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

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...

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Autores principales: Ginghina, Octav, Hudita, Ariana, Zamfir, Marius, Spanu, Andrada, Mardare, Mara, Bondoc, Irina, Buburuzan, Laura, Georgescu, Sergiu Emil, Costache, Marieta, Negrei, Carolina, Nitipir, Cornelia, Galateanu, Bianca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959149/
https://www.ncbi.nlm.nih.gov/pubmed/35356214
http://dx.doi.org/10.3389/fonc.2022.856575
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author Ginghina, Octav
Hudita, Ariana
Zamfir, Marius
Spanu, Andrada
Mardare, Mara
Bondoc, Irina
Buburuzan, Laura
Georgescu, Sergiu Emil
Costache, Marieta
Negrei, Carolina
Nitipir, Cornelia
Galateanu, Bianca
author_facet Ginghina, Octav
Hudita, Ariana
Zamfir, Marius
Spanu, Andrada
Mardare, Mara
Bondoc, Irina
Buburuzan, Laura
Georgescu, Sergiu Emil
Costache, Marieta
Negrei, Carolina
Nitipir, Cornelia
Galateanu, Bianca
author_sort Ginghina, Octav
collection PubMed
description 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.
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spelling pubmed-89591492022-03-29 Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification Ginghina, Octav Hudita, Ariana Zamfir, Marius Spanu, Andrada Mardare, Mara Bondoc, Irina Buburuzan, Laura Georgescu, Sergiu Emil Costache, Marieta Negrei, Carolina Nitipir, Cornelia Galateanu, Bianca Front Oncol Oncology 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. Frontiers Media S.A. 2022-03-08 /pmc/articles/PMC8959149/ /pubmed/35356214 http://dx.doi.org/10.3389/fonc.2022.856575 Text en Copyright © 2022 Ginghina, Hudita, Zamfir, Spanu, Mardare, Bondoc, Buburuzan, Georgescu, Costache, Negrei, Nitipir and Galateanu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Ginghina, Octav
Hudita, Ariana
Zamfir, Marius
Spanu, Andrada
Mardare, Mara
Bondoc, Irina
Buburuzan, Laura
Georgescu, Sergiu Emil
Costache, Marieta
Negrei, Carolina
Nitipir, Cornelia
Galateanu, Bianca
Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification
title Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification
title_full Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification
title_fullStr Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification
title_full_unstemmed Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification
title_short Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification
title_sort liquid biopsy and artificial intelligence as tools to detect signatures of colorectal malignancies: a modern approach in patient’s stratification
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959149/
https://www.ncbi.nlm.nih.gov/pubmed/35356214
http://dx.doi.org/10.3389/fonc.2022.856575
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