Evidence map›Paper›PMID 42218257›Full record

ArticleNPJ digital medicine2026

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

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. The Five-Decade Journey of Small Cell Lung Cancer.Cancer communications (London, England) · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Lin Yang *Department 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.
Ruyu Sheng *Institute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P. R. China.
Zijian Yang *Institute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P. R. China.
Yibo ZhangInstitute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P. R. China.
Taolue WangInstitute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P. R. China.
Fan YangDepartment 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.
Shilong LiuDepartment of Thoracic Radiation Oncology, Harbin Medical University Cancer Hospital, Harbin, P. R. China. liushilong1984@hrbmu.edu.cn.
Meng ZhouInstitute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P. R. China. zhoumeng@wmu.edu.cn.

Funding

CAMS Innovation Fund for Medical Sciences (CIFMS) 2024-I2M-C&T-A-005National High Level Hospital Clinical Research Funding LC2024L01
6 · The paper itself

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.

Identifiers

PMID42218257
PMCPMC13639039

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.