{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:01:02Z","timestamp":1790035262828,"version":"4.0.1"},"reference-count":138,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Medicine and Pharmacy Carol Davila"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with prognosis critically dependent on the stage at diagnosis. Traditional tissue biopsy presents well-known limitations, including tumor heterogeneity and invasiveness. Liquid biopsy, encompassing the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and other cell-free biomarkers, has emerged as a transformative approach for non-invasive tumor profiling. This comprehensive narrative review outlines the recent evidence published on the current state and future perspectives of liquid biopsy in CRC, with a focused emphasis on the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in data analysis and clinical translation. Methods: A narrative review of the literature was conducted by searching PubMed\/MEDLINE, EMBASE, and ClinicalTrials.gov for articles published between January 2020 and January 2026, using a predefined Boolean search string combining terms related to liquid biopsy biomarkers, colorectal cancer, and artificial intelligence methodologies. Filters were applied to include only English-language human studies. Additional relevant sources were consulted to ensure comprehensive coverage of the available literature. Liquid biopsy platforms, particularly ctDNA sequencing and methylation profiling, demonstrate increasing clinical utility across the CRC care continuum from population screening to post-surgical minimal residual disease (MRD) detection and real-time therapy monitoring. AI-driven analytical frameworks, including Random Forest, Convolutional Neural Networks, LSTM models, and more recently Large Language Models (LLMs), substantially augment the sensitivity and specificity of liquid biopsy interpretation, enabling multimodal data integration. The convergence of liquid biopsy technology and AI-driven analytics represents a paradigm shift toward precision oncology in CRC. Remaining challenges include analytical standardization, model explainability, regulatory harmonization, and equitable access. Future integration of federated learning frameworks and LLM-based clinical decision support tools will be essential for responsible clinical translation.<\/jats:p>","DOI":"10.3390\/ijms27093951","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:46:11Z","timestamp":1777455971000},"page":"3951","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence\u2014A Comprehensive Review of Novel Literature"],"prefix":"10.3390","volume":"27","author":[{"given":"Dan Nicolae","family":"Paduraru","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"Surgery Department, CF2 Clinical Hospital, 011464 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5145-5404","authenticated-orcid":false,"given":"Alexandru Cosmin","family":"Palc\u0103u","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"Surgery Department, CF2 Clinical Hospital, 011464 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5340-8929","authenticated-orcid":false,"given":"Gabriel-Petre","family":"Gorecki","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Titu Maiorescu University, 031593 Bucharest, Romania"},{"name":"National Institute for Research and Development in Microtechnologies, 077190 Voluntari, Romania"},{"name":"Department of Anesthesia and Intensive Care, CF2 Clinical Hospital, 011464 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8008-4408","authenticated-orcid":false,"given":"Alexandru","family":"Dinulescu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"Department of Pediatrics, Emergency Hospital for Children \u201cGrigore Alexandrescu\u201d, 011743 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria-Luiza","family":"B\u0103ean","sequence":"additional","affiliation":[{"name":"Department of Fundamental Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy \u201cCarol Davila\u201d, 050474 Bucharest, Romania"},{"name":"Department of Clinical Laboratory of Radiology and Medical Imaging, CF2 Clinical Hospital, 011464 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1136\/gutjnl-2022-327736","article-title":"Global burden of colorectal cancer in 2020 and 2040: Incidence and mortality estimates from GLOBOCAN","volume":"72","author":"Morgan","year":"2023","journal-title":"Gut"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Marcellinaro, R., Spoletini, D., Grieco, M., Avella, P., Cappuccio, M., Troiano, R., Lisi, G., Garbarino, G.M., and Carlini, M. 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