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Related Experiment Video

Updated: Jun 20, 2026

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

Development and Internal Validation of a Clinical Data-Based Machine Learning Web Calculator for Predicting

Jiao Feng1, Ruiyang Wu2, Jin Chen2

  • 1Department of Gastrointestinal Surgery, The Affiliated Chengdu 363 Hospital of Southwest Medical University, Chengdu, China.

European Journal of Breast Health
|June 19, 2026
PubMed
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Machine learning models can predict granulomatous lobular mastitis (GLM) recurrence. A random forest model, validated on multicenter data, is now a web calculator to aid clinical decisions.

Area of Science:

  • Medical research
  • Machine learning in healthcare
  • Oncology

Background:

  • Granulomatous lobular mastitis (GLM) has a high recurrence rate and lacks standard treatment.
  • Accurate prognostic prediction is vital for managing GLM.
  • Existing predictive models are limited by small, single-center datasets.

Purpose of the Study:

  • To develop and validate machine learning models for predicting GLM recurrence using a large, multicenter dataset.
  • To create a clinical web calculator for personalized GLM recurrence risk assessment.

Main Methods:

  • A retrospective cohort study involving 318 GLM patients from two tertiary hospitals (2019-2024).
  • Five machine learning models were trained and evaluated.
  • Model performance was assessed using accuracy, AUC, F1-score, sensitivity, and specificity.
Keywords:
Granulomatous mastitismachine learningrecurrence

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Main Results:

  • All five models showed comparable discriminatory performance (AUCs 0.778-0.808).
  • The random forest (RF) model demonstrated balanced performance and was selected for deployment.
  • Key predictors identified by the RF model include white blood cell count, age, tumor origin, and treatment modalities.

Conclusions:

  • A multicenter random forest model was developed and implemented as an accessible web calculator.
  • This tool aids in personalized recurrence prediction and treatment decisions for GLM.
  • The model serves as a risk stratification aid to support, not replace, clinical judgment.