{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T02:29:24Z","timestamp":1788402564678,"version":"build-2803163510"},"reference-count":46,"publisher":"Elsevier BV","issue":"8","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","academicradiology.org","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1016\/j.acra.2023.11.007","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T12:35:35Z","timestamp":1702298135000},"page":"3133-3141","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["Correlation of Radiomics with Treatment Response in Liver Metastases"],"prefix":"10.1016","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8383-3678","authenticated-orcid":false,"given":"Leila","family":"Mostafavi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Homayounieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Lades","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Primak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victorine","family":"Muse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gordon J.","family":"Harris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mannudeep K.","family":"Kalra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subba R.","family":"Digumarthy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2023.11.007_bib1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1093\/jnci\/92.3.205","article-title":"New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada.","volume":"92","author":"Therasse","year":"2000","journal-title":"J Natl Cancer Inst"},{"key":"10.1016\/j.acra.2023.11.007_bib2","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.ejca.2008.10.026","article-title":"New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).","volume":"45","author":"Eisenhauer","year":"2009","journal-title":"Eur J Cancer"},{"issue":"6","key":"10.1016\/j.acra.2023.11.007_bib3","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.acra.2015.01.015","article-title":"Pitfalls in RECIST data extraction for clinical trials: beyond the basics.","volume":"22","author":"Abramson","year":"2015","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2023.11.007_bib4","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1007\/s00261-018-1752-4","article-title":"Pearls and pitfalls of response evaluation criteria in solid tumors (RECIST) v1.1 non-target lesion assessment.","volume":"44","author":"Morse","year":"2019","journal-title":"Abdom Radiol"},{"issue":"13","key":"10.1016\/j.acra.2023.11.007_bib5","doi-asserted-by":"crossref","first-page":"2574","DOI":"10.1200\/JCO.2003.01.144","article-title":"Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response.","volume":"21","author":"Erasmus","year":"2003","journal-title":"J Clin Oncol"},{"issue":"6","key":"10.1016\/j.acra.2023.11.007_bib6","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/j.ejrad.2013.02.018","article-title":"Exploring intra- and inter-reader variability in uni-dimensional, bi-dimensional, and volumetric measurements of solid tumors on CT scans reconstructed at different slice intervals.","volume":"82","author":"Zhao","year":"2013","journal-title":"Eur J Radiol"},{"issue":"3","key":"10.1016\/j.acra.2023.11.007_bib7","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.ejrad.2015.12.019","article-title":"Comparison of CT volumetric measurement with RECIST response in patients with lung cancer.","volume":"85","author":"Hayes","year":"2016","journal-title":"Eur J Radiol"},{"issue":"6","key":"10.1016\/j.acra.2023.11.007_bib8","doi-asserted-by":"crossref","first-page":"765","DOI":"10.5009\/gnl19197","article-title":"The modified response evaluation criteria in solid tumors (RECIST) yield a more accurate prognoses than the RECIST 1.1 in hepatocellular carcinoma treated with transarterial radioembolization.","volume":"14","author":"Lee","year":"2020","journal-title":"Gut Liver"},{"issue":"2","key":"10.1016\/j.acra.2023.11.007_bib9","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Images are more than pictures, they are data.","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"issue":"12","key":"10.1016\/j.acra.2023.11.007_bib10","first-page":"749","article-title":"Radiomics: the bridge between medical imaging and personalized medicine.","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat\u00a0Rev\u00a0Clin\u00a0Oncol"},{"issue":"4","key":"10.1016\/j.acra.2023.11.007_bib11","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis.","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J\u00a0Cancer"},{"issue":"21","key":"10.1016\/j.acra.2023.11.007_bib12","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ab489f","article-title":"A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis.","volume":"64","author":"Xu","year":"2019","journal-title":"Phys Med Biol"},{"issue":"6","key":"10.1016\/j.acra.2023.11.007_bib13","doi-asserted-by":"crossref","first-page":"485","DOI":"10.5152\/dir.2019.19321","article-title":"Radiomics with artificial intelligence: a practical guide for beginners.","volume":"25","author":"Ko\u00e7ak","year":"2019","journal-title":"Diagn Interv Radiol"},{"issue":"5","key":"10.1016\/j.acra.2023.11.007_bib14","doi-asserted-by":"crossref","first-page":"e185","DOI":"10.1002\/mp.13678","article-title":"Machine and deep learning methods for radiomics.","volume":"47","author":"Avanzo","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.acra.2023.11.007_bib15","article-title":"Deep