Shape-based tumor microenvironment analysis to differentiate non-small cell lung cancer subtypes: a radio-pathomic study

Saarthak Kapse; Rajarsi Gupta; Prateek Prasanna

doi:10.1117/12.2613167

4 April 2022 Shape-based tumor microenvironment analysis to differentiate non-small cell lung cancer subtypes: a radio-pathomic study

Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna

Author Affiliations +

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 1203910 (2022) https://doi.org/10.1117/12.2613167
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Shaped-based descriptors from Computed Tomography (CT) scans and whole slide digital pathology images were used to differentiate the two major histopathological subtypes of non-small-cell lung cancer (NSCLC). Our two hypotheses are 1) Encoding information on local heterogeneity will augment the model’s classification capabilities 2) Shape-based biomarkers from radiology and pathology can complement each other. Shape features were extracted from the tumor map from pathology and radiology images. In pathology, tumor-microenvironment features were encoded by clustering the tumor map into phenotype maps. These features performed better than the features from whole tumor map. Integration of radio-pathomics performed best, achieving 0.802 AUC.

Conference Presentation

Citation Download Citation

Saarthak Kapse, Rajarsi Gupta, and Prateek Prasanna "Shape-based tumor microenvironment analysis to differentiate non-small cell lung cancer subtypes: a radio-pathomic study", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 1203910 (4 April 2022); https://doi.org/10.1117/12.2613167

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