Evidence map›Paper›PMID 42369144›Full record

ArticleFrontiers in medicine2026

Self-supervised 3D deep learning on preoperative contrast-enhanced computed tomography for predicting high pathologic nodal burden in esophageal squamous cell carcinoma: temporal and external multicohort validation.

Qian Li, Yongxin Li, Jing Bai, Jinze Zhang, Zhenkai Nie, Yanxin Ren, Zhantao Li, Jin Guo, Meng Li, Tingting Zhang and 1 more

Abstract read
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Article in Frontiers in medicine, 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

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Qian Li *Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Yongxin Li *Department of Thoracic Surgery, Beijing Genertec Aerospace Hospital, Beijing, China.
Jing BaiDepartment of Pharmacy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Jinze ZhangDepartment of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Zhenkai NieDepartment of Thoracic Surgery, Beijing Genertec Aerospace Hospital, Beijing, China.
Yanxin RenDepartment of Thoracic Surgery, Beijing Genertec Aerospace Hospital, Beijing, China.
Zhantao LiDepartment of Thoracic Surgery, Beijing Genertec Aerospace Hospital, Beijing, China.
Jin GuoSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Meng LiDepartment of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Tingting ZhangDepartment of Radiology, Yichang Central People's Hospital, Yichang, China.
Xinbo LiuDepartment of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Accurate preoperative identification of high nodal burden (pathologic N2 or N3; hereafter N2+) is important in esophageal squamous cell carcinoma, but contrast-enhanced computed tomography criteria based mainly on nodal size and morphology have limited sensitivity. Methods: In this retrospective multicohort study, 1,060 consecutive patients with esophageal squamous cell carcinoma who underwent preoperative contrast-enhanced computed tomography and curative-intent esophagectomy with lymphadenectomy were enrolled from two centers. Center A contributed a development cohort ( Results: The self-supervised model achieved area under the receiver operating characteristic curve values of 0.881 (95% confidence interval, 0.793-0.955) in the internal test cohort, 0.860 (95% confidence interval, 0.810-0.903) in the temporal cohort, and 0.860 (95% confidence interval, 0.810-0.906) in the external cohort. In the external cohort, sensitivity and specificity at the main operating point were 0.581 and 0.845 for the self-supervised model versus 0.339 and 0.784 for the guideline-inspired comparator. Calibration also improved with self-supervised pretraining (Brier score, 0.148; expected calibration error, 0.053). Conclusion: A contrast-enhanced computed tomography-only self-supervised three-dimensional model predicted high pathologic nodal burden in esophageal squamous cell carcinoma with robust temporal and external validation and showed numerically higher performance than a transparent computed tomography-only comparator. Calibrated risk estimates may help prioritize additional nodal workup when staging resources are limited or routine computed tomography findings are equivocal.

Indexed as

contrast-enhanced computed tomographydeep learningesophageal squamous cell carcinomaexternal validationnodal stagingrisk predictionself-supervised learning

Identifiers

PMID42369144
PMCPMC13293925

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