A Pathological Image-Based Pathomics Signature Combined with Clinicopathological Features for Predicting Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma: A Development and External Validation Study

Background: Esophageal squamous cell carcinoma (ESCC) accounts for over 80% of esophageal cancer cases globally. Lymph node status is the strongest determinant of survival after resection, yet occult disease is found in about 17% of patients classified as node-negative. We developed a postoperative model estimating the probability of lymph node metastasis (LNM) from the resection specimen, combining nuclear features with clinicopathological variables. Methods: Of 477 consecutive ESCC patients undergoing radical esophagectomy without neoadjuvant therapy, 416 were analyzed (LNM 217, 52.2%). Nuclear features were extracted from H&E whole-slide images (20×) by QuPath optical-density detection; LASSO logistic regression selected 17. A nomogram combining the pathomics score with age, sex, tumor location, pT category and grade was validated by 10-fold cross-validation (out-of-fold, OOF) and applied without refitting to 101 patients from another institution. Results: OOF AUC was 0.821 (95% CI 0.779–0.858) for the combined model, versus 0.791 (clinical) and 0.762 (pathomics alone). The pathomics score remained an independent predictor (adjusted OR 2.47, 95% CI 1.80–3.39; p < 0.001). LNM prevalence rose across tertiles (15.8%, 55.1%, 85.6%; p < 0.001). Among 199 node-negative patients, high pathomics risk predicted worse 3-year overall survival (77.2% vs. 94.3%; p = 0.005) and progression-free survival (75.5% vs. 88.9%; p = 0.007). Externally (n = 101; LNM 24.8%), AUCs were 0.812, 0.801 and 0.697, and adding pathomics did not significantly improve discrimination. Conclusions: Within the development cohort, pathomics added modest but significant incremental value and identified node-negative patients at elevated risk. Because the model requires the resection specimen, it cannot guide preoperative treatment selection. External validation did not confirm incremental value: the clinical component; the pathomics signature did not.

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Journal
Cancers
Published
2026-10-09
DOI
https://doi.org/10.3390/cancers18203248
Primary Topic
AI in cancer detection
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article
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article

A Pathological Image-Based Pathomics Signature Combined with Clinicopathological Features for Predicting Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma: A Development and External Validation Study

Jieming Lu, Kaiming Peng, 康明强, 陈舒晨 et al.
Cancers
AI in cancer detection
article

A Pathological Image-Based Pathomics Signature Combined with Clinicopathological Features for Predicting Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma: A Development and External Validation Study

Jieming Lu, Kaiming Peng, 康明强, 陈舒晨, Weiguang Zhang, Peipei Zhang, Jinlong Fang, Junhuang Lin
article en

Abstract

Background: Esophageal squamous cell carcinoma (ESCC) accounts for over 80% of esophageal cancer cases globally. Lymph node status is the strongest determinant of survival after resection, yet occult disease is found in about 17% of patients classified as node-negative. We developed a postoperative model estimating the probability of lymph node metastasis (LNM) from the resection specimen, combining nuclear features with clinicopathological variables. Methods: Of 477 consecutive ESCC patients undergoing radical esophagectomy without neoadjuvant therapy, 416 were analyzed (LNM 217, 52.2%). Nuclear features were extracted from H&E whole-slide images (20×) by QuPath optical-density detection; LASSO logistic regression selected 17. A nomogram combining the pathomics score with age, sex, tumor location, pT category and grade was validated by 10-fold cross-validation (out-of-fold, OOF) and applied without refitting to 101 patients from another institution. Results: OOF AUC was 0.821 (95% CI 0.779–0.858) for the combined model, versus 0.791 (clinical) and 0.762 (pathomics alone). The pathomics score remained an independent predictor (adjusted OR 2.47, 95% CI 1.80–3.39; p < 0.001). LNM prevalence rose across tertiles (15.8%, 55.1%, 85.6%; p < 0.001). Among 199 node-negative patients, high pathomics risk predicted worse 3-year overall survival (77.2% vs. 94.3%; p = 0.005) and progression-free survival (75.5% vs. 88.9%; p = 0.007). Externally (n = 101; LNM 24.8%), AUCs were 0.812, 0.801 and 0.697, and adding pathomics did not significantly improve discrimination. Conclusions: Within the development cohort, pathomics added modest but significant incremental value and identified node-negative patients at elevated risk. Because the model requires the resection specimen, it cannot guide preoperative treatment selection. External validation did not confirm incremental value: the clinical component; the pathomics signature did not.

CancersVol. 18(20)
Fujian Medical University (CN), Union Hospital (CN)
Openalex Percentile: Top 12%
AI in cancer detection
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