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Abstract：
Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.
摘要：
目的建立基于术前MRI的机器学习多模态模型并进行验证,外科全载玻片成像（WSI），以及预测前列腺癌（PCa）根治性前列腺切除术（RP）后生化复发（BCR）的临床变量。材料与方法本回顾性研究(2015年9月至2021年4月)，363名接受RP的男性PCa患者被分为训练（n=254；中位年龄，69年[IQR，64-74岁])和测试(n=109；中位年龄，70年[IQR，65-75岁])的比率为7:3。主要终点是无生化复发生存期。应用最小绝对收缩和选择算子Cox算法选择独立临床变量并构建临床签名。使用术前MRI和手术WSI数据构建影像组学签名和病理组学签名，分别。通过结合影像组学签名构建了多模态模型，pathomics签名,和临床签名。使用哈雷尔一致性指数(C指数)，评估了多模态模型对BCR的预测性能，并与所有单模态模型进行了比较，包括影像组学签名，pathomics签名,和临床签名。结果在测试队列中，影像组学和病理组学特征均实现了BCR预测的良好性能（C指数：分别为0.742和0.730）。多模态模型表现出最佳的预测性能，测试集上的C指数为0.860，显着高于所有单模态模型（所有P≤0.01）。结论多模态模型可有效预测PCa患者RP后的BCR，因此可能为辅助术后个体化治疗提供新的准确工具。关键词:磁共振成像,尿路,骨盆,比较研究补充材料可用于本文。©RSNA,2024.
