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Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.
- Source :
-
Cancer Immunology, Immunotherapy . Aug2024, Vol. 73 Issue 8, p1-10. 10p. - Publication Year :
- 2024
-
Abstract
- Background: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC). Methods: Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual's tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data. Results: The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772–0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use. Conclusion: The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03407004
- Volume :
- 73
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- Cancer Immunology, Immunotherapy
- Publication Type :
- Academic Journal
- Accession number :
- 177647716
- Full Text :
- https://doi.org/10.1007/s00262-024-03724-3