Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis.

Autor:	Russo V; Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy., Lallo E; Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy., Munnia A; Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy., Spedicato M; Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy., Messerini L; Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy., D'Aurizio R; Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy., Ceroni EG; Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy., Brunelli G; Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy., Galvano A; Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy., Russo A; Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy., Landini I; Department of Health Sciences, University of Florence, 50139 Florence, Italy., Nobili S; Department of Neurosciences, Imaging and Clinical Sciences, 'G. D'Annunzio' Chieti-Pescara, 66100 Chieti, Italy., Ceppi M; Clinical Epidemiology Unit, IRCCS-Ospedale Policlinico San Martino, 16131 Genova, Italy., Bruzzone M; Clinical Epidemiology Unit, IRCCS-Ospedale Policlinico San Martino, 16131 Genova, Italy., Cianchi F; Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy., Staderini F; Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy., Roselli M; Medical Oncology Unit, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy., Riondino S; Medical Oncology Unit, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy., Ferroni P; BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Roma, 00166 Rome, Italy.; Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy., Guadagni F; BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Roma, 00166 Rome, Italy.; Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy., Mini E; Department of Health Sciences, University of Florence, 50139 Florence, Italy., Peluso M; Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy.
Jazyk:	angličtina
Zdroj:	Cancers [Cancers (Basel)] 2022 Aug 19; Vol. 14 (16). Date of Electronic Publication: 2022 Aug 19.
DOI:	10.3390/cancers14164012
Abstrakt:	Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.
Databáze:	MEDLINE
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