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Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy?

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World Journal of Hepatology 2022年第1期14卷 244-259页

作者： Etienne Rabe Dania Cioni Laura Baglietto Marco Fornili Michela Gabelloni Emanuele Neri Academic radiology Master in Oncologic ImagingDepartment of Translational ResearchUniversity of PisaPisa 56126Italy bay radiology-cancercare oncology centre Bay RadiologyPort Elizabeth 6001Eastern CapeSouth Africa Department of Clinical and Experimental Medicine University of PisaPisa 56126Italy

BACKGROUND Artificial intelligence in radiology has the potential to assist with the diagnosis,prognostication and therapeutic response prediction of various cancers.A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases,however,the results have *** metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not *** study was designed to determine if the computed tomography(CT)texture analysis results of non-necrotic colorectal liver metastases differ from previous reports.A larger range of texture features were also evaluated to identify potential new *** To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases(CRLMs).METHODS Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology *** inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm(diagnosed on archived 1.25 mm portal venous phase CT(FOLFOX,FOLFIRI,FOLFOXIRI,CAPE-OX,CAPE-IRI or capecitabine).The final study cohort consisted of 29 *** treatment response of the CRLMs was classified according to the RECIST 1.1 *** means of CT texture analysis,various first and second order texture features were extracted from a single nonnecrotic target CRLM in each responding and non-responding *** between features and response to chemotherapy were assessed by logistic regression *** prognostic accuracy of selected features was evaluated by using the area under the *** There were 15 responders(partial response)and 14 non-responders(7 stable and 7 with progressive disease).The responders presented with a higher number of CRLMs(P=0.05).In univariable an

关键词： Colorectal cancer Liver metastases Radiomics Computed tomography texture analysis Response assessment

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Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training

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World Journal of radiology 2023年第12期15卷 359-369页

作者： Matthew Grudza Brandon Salinel Sarah Zeien Matthew Murphy Jake Adkins Corey T Jensen Curtis bay Vikram Kodibagkar Phillip Koo Tomislav Dragovich Michael A Choti Madappa Kundranda Tanveer Syeda-Mahmood Hong-Zhi Wang John Chang School of Biological Health and Systems Engineering Arizona State UniversityTempeAZ 85287United States Department of radiology Banner MD Anderson Cancer CenterGilbertAZ 85234United States School of Osteopathic Medicine A.T.Still UniversityMesaAZ 85206United States Department of Abdominal Imaging MD Anderson Cancer CenterHoustonTX 77030United States Department of Abdominal Imaging University Texas MD Anderson Cancer CenterHoustonTX 77030United States Department of Interdisciplinary Sciences A.T.Still UniversityMesaAZ 85206United States School of Biological and Health Systems Engineering Arizona State UniversityTempeAZ 85287United States Division of Cancer Medicine Banner MD Anderson Cancer CenterGilbertAZ 85234United States Department of Surgical oncology Banner MD Anderson Cancer CenterGilbertAZ 85234United States IBM Almaden Research Center IBMSan JoseCA 95120United States

BACKGROUND Missing occult cancer lesions accounts for the most diagnostic errors in retrospective radiology reviews as early cancer can be small or subtle,making the lesions difficult to *** is the most effective technique for reducing these events and can be economically implemented with the advent of artificial intelligence(AI).AIM To achieve appropriate AI model training,a large annotated dataset is necessary to train the AI *** goal in this research is to compare two methods for decreasing the annotation time to establish ground truth:Skip-slice annotation and AI-initiated *** We developed a 2D U-Net as an AI second observer for detecting colorectal cancer(CRC)and an ensemble of 5 differently initiated 2D U-Net for ensemble *** model was trained with 51 cases of annotated CRC computed tomography of the abdomen and pelvis,tested with 7 cases,and validated with 20 cases from The Cancer Imaging Archive *** sensitivity,false positives per case,and estimated Dice coefficient were obtained for each method of *** compared the two methods of annotations and the time reduction associated with the *** time differences were tested using Friedman’s two-way analysis of *** Sparse annotation significantly reduces the time for annotation particularly skipping 2 slices at a time(P<0.001).Reduction of up to 2/3 of the annotation does not reduce AI model sensitivity or false positives per *** initializing human annotation with AI reduces the annotation time,the reduction is minimal,even when using an ensemble AI to decrease false *** Our data support the sparse annotation technique as an efficient technique for reducing the time needed to establish the ground truth.

关键词： Artificial intelligence Colorectal cancer Detection

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