Radiation Oncology Clinic, Oncology Institute of Southern Switzerland, Via Gallino, 6500, Bellinzona, Switzerland.
Information and Communications Technology, Ente Ospedaliero Cantonale, Bellinzona, Switzerland.
Strahlenther Onkol. 2020 Oct;196(10):943-951. doi: 10.1007/s00066-020-01677-x. Epub 2020 Sep 1.
The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer.
The study population consisted of 31 prostate cancer patients. Radiomics features were extracted from weekly CBCT scans performed for verifying treatment position. From the data, logistic-regression models were learned for establishing tumor stage, Gleason score, level of prostate-specific antigen, and risk stratification, and for predicting biochemical recurrence. Performance of the learned models was assessed using the area under the receiver operating characteristic curve (AUC-ROC) or the area under the precision-recall curve (AUC-PRC).
Results suggest that the histogram-based Energy and Kurtosis features and the shape-based feature representing the standard deviation of the maximum diameter of the prostate gland during treatment are predictive of biochemical relapse and indicative of patients at high risk.
Our results suggest the usefulness of CBCT-based radiomics for treatment definition in prostate cancer.
本研究旨在探讨基于锥形束 CT（CBCT）的放射组学在前列腺癌风险分层和生化复发预测中的价值。
该研究人群包括 31 例前列腺癌患者。从每周进行的用于验证治疗位置的 CBCT 扫描中提取放射组学特征。从这些数据中，学习了逻辑回归模型，用于建立肿瘤分期、Gleason 评分、前列腺特异性抗原水平和风险分层，并预测生化复发。使用接收者操作特征曲线下的面积（AUC-ROC）或精度-召回曲线下的面积（AUC-PRC）评估所学习模型的性能。
结果表明，基于直方图的能量和峰度特征以及代表治疗期间前列腺最大直径标准差的形状特征与生化复发相关，提示患者存在高风险。
我们的结果表明，基于 CBCT 的放射组学在前列腺癌的治疗定义中具有一定的应用价值。
