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Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT
Mirjam Schöneck1, Nicolas Rehbach1, Lars Lotter-Becker1
1Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.
Life (Basel, Switzerland)
|January 25, 2025
Summary
Machine learning struggled to predict Kirsten Rat Sarcoma viral oncogene homolog (KRAS) mutations in non-small-cell lung cancer using CT radiomics. Model transferability was poor, indicating current methods are insufficient for clinical use.
Area of Science:
- Oncology
- Radiology
- Machine Learning
Background:
- Kirsten Rat Sarcoma viral oncogene homolog (KRAS) mutations are common in non-small-cell lung cancer (NSCLC), impacting treatment and prognosis.
- Radiomic features from CT images offer potential for non-invasive mutation detection.
Purpose of the Study:
- To develop and validate a machine learning pipeline for identifying KRAS mutations in NSCLC patients using CT-derived radiomic features.
- To assess the transferability and clinical applicability of the developed machine learning model.
Main Methods:
- A machine learning pipeline was applied to radiomic features extracted from public and internal CT datasets.
- Statistical analyses (t-test, Mann-Whitney U test) and dimensionality reduction were performed.
- A five-fold cross-validation was used for training and external validation between datasets.
- Data balancing and harmonization techniques were explored to improve model performance.
Main Results:
- Significant differences in radiomic features were observed between the two datasets (p < 0.05), hindering model transferability (F1 score = 0.41).
- Data balancing and harmonization techniques did not improve KRAS mutation classification accuracy.
- The highest achieved test F1 score was 0.67, deemed insufficient for clinical application.
Conclusions:
- Current machine learning approaches using radiomic features alone are not accurate enough for predicting KRAS mutations in NSCLC.
- Model transferability remains a significant challenge due to dataset variability.
- Future research should consider KRAS submutations and utilize larger, multicentric, and multi-scanner datasets for robust predictive model development.
