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Updated: May 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Optimising coronary imaging decisions with machine learning: an external validation study.

L Malin Overmars¹, Bram van Es², Floor Groepenhoff³

¹Central Diagnostic Laboratory, University Medical Centre Utrecht, Utrecht, The Netherlands l.m.overmars-2@umcutrecht.nl.

|April 25, 2025

Summary

This summary is machine-generated.

Sex-stratified machine learning algorithms using electronic health records (EHRs) show high negative predictive values for excluding coronary stenosis. While promising, further refinement is needed before widespread clinical use.

Keywords:

Angina Pectoris Chest Pain Coronary Stenosis Diagnostic Imaging Electronic Health Records

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Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

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Published on: August 9, 2024

Area of Science:

Cardiology
Medical Informatics
Machine Learning

Background:

Diagnosing coronary stenosis is challenging and current methods like CT and angiography are costly and invasive.
Electronic health records (EHRs) offer a potential non-invasive alternative for excluding coronary stenosis.
External validation of sex-stratified algorithms is crucial for assessing generalizability across different healthcare settings.

Purpose of the Study:

To externally validate sex-stratified machine learning algorithms for predicting the absence of coronary stenosis.
To evaluate algorithm performance in diverse clinical settings using EHR data.

Main Methods:

Sex-stratified XGBoost algorithms were developed using EHR data from 14,674 patients.
Algorithms were externally tested on EHR data from 9,252 patients across 13 cardiology centers.
Absence of coronary stenosis was determined via text mining of radiology reports; performance was measured by negative predictive values (NPVs) and specificities.

Main Results:

In the training cohort, algorithms achieved NPVs of 0.95 (men) and 0.93 (women) with specificities of 0.14 (men) and 0.26 (women).
In the testing cohort, NPVs were 0.89 (men) and 0.87 (women), with specificities of 0.07 (men) and 0.18 (women).
High NPVs were observed across different settings, indicating strong predictive power for the absence of stenosis.

Conclusions:

Sex-stratified machine learning algorithms using EHR data can non-invasively predict the absence of coronary stenosis with high NPVs.
The modest specificity suggests limitations for immediate clinical adoption.
Further research and refinement are necessary before these algorithms can be widely implemented in clinical practice.