Deep Learning-Based Cross-Cancer Morphological Analysis: Identifying Histopathological Patterns in Breast and Lung Cancer

Abstract

The efficacy of Cancer treatment often varies across different types of cancers. This study aims to investigate any pattern relationship in histopathological images of different cancer types to find any potential correlation between those patterns. Using deep image analysis techniques and artificial intelligence (AI), we extract, analyze, and compare the morphological parameters of cancer images to identify potential indicators of that treatment effective for one type that might be applicable for its correspondence. This research applied advanced image analysis, artificial intelligence (AI), machine learning, and more sophisticated statistical analysis to find the required pattern relationship for those parameters. The study answers the question regarding the correlation of different measurement parameters across different varieties of cancer cells. The model achieved an impressive ROC-AUC score of 0.967, an F1-score of 0.805, and Cohen's kappa coefficient of 0.767, indicating a high level of agreement and predictive performance. The overall accuracy of the model was 81%, with both macro and weighted averages also at 81%. These results provide strong evidence of meaningful pattern relationships across different cancer types, potentially enhancing treatments' applicability across various cancers.