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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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Prostate Cancer Detection using Deep Convolutional Neural Networks.

Sunghwan Yoo¹, Isha Gujrathi¹, Masoom A Haider^1,2,3,4

¹Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.

Scientific Reports

|December 22, 2019

Summary

This summary is machine-generated.

This study introduces an automated deep learning pipeline using convolutional neural networks (CNNs) for detecting clinically significant prostate cancer (PCa) from diffusion-weighted MRI (dWI). The AI model shows promising performance in identifying cancer on both image slices and at the patient level.

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Area of Science:

Medical Imaging
Artificial Intelligence in Oncology
Prostate Cancer Diagnostics

Background:

Prostate cancer is a leading cause of cancer death in North America.
Diffusion-weighted magnetic resonance imaging (dWI) is crucial for computer-aided detection (CAD) of prostate cancer.
Deep convolutional neural networks (CNNs) show potential for enhancing CAD tools in medical imaging.

Purpose of the Study:

To develop and implement an automated CNN-based pipeline for detecting clinically significant prostate cancer (PCa).
To evaluate the pipeline's performance on axial dWI images at both slice and patient levels.

Main Methods:

An automated pipeline utilizing CNNs was developed for prostate cancer detection.
The dataset comprised dWI images from 427 patients (175 with PCa, 252 without).
A dedicated test set of 108 patients was used for performance evaluation.

Main Results:

The CNN pipeline achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI: 0.84-0.90) at the slice level.
The pipeline demonstrated an AUC of 0.84 (95% CI: 0.76-0.91) at the patient level.
These results indicate strong performance in identifying clinically significant prostate cancer.

Conclusions:

The developed automated CNN pipeline is a promising tool for detecting clinically significant prostate cancer.
The findings support the integration of AI-driven CAD tools in prostate cancer diagnostics using dWI.
Further validation on larger datasets may enhance clinical applicability.