From associations to clinical practice: translating inflammatory-nutritional indices into a machine learning-driven model for breast cancer risk stratification with cross-ethnic validation - Report - MDSpire

From associations to clinical practice: translating inflammatory-nutritional indices into a machine learning-driven model for breast cancer risk stratification with cross-ethnic validation

  • By

  • Yue Li

  • Ting Ding

  • Xiaoyan Zhou

  • Chao Lu

  • Yue Zhang

  • Qian He

  • Jiangbo Ding

  • July 15, 2026

Share

Clinical Report: Translating Inflammatory-Nutritional Indices into a Machine Learning Model for Breast Cancer Risk Assessment

Overview

This study evaluates inflammatory-nutritional indices related to breast cancer risk and mortality, developing a machine learning model validated across ethnicities.

Background

Breast cancer is the most prevalent cancer among women and a leading cause of cancer-related mortality worldwide. Inflammatory conditions are significant determinants of survival, necessitating the exploration of inflammatory-nutritional indices in breast cancer risk assessment.

Data Highlights

IndexAssociationOdds Ratio (OR)Hazard Ratio (HR)
Advanced Lung Cancer Inflammation Index (ALI)Inversely associated with BC risk0.64 (95% CI 0.45–0.91)0.41 (95% CI 0.18–0.90)
Neutrophil Percentage-to-Albumin Ratio (NPAR)Positively associated with BC risk--
Systemic Inflammation Response Index (SIRI)Positively associated with BC risk--
Neutrophil-to-Lymphocyte Ratio (NLR)Positively associated with BC risk--

Key Findings

  • ALI was inversely associated with breast cancer risk and all-cause mortality.
  • NPAR, SIRI, and NLR showed positive associations with breast cancer risk.
  • ALI outperformed other indices in predicting mortality.
  • The machine learning model achieved an AUC of 0.832 on the test set.
  • External validation yielded AUCs of 0.781 (NHANES) and 0.730 (Chinese cohort).

Clinical Implications

The findings indicate that inflammatory-nutritional indices can serve as predictors of breast cancer risk and mortality.

Conclusion

The study demonstrates the use of machine learning approaches leveraging inflammatory-nutritional indices for breast cancer risk assessment.

Related Resources & Content

  1. Frontiers in Immunology, 2026 -- Machine learning-driven identification and immunohistochemical validation of an integrated immune-inflammatory phenotype for disease-free survival stratification in breast cancer
  2. Frontiers in Medicine, 2026 -- Integrating Machine Learning and Clinicopathological Data to Stratify Survival Risk in Young Women with Localized Breast Cancer
  3. Frontiers in Oncology, 2026 -- Prognostic Evaluation Using Nutrition-Inflammation Biomarkers from Routine Blood Tests in Metastatic Breast Cancer: A Boruta Algorithm-Optimized Feature Selection Study
  4. Frontiers in Medicine, 2026 -- Development and validation of an interpretable machine learning model for predicting 5-year recurrence in breast cancer
  5. ACR Appropriateness Criteria® Female Breast Cancer Screening: 2025 Update - PubMed
  6. Risk-Based vs Annual Breast Cancer Screening: The WISDOM Randomized Clinical Trial | Trials | JAMA | JAMA Network
  7. ACR Appropriateness Criteria® Female Breast Cancer Screening: 2025 Update - PubMed
  8. Risk-Based vs Annual Breast Cancer Screening: The WISDOM Randomized Clinical Trial | Trials | JAMA | JAMA Network
  9. https://www.journalofoncology.org/pdf/50c58720-e50d-4950-b386-1d4e1867ef9a/articles/jos.galenos.2025.2025-5-5/145-160.pdf

Original Source(s)

From associations to clinical practice: translating inflammatory-nutritional indices into a machine learning-driven model for breast cancer risk stratification with cross-ethnic validation

  • by Yue Li, Ting Ding, Xiaoyan Zhou, Chao Lu, Yue Zhang, Qian He, Jiangbo Ding

  • July 15, 2026

Related Content

Baptist Health Cancer Care to Present Research on Precision Radiation Therapy Across Complex Cancers at ASTRO 2026

Experts across Baptist Health Herbert Wertheim Cancer Institute and Eugene M. & Christine E. Lynn Cancer Institute will present new radiation oncology research and clinical expertise at the 2026 Annual Meeting of the American Society for Radiation Oncology (ASTRO), September 26-30, 2026, in Boston.

  • by

  • July 7, 2026

  • 8 min
Therapeutic assessment of Nerium oleander-derived exosome-like nanoparticles against triple-negative breast cancer: a preliminary in vitro and network pharmacology approach

Editorial: Advancement of chemotherapy in breast cancer: predictive markers, resistance mechanism and therapeutic strategies