Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy
- Author(s)
- Kothari, G; Hardcastle, N; Perera, R; Jackson, P; Lee, S; Silver, JD; Gaudreault, M; Li, J; Brown, C; Mitchell, PLR; Kron, T; Welsh, JW; Theelen, Wsme; Siva, S;
- Journal Title
- Scientific Reports
- Publication Type
- Online publication before print
- Abstract
- Quantitative imaging is an emerging field that may allow prediction of oncological outcomes. We investigate whether radiomics and deep learning can predict outcomes in metastatic non-small cell lung cancer utilizing randomized trials of PD-1 inhibitors + /- stereotactic ablative body radiotherapy: PEMBRO-RT(NCT02492568), NIVORAD(ACTRN12616000352404) and MDACC(NCT02444741). A random forest model developed on PEMBRO-RT using radiomics features had an AUC of 0.57 for prediction of per-lesion progressive disease on immunotherapy compared to an AUC of 0.92 for a deep learning model. A random forest survival model using radiomics features for overall survival (progression free survival) had a concordance index of 0.63(0.59) and improved to 0.67(0.65) by adding clinical features, including PD-L1 and treatment arm. Validation on NIVORAD and MDACC revealed reduced AUCs. Overall, a deep learning compared to a radiomics model demonstrated excellent predictive value for per-lesion progressive disease for patients on immunotherapy. Models had reduced performance on external validation. Research improving generalizability is required for clinical translation.
- Keywords
- Deep learning; Immunotherapy; Lung cancer; Non-small cell lung cancer; Radiomics; Stereotactic ablative body radiotherapy
- Department(s)
- Radiation Oncology; Physical Sciences; Cancer Imaging; Laboratory Research
- Publisher's Version
- https://doi.org/10.1038/s41598-026-53520-5
- Open Access at Publisher's Site
https://doi.org/10.1038/s41598-026-53520-5- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2026-06-04 11:59:37
Last Modified: 2026-06-04 12:00:13