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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements.

Ciprian Cosmin Secasan^1,2, Darian Onchis³, Razvan Bardan^1,2

¹Department of Urology, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania.

Current Oncology (Toronto, Ont.)

|June 23, 2022

Summary

Artificial intelligence combined with shear wave elastography shows high accuracy in predicting prostate cancer. This AI system aids in identifying cancerous lesions, improving diagnostic capabilities for urologists.

Keywords:

artificial intelligence system prostate cancer shear wave elastography

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Area of Science:

Medical imaging
Artificial intelligence
Oncology

Background:

Prostate cancer diagnosis relies on biopsies, which can be invasive.
Shear wave elastography (SWE) offers a non-invasive method to assess tissue stiffness.
Integrating AI with SWE may enhance diagnostic accuracy for prostate cancer.

Purpose of the Study:

To develop an artificial intelligence (AI) system for prostate cancer prediction.
To evaluate the efficacy of bi-dimensional shear wave elastography (BDSWE) in conjunction with AI.
To compare AI-driven BDSWE analysis with histopathological examination of prostate biopsy specimens.

Main Methods:

A prospective study involving 356 patients undergoing prostate biopsy for cancer suspicion.
Bi-dimensional shear wave ultrasonography was performed, followed by systematic transrectal prostate biopsy.
Three machine learning algorithms (logistic regression, decision tree, dense neural network) were applied to elastographic data from 223 confirmed prostate cancer cases.

Main Results:

The dense neural network achieved an Area Under the Curve (AUC) of 0.94.
An ensemble model combining three machine learning algorithms reached a 98% accuracy rate.
Upsampling the neural network's training set slightly improved its predictive performance.

Conclusions:

Bi-dimensional shear wave elastography shows significant potential for predicting prostate cancer lesions.
AI and machine learning algorithms substantially enhance the diagnostic utility of BDSWE.
This AI-powered approach could serve as a valuable tool in prostate cancer diagnostics.