Deep learning based histomorphological phenotyping and prognostic stratification for combined SCLC and LCNEC
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Published on: June 3, 2022
Lin Yang1, Ruyu Sheng2, Zijian Yang2
1Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China. yanglin@cicams.ac.cn.
Abstract:
Prognostic stratification for combined small and large cell neuroendocrine lung carcinoma (cSCLC-LCNEC) remains challenging. We introduce GTBIS, an interpretable deep learning model for histomorphological phenotyping and prognostic stratification of cSCLC-LCNEC from pathology images. In multicenter cohorts (n = 670), GTBIS accurately differentiated SCLC from LCNEC. When applied to cSCLC-LCNEC patients treated with chemoradiotherapy, GTBIS stratified them into subgroups with different prognoses. Patients in the favorable-prognosis subgroup (SCLC-like phenotype) exhibited significantly better five-year overall survival (100% vs. 39.5%) and disease-free survival (87.5% vs. 36.0%) than those in the poor-prognosis subgroup (LCNEC-like phenotype). Multivariable analysis confirmed GTBIS classification as a strong, independent prognostic factor. Multimodal interpretability analyses linked the favorable-prognosis phenotype to proliferative pathways, whereas the poor-prognosis phenotype to epithelial-mesenchymal transition, hypoxia, and metabolic reprogramming. In conclusion, GTBIS shows promise as a complementary histology-based tool for prognostic stratification, enabling more personalized management of cSCLC-LCNEC.
