Robust Multicenter CT Radiogenomics for Dual EGFR and KRAS Prediction in Lung Cancer with Stability-Aware Modeling and SHAP Interpretation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910155779604480 |
|---|---|
| author | Mehrnia, Somayeh Sadat Razavi, Fatemeh Abedini, Helia Rahimi, Niloofar Rahmim, Arman Salmanpour, Mohammad |
| author_facet | Mehrnia, Somayeh Sadat Razavi, Fatemeh Abedini, Helia Rahimi, Niloofar Rahmim, Arman Salmanpour, Mohammad |
| contents | Accurate identification of EGFR and KRAS mutations is essential for precision therapy in non-small cell lung cancer (NSCLC), but tissue genotyping is invasive and may not capture tumor heterogeneity. CT-based radiogenomics offers a noninvasive alternative, although generalization across centers remains challenging. We benchmarked handcrafted radiomics features (HRF), deep feature representations (DFR), and their fusion for three-class mutation prediction (wild-type, KRAS-mutant, and EGFR-mutant) with external testing. We curated 1,023 thoracic CT scans from 12 public datasets across more than 20 centers, including 136 patients with EGFR/KRAS labels. IBSI-compliant HRFs were extracted with standardized preprocessing, and DFRs were derived using PySERA. HRF-only, DFR-only, and fused HRF+DFR pipelines were evaluated using five-fold cross-validation and external testing. A semi-supervised pseudo-labeling strategy leveraged unlabeled CT scans, and SHAP supported interpretability. In external testing, HRF-based models generalized best, achieving AUC 0.77 +/- 0.07 and accuracy 0.77 +/- 0.00. DFR-based models showed a larger drop from cross-validation to external testing, with best external AUC around 0.57 +/- 0.05. Fusion improved robustness over DFR-only models but did not consistently outperform HRFs. SHAP identified morphology- and heterogeneity-related radiomic phenotypes as key predictors. Standardized handcrafted radiomics within a multicenter semi-supervised framework may provide a generalizable and interpretable approach for CT-based EGFR/KRAS stratification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24922 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Multicenter CT Radiogenomics for Dual EGFR and KRAS Prediction in Lung Cancer with Stability-Aware Modeling and SHAP Interpretation Mehrnia, Somayeh Sadat Razavi, Fatemeh Abedini, Helia Rahimi, Niloofar Rahmim, Arman Salmanpour, Mohammad Medical Physics F.2.2; I.2.7 Accurate identification of EGFR and KRAS mutations is essential for precision therapy in non-small cell lung cancer (NSCLC), but tissue genotyping is invasive and may not capture tumor heterogeneity. CT-based radiogenomics offers a noninvasive alternative, although generalization across centers remains challenging. We benchmarked handcrafted radiomics features (HRF), deep feature representations (DFR), and their fusion for three-class mutation prediction (wild-type, KRAS-mutant, and EGFR-mutant) with external testing. We curated 1,023 thoracic CT scans from 12 public datasets across more than 20 centers, including 136 patients with EGFR/KRAS labels. IBSI-compliant HRFs were extracted with standardized preprocessing, and DFRs were derived using PySERA. HRF-only, DFR-only, and fused HRF+DFR pipelines were evaluated using five-fold cross-validation and external testing. A semi-supervised pseudo-labeling strategy leveraged unlabeled CT scans, and SHAP supported interpretability. In external testing, HRF-based models generalized best, achieving AUC 0.77 +/- 0.07 and accuracy 0.77 +/- 0.00. DFR-based models showed a larger drop from cross-validation to external testing, with best external AUC around 0.57 +/- 0.05. Fusion improved robustness over DFR-only models but did not consistently outperform HRFs. SHAP identified morphology- and heterogeneity-related radiomic phenotypes as key predictors. Standardized handcrafted radiomics within a multicenter semi-supervised framework may provide a generalizable and interpretable approach for CT-based EGFR/KRAS stratification. |
| title | Robust Multicenter CT Radiogenomics for Dual EGFR and KRAS Prediction in Lung Cancer with Stability-Aware Modeling and SHAP Interpretation |
| topic | Medical Physics F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2603.24922 |