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February 26, 2026World Journal of Surgical OncologyOpen Access

Development of a predictive model for pathological complete response following neoadjuvant immunotherapy and chemotherapy in locally advanced resectable esophageal squamous cell carcinoma

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Authors

YZYilei ZhangHXHounai XieDZDeguo Zhang

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Overview

Predictive model uncovers factors for complete response in esophageal cancer, indicating personalized therapy potential.

Key Points

  • This research aims to develop a predictive model for pathological complete response following neoadjuvant immunotherapy and chemotherapy in esophageal squamous cell carcinoma.
  • Analyzed data from 517 patients with locally advanced esophageal squamous cell carcinoma
  • Patients underwent neoadjuvant immunotherapy and chemotherapy or chemotherapy alone
  • Employed univariate and multivariate logistic regression for factor identification
  • Assessed predictive performance using ROC curve analysis and clinical impact curves
  • 29.21% of patients achieved pathological complete response
  • Independent predictors of pCR included differentiation, treatment type, and preoperative blood parameters
  • Predictive model demonstrated AUC values of 0.809, 0.786, and 0.806 for training, internal, and external validation groups respectively

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/699fe36b95ddcd3a253e748ehttps://doi.org/10.1186/s12957-026-04275-w
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1349. PATHOLOGIC COMPLETE RESPONSE IN ESOPHAGEAL SQUAMOUS CELL CANCER: DO DIVERSE NEOADJUVANT MODALITIES PREDICT PROGNOSTIC DIFFERENCES?2025
  2. 2A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma2024 · 18 citations
  3. 3Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma2024 · 6 citations
  4. 4An interpretable machine learning model using multimodal pretreatment features predicts pathological complete response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma2025 · 4 citations
  5. 5Nomogram for predicting pathologic complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma2024 · 2 citations