School of Medical Imaging, Binzhou Medical University, Yantai, China.
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
J Magn Reson Imaging. 2021 May;53(5):1550-1558. doi: 10.1002/jmri.27486.
Previous studies on the value of radiomics for diagnosing clinically significant prostate cancer (csPCa) only utilized intralesional features. However, the role of tumor microenvironment is important in tumor generation and progression. The aim of this study is to build and validate a nomogram based on perilesional and intralesional radiomics features and clinical factors for csPCa. This is a retrospective study, which included 140 patients who underwent prostate magnetic resonance imaging (MRI). This study used 3.0T T2-weighted imaging, apparent diffusion coefficient maps (derived from diffusion-weighted images), and dynamic contrast-enhanced MRI. Region of interest (ROI)s were segmented by two radiologists. Intralesional and combined radiomics signatures were built based on radiomics features extracted from intralesional and the combination of radiomics features extracted from intralesional and perilesional volumes. Serum total prostate-specific antigen level and combined radiomics signature scores were used to construct a diagnostic nomogram. Intraclass correlation efficient analysis was used to test intra- and inter-rater agreement of radiomics features. Min-max scalar was used for normalization. One-way analysis of variance or the Mann-Whitney U-test was used for univariate analysis. Receiver operating characteristic curve analysis, accuracy, balanced accuracy, and F1-score were used to evaluate radiomics signatures and the nomogram. Also, the nomogram was evaluated using decision curve analysis in testing cohort. Delong test was used to compare area under the curves (AUCs). Statistical significance was set at p < 0.05. In testing cohort, AUC, accuracy, balanced accuracy, and F1-score of combined radiomics signature (0.94, 0.83, 0.80, and 0.87, respectively) were all higher than that of intralesional radiomics signature (0.90, 0.77, 0.74, and 0.83, respectively). The difference between AUCs was insignificant (p of 0.19). AUC, accuracy, balanced accuracy, and F1-score of the nomogram were 0.96, 0.94, 0.95, and 0.95, respectively. Nomogram was clinically useful when threshold probability of a patient is higher than 0.06. Perilesional radiomics features improved the discrimination ability of the radiomics signature. Diagnostic nomogram had a good performance. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 2.
先前关于放射组学诊断临床显著前列腺癌（csPCa）价值的研究仅利用了肿瘤内特征。然而，肿瘤微环境在肿瘤的发生和发展中起着重要作用。本研究旨在建立并验证一个基于肿瘤旁和肿瘤内放射组学特征及临床因素的预测 csPCa 的列线图。这是一项回顾性研究，共纳入 140 例接受前列腺磁共振成像（MRI）检查的患者。本研究使用 3.0T T2 加权成像、表观扩散系数图（源自扩散加权图像）和动态对比增强 MRI。两位放射科医生对感兴趣区（ROI）进行了分割。基于肿瘤内提取的放射组学特征和肿瘤内与肿瘤旁容积提取的放射组学特征的组合，构建了肿瘤内和联合放射组学特征。使用血清总前列腺特异性抗原水平和联合放射组学特征评分构建诊断列线图。采用组内相关系数分析检验放射组学特征的组内和组间一致性。采用最小-最大标度进行归一化。采用单因素方差分析或 Mann-Whitney U 检验进行单变量分析。采用受试者工作特征曲线分析、准确性、平衡准确性和 F1 评分评估放射组学特征和列线图。还使用测试队列中的决策曲线分析评估了列线图。采用 Delong 检验比较曲线下面积（AUC）。p 值小于 0.05 为统计学意义。在测试队列中，联合放射组学特征（AUC、准确性、平衡准确性和 F1 评分分别为 0.94、0.83、0.80 和 0.87）的 AUC、准确性、平衡准确性和 F1 评分均高于肿瘤内放射组学特征（AUC、准确性、平衡准确性和 F1 评分分别为 0.90、0.77、0.74 和 0.83）。AUC 之间的差异无统计学意义（p=0.19）。列线图的 AUC、准确性、平衡准确性和 F1 评分为 0.96、0.94、0.95 和 0.95。当患者的阈值概率高于 0.06 时，列线图具有临床应用价值。肿瘤旁放射组学特征提高了放射组学特征的鉴别能力。诊断列线图具有良好的性能。证据水平：3。技术效能分期：2。
