Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles (UCLA), Los Angeles, CA, 90024, USA.
Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.
Abdom Radiol (NY). 2018 Sep;43(9):2487-2496. doi: 10.1007/s00261-018-1495-2.
We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer.
In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space.
Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate.
We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.
我们提出了一种基于 T2MR 的前列腺肿瘤发生概率模型生成方法，以指导靶向活检的解剖部位选择，并作为一种诊断工具，辅助前列腺癌的影像学评估。
在我们的研究中，对 266 例接受根治性前列腺切除术的患者的 3DT2 加权 MR 图像进行了回顾性前列腺和任何放射学发现的分割。随后的组织病理学分析确定了肿瘤的真实情况和 Gleason 分级。随机选择 19 名受试者的子集用于生成多受试者衍生的前列腺模板。随后，使用涉及仿射和非刚性 B 样条变换的级联配准算法将每个受试者的前列腺配准到模板上。放射学发现的相应变换产生了肿瘤发生的基于人群的概率模型。通过测量肿瘤在前列腺模板中的正确放置比例（即，在转换到前列腺模板空间后肿瘤保持其在前列腺内解剖位置的肿瘤数量），对我们的概率模型构建方法的质量进行了统计学评估。
用被认为具有临床意义的肿瘤构建的概率模型显示出肿瘤的异质性分布，在前部中央过渡区和基底部到中部后外周区发生肿瘤的可能性更高。在分析的 250 个 MR 病变中，248 个在转换到前列腺后保持了与前列腺区相对于前列腺的原始解剖位置。
我们提出了一种生成前列腺肿瘤发生概率模型的稳健方法，该模型可以辅助临床决策，例如选择磁共振引导下前列腺活检的解剖部位。
