METHODS: Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA.
RESULTS: The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78-0.91), 0.77 (95% CI: 0.62-0.91) and 0.84 (95% CI: 0.70-0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram.
CONCLUSIONS: The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.
方法:回顾性分析了268例前列腺癌(PCa)患者在前列腺切除术前接受18F-PSMA-1007PET/CT的数据,用于模型构建和验证(训练队列:n=156;内部验证队列:n=65;外部验证队列:n=47)。提取PET和CT的影像组学特征(RF)。然后,Rad分数是使用逻辑回归分析构建的,基于通过最大相关性和最小冗余选择的25个最佳RF,以及最小绝对收缩和选择运算符。建立列线图来预测由持续PSA决定的短期预后。
结果:由25个RFs组成的Rad评分在所有队列中对持续性PSA进行分类时显示出良好的区分度(均P<0.05)。基于逻辑分析,影像组学-临床联合模型,其中包含最佳的RF和预测的临床变量,在AUC为0.85时表现最佳(95%CI:0.78-0.91),训练中0.77(95%CI:0.62-0.91)和0.84(95%CI:0.70-0.93),内部验证和外部验证队列。在所有队列中,校准曲线校准良好.对决策曲线的分析显示,影像组学-临床组合列线图具有更大的临床实用性。
结论:影像组学-临床组合列线图是一种新的工具,用于术前个体化预测PCa患者的短期预后。