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Transfer Learning for Mortality Prediction in Non-Small Cell Lung Cancer with Low-Resolution Histopathology Slide

Matthew Clark¹, Christopher Meyer¹, Jaime Ramos-Cejudo^2,3

¹Center for Translational Data Science, University of Chicago, Chicago, IL.

Studies in Health Technology and Informatics

|January 25, 2024

Summary

Transfer learning using high-resolution pathology scans significantly improves neural network models for predicting outcomes in non-small cell lung cancer (NSCLC) from low-resolution images.

Keywords:

deep learning medical images pathology prognosis transfer learning

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Area of Science:

Digital pathology
Computational oncology
Artificial intelligence in medicine

Background:

High-resolution histopathology slides are crucial for cancer prediction.
Low-resolution images present challenges for prognostic model training.
Access to high-resolution data may be limited in clinical settings.

Purpose of the Study:

To evaluate strategies for training prognostic models using low-resolution histopathology snapshots in non-small cell lung cancer (NSCLC).
To compare the effectiveness of different transfer learning approaches for NSCLC prognostic modeling with limited resolution data.

Main Methods:

Utilized data from the Veterans Affairs Precision Oncology Data Repository for non-small cell lung cancer (NSCLC) cases.
Trained neural network prognostic models using low-resolution histopathology images.
Compared three strategies: no transfer learning, transfer learning from general images, and transfer learning from high-resolution histopathology scans.

Main Results:

Transfer learning utilizing high-resolution histopathology scans demonstrated significantly superior performance compared to other methods.
Models trained without transfer learning or with general domain transfer learning showed lower predictive accuracy.
The study identified optimal strategies for leveraging limited-resolution pathology data.

Conclusions:

Transfer learning from high-resolution histopathology scans is an effective strategy for developing prognostic models in NSCLC when only low-resolution images are available.
This approach enhances the utility of low-resolution pathology slide snapshots in clinical informatics.
The findings support the development of robust prognostic tools for NSCLC integrating diverse data sources.