Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer Journal Article
| Authors: | Vanguri, R. S.; Luo, J.; Aukerman, A. T.; Egger, J. V.; Fong, C. J.; Horvat, N.; Pagano, A.; Araujo-Filho, J. A. B.; Geneslaw, L.; Rizvi, H.; Sosa, R.; Boehm, K. M.; Yang, S. R.; Bodd, F. M.; Ventura, K.; Hollmann, T. J.; Ginsberg, M. S.; Gao, J.; MSK MIND Consortium; Hellmann, M. D.; Sauter, J. L.; Shah, S. P. |
| Article Title: | Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer |
| Abstract: | Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74–0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52–0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65–0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning. © 2022, The Author(s). |
| Journal Title: | Nature Cancer |
| Volume: | 3 |
| Issue: | 10 |
| ISSN: | 2662-1347 |
| Publisher: | Nature Research |
| Date Published: | 2022-10-01 |
| Start Page: | 1151 |
| End Page: | 1164 |
| Language: | English |
| DOI: | 10.1038/s43018-022-00416-8 |
| PUBMED: | 36038778 |
| PROVIDER: | scopus |
| PMCID: | PMC9586871 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 November 2022 -- Source: Scopus |
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