Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification

Abstract

Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient’s tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

“…During colonoscopy, computer-aided diagnosis (CADx) algorithms can be used to characterize the region based on the probability of its malignancy (18,32). Kambe et al conducted a randomized control trial to investigate whether a CADe model could reduce AMR during colonoscopy (34).…”

Liquid biopsy approaches and immunotherapy in colorectal cancer for precision medicine: Are we there yet?

“…Furthermore, recent studies found that ctDNA was detected postoperatively in approximately 5% to 30% of patients with stage II to III colon cancer and has established a strong prognostic capacity in numerous observational studies (127). Since the last decade, in CRC, the introduction of next-generation sequencing (NGS) technology has led to the discovery of ctDNA in plasma, which is an encouraging practice (128,128). Some research reports revealed that ctDNA methylation has a notable sensitivity compared to traditional serum tumor markers in patients with initial-stage CRC and is a significant biomarker for the diagnosis of CRC (129)(130)(131).…”

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

“…There are harmful side effects from the medications used to reduce tumours. A few specific hormones' effects on the body are the basis of hormone-level therapy [13,14]. When it comes to patients with prostate or breast cancer, hormones play a significant role.…”

“…Despite all these benefits and the obvious advancement in current practices, widely used rule-based systems show a number of drawbacks, including expensive development costs, system limitations caused by the challenging encoding of complex relationships, and a need for in-depth medical knowledge. As a result, the identification of such data patterns has improved thanks to its integration with AI systems [14].…”