Partial Wave Spectroscopy (PWS) is a nanoscale sensitive imaging technique, emerging as a potent modality for minimally invasive and cost-efficient early risk stratification for colorectal cancer. Feature engineering on the PWS map of nuclear chromatin structures, we developed 50 features extracted from the map as potentially representative of future colorectal cancer development risk. Along with feature extraction utilizing convolutional neural networks, classification model is developed with 10-fold cross validation from a multicenter data consisting of a total of 187 patients. Our newly developed feature engineering results in improved performance of the early risk stratification model for colorectal cancer.
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