MRI-based prostate cancer classification using 3D efficient capsule network

Yuheng Li ^1,2, Jacob Wynne ¹, Jing Wang ¹

⁰Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.

Medical Physics +

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February 12, 2024

View abstract on PubMed

Summary

A novel 3D Efficient CapsNet accurately predicts prostate cancer (PCa) risk from MRI scans. This non-invasive tool aids in personalized treatment and reduces unnecessary biopsies.

Area Of Science

Medical Imaging
Artificial Intelligence
Oncology

Background

Prostate cancer (PCa) risk stratification relies on biopsy, an invasive procedure.
Magnetic resonance imaging (MRI) offers non-invasive characterization but faces interpretation variability.
Gleason score (GS) is crucial for PCa risk but currently requires invasive assessment.

Purpose Of The Study

To develop a 3D Efficient Capsule Network (CapsNet) for non-invasive prediction of PCa risk using T2-weighted (T2W) MRI.
To overcome limitations of Convolutional Neural Networks (CNNs) in encoding spatial information for improved robustness.
To stratify PCa risk based on quantitative MRI analysis.

Main Methods

Utilized 3D CNN modules for spatial feature extraction and primary capsule layers for vector encoding.
Integrated fully-connected capsule layers (FC Caps) to create a deeper hierarchy for PCa grading.
Employed a novel dynamic weighted margin loss function to address data imbalance.
Evaluated the method on a public dataset of 976 PCa T2W MRI scans.

Main Results

The 3D Efficient CapsNet achieved high performance in classifying PCa risk, with AUCs up to 0.83 for low vs. high grade.
The model outperformed state-of-the-art radiomics and deep learning methods in PCa risk stratification.
A weighted Cohen's Kappa score of 0.41 indicated moderate agreement with ground truth PCa risks.

Conclusions

A novel 3D Efficient CapsNet demonstrates feasibility for non-invasive PCa risk stratification using T2W MRI.
This tool has the potential to personalize PCa treatment and decrease the need for invasive biopsies.
The developed method offers a promising non-invasive approach for assessing PCa risk from MRI data.

Keywords:

MRI neural network prostate cancer risk classification