Objectives: This study aims to explore the role of radiomics features (RFs) from prostate subregions, including the tumor microenvironment (TME), in predicting persistent PSA.Methods: In retrospective analysis, we segregated 354 patients with pathologically confirmed localized prostate cancer (PCa) into training, internal validation, and external validation cohorts. The prostate on18F-prostate-specific membrane antigen (PSMA)-1007 positron emission tomography/computed tomography (PET/CT) was partitioned into three zones based on the maximum standardized uptake value (SUVmax) (zone-intra: 45–100% SUVmax; zone-peri: 20–45% SUVmax; zone-norm: 0–20% SUVmax). RFs from these zones were harnessed to develop five radiomics models [model-intra; model-peri; model-norm; model-ip; model-ipn]. Three optimal radiomics models were further integrated with the PSA model to construct combined models. Model performance was evaluated using the receiver operating characteristic (ROC) curves and the area under the curve (AUC).Results: Utilizing least absolute shrinkage and selection operator (LASSO) and logistic regression, five radiomics models were constructed, with model-ip, model-ipn, and model-intra showing superior performance [training cohort AUCs: 0.76 (0.68–0.83), 0.75 (0.68–0.83), 0.76 (0.68–0.83); internal validation cohort AUCs: 0.76 (0.65–0.88), 0.72 (0.57–0.86), 0.70 (0.55–0.86); external validation cohort AUCs: 0.70 (0.50–0.86), 0.55 (0.36–0.73), 0.53 (0.34–0.72)]. Notably, the combined model incorporating model-ip and the PSA model exhibited optimal performance [training cohort AUC: 0.78 (0.71–0.85); internal validation cohort AUC: 0.78 (0.67–0.90); external validation cohort AUC: 0.89 (0.72–0.98)].Conclusions: The RFs in different subregions on18F-PSMA-1007 PET/CT have varying effectiveness in predicting persistent PSA. A radiomics model that encompasses the 20–45% SUVmax and 45–100% SUVmax zones, when combined with the PSA model, enhances predictive accuracy.
目的：本研究旨在探讨前列腺亚区（包括肿瘤微环境）的影像组学特征在预测持续性前列腺特异性抗原（PSA）中的作用。 方法：通过回顾性分析，将354例经病理确诊的局限性前列腺癌患者分为训练队列、内部验证队列和外部验证队列。基于最大标准化摄取值（SUVmax），将18F-前列腺特异性膜抗原（PSMA）-1007正电子发射断层扫描/计算机断层扫描（PET/CT）图像中的前列腺划分为三个区域（区域-内部：45–100% SUVmax；区域-周边：20–45% SUVmax；区域-正常：0–20% SUVmax）。利用这些区域的影像组学特征构建了五个影像组学模型[模型-内部；模型-周边；模型-正常；模型-内部+周边；模型-内部+周边+正常]。进一步将三个最优影像组学模型与PSA模型结合构建联合模型。采用受试者工作特征曲线及曲线下面积评估模型性能。 结果：通过最小绝对收缩与选择算子及逻辑回归构建了五个影像组学模型，其中模型-内部+周边、模型-内部+周边+正常及模型-内部表现较优[训练队列AUC：0.76 (0.68–0.83)、0.75 (0.68–0.83)、0.76 (0.68–0.83)；内部验证队列AUC：0.76 (0.65–0.88)、0.72 (0.57–0.86)、0.70 (0.55–0.86)；外部验证队列AUC：0.70 (0.50–0.86)、0.55 (0.36–0.73)、0.53 (0.34–0.72)]。值得注意的是，结合模型-内部+周边与PSA模型的联合模型表现出最佳性能[训练队列AUC：0.78 (0.71–0.85)；内部验证队列AUC：0.78 (0.67–0.90)；外部验证队列AUC：0.89 (0.72–0.98)]。 结论：18F-PSMA-1007 PET/CT中不同亚区的影像组学特征对预测持续性PSA具有不同的效能。涵盖20–45% SUVmax与45–100% SUVmax区域的影像组学模型与PSA模型结合可提高预测准确性。
肿瘤(癌症)患者之家