Automated Fuhrman Grading of Renal Carcinoma
Since Spring 2013 I have been researching clinical diagnosis decision support systems for segmentation, feature extraction, and classification of microscopy for Fuhrman grading of renal carcinoma with the bio-imaging laboratory at Georgia Tech.
By developing accurate grade classifiers we identify novel features contributing to accuracy and provide semantic interpretation of these features linking them with biological markers that pathologists use in their own grading judgements. Parts of this research have been published at the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’ 14) [1].
The EMBC paper abstract summarizes the published portion of this research:
Pattern recognition in tissue biopsy images can assist in clinical diagnosis and identify relevant image characteristics linked with various biological characteristics. Although previous work suggests several informative imaging features for pattern recognition, there exists a semantic gap between characteristics of these features and pathologists’ interpretation of histopathological images. To address this challenge, we develop a clinical decision support system for automated Fuhrman grading of renal carcinoma biopsy images. We extract 1316 color, shape, texture and topology features and develop one vs. all models for four Fuhrman grades. Our models are highly accurate with 90.4% accuracy in a four-class prediction. Predictivity analysis suggests good generalization of the model development methodology through robustness to dataset sampling in cross-validation. We provide a semantic interpretation for the imaging features used in these models by linking features to pathologists’ grading criteria. Our study identifies novel imaging features that are semantically linked to Fuhrman grading criteria.
I hope to open source portions of the classification system (MATLAB and C) in the near future, including a from-scratch shared memory parallel implementation of Hanchuan Peng’s excellent mRMR feature ranking specialized for the unique analysis in our work requiring exhaustive ranking.
References
- [1]A. Champion, G. Lu, M. Walker, S. Kothari, A. Osunkoya, and M. D. Wang, “Semantic Interpretation of Robust Imaging Features for Fuhrman Grading of Renal Carcinoma,” in Engineering in Medicine and Biology Society, 2014. EMBC 2014. Annual International Conference of the IEEE, 2014.