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Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study.

Jose Lozano-Montoya^1,2, Emilio Soria-Olivas³, Almudena Fuster-Matanzo²

¹Universitat de València, Valencia, Valencia, Spain.

JMIR Formative Research

|June 11, 2026

Summary

This summary is machine-generated.

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Simplatab, a radiomics-specific automated machine learning (AutoML) tool, balances performance and efficiency for radiomics classification. While general-purpose AutoML frameworks offer greater accessibility, Simplatab provides a user-friendly option for researchers.

Area of Science:

Radiomics and Machine Learning
Medical Image Analysis
Computational Biology

Background:

Automated machine learning (AutoML) frameworks can simplify predictive model development in radiomics.
However, their effectiveness for radiomics-specific challenges is not well-established.

Purpose of the Study:

To evaluate general-purpose and radiomics-specific AutoML frameworks for performance, efficiency, and accessibility in radiomics classification.
To guide researchers and identify areas for AutoML development in radiomics.

Main Methods:

Tested 6 general-purpose and 5 radiomics-specific AutoML frameworks on 10 diverse radiomics datasets.
Utilized predefined parameters, standardized cross-validation, and metrics including area under the receiver operating characteristic curve (AUC) and runtime.

Keywords:

automated machine learning classification comparative study medical imaging radiomics

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Main Results:

Simplatab, a radiomics-specific tool, offered the best balance of performance (mean AUC 78.46%) and efficiency (1.1h runtime).
General-purpose frameworks showed higher accessibility and ease of use, though Simplatab's performance was comparable to intensive general-purpose solutions.
Several radiomics-specific frameworks were excluded due to obsolescence or high computational demands.

Conclusions:

Simplatab presents a viable option for radiomics classification, balancing performance, efficiency, and accessibility.
No single framework achieved absolute predictive superiority, indicating a need for continued AutoML development in radiomics.