed PubMed Central Google Scholar
Bunkhumpornpat C, Boonchieng E, Chouvatut V, Lipsky D. FLEX-SMOTE: synthetic over-sampling technique that flexibly adjusts to different minority class distributions. Patterns (New York NY). 2024;5(11):101073. https://doi.org/10.1016/j.patter.2024.101073.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77. https://doi.org/10.1148/radiol.2015151169.
Mattoni S, Farolfi A, Formaggio F, Bruno G, Caroli P, Cerci JJ, Eiber M, Fendler WP, Golfieri R, Herrmann K, et al. PSMA PET for the evaluation of liver metastases in castration-resistant prostate cancer patients: a multicenter retrospective study. Cancers (Basel). 2022;14(22). https://doi.org/10.3390/cancers14225680.
Gülbahar Ateş S, Demirel BB, Kekilli E, Öztürk E, Uçmak G. Primary tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET/CT for the prediction of biochemical recurrence in prostate cancer. Rev Esp Med Nucl Imagen Mol (Engl Ed). 2024;500032. https://doi.org/10.1016/j.remnie.2024.500032.
Khateri M, Babapour Mofrad F, Geramifar P, Jenabi E. Machine learning-based analysis of (68)Ga-PSMA-11 PET/CT images for Estimation of prostate tumor grade. Phys Eng Sci Med. 2024;47(2):741–53. https://doi.org/10.1007/s13246-024-01402-3.
Ghezzo S, Mongardi S, Bezzi C, Samanes Gajate AM, Preza E, Gotuzzo I, Baldassi F, Jonghi-Lavarini L, Neri I, Russo T, et al. External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on (68)Ga-PSMA PET images. Front Med (Lausanne). 2023;10:1133269. https://doi.org/10.3389/fmed.2023.1133269.
Lao J, Chen Y, Li ZC, Li Q, Zhang J, Liu J, Zhai G. A deep Learning-Based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep. 2017;7(1):10353. https://doi.org/10.1038/s41598-017-10649-8.
Article PubMed PubMed Central Google Scholar
Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys. 2020;47(5):e185–202. https://doi.org/10.1002/mp.13678.
Comments (0)