Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer
Abstract Lung cancer, the leading cause of cancer-related deaths globally, includes non-small cell lung cancer (NSCLC) (85% of cases) and small cell lung cancer (SCLC) (13–15%). While accurate diagnosis and treatment selection are critical, the absence of reliable predictive or prognostic biomarkers...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00939-0 |
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| Summary: | Abstract Lung cancer, the leading cause of cancer-related deaths globally, includes non-small cell lung cancer (NSCLC) (85% of cases) and small cell lung cancer (SCLC) (13–15%). While accurate diagnosis and treatment selection are critical, the absence of reliable predictive or prognostic biomarkers remains a significant challenge. This study explored the combined use of radiomics from CT scans and pathomics from H&E slides in three contexts: (1) predicting disease recurrence in early-stage NSCLC, (2) predicting immunotherapy response in advanced-stage NSCLC, and (3) predicting chemotherapy response in SCLC. The integrated radio-pathomic model significantly outperformed individual models. In early-stage NSCLC (N = 194), it achieved an HR of 8.35 (C-index: 0.71, p = 0.0043). In advanced-stage NSCLC (N = 35), the combined model improved predictive performance (AUC: 0.75, p = 0.042). In SCLC (N = 50), the integrated model showed an AUC of 0.78, surpassing both radiomic and pathomic models. These findings highlight the potential of combining radiomics and pathomics for improved lung cancer risk stratification and treatment prediction. |
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| ISSN: | 2397-768X |