Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

Abstract

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

Focused Ultrasound as Targeted Therapy for Colorectal Cancer: A Comprehensive Review

“…Furthermore, the group receiving both HIFU and surgical excision exhibited a higher count of granzyme B and perforin-positive tumor-infiltrating lymphocytes (TILs) within the tumors compared to the group undergoing surgical resection alone, suggesting their activation [55]. These findings illustrate that the immunostimulatory impact of thermal ablation seen in preclinical studies can be reproduced in clinical settings, thereby influencing the immune microenvironments of patients [6]. While thermal tissue ablation can lead to cell destruction and the release of cancer-associated antigens, there is speculation that after HIFU treatment, some of these antigens may become thermally stable due to the drastic thermal escalation (>56 • C) induced within the tissue environment [7].…”

“…Other risk factors for colorectal cancer are dyslipidemia and nonalcoholic fatty liver disease (NAFLD), which entails integrating lipid screening into routine health assessments [5]. Approaches to prevent NAFLD include lifestyle changes promoting weight loss and managing associated conditions like diabetes and dyslipidemia [6]. Dealing with issues surrounding nicotine dependence and quitting smoking requires the adoption of extensive tobacco control strategies and public awareness campaigns emphasizing the contradictory effects of smoking and its associated risk factors linked with colorectal cancer, as depicted in Figure 1 [7].…”

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

“…The complex nature of colorectal cancer and the vast amount of available patient data make it an ideal candidate for the application of AI and machine learning algorithms to improve diagnosis, prognosis, and therapeutic decision-making. 211 One of the most promising applications of AI in precision medicine for colorectal cancer is in the field of medical imaging. AI-powered image analysis can help radiologists and pathologists identify tumor features, such as size, location, and the presence of metastases, with higher accuracy and efficiency.…”

“…Artificial intelligence (AI) and machine learning have emerged as powerful future technologies with the potential to revolutionize precision medicine for colorectal cancer treatment. The complex nature of colorectal cancer and the vast amount of available patient data make it an ideal candidate for the application of AI and machine learning algorithms to improve diagnosis, prognosis, and therapeutic decision-making …”

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

“…One domain that has significantly benefited from these advancements is healthcare, where numerous applications have been proposed and implemented to facilitate medical diagnosis [15,16]. These applications have demonstrated remarkable accuracy in diagnosing a wide range of prevalent diseases, including cancer, eye diseases, heart diseases, skin lesions, gastrointestinal diseases, respiratory diseases, and diabetes [17][18][19][20][21][22][23]. The availability and widespread use of these applications have empowered medical professionals to make critical decisions for their patients with increased confidence, resulting in significant benefits for the medical field and healthcare services as a whole.…”