Abstract：
OBJECTIVE: To establish and validate novel predictive models for predicting bone metastasis (BM) in newly diagnosed prostate adenocarcinoma (PCa) via single-photon emission computed tomography radiomics.
METHODS: In a retrospective review of the clinical single-photon emission computed tomography (SPECT) database, 176 patients (training set: n = 140; validation set: n = 36) who underwent SPECT/CT imaging and were histologically confirmed to have newly diagnosed PCa from June 2016 to June 2022 were enrolled. Radiomic features were extracted from the region of interest (ROI) in a targeted lesion in each patient. Clinical features, including age, total prostate-specific antigen (t-PSA), and Gleason grades, were included. Statistical tests were then employed to eliminate irrelevant and redundant features. Finally, four types of optimized models were constructed for the prediction. Furthermore, fivefold cross-validation was applied to obtain sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation. The clinical usefulness of the multivariate models was estimated through decision curve analysis (DCA).
RESULTS: A radiomics signature consisting of 27 selected features which were obtained by radiomics\' LASSO treatment was significantly correlated with bone status (P < 0.01 for both training and validation sets). Collectively, the models showed good predictive efficiency. The AUC values ranged from 0.87 to 0.98 in four models. The AUC values of the human experts were 0.655 and 0.872 in the training and validation groups, respectively. Most radiomic models showed better diagnostic accuracy than human experts in the training and validation groups. DCA also demonstrated the superiority of the radiomics models compared to human experts.
CONCLUSIONS: Radiomics models are superior to humans in differentiating between benign bone and prostate cancer bone metastases; it can be used to facilitate personalized prediction of BM in newly diagnosed PCa patients.
METHODS: In a retrospective review of the clinical single-photon emission computed tomography (SPECT) database, 176 patients (training set: n = 140; validation set: n = 36) who underwent SPECT/CT imaging and were histologically confirmed to have newly diagnosed PCa from June 2016 to June 2022 were enrolled. Radiomic features were extracted from the region of interest (ROI) in a targeted lesion in each patient. Clinical features, including age, total prostate-specific antigen (t-PSA), and Gleason grades, were included. Statistical tests were then employed to eliminate irrelevant and redundant features. Finally, four types of optimized models were constructed for the prediction. Furthermore, fivefold cross-validation was applied to obtain sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation. The clinical usefulness of the multivariate models was estimated through decision curve analysis (DCA).
RESULTS: A radiomics signature consisting of 27 selected features which were obtained by radiomics\' LASSO treatment was significantly correlated with bone status (P < 0.01 for both training and validation sets). Collectively, the models showed good predictive efficiency. The AUC values ranged from 0.87 to 0.98 in four models. The AUC values of the human experts were 0.655 and 0.872 in the training and validation groups, respectively. Most radiomic models showed better diagnostic accuracy than human experts in the training and validation groups. DCA also demonstrated the superiority of the radiomics models compared to human experts.
CONCLUSIONS: Radiomics models are superior to humans in differentiating between benign bone and prostate cancer bone metastases; it can be used to facilitate personalized prediction of BM in newly diagnosed PCa patients.
摘要：
目的：通过单光子发射计算机断层扫描影像组学，建立并验证新诊断的前列腺腺癌（PCa）骨转移（BM）的预测模型。
方法：在对临床单光子发射计算机断层扫描（SPECT）数据库的回顾性审查中，纳入176例患者（训练集：n=140；验证集：n=36），从2016年6月至2022年6月接受SPECT/CT成像并经组织学证实为新诊断的PCa。从每位患者的目标病变的感兴趣区域（ROI）中提取放射学特征。临床特征,包括年龄,总前列腺特异性抗原（t-PSA），和格里森等级，包括在内。然后采用统计测试来消除不相关和冗余的特征。最后,构建了四类优化模型进行预测。此外，应用五倍交叉验证来获得灵敏度，特异性，准确度,和用于性能评估的曲线下面积(AUC)。通过决策曲线分析(DCA)估计多变量模型的临床有用性。
结果：通过影像组学LASSO治疗获得的由27个选定特征组成的影像组学特征与骨骼状态显着相关（训练集和验证集P<0.01）。总的来说，模型表现出良好的预测效率。在四个模型中AUC值范围为0.87至0.98。在训练和验证组中，人类专家的AUC值分别为0.655和0.872，分别。在训练和验证组中，大多数放射学模型显示出比人类专家更好的诊断准确性。与人类专家相比，DCA还证明了影像组学模型的优越性。
结论：Radiomics模型在区分良性骨转移和前列腺癌骨转移方面优于人类；它可用于促进新诊断PCa患者BM的个性化预测。
方法：在对临床单光子发射计算机断层扫描（SPECT）数据库的回顾性审查中，纳入176例患者（训练集：n=140；验证集：n=36），从2016年6月至2022年6月接受SPECT/CT成像并经组织学证实为新诊断的PCa。从每位患者的目标病变的感兴趣区域（ROI）中提取放射学特征。临床特征,包括年龄,总前列腺特异性抗原（t-PSA），和格里森等级，包括在内。然后采用统计测试来消除不相关和冗余的特征。最后,构建了四类优化模型进行预测。此外，应用五倍交叉验证来获得灵敏度，特异性，准确度,和用于性能评估的曲线下面积(AUC)。通过决策曲线分析(DCA)估计多变量模型的临床有用性。
结果：通过影像组学LASSO治疗获得的由27个选定特征组成的影像组学特征与骨骼状态显着相关（训练集和验证集P<0.01）。总的来说，模型表现出良好的预测效率。在四个模型中AUC值范围为0.87至0.98。在训练和验证组中，人类专家的AUC值分别为0.655和0.872，分别。在训练和验证组中，大多数放射学模型显示出比人类专家更好的诊断准确性。与人类专家相比，DCA还证明了影像组学模型的优越性。
结论：Radiomics模型在区分良性骨转移和前列腺癌骨转移方面优于人类；它可用于促进新诊断PCa患者BM的个性化预测。
