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European Radiology
Published in: European Radiology 11/2022

04-05-2022 | Prostate Cancer | Imaging Informatics and Artificial Intelligence

RETRACTED ARTICLE: Biparametric MR signal characteristics can predict histopathological measures of prostate cancer

Authors: Minh Nguyen Nhat To, Jin Tae Kwak

Published in: European Radiology | Issue 11/2022

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Abstract

Objectives

The aim of this study was to establish a new data-driven metric from MRI signal intensity that can quantify histopathological characteristics of prostate cancer.

Methods

This retrospective study was conducted on 488 patients who underwent biparametric MRI (bp-MRI), including T2-weighted imaging (T2W) and apparent diffusion coefficient (ADC) of diffusion-weighted imaging, and having biopsy-proven prostate cancer between August 2011 and July 2015. Forty-two of the patients who underwent radical prostatectomy and the rest of 446 patients constitute the labeled and unlabeled datasets, respectively. A deep learning model was built to predict the density of epithelium, epithelial nuclei, stroma, and lumen from bp-MRI, called MR-driven tissue density. On both the labeled validation set and the whole unlabeled dataset, the quality of MR-driven tissue density and its relation to bp-MRI signal intensity were examined with respect to different histopathologic and radiologic conditions using different statistical analyses.

Results

MR-driven tissue density and bp-MRI of 446 patients were evaluated. MR-driven tissue density was significantly related to bp-MRI (p < 0.05). The relationship was generally stronger in cancer regions than in benign regions. Regarding cancer grades, significant differences were found in the intensity of bp-MRI and MR-driven tissue density of epithelium, epithelial nuclei, and stroma (p < 0.05). Comparing MR true-negative to MR false-positive regions, MR-driven lumen density was significantly different, similar to the intensity of bp-MRI (p < 0.001).

Conclusions

MR-driven tissue density could serve as a reliable histopathological measure of the prostate on bp-MRI, leading to an improved understanding of prostate cancer and cancer progression.

Key Points

• Semi-supervised deep learning enables non-invasive and quantitative histopathology in the prostate from biparametric MRI.
• Tissue density derived from biparametric MRI demonstrates similar characteristics to the direct estimation of tissue density from histopathology images.
• The analysis of MR-driven tissue density reveals significantly different tissue compositions among different cancer grades as well as between MR-positive and MR-negative benign.
Appendix
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Metadata
Title
RETRACTED ARTICLE: Biparametric MR signal characteristics can predict histopathological measures of prostate cancer
Authors
Minh Nguyen Nhat To
Jin Tae Kwak
Publication date
04-05-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 11/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08808-1

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