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

Akimasa Urakami; Hidetaka Arimura; Yukihisa Takayama; Fumio Kinoshita; Kenta Ninomiya; Kenjiro Imada; Sumiko Watanabe; Akihiro Nishie; Yoshinao Oda; Kousei Ishigami

doi:10.1002/pros.24278

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

Prostate. 2022 Feb;82(3):330-344. doi: 10.1002/pros.24278. Epub 2021 Dec 8.

Authors

Affiliations

¹ Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
² Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
³ Department of Radiology, Faculty of Medicine, Fukuoka University, Fukuoka, Japan.
⁴ Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
⁵ Department of Urology, Prostate, Kidney, Adrenal Surgery, Kyushu University Hospital, Fukuoka, Japan.
⁶ Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.

PMID: 35014713
DOI: 10.1002/pros.24278

Abstract

Purpose: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: dynamic contrast-enhanced images; grade group; joint histogram; multiparametric MR; prostate cancer.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Contrast Media / pharmacology
Histological Techniques / methods*
Humans
Image Processing, Computer-Assisted
Male
Middle Aged
Multimodal Imaging
Multiparametric Magnetic Resonance Imaging / methods*
Prostatic Neoplasms / diagnosis*
ROC Curve
Radiographic Image Enhancement / methods*
Reproducibility of Results
Risk Assessment* / methods
Risk Assessment* / statistics & numerical data

Substances

Contrast Media