Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms

Abstract

Detecting breast cancer (BC) at the initial stages of progression has always been regarded as a lifesaving intervention. With modern technology, extensive studies have unraveled the complexity of BC, but the current standard practice of early breast cancer screening and clinical management of cancer progression is still heavily dependent on tissue biopsies, which are invasive and limited in capturing definitive cancer signatures for more comprehensive applications to improve outcomes in BC care and treatments. In recent years, reviews and studies have shown that liquid biopsies in the form of blood, containing free circulating and exosomal microRNAs (miRNAs), have become increasingly evident as a potential minimally invasive alternative to tissue biopsy or as a complement to biomarkers in assessing and classifying BC. As such, in this review, the potential of miRNAs as the key BC signatures in liquid biopsy are addressed, including the role of artificial intelligence (AI) and machine learning platforms (ML), in capitalizing on the big data of miRNA for a more comprehensive assessment of the cancer, leading to practical clinical utility in BC management.

An Integrated Care Approach to Improve Well-Being in Breast Cancer Patients

“…Moreover, in recent years, liquid biopsy and circulating biomarkers helped address medical needs by providing a non-invasive tool for patient in course of treatments and during follow-up. Although their role in BC prognosis has now been deeply evaluated [ 121 ], it is still not clear wheter this new non-invasive approach could be useful to address molecular benefits of holistic approaches on QoL and how can we integrate these techniques in machine-learning programmes [ 122 ].…”

The Role of MicroRNAs in Breast Cancer and the Challenges of Their Clinical Application

“…Circulating miRNAs refer to extracellular miRNAs found in body fluids, including blood, serum, plasma, breast milk, saliva, and urine [115]. They exist either as free-circulating miRNAs, enclosed within extracellular vesicles (EVs) like exosomes or bound to a protein [116][117][118].…”

“…AI and machine learning algorithms can also enhance the discovery process for miRNA biomarkers. Several machine learning models and algorithms, including Support Vector Machines (SVM), Hidden Markov Models, Neural Networks and Random Forest algorithms, have been effectively employed to construct predictive models [116]. By enabling early detection and risk assessment, these models assist clinicians in improving patient outcomes.…”

Comparison of machine learning models on breast cancer risk prediction challenge

“…Compared with the methods mentioned, machine or artificial intelligence diagnostics take advantage. The machine learning algorithms enhance the accuracy of diagnosing BC significantly, and they can identify females at high risk of triple-negative breast carcinoma (TNBC) for early treatment [6]. Similarly, deep learning is also an effective tool in detecting cancer.…”