Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.
前列腺癌（PCa）是男性中诊断率最高的非皮肤癌。尽管根治性前列腺切除术等疗法被认为具有治愈效果，但仍可能形成远处转移，导致生化复发（BCR）。本研究利用多参数磁共振成像（MP-MRI）计算的影像组学特征，评估其预测生化复发和前列腺癌存在的能力。研究共纳入279例患者数据，其中46例出现生化复发，所有患者均在术前接受了MP-MRI检查。术后，采用基于T2加权成像（T2WI）建模的患者特异性3D打印切片夹具对前列腺进行切片。切片组织经染色、数字化处理后，由泌尿生殖系统专科培训的病理学家标注癌灶存在区域。数字化切片与标注结果与T2WI图像进行配准，并针对整个前列腺及癌灶区域计算影像组学特征。研究采用树回归模型评估影像组学特征预测生化复发的能力，并利用相同特征构建树分类模型对癌变区域进行分类。结果显示，10个影像组学特征预测最终生化复发的曲线下面积（AUC）达0.97，癌灶分类准确率为89.9%。本研究展示了一种基于影像组学特征的工具有助于筛查前列腺癌存在并评估患者预后（以生化复发为判断标准）。
肿瘤(癌症)患者之家