Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study

Clinical Snapshot

Key Findings

• P Value: Statistical significance not reported for performance comparisons

• Effect Size: Simplatab achieved highest mean AUC-ROC of 78.46% (SD 12.22%)

• Primary Outcome: Area under the receiver operating characteristic curve (AUC-ROC)

• Nnt Or Sensitivity: Runtime efficiency: Simplatab averaged 1.1 hours computational time

• Confidence Interval: Not reported

Clinical Application

High feasibility for implementation in research settings with appropriate computational resources Relevant to Australian medical imaging research institutions and TGA-regulated medical device development involving radiomics Medical imaging researchers and clinicians developing radiomics-based predictive models

Abstract

BACKGROUND: Automated machine learning (AutoML) frameworks can lower technical barriers for predictive and prognostic model development in radiomics by enabling researchers without programming expertise to build models. However, their effectiveness in addressing radiomics-specific challenges remains unclear. OBJECTIVE: This study aimed to evaluate the performance, efficiency, and accessibility of general-purpose and radiomics-specific AutoML frameworks on diverse radiomics classification tasks, thereby guiding researchers and highlighting development needs for radiomics. METHODS: A total of 10 public and private radiomics datasets with varied imaging modalities (computed tomography and magnetic resonance imaging), sizes, anatomies, and end points were used. Six general-purpose and 5 radiomics-specific frameworks were tested with predefined parameters using standardized cross-validation. Evaluation metrics included area under the receiver operating characteristic curve, runtime, and qualitative aspects related to software status, accessibility, and interpretability. RESULTS: Simplatab, a radiomics-specific tool with a no-code interface, achieved the best overall balance between performance and computational efficiency, recording the highest average test area under the receiver operating characteristic curve (mean 78.46%, SD 12.22%) with a moderate runtime (1.1 h). However, its performance was not statistically superior to the most intensive general-purpose solutions. Most radiomics-specific frameworks were excluded from the performance analysis due to obsolescence, extensive programming requirements, or computational inefficiency. Conversely, general-purpose frameworks demonstrated higher accessibility and ease of implementation. CONCLUSIONS: While no single framework demonstrated absolute predictive superiority, Simplatab provides an effective balance of performance, efficiency, and accessibility for radiomics classification problems. However, continued efforts are needed to further mature AutoML solutions in the radiomics domain.

References

1. 1.Lozano-Montoya, J., Soria-Olivas, E., Fuster-Matanzo, A., Alberich-Bayarri, A., & Jimenez-Pastor, A. (2026). Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study. JMIR Formative Research. https://doi.org/10.2196/91492