Duenweg Savannah R, Bobholz Samuel A, Barrett Michael J, Lowman Allison K, Winiarz Aleksandra, Nath Biprojit, Stebbins Margaret, Bukowy John, Iczkowski Kenneth A, Jacobsohn Kenneth M, Vincent-Sheldon Stephanie, LaViolette Peter S
Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA.
Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA.
Cancers (Basel). 2023 Sep 6;15(18):4437. doi: 10.3390/cancers15184437.
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）是男性中诊断最多的非皮肤癌。尽管诸如根治性前列腺切除术等疗法被认为可治愈，但仍可能形成远处转移，导致生化复发（BCR）。本研究使用从多参数磁共振成像（MP-MRI）计算得出的放射组学特征来评估其预测BCR和前列腺癌存在的能力。本研究评估了总共279例患者的数据，其中46例经历了BCR，这些患者在手术前接受了MP-MRI检查。手术后，使用根据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，并由一名接受过泌尿专科培训的病理学家标注是否存在癌症。将数字化切片和标注与T2WI进行配准，并在整个前列腺和癌性病变中计算放射组学特征。拟合了一个树回归模型来评估放射组学特征预测BCR的能力，并使用相同的放射组学特征拟合了一个树分类模型来对癌症区域进行分类。我们发现，10个放射组学特征预测最终BCR的曲线下面积（AUC）为0.97，对癌症分类的准确率为89.9%。本研究展示了一种基于放射组学特征的工具在筛查前列腺癌存在情况和评估患者预后（由生化复发确定）方面的应用。
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