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Development of a prognostic nomogram for esophageal squamous cell carcinoma patients received radiotherapy based on clinical risk factors

  • 0Department of Computed Tomography and Magnetic Resonance Imaging, The Fourth Hospital of Hebei Medical University, Hebei, Shijiazhuang, China.
Frontiers in Oncology +

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September 6, 2024

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September 6, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new nomogram accurately predicts locoregional recurrence-free survival (LRFS) in esophageal squamous cell carcinoma (ESCC) patients after radiotherapy (RT). This tool helps identify high-risk patients for tailored treatment strategies.

Area Of Science

  • Oncology
  • Radiation Oncology
  • Medical Imaging

Background

  • Esophageal squamous cell carcinoma (ESCC) is a significant cause of cancer mortality.
  • Radiotherapy (RT) is a primary treatment modality for ESCC, but predicting treatment outcomes remains challenging.
  • Locoregional recurrence (LR) is a major determinant of survival in ESCC patients treated with RT.

Purpose Of The Study

  • To develop and validate a predictive nomogram for locoregional recurrence-free survival (LRFS) in ESCC patients undergoing RT.
  • To identify key clinical risk factors influencing LRFS after radiotherapy for ESCC.

Main Methods

  • A cohort of 574 ESCC patients treated with RT was retrospectively analyzed.
  • Patients were divided into training (70%) and validation (30%) sets.
  • A nomogram was constructed using Cox regression analysis and validated for accuracy (C-index, AUC), calibration (Hosmer-Lemeshow test), and clinical utility (DCA).

Main Results

  • Multivariate analysis identified T stage, N stage, GTV dose, location, MWT, NS, Δ CT value, and chemotherapy as independent predictors of LRFS.
  • The developed nomogram demonstrated superior predictive accuracy compared to the TNM staging system in both training and validation cohorts.
  • The nomogram effectively stratified patients into low, medium, and high-risk groups with significant differences in LRFS.

Conclusions

  • The clinical risk factor-based nomogram provides a reliable tool for predicting LRFS in ESCC patients receiving RT.
  • This predictive model can aid in personalized treatment planning and risk stratification for improved patient management.

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