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

作者：Valentina Russo, Eleonora Lallo, Armelle Munnia, Miriana Spedicato, Luca Messerini, Romina D’Aurizio, Elia Giuseppe Ceroni, Giulia Brunelli, Antonio Galvano, Antonio Russo, Ida Landini, Stefania Nobili, Marcello Ceppi, Marco Bruzzone, Fabio Cianchi, Fabio Staderini, Mario Roselli, Silvia Riondino, Patrizia Ferroni, Fiorella Guadagni, Enrico Mini, Marco E. M. Peluso · 发表于：Cancers · 年份：2022 · DOI：10.3390/cancers14164012 · 被引用次数：53 · 研究领域：Colorectal Cancer Treatments and Studies、Radiomics and Machine Learning in Medical Imaging、Colorectal and Anal Carcinomas

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 ...