Systematic review finds varying predictive accuracy in machine learning models for lung cancer treatment outcomes using xenograft models, highlighting potential improvements.
The predictive ability of machine learning algorithms in determining treatment outcomes for lung cancer is investigated in this work utilizing patient-derived xenograft (PDX) mouse models. The review focuses on panitumumab, an epidermal growth factor receptor (EGFR)-targeted drug, across a variety of xenograft models. In addition to the original study, a systematic evaluation was undertaken between 2009 and 2024 to evaluate machine learning applications for predicting lung cancer outcomes using xenograft models. Decision trees, neural networks, and support vector machines were found to be the most commonly investigated models. The evaluation stresses methodological variety, computational techniques, and the types of data used. Inclusion criteria include research that combines many procedures and data types, whereas exclusion criteria target studies that lack detailed approach information or are presented in non-English languages. The findings reveal differing degrees of accuracy among models and give insights on areas for development. Patients' clinical records, gene expression datasets, and molecular profiling are all primary data sources. The systematic review intends to uncover patterns, obstacles, and overall predictive model performance, which will serve as a platform for future research to improve lung cancer prediction and treatment results.
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Idowu et al. (2025) studied this question.
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