Department of Radiology, University of Southern California, Los Angeles, CA, USA.
USC Institute of Urology, Los Angeles, CA, USA.
Sci Rep. 2019 Feb 7;9(1):1570. doi: 10.1038/s41598-018-38381-x.
Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods. This paper presents a systematic and rigorous framework comprised of classification, cross-validation and statistical analyses that was developed to identify the best performing classifier for PCa risk stratification based on mpMRI-derived radiomic features derived from a sizeable cohort. This classifier performed well in an independent validation set, including performing better than PI-RADS v2 in some aspects, indicating the value of objectively interpreting mpMRI images using radiomics and classification methods for PCa risk assessment.
多参数磁共振成像（mpMRI）在前列腺癌（PCa）的临床评估中变得越来越重要，但由于其相对主观的性质，其解读通常存在差异。放射组学和分类方法已显示出提高基于 mpMRI 的 PCa 评估准确性和客观性的潜力。然而，这些研究仅限于少数分类方法，仅使用 AUC 评分进行评估，并且对放射组学和分类方法的所有可能组合没有进行严格评估。本文提出了一个系统和严格的框架，包括分类、交叉验证和统计分析，旨在基于从大量队列中提取的基于 mpMRI 的放射组学特征，确定用于 PCa 风险分层的最佳分类器。该分类器在独立验证集中表现良好，包括在某些方面优于 PI-RADS v2，这表明使用放射组学和分类方法客观地解释 mpMRI 图像对 PCa 风险评估具有价值。
