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Machine Learning for Lung Cancer Subtype Classification: Combining Clinical, Histopathological, and Biophysical

Aiga Andrijanova¹, Lasma Bugovecka², Sergejs Isajevs³

¹SIA "APPLY", Ieriku Street 5, LV-1084 Riga, Latvia.

Diagnostics (Basel, Switzerland)

|January 25, 2025

Summary

Integrating atomic force microscopy (AFM) with clinical data shows promise for lung cancer classification. Key features like sex and invasion status significantly simplify diagnosis, though larger studies are needed for validation.

Keywords:

Bayesian networks atomic force microscopy biophysical properties cancer biomarkers cancer subtype classification cell mechanics lung cancer machine learning non-small cell lung cancer personalized medicine

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Area of Science:

Oncology
Biophysics
Computational Biology

Background:

Accurate lung cancer subtype classification is vital for treatment but traditional methods are costly and complex.
Atomic force microscopy (AFM) offers a potential alternative for biophysical characterization.
This study explores integrating AFM data with clinical and histopathological information.

Purpose of the Study:

To evaluate the utility of combining AFM-derived biophysical data with conventional methods for lung cancer subtype classification.
To assess the performance of machine learning models, particularly Bayesian Networks, in this integrated approach.
To identify key predictive features for improved diagnostic accuracy.

Main Methods:

A novel dataset was created, incorporating clinical, histopathological, and AFM biophysical data from 37 lung cancer patients.
Machine learning techniques, with a focus on Bayesian Networks, were employed for classification.
Leave-One-Out Cross-Validation was used to rigorously assess model performance.

Main Results:

Integrating AFM biophysical features improved lung cancer classification accuracy from 86.49% to 89.19% using a Bayesian Network model.
Four highly predictive features were identified: sex, vascular invasion, perineural invasion, and ALK mutation.
A simplified model using these key features achieved comparable performance with substantially reduced complexity.

Conclusions:

AFM-derived measurements show potential for enhancing lung cancer subtype classification.
The study demonstrates the feasibility of incorporating biophysical data into cancer classification frameworks.
Larger datasets are required to fully validate the impact of AFM and confirm these findings.