Abstract：
BACKGROUND: Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT.
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.
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.
摘要：
背景：根治性前列腺切除术后前列腺特异性抗原（PSA）水平升高表明预后不良，可能与前列腺周围脂肪组织（PPAT）相关。因此，我们旨在构建基于PPAT的18F-PSMA-1007PET/CT的动态在线列线图来预测肿瘤的短期预后。
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。
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。
