Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States.
Department of Urology, University of California, San Francisco, CA, United States; Department of Pathology, University of California, San Francisco, CA, United States.
Magn Reson Imaging. 2023 Jun;99:48-57. doi: 10.1016/j.mri.2023.01.006. Epub 2023 Jan 11.
Multi-parametric MRI (mpMRI) has proven itself a clinically useful tool to assess prostate cancer (PCa). Our objective was to generate PCa risk maps to quantify the volume and location of both all PCa and high grade (Gleason grade group ≥ 3) PCa. Such capabilities would aid physicians and patients in treatment decisions, targeting biopsy, and planning focal therapy. A cohort of men with biopsy proven prostate cancer and pre-prostatectomy mpMRI were studied. PCa and benign ROIs (1524) were identified on mpMRI and histopathology with histopathology serving as the reference standard. Logistic regression models were created to differentiate PCa from benign tissues. The MRI images were registered to ensure correct overlay. The cancer models were applied to each image voxel within prostates to create probability maps of cancer and of high-grade cancer. Use of an optimum probability threshold quantified PCa volume for all lesions >0.1 cc. Accuracies were calculated using area under the curve (AUC) for the receiver operating characteristic (ROC). The PCa models utilized apparent diffusion coefficient (ADC), T2 weighted (T2W), dynamic contrast-enhanced MRI (DCE MRI) enhancement slope, and DCE MRI washout as the statistically significant MRI scans. Application of the PCa maps method provided total PCa volume and individual lesion volumes. The AUCs derived from lesion analysis were 0.91 for all PCa and 0.73 for high-grade PCa. At the optimum threshold, the PCa maps detected 135 / 150 (90%) histopathological lesions >0.1 cc. This study showed the feasibility of cancer risk maps, created from pre-prostatectomy, mpMR images validated with histopathology, to detect PCa lesions >0.1 cc. The method quantified the volume of cancer within the prostate. Method improvements were identified by determining root causes for over and underestimation of cancer volumes. The maps have the potential for improved non-invasive capability in quantitative detection, localization, volume estimation, and MRI characterization of PCa.
多参数 MRI（mpMRI）已被证明是一种用于评估前列腺癌（PCa）的临床有用工具。我们的目标是生成 PCa 风险图，以量化所有 PCa 和高级别（Gleason 分级组≥3）PCa 的体积和位置。这些功能将有助于医生和患者做出治疗决策、靶向活检和规划局灶性治疗。本研究纳入了一组经活检证实患有前列腺癌和术前 mpMRI 的男性患者。mpMRI 和组织病理学上均识别出 PCa 和良性 ROI（1524 个），并以组织病理学作为参考标准。建立了逻辑回归模型以区分 PCa 和良性组织。将 MRI 图像进行配准以确保正确叠加。将癌症模型应用于前列腺内的每个图像体素，以创建癌症和高级别癌症的概率图。使用最佳概率阈值量化了所有>0.1 cc 的病变的 PCa 体积。使用受试者工作特征（ROC）的曲线下面积（AUC）计算了准确性。PCa 模型利用表观扩散系数（ADC）、T2 加权（T2W）、动态对比增强 MRI（DCE MRI）增强斜率和 DCE MRI 洗脱作为具有统计学意义的 MRI 扫描。应用 PCa 图谱方法提供了总 PCa 体积和单个病变体积。病变分析得出的 AUC 分别为所有 PCa 的 0.91 和高级别 PCa 的 0.73。在最佳阈值下，PCa 图谱检测到 135/150（90%）>0.1 cc 的组织病理学病变。本研究表明，从术前 mpMRI 图像创建验证组织病理学的癌症风险图是可行的，可检测>0.1 cc 的 PCa 病变。该方法量化了前列腺内的癌症体积。通过确定癌症体积高估和低估的根本原因，确定了方法改进。该图谱具有提高 PCa 的定量检测、定位、体积估计和 MRI 特征描述的非侵入性能力的潜力。
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