Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Background: CT imaging is vital for lung cancer management, offering detailed
visualization for AI-based prognosis. However, supervised learning SL models
require large labeled datasets, limiting their real-world application in
settings with scarce annotations.
Methods: We analyzed CT scans from 977 patients across 12 datasets extracting
1218 radiomics features using Laplacian of Gaussian and wavelet filters via
PyRadiomics Dimensionality reduction was applied with 56 feature selection and
extraction algorithms and 27 classifiers were benchmarked A semi supervised
learning SSL framework with pseudo labeling utilized 478 unlabeled and 499
labeled cases Model sensitivity was tested in three scenarios varying labeled
data in SL increasing unlabeled data in SSL and scaling both from 10 percent to
100 percent SHAP analysis was used to interpret predictions Cross validation
and external testing in two cohorts were performed.
Results: SSL outperformed SL, improving overall survival prediction by up to
17 percent. The top SSL model, Random Forest plus XGBoost classifier, achieved
0.90 accuracy in cross-validation and 0.88 externally. SHAP analysis revealed
enhanced feature discriminability in both SSL and SL, especially for Class 1
survival greater than 4 years. SSL showed strong performance with only 10
percent labeled data, with more stable results compared to SL and lower
variance across external testing, highlighting SSL's robustness and cost
effectiveness.
Conclusion: We introduced a cost-effective, stable, and interpretable SSL
framework for CT-based survival prediction in lung cancer, improving
performance, generalizability, and clinical readiness by integrating SHAP
explainability and leveraging unlabeled data.
