Radio-pathomic maps of complex histo-morphometric features trained with whole mount prostate histology differentiate prostate cancer on MPMRI

Motivation: The motivation of this study is to develop novel methods for mapping non-invasively the underlying history-morphometric features of prostate cancer. Goal(s): The goal of this study is to develop and demonstrate radio-pathomic mapping techniques to enable noninvasive detection of prostate cancer presence and distinction from benign tissue using MRI. Approach: Our approach was to align multiparametric MRI with digitized histology slides from prostatectomy specimens, then predict quantitative histological features from MRI intensities, and use these predicted features to classify cancer versus noncancer regions. Results: Our models can distinguish cancerous from noncancerous prostate tissue with 70% accuracy. Impact: This study uses radio-pathomic mapping for noninvasive prostate cancer detection, demonstrating the potential to differentiate cancerous vs benign prostate tissue using imaging surrogates of microstructural features discernible only on histology.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/0723
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Radio-pathomic maps of complex histo-morphometric features trained with whole mount prostate histology differentiate prostate cancer on MPMRI

Fitzgerald Kyereme, Biprojit Nath, Kenneth A. Iczkowski, Michael Flatley et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

Radio-pathomic maps of complex histo-morphometric features trained with whole mount prostate histology differentiate prostate cancer on MPMRI

Fitzgerald Kyereme, Biprojit Nath, Kenneth A. Iczkowski, Michael Flatley, Aleksandra Winiarz, Peter S. LaViolette, Anjishnu Banerjee, Samuel Bobholz, Savannah Duenweg, Allison Lowman
article en

Abstract

Motivation: The motivation of this study is to develop novel methods for mapping non-invasively the underlying history-morphometric features of prostate cancer. Goal(s): The goal of this study is to develop and demonstrate radio-pathomic mapping techniques to enable noninvasive detection of prostate cancer presence and distinction from benign tissue using MRI. Approach: Our approach was to align multiparametric MRI with digitized histology slides from prostatectomy specimens, then predict quantitative histological features from MRI intensities, and use these predicted features to classify cancer versus noncancer regions. Results: Our models can distinguish cancerous from noncancerous prostate tissue with 70% accuracy. Impact: This study uses radio-pathomic mapping for noninvasive prostate cancer detection, demonstrating the potential to differentiate cancerous vs benign prostate tissue using imaging surrogates of microstructural features discernible only on histology.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Good health and well-being
Openalex Percentile: Top 64%
Radiomics and Machine Learning in Medical Imaging
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