Author Correction: Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images

作者更正:人工智能能够解读组织学图像中前列腺癌的恶性程度分级

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Abstract

PURPOSE OF REVIEW: To review the current risk and prognostic stratification systems in localised prostate cancer. To explore some of the most promising adjuncts to clinical models and what the evidence has shown regarding their value. RECENT FINDINGS: There are many new biomarker-based models seeking to improve, optimise or replace clinical models. There are promising data on the value of MRI, radiomics, genomic classifiers and most recently artificial intelligence tools in refining stratification. Despite the extensive literature however, there remains uncertainty on where in pathways they can provide the most benefit and whether a biomarker is most useful for prognosis or predictive use. Comparisons studies have also often overlooked the fact that clinical models have themselves evolved and the context of the baseline used in biomarker studies that have shown superiority have to be considered. SUMMARY: For new biomarkers to be included in stratification models, well designed prospective clinical trials are needed. Until then, there needs to be caution in interpretation of their use for day-to-day decision making. It is critical that users balance any purported incremental value against the performance of the latest clinical classification and multivariate models especially as the latter are cost free and widely available.

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