Cancer Grade

Cancer grading, the process of classifying tumors based on their aggressiveness and likely prognosis, is crucial for treatment planning and patient management. Current research focuses on improving grading accuracy and efficiency through advanced machine learning techniques, employing models like vision transformers, convolutional neural networks, and large language models to analyze diverse data sources including histopathology images, immunohistochemistry data, and multiparametric MRI scans. These efforts aim to reduce reliance on subjective human assessment, improve diagnostic consistency, and ultimately lead to more personalized and effective cancer care. The integration of multiple data modalities and the development of robust, explainable AI models are key themes driving progress in this field.