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<p>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 (<i>n</i> = 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.</p>

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Deep learning based histomorphological phenotyping and prognostic stratification for combined SCLC and LCNEC

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月30日

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  • Lin Yang,
  • Ruyu Sheng,
  • Zijian Yang,
  • Yibo Zhang,
  • Taolue Wang,
  • Fan Yang,
  • Shilong Liu,
  • Meng Zhou

摘要

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.