Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China.
Eur J Nucl Med Mol Imaging. 2024 Jul;51(9):2806-2818. doi: 10.1007/s00259-024-06734-6. Epub 2024 May 1.
Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer.
A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients.
The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60-0.91) in the training cohort and 0.71 (95% CI: 0.63-0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47-4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66-0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63-0.89, p < 0.001) in the validation cohort.
We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.
根治性前列腺切除术（RP）后生化复发（BCR）是前列腺癌患者的重大关注点。需要可靠的预测模型来识别有 BCR 风险的患者，并促进适当的管理。本研究旨在基于术前[18F]PSMA-1007 PET 开发和验证一种临床放射组学模型，以预测接受 RP 治疗的前列腺癌患者的无 BCR 生存（BRFS）。
回顾性分析了 236 例经组织学证实的前列腺癌患者，这些患者均接受了 RP 手术，所有患者均接受了术前[18F]PSMA-1007 PET/CT 扫描。从 PET 图像上的原发肿瘤区域提取放射组学特征。使用最小绝对收缩和选择算子（LASSO）Cox 回归模型开发放射组学特征。使用 Harrell 一致性指数（C 指数）评估放射组学特征预测 BRFS 的性能。使用放射组学特征和临床特征构建临床放射组学列线图。该模型在 98 例独立患者队列中进行了外部验证。
放射组学特征由三个特征组成，在训练队列中的 C 指数为 0.76（95%CI：0.60-0.91），在验证队列中的 C 指数为 0.71（95%CI：0.63-0.79）。在多变量分析中，放射组学特征仍然是 BRFS 的独立预测因子（HR：2.48，95%CI：1.47-4.17，p<0.001）。临床放射组学列线图在训练队列中显著提高了预测性能（C 指数：0.81，95%CI：0.66-0.95，p=0.007）和验证队列中（C 指数：0.78，95%CI：0.63-0.89，p<0.001）。
我们开发并验证了一种新的基于[18F]PSMA-1007 PET 的临床放射组学模型，可预测前列腺癌患者 RP 后 BRFS。该模型可用于识别 BCR 风险较高的患者，从而实现个体化风险分层和量身定制的管理策略。
