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Updated: Feb 23, 2026

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Research on Lung Cancer Classification Based on Multidimensional Hematological Indicators and Machine Learning

Fan Jia1, Jianmin Xu1, Lijun Zeng1

  • 1Department of Clinical Laboratory, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.

Journal of Evidence-Based Medicine
|February 21, 2026
PubMed
Summary

Machine learning models using blood tests accurately subtype lung cancer. These non-invasive methods aid personalized treatment by distinguishing small cell lung cancer and non-small cell lung cancer, and lung squamous cell carcinoma from lung adenocarcinoma.

Keywords:
differential diagnosishematological indicatorslung cancermachine learning

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Area of Science:

  • Oncology
  • Bioinformatics
  • Machine Learning

Background:

  • Accurate lung cancer subtyping is essential for personalized medicine and improved patient outcomes.
  • Current methods like pathological biopsy can be invasive and lack dynamic monitoring capabilities.

Purpose of the Study:

  • To develop and validate non-invasive machine learning models for lung cancer subtyping using hematological indicators.
  • To compare the clinical applicability of these models against traditional diagnostic methods.

Main Methods:

  • Utilized data from 771 lung cancer patients for model development and validation.
  • Screened ten supervised learning algorithms, including XGBoost and Random Forest.
  • Validated models on an independent cohort of 510 lung cancer cases from two clinical centers.

Main Results:

  • The XGBoost model achieved 95% accuracy in differentiating small cell lung cancer from non-small cell lung cancer.
  • The Random Forest model achieved 91% accuracy in distinguishing lung squamous cell carcinoma from lung adenocarcinoma.
  • Both models demonstrated significant clinical applicability in independent validation.

Conclusions:

  • Machine learning models integrating hematological data offer a non-invasive, repeatable, and dynamic approach to lung cancer subtyping.
  • These models serve as a valuable complement to pathological biopsy, enhancing diagnostic accuracy and facilitating personalized treatment strategies.