Xue Yang*
Through the integration of radiomic and pathomic signatures from the MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) image and H&E-stained whole-slide images, they developed a radio-pathomics integrated prediction system With progressively investigated belief systems and advances for possible utilizations of computerized reasoning (man-made intelligence) in oncology, we here depict a comprehensive and organized idea named wise oncology. Oncology, radiology, pathology, molecular biology, multi-omics, and computer science are all included in the definition of intelligent oncology, which aims to promote cancer prevention, screening, early diagnosis, and precise treatment. The rapid advancement of AI technologies like natural language processing, machine/deep learning, computer vision, and robotic process automation has made it easier to advance intelligent oncology. We are optimistic that intelligent oncology will play a pivotal role for the future of basic, translational, and clinical oncology despite the fact that the concept and applications of intelligent oncology are still in their infancy and face numerous obstacles.
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