Evidence map›Paper›PMID 41943992›Full record

ArticleThoracic cancer2026

Optimizing Surgery Strategies in Stage IB Lung Squamous Cell Carcinoma: Insights from Interpretable Machine Learning.

Qunzhe Ding, Chutong Lin, Yatsu Lam, Fuxin Guo, Shanwu Ma, Guangliang Qiang

Abstract read
In one paragraph

Article in Thoracic cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qunzhe DingSchool of Information Management, Wuhan University, Wuhan, Hubei, China.
Chutong LinDepartment of Thoracic Surgery, Peking University Third Hospital, Beijing, China.ORCID https://orcid.org/0000-0002-0626-220X
Yatsu LamDepartment of Oncology, Putuo Hospital Affiliated Shanghai University of Traditional Chinese Medicine, Shanghai, China.ORCID https://orcid.org/0009-0009-8898-7543
Fuxin GuoDepartment of Radiation Oncology, Peking University Third Hospital, Beijing, China.
Shanwu MaDepartment of Thoracic Surgery, Peking University Third Hospital, Beijing, China.ORCID https://orcid.org/0000-0002-3906-5958
Guangliang QiangDepartment of Thoracic Surgery, Peking University Third Hospital, Beijing, China.ORCID https://orcid.org/0000-0002-7809-1892

Funding

Project of the China Association for Promotion of Health Science and Technology JKHY2023003the Key Clinical Projects of Peking University Third Hospital BYSYRCYJ2023001the Key Clinical Projects of Peking University Third Hospital BYSYZD2025049
6 · The paper itself

Abstract

backgroundStage IB lung squamous-cell carcinoma (LSCC) lacks individualized survival prediction tools. There's debate on adjuvant chemotherapy's benefit. This study aimed to develop an interpretable machine-learning model for stage IB LSCC survival prediction and re-evaluate postoperative chemotherapy's added value.

methodsA total of 6445 patients with stage IB LSCC diagnosed between 2000 and 2015 were extracted from the SEER database. Patients from 2000 to 2014 (n = 5740) were split 7:3 into training and internal validation cohorts, while those from 2015 (n = 705) served as the external validation cohort. Six machine-learning algorithms (including logistic regression and gradient-boosting models) were trained to predict 1-, 3-, and 5-year overall survival (OS) with hyperparameter optimization via 10-fold cross-validation. SHAP analysis ensured model interpretability, and 1:1 nearest-neighbor propensity-score matching evaluated chemotherapy benefit in completely resected patients.

resultsThe LightGBM model achieved the best discriminative performance (AUC = 0.834, 0.828, 0.800 for 1-, 3-, 5-year OS) with excellent generalizability in external validation. SHAP analysis identified treatment modality as the top survival predictor; both surgery alone and surgery plus chemotherapy improved survival, but no significant OS difference was observed between the two strategies across all timepoints, consistent across subgroups (age, tumor size, etc.). Propensity-score matching of 565 patients confirmed similar outcomes (median OS: 64 vs. 62 months; HR = 1.01, 95% CI: 0.82-1.20; p = 0.893).

conclusionThis study gives individualized survival estimates for stage IB LSCC, backing a risk-adapted conservative adjuvant treatment approach. High-risk subgroups got no extra benefit from postoperative chemotherapy, which may help integrate precision medicine and shared decision-making in early-stage LSCC management.

Indexed as

Carcinoma, Squamous CellLung NeoplasmsMachine LearningAgedBoosting Machine Learning AlgorithmsChemotherapy, AdjuvantFemaleHumansMaleMiddle AgedNeoplasm StagingPrognosisclinical decision supportmachine learningSHapley additive exPlanationssquamous cell carcinoma of the lungsurvival prediction

Identifiers

PMID41943992
PMCPMC13054524

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LicenceCC BY-NC-ND
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Registered trials

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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 OpenQuestion graph.