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Radiomics and Machine Learning in Medical Imaging / Ai in Cancer Detection · Journal article
Journal of Applied Statistics · September 10, 2026
Raises a question worth testing. It does not answer one.
This is a methodological paper comparing three machine learning algorithms for variable selection in ultra-high dimensional radiomics data. The authors propose extensions (SIS+GOAL and SIS+OAL) to handle radiomics feature sets and report that SIS+GOAL performed optimally in their simulation and two observational cancer radiomics studies, but no clinical endpoint or patient outcome data are presented.
Algorithm comparison study using simulation and observational data. Simulation study and two observational radiomics datasets in cancer; osteosarcoma and gliosarcoma patients; specific eligibility criteria and clinical characteristics not described in source text.. Intervention: SIS+GOAL, SIS+OAL, and causal ball screening (CBS) algorithms for variable selection in ultra-high dimensional radiomics data. Compared with: Outcome-adaptive lasso (OAL), generalized outcome-adaptive lasso (GOAL), and causal ball screening (CBS).
SIS+GOAL identified as the optimal variable selection algorithm across simulation study and two radiomics datasets in osteosarcoma and gliosarcoma Proposed SIS+GOAL and SIS+OAL procedures extend outcome-adaptive lasso methods to ultra-high dimensional data via ball covariance screening step Asymptotic properties of SIS+GOAL and SIS+OAL procedures derived theoretically
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This work is methodological and does not directly inform clinical decision-making. Researchers developing radiomics models or working with ultra-high dimensional imaging data may consider these algorithms for confounder selection, but validation on clinical endpoints and generalizability to other cancer types and imaging modalities remain unclear.
This is a methodological comparison study using simulation and observational radiomics data to evaluate machine learning algorithms for confounder selection; it does not test a clinical hypothesis or report a patient outcome.
As stated by the source record.
This work is methodological and does not directly inform clinical decision-making. Researchers developing radiomics models or working with ultra-high dimensional imaging data may consider these algorithms for confounder selection, but validation on clinical endpoints and generalizability to other cancer types and imaging modalities remain unclear.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Radiomics is an emerging area of medical imaging data analysis for diseases such as cancer. It involves the conversion of digital medical images into mineable ultra-high dimensional data, where, the number of extracted radiomics features is much larger than the number of observations. Machine learning algorithms are widely used in radiomics data analysis to develop powerful decision support models to improve precision in diagnosis, assessment of prognosis and prediction of therapy response. However, machine learning algorithms for causal inference have not been previously employed in radiomics analysis. In this paper, we evaluate the value of machine learning algorithms for causal inference in radiomics. We compare three recent competitive variable selection algorithms for causal inference: outcome-adaptive lasso (OAL), generalized outcome-adaptive lasso (GOAL), and causal ball screening (CBS). We used a ball covariance screening step to extend GOAL and OAL to ultra-high dimensional data, called SIS+GOAL and SIS+OAL. The proposed SIS+GOAL and SIS+OAL procedures can perform variable selection for ultra-high dimensional data analysis. Asymptotic properties of the proposed SIS+GOAL and SIS+OAL procedures are derived. We compared SIS+GOAL, SIS+OAL and CBS using simulation study and two radiomics datasets in cancer, osteosarcoma and gliosarcoma. The two radiomics studies and the simulation study identified SIS+GOAL as the optimal variable selection algorithm in the scenarios considered.
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