Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Department of Urology, National Region Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Cancer Med. 2024 Jul;13(14):e70001. doi: 10.1002/cam4.70001.
The aim of this study was to assess the potential application of a radiomics features-based nomogram for predicting therapeutic responses to neoadjuvant chemohormonal therapy (NCHT) in patients with high-risk non-metastatic prostate cancer (PCa).
Clinicopathologic information was retrospectively collected from 162 patients with high-risk non-metastatic PCa receiving NCHT and radical prostatectomy at our center. The postoperative pathological findings were used as the gold standard for evaluating the efficacy of NCHT. The least absolute shrinkage and selection operator (LASSO) was conducted to develop radiomics signature. Multivariate logistic regression analyses were conducted to identify the predictors of a positive pathological response to NCHT, and a nomogram was constructed based on these predictors.
Sixty-three patients (38.89%) experienced positive pathological response to NCHT. Receiver operating characteristic analyses showed that the area under the curve (AUC) of periprostatic fat (PPF) radiomics signature was 0.835 (95% CI, 0.754-0.898), while the AUC of intratumoral radiomics signature was 0.822 (95% CI, 0.739-0.888). Multivariate logistic regression analysis revealed that PSA level, PPF radiomics signature and intratumoral radiomics signature were independent predictors of positive pathological response. A nomogram based on these three predictors was constructed. The AUC was 0.908 (95% CI, 0.839-0.954). The Hosmer-Lemeshow goodness-of-fit test showed that the nomogram was well calibrated. Decision curve analysis revealed the favorable clinical practicability of the nomogram. The nomogram was successfully validated in the validation cohort. Kaplan-Meier analyses showed that nomogram and positive pathological response were significantly related with survival of PCa.
The radiomics-clinical nomogram based on mpMRI radiomics features exhibited superior predictive ability for positive pathological response to NCHT in high-risk non-metastatic PCa.
本研究旨在评估基于放射组学特征的列线图在预测高危非转移性前列腺癌（PCa）患者新辅助化疗激素治疗（NCHT）疗效中的潜在应用。
回顾性收集了 162 例在我院接受 NCHT 和根治性前列腺切除术的高危非转移性 PCa 患者的临床病理资料。术后病理结果作为评价 NCHT 疗效的金标准。采用最小绝对值收缩和选择算子（LASSO）进行放射组学特征分析。采用多因素逻辑回归分析识别预测 NCHT 阳性病理反应的指标，并基于这些指标构建列线图。
63 例（38.89%）患者 NCHT 后病理反应阳性。受试者工作特征曲线分析显示，前列腺周围脂肪（PPF）放射组学特征的曲线下面积（AUC）为 0.835（95%可信区间，0.754-0.898），肿瘤内放射组学特征的 AUC 为 0.822（95%可信区间，0.739-0.888）。多因素逻辑回归分析显示，PSA 水平、PPF 放射组学特征和肿瘤内放射组学特征是 NCHT 阳性病理反应的独立预测指标。基于这三个预测指标构建了列线图。AUC 为 0.908（95%可信区间，0.839-0.954）。Hosmer-Lemeshow 拟合优度检验表明，该列线图拟合良好。决策曲线分析表明，该列线图具有良好的临床实用性。在验证队列中成功验证了该列线图。Kaplan-Meier 分析表明，列线图和阳性病理反应与 PCa 患者的生存显著相关。
基于 mpMRI 放射组学特征的放射组学-临床列线图在预测高危非转移性 PCa 患者 NCHT 阳性病理反应方面具有较好的预测能力。
