Stratification of prostate cancer patients into low‐ and high‐grade groups using multiparametric magnetic resonance radiomics with dynamic contrast‐enhanced image joint histograms

直方图 接收机工作特性 前列腺癌 磁共振成像 医学 模式识别(心理学) 人工智能 核医学 前列腺 癌症 计算机科学 放射科 图像(数学) 内科学
作者
Akimasa Urakami,Hidetaka Arimura,Yoshiaki Takayama,Fumio Kinoshita,Kenta Ninomiya,Kenjiro Imada,Soichi Watanabe,Akihiro Nishie,Yoshinao Oda,Kousei Ishigami
出处
期刊:The Prostate [Wiley]
卷期号:82 (3): 330-344 被引量:4
标识
DOI:10.1002/pros.24278
摘要

This study aimed to investigate the potential of stratification of prostate cancer patients into low- and high-grade groups (GGs) using multiparametric magnetic resonance (mpMR) radiomics in conjunction with two-dimensional (2D) joint histograms computed with dynamic contrast-enhanced (DCE) images.A total of 101 prostate cancer regions extracted from the MR images of 44 patients were identified and divided into training (n = 31 with 72 cancer regions) and test datasets (n = 13 with 29 cancer regions). Each dataset included low-grade tumors (International Society of Urological Pathology [ISUP] GG ≤ 2) and high-grade tumors (ISUP GG ≥ 3). A total of 137,970 features consisted of mpMR image (16 types of images in four sequences)-based and joint histogram (DCE images at 10 phases)-based features for each cancer region. Joint histogram features can visualize temporally changing perfusion patterns in prostate cancer based on the joint histograms between different phases or subtraction phases of DCE images. Nine signatures (a set of significant features related to GGs) were determined using the best combinations of features selected using the least absolute shrinkage and selection operator. Further, support vector machine models with the nine signatures were built based on a leave-one-out cross-validation for the training dataset and evaluated with receiver operating characteristic (ROC) curve analysis.The signature showing the best performance was constructed using six features derived from the joint histograms, DCE original images, and apparent diffusion coefficient maps. The areas under the ROC curves for the training and test datasets were 1.00 and 0.985, respectively.This study suggests that the proposed approach with mpMR radiomics in conjunction with 2D joint histogram computed with DCE images could have the potential to stratify prostate cancer patients into low- and high-GGs.

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