Artificial intelligence in bladder cancer prognosis: a pathway for personalized medicine. Issue 4 (July 2021)

• Record Type: Journal Article
• Authors: Pinar, Ugo; Pradere, Benjamin; Roupret, Morgan
• Is Part Of: Current opinion in urology. Volume 31:Issue 4(2021)
• Publication Date: 2021-07
• Subjects: artificial intelligence -- bladder cancer -- machine learning -- precision medicine -- prognosis; Urology -- Periodicals; Review Literature -- Bibliography; Review Literature -- Periodicals; Male Urogenital Diseases -- Bibliography; Male Urogenital Diseases -- Periodicals; Female Urogenital Diseases -- Bibliography; Female Urogenital Diseases -- Periodicals; Urologic Diseases -- Bibliography; Urologic Diseases -- Periodicals; Periodicals; 616.6005
• DOI: 10.1097/MOU.0000000000000882
• Languages: English
• ISSNs: 1473-6586

Abstract

Abstract : Purpose of review: This review aims to provide an update of the results of studies published in the last 2 years involving the use of artificial intelligence in bladder cancer (BCa) prognosis. Recent findings: Recently, many studies evaluated various artificial intelligence models to predict BCa evolution using either deep learning or machine learning. Many trials evidenced a better prediction of recurrence-free survival and overall survival for muscle invasive BCa (MIBC) for deep learning-based models compared with clinical stages. Improvements in imaging associated with the development of deep learning neural networks and radiomics seem to improve post neo-adjuvant chemotherapy response. One study showed that digitalized histology could predict nonmuscle invasive BCa recurrence. Summary: BCa prognosis could be better assessed using artificial intelligence models not only in the case of MIBC but also NMIBC. Many studies evaluated its role for the prediction of overall survival and recurrence-free survival but there is still little data in the case of NMIBC. Recent findings showed that artificial intelligence could lead to a better assessment of BCa prognosis before treatment and to personalized medicine.