Stain normalization matters: impact on feature relevance and classifier performance in mCRC therapy response
Summary
Predicting chemotherapy response in metastatic colorectal cancer (mCRC) is crucial. Stain normalization significantly impacts AI model performance, with color deconvolution methods showing superior results for predicting treatment outcomes.
Area of Science:
- Digital pathology
- Artificial intelligence in oncology
- Computational pathology
Background:
- Metastatic colorectal cancer (mCRC) poses a significant challenge, with limited patient response to first-line chemotherapy.
- Histopathological images are valuable for AI-driven prediction but suffer from staining variability, hindering consistent analysis.
- Stain normalization techniques aim to standardize image appearance, but the impact of target patch selection is not well understood.
Purpose of the Study:
- To investigate the influence of target patch selection on stain normalization methods for histopathological images.
- To compare traditional stain normalization techniques with a generative model (CycleGAN) that bypasses target patch selection.
- To evaluate the impact of stain normalization on feature extraction and subsequent prediction of chemotherapy response in mCRC.
Main Methods:
- Comparison of stain normalization methods, including convolutional-based approaches with varying target patch selections and a CycleGAN generative model.
- Analysis of the effects of normalization on image color appearance and structural content.
- Feature extraction and classification using machine learning models to predict mCRC chemotherapy response.
Main Results:
- Target patch selection in stain normalization affected both color and structural content of normalized images.
- The CycleGAN model eliminated the need for target patch selection but showed moderate overall performance.
- Color-deconvolution-based stain normalization yielded more relevant features and superior performance in predicting mCRC chemotherapy response.
- The best classifier achieved an AUC of 0.83 (training) and 0.73 (test), indicating high predictive potential.
Conclusions:
- Stain normalization is critical for robust AI-driven predictive modeling in digital pathology, influencing feature extraction and classifier performance.
- Effective management of staining variations is essential for reliable prediction of mCRC treatment response.
- The developed model shows promise for assisting clinical decisions in mCRC therapy by predicting patient response to chemotherapy.
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