基于全滑动成像和双参数磁共振成像的多模态模型的开发与验证,用于预测前列腺癌术后生化复发。

IF 5.6 Q1 ONCOLOGY Radiology. Imaging cancer Pub Date : 2024-05-01 DOI:10.1148/rycan.230143
Chenhan Hu, Xiaomeng Qiao, Renpeng Huang, Chunhong Hu, Jie Bao, Ximing Wang
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摘要

目的 开发并验证一种基于术前磁共振成像、手术全切片成像(WSI)和临床变量的机器学习多模态模型,用于预测前列腺癌(PCa)根治性前列腺切除术(RP)后的生化复发(BCR)。材料与方法 在这项回顾性研究中(2015 年 9 月至 2021 年 4 月),363 名接受前列腺癌根治术的男性 PCa 患者按 7:3 的比例被分为训练组(n = 254;中位年龄 69 岁 [IQR 64-74 岁])和测试组(n = 109;中位年龄 70 岁 [IQR 65-75 岁])。主要终点是无生化复发生存期。采用最小绝对收缩和选择算子 Cox 算法选择独立的临床变量并构建临床特征。放射组学特征和病理组学特征分别使用术前 MRI 和手术 WSI 数据构建。结合放射组学特征、病理组学特征和临床特征,构建了多模态模型。使用哈雷尔一致性指数(C指数)评估多模态模型对BCR的预测性能,并与所有单模态模型(包括放射组学特征、病理组学特征和临床特征)进行比较。结果 放射性组学特征和病理组学特征对测试队列的 BCR 预测均有良好的表现(C 指数分别为 0.742 和 0.730)。多模态模型的预测性能最好,在测试集上的 C 指数为 0.860,明显高于所有单模态模型(所有 P 均小于 0.01)。结论 多模态模型可有效预测PCa患者RP术后的BCR,因此可为术后个体化治疗提供一个新兴的准确工具。关键词磁共振成像、泌尿系统、骨盆、比较研究 本文有补充材料。© RSNA, 2024.
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Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.

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.

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