Chen Yuntian, Zhao Jinge, Ye Lei, Zhao Diwei, Zhu Sha, Fang Bangwei, Zhao Fengnian, Yang Ling, Liu Zhenhua, Dai Jindong, Xu Nanwei, Tang Yanfeng, Liu Haolin, Wang Zhipeng, Tu Xiang, Zhou Fangjian, Wei Qiang, Ye Dingwei, Song Bin, Li Yonghong, Zhu Yao, Shen Pengfei, Zeng Hao, Yao Jin, Sun Guangxi
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Quant Imaging Med Surg. 2025 Jul 1;15(7):5991-6004. doi: 10.21037/qims-2024-2708. Epub 2025 Jun 30.
Genetic testing for pathogenic DNA damage repair gene (pDDRg) mutations has clinical benefits for prostate cancer (PCa) patients, but its real-world application faces challenges due to its high associated costs. We sought to develop a magnetic resonance imaging (MRI)-based radiomics model capable of assessing the likelihood of PCa patients harboring pDDRg mutations. We then rigorously validated its predictive value in two external validation cohorts.
A total of 225 patients with both multiparametric MRI data before prostate biopsy and genetic testing information for pDDRg mutations were included in this study. The radiomics features were extracted from the T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences of the MRI images in the training cohort (N=101) using the least absolute shrinkage and selection operator (LASSO) algorithm. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) were used to validate the predictive value of the model in both the internal (N=41) and external (N=83) validation cohorts.
In total, 48 of the 225 (21.3%) patients in our cohort were identified by genetic testing as having positive pDDRg mutations, including (N=13), (N=15), (N=9), and other pDDRg mutations (N=17). Thirteen radiomics features from T2WI (N=7) and ADC sequences (N=6) were extracted to develop a model predicting pDDRg mutation carriers. The radiomics-based model had AUC values of 0.824 [95% confidence interval (CI): 0.677-0.923] in the internal validation dataset and 0.836 (95% CI: 0.738-0.908) in the external validation dataset. Notably, setting the cut-off value as "zero misseddignoses" resulted in a potential reduction of around 25% in unnecessary gene testing across both the internal and external validation datasets.
Our MRI radiomics-based predictive model is a promising pre-testing tool for pDDRg mutation prediction in patients with PCa. Prospective studies need to be conducted to further validate the power of this predictive model before its clinical application.
对致病性DNA损伤修复基因（pDDRg）突变进行基因检测对前列腺癌（PCa）患者具有临床益处，但其实际应用因相关成本高昂而面临挑战。我们试图开发一种基于磁共振成像（MRI）的放射组学模型，能够评估PCa患者携带pDDRg突变的可能性。然后，我们在两个外部验证队列中严格验证了其预测价值。
本研究共纳入225例在前列腺活检前有多参数MRI数据且有pDDRg突变基因检测信息的患者。使用最小绝对收缩和选择算子（LASSO）算法从训练队列（N = 101）中MRI图像的T2加权成像（T2WI）和表观扩散系数（ADC）序列中提取放射组学特征。采用受试者操作特征（ROC）曲线下面积（AUC）值和决策曲线分析（DCA）来验证该模型在内部（N = 41）和外部（N = 83）验证队列中的预测价值。
在我们队列的225例患者中，共有48例（21.3%）通过基因检测被确定为pDDRg突变阳性，包括（N = 13）、（N = 15）、（N = 9）和其他pDDRg突变（N = 17）。从T2WI（N = 7）和ADC序列（N = 6）中提取了13个放射组学特征，以建立一个预测pDDRg突变携带者的模型。基于放射组学的模型在内部验证数据集中的AUC值为0.824 [95%置信区间（CI）：0.677 - 0.923]，在外部验证数据集中为0.836（95% CI：0.738 - 0.908）。值得注意的是，将临界值设定为“零漏诊”可使内部和外部验证数据集的不必要基因检测潜在减少约25%。
我们基于MRI放射组学的预测模型是一种很有前景的用于预测PCa患者pDDRg突变的检测前工具。在其临床应用之前，需要进行前瞻性研究以进一步验证该预测模型的效能。
