The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
The Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Radiol Med. 2022 Oct;127(10):1170-1178. doi: 10.1007/s11547-022-01541-1. Epub 2022 Aug 26.
PET-based radiomics features could predict the biological characteristics of primary prostate cancer (PCa). However, the optimal thresholds to predict the biological characteristics of PCa are unknown. This study aimed to compare the predictive power of F-PSMA-1007 PET radiomics features at different thresholds for predicting multiple biological characteristics.
One hundred and seventy-three PCa patients with complete preoperative F-PSMA-1007 PET examination and clinical data before surgery were collected. The prostate lesions' volumes of interest were semi-automatically sketched with thresholds of 30%, 40%, 50%, and 60% maximum standardized uptake value (SUVmax). The radiomics features were respectively extracted. The prediction models of Gleason score (GS), extracapsular extension (ECE), and vascular invasion (VI) were established using the support vector machine. The performance of models from different thresholding regions was assessed using receiver operating characteristic curve and confusion matrix-derived indexes.
For predicting GS, the 50% SUVmax model showed the best predictive performance in training (AUC, 0.82 [95%CI 0.74-0.88]) and testing cohorts (AUC, 0.80 [95%CI 0.66-0.90]). For predicting ECE, the 40% SUVmax model exhibit the best predictive performance (AUC, 0.77 [95%CI 0.68-0.84] and 0.77 [95%CI 0.63-0.88]). As for VI, the 50% SUVmax model had the best predictive performance (AUC, 0.74 [95%CI 0.65-0.82] and 0.74 [95%CI 0.56-0.82]).
The F-1007-PSMA PET-based radiomics features at 40-50% SUVmax showed the best predictive performance for multiple PCa biological characteristics evaluation. Compared to the single PSA model, radiomics features may provide additional benefits in predicting the biological characteristics of PCa.
基于 PET 的放射组学特征可预测原发性前列腺癌（PCa）的生物学特征。然而，预测 PCa 生物学特征的最佳阈值尚不清楚。本研究旨在比较 F-PSMA-1007 PET 放射组学特征在不同阈值下预测多种生物学特征的预测能力。
收集了 173 例术前接受完整 F-PSMA-1007 PET 检查和手术前临床数据的 PCa 患者。使用 30%、40%、50%和 60%最大标准化摄取值（SUVmax）的感兴趣区半自动勾画前列腺病变体积。分别提取放射组学特征。使用支持向量机建立预测 Gleason 评分（GS）、包膜外侵犯（ECE）和血管侵犯（VI）的模型。使用受试者工作特征曲线和混淆矩阵衍生指标评估来自不同阈值区域的模型性能。
对于预测 GS，50%SUVmax 模型在训练（AUC，0.82[95%CI 0.74-0.88]）和测试队列（AUC，0.80[95%CI 0.66-0.90]）中表现出最佳预测性能。对于预测 ECE，40%SUVmax 模型表现出最佳预测性能（AUC，0.77[95%CI 0.68-0.84]和 0.77[95%CI 0.63-0.88]）。对于 VI，50%SUVmax 模型具有最佳预测性能（AUC，0.74[95%CI 0.65-0.82]和 0.74[95%CI 0.56-0.82]）。
在 40%-50%SUVmax 水平，基于 F-1007-PSMA PET 的放射组学特征在评估多种 PCa 生物学特征方面具有最佳预测性能。与单一 PSA 模型相比，放射组学特征可能在预测 PCa 生物学特征方面提供额外的益处。
