The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
Background: Gastrointestinal stromal tumors are mesenchymal tumors of the gastrointestinal tract of varying malignant potential that are believed to originate from neoplastic transformation of the interstitial cells of Cajal. They may occur anywhere along the gastrointestinal tract, but most commonly arise in the stomach or small intestine. They usually grow exophytically invading adjacent organs or perforating into the peritoneal cavity. They may also cause bleeding or obstructive symptoms. Intussusception and obstruction is a very uncommon presentation of these lesions because of their tendency to grow in an exraluminal fashion.
Predictive nomograms for the presence of synchronous liver, lung, and bone metastasis were developed and may be used to predict the probability of distant disease in rectal cancer patients.
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