Ferro Matteo, de Cobelli Ottavio, Musi Gennaro, Del Giudice Francesco, Carrieri Giuseppe, Busetto Gian Maria, Falagario Ugo Giovanni, Sciarra Alessandro, Maggi Martina, Crocetto Felice, Barone Biagio, Caputo Vincenzo Francesco, Marchioni Michele, Lucarelli Giuseppe, Imbimbo Ciro, Mistretta Francesco Alessandro, Luzzago Stefano, Vartolomei Mihai Dorin, Cormio Luigi, Autorino Riccardo, Tătaru Octavian Sabin
Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy.
Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy.
Ther Adv Urol. 2022 Jul 4;14:17562872221109020. doi: 10.1177/17562872221109020. eCollection 2022 Jan-Dec.
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
前列腺癌（PCa）是全球男性人群中最常被诊断出的恶性肿瘤。其诊断、侵袭性疾病的识别以及治疗后的随访需要更全面和整体的方法。放射组学是以定量方式提取和解释图像表型。通过成像模态的进步以及人工智能技术的潜在力量，将这些特征转化为临床结果预测，放射组学可能会带来优势。本文概述了当前的方法学证据，并回顾了有关PCa患者放射组学的现有文献，强调了其在个性化治疗和未来应用方面的潜力。
