Purpose: The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learning strategies, aiming to enhance clinical decision-making and improve patient outcomes in oncology.
Procedures: The study utilizes histopathological slide images from the NCT-CRC-HE-100 K and PAIP 2020 databases. Key procedures include self-attentive adversarial stain normalization for data standardization, tumor delineation via a Slimmable Transformer, and radiomics feature extraction using a hybrid quantum-classical neural network.
Results: The proposed system reaches 99% accuracy when identifying colorectal cancer MSI status. It shows the model is good at telling the difference between MSI and MSS tumors and can be used in real medical care for cancer.
Conclusions: Our research shows that the new system improves colorectal cancer MSI status determination better than previous methods. Our optimized processing technology works better than other methods to divide and analyze tissue features making the system good for improving patient care decisions.
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