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Related Experiment Video

Updated: Jun 26, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Predicting Post-Radiotherapy Lymphocyte Recovery for Individualized Risk Stratification in Locally Advanced

Hongshan Ji1, Yuhao Su1, Menglu Liu1

  • 1Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China.

Current Oncology (Toronto, Ont.)
|June 25, 2026
PubMed
Summary

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Post-radiotherapy lymphocyte recovery in esophageal cancer patients significantly predicts survival. A new model identifies key recovery predictors, aiding in personalized risk assessment for improved outcomes.

Area of Science:

  • Oncology
  • Radiation Oncology
  • Immunology

Background:

  • Prognostic value of post-radiotherapy (RT) lymphocyte recovery in locally advanced esophageal squamous cell carcinoma (ESCC) is unclear.
  • Lack of tools to predict lymphocyte recovery hinders personalized treatment strategies.

Purpose of the Study:

  • Evaluate lymphocyte recovery as a survival predictor in ESCC patients post-RT.
  • Develop and validate a predictive model for lymphocyte recovery.

Main Methods:

  • Analysis of 233 ESCC patients (2019-2024) with 7:3 training:validation split.
  • Lymphocyte recovery assessed at 1 and 3 months post-RT using absolute lymphocyte count (ALC) changes.
  • Multivariate logistic regression and nomogram development for prediction model.
Keywords:
esophageal squamous cell carcinomaindividualized risk stratificationlymphocyte recoveryprediction modelradiation-induced lymphopeniaradiotherapy

Related Experiment Videos

Last Updated: Jun 26, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Main Results:

  • Overall survival (OS) and progression-free survival (PFS) significantly differed across recovery groups (no recovery, partial recovery, full recovery).
  • Median OS: 16.0 (no recovery), 26.0 (partial), 50.0 (full) months; Median PFS: 10.2, 12.0, 36.6 months.
  • Independent predictors: ECOG 0, thoracic spine V5 < 57.3%; nomogram showed AUCs of 0.77 (training) and 0.75 (validation).

Conclusions:

  • Superior lymphocyte recovery post-RT is associated with improved survival in ESCC.
  • The developed nomogram can potentially facilitate individualized risk stratification for ESCC patients.
  • External validation of the model is recommended for broader clinical application.