Li Licong, Xu Jian, Bian Shuying, Yao Fei, Lin Qi, Zhou Meiyan, Yang Yunjun, Song Meiyao, Pan Yixuan, Shen Qinyang, Zhuang Yuandi, Lin Jie
The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
The Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
Cancers (Basel). 2025 Aug 28;17(17):2807. doi: 10.3390/cancers17172807.
: This study aims to explore the role of radiomics features (RFs) from prostate subregions, including the tumor microenvironment (TME), in predicting persistent PSA. : In retrospective analysis, we segregated 354 patients with pathologically confirmed localized prostate cancer (PCa) into training, internal validation, and external validation cohorts. The prostate on F-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). : 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)]. : The RFs in different subregions on F-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.
本研究旨在探讨前列腺亚区域（包括肿瘤微环境）的放射组学特征在预测持续性前列腺特异性抗原（PSA）方面的作用。
在回顾性分析中，我们将354例经病理证实的局限性前列腺癌（PCa）患者分为训练组、内部验证组和外部验证组。基于最大标准化摄取值（SUVmax），将F-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）上的前列腺分为三个区域（区域内：45 - 100% SUVmax；区域周围：20 - 45% SUVmax；区域正常：0 - 20% SUVmax）。利用这些区域的放射组学特征开发了五个放射组学模型[模型内；模型周围；模型正常；模型ip；模型ipn]。进一步将三个最佳放射组学模型与PSA模型整合以构建联合模型。使用受试者工作特征（ROC）曲线和曲线下面积（AUC）评估模型性能。
利用最小绝对收缩和选择算子（LASSO）和逻辑回归构建了五个放射组学模型，模型ip、模型ipn和模型内表现出更好的性能[训练组AUC：0.76（0.68 - 0.83），0.75（0.68 - 0.83），0.76（0.68 - 0.83）；内部验证组AUC：0.76（0.65 - 0.88），0.72（0.57 - 0.86），0.70（0.55 - 0.86）；外部验证组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ip和PSA模型的联合模型表现出最佳性能[训练组AUC：0.78（0.71 - 0.85）；内部验证组AUC：0.78（0.67 - 0.90）；外部验证组AUC：0.89（0.72 - 0.98）]。
F-PSMA - 1007 PET/CT不同亚区域的放射组学特征在预测持续性PSA方面具有不同的有效性。一个包含20 - 45% SUVmax和45 - 100% SUVmax区域的放射组学模型与PSA模型相结合时，可提高预测准确性。