learning with radiomics for disease diagnosis and treatment: challenges and potential.","volume":"12","author":"Zhang","year":"2022","journal-title":"Front Oncol"},{"issue":"1","key":"10.1016\/j.acra.2023.11.007_bib16","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000013963","article-title":"Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?","volume":"98","author":"Digumarthy","year":"2019","journal-title":"Medicine (Baltimore)"},{"issue":"26","key":"10.1016\/j.acra.2023.11.007_bib17","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000011172","article-title":"CT texture analysis of histologically proven benign and malignant lung lesions.","volume":"97","author":"Digumarthy","year":"2018","journal-title":"Medicine (Baltimore)"},{"issue":"2","key":"10.1016\/j.acra.2023.11.007_bib18","doi-asserted-by":"crossref","first-page":"398","DOI":"10.2214\/AJR.19.22164","article-title":"Semiautomatic segmentation and radiomics for dual-energy CT: a pilot study to differentiate benign and malignant hepatic lesions.","volume":"215","author":"Homayounieh","year":"2020","journal-title":"Am J Roentgenol"},{"issue":"1","key":"10.1016\/j.acra.2023.11.007_bib19","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s40644-019-0223-7","article-title":"Predicting malignant potential of subsolid nodules: can radiomic preempt longitudinal follow up CT?","volume":"19","author":"Digumarthy","year":"2019","journal-title":"Cancer Imaging"},{"issue":"4","key":"10.1016\/j.acra.2023.11.007_bib20","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.21037\/tlcr-20-347","article-title":"Radiomic features of primary tumor by lung cancer stage: analysis in BRAF mutated non-small cell lung cancer.","volume":"9","author":"Padole","year":"2020","journal-title":"Transl Lung Cancer Res"},{"issue":"2","key":"10.1016\/j.acra.2023.11.007_bib21","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s00330-020-07141-9","article-title":"Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives.","volume":"31","author":"Chetan","year":"2021","journal-title":"Eur Radiol"},{"issue":"1","key":"10.1016\/j.acra.2023.11.007_bib22","first-page":"1","article-title":"Bladder cancer treatment response assessment in CT using radiomics with deep learning.","volume":"7","author":"Cha","year":"2017","journal-title":"Sci\u00a0Rep"},{"issue":"6","key":"10.1016\/j.acra.2023.11.007_bib23","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1007\/s00259-020-05142-w","article-title":"Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.","volume":"48","author":"Wesdorp","year":"2021","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"1","key":"10.1016\/j.acra.2023.11.007_bib24","first-page":"1","article-title":"Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI","volume":"19","author":"Braman","year":"2017","journal-title":"Breast Cancer Res"},{"key":"10.1016\/j.acra.2023.11.007_bib25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0231227","article-title":"Deciphering the tumor microenvironment through radiomics in non-small cell lung cancer: correlation with immune profiles","volume":"15","author":"Yoon","year":"2020","journal-title":"PLoS One"},{"issue":"1","key":"10.1016\/j.acra.2023.11.007_bib26","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s40644-021-00400-y","article-title":"Are radiomics features universally applicable to different organs?","volume":"21","author":"Lee","year":"2021","journal-title":"Cancer Imaging"},{"key":"10.1016\/j.acra.2023.11.007_bib27","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21551","article-title":"Cancer statistics, 2019.","volume":"69","author":"Siegel","year":"2019","journal-title":"CA Cancer J Clin"},{"key":"10.1016\/j.acra.2023.11.007_bib28","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1111\/joim.12084","article-title":"Breast cancer as a systemic disease: a view of metastasis.","volume":"274","author":"Redig","year":"2013","journal-title":"J Intern Med"},{"key":"10.1016\/j.acra.2023.11.007_bib29","doi-asserted-by":"crossref","first-page":"5937","DOI":"10.2147\/CMAR.S178395","article-title":"Incidence and prognostic factors of patients with synchronous liver metastases upon initial diagnosis of breast cancer: a population-based study","volume":"10","author":"Zhao","year":"2018","journal-title":"Cancer Manag Res"},{"issue":"10","key":"10.1016\/j.acra.2023.11.007_bib30","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1007\/s11548-020-02212-0","article-title":"Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT","volume":"15","author":"Homayounieh","year":"2020","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"7","key":"10.1016\/j.acra.2023.11.007_bib31","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.acra.2020.09.021","article-title":"Prediction of coronary calcification and stenosis: role of radiomics from low-dose CT","volume":"28","author":"Homayounieh","year":"2021","journal-title":"Acad Radiol"},{"issue":"6","key":"10.1016\/j.acra.2023.11.007_bib32","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1007\/s10554-021-02178-3","article-title":"Use of radiomics to differentiate left atrial appendage thrombi and mixing artifacts on single-phase CT angiography.","volume":"37","author":"Ebrahimian","year":"2021","journal-title":"Int J Cardiovasc Imaging"},{"issue":"3","key":"10.1016\/j.acra.2023.11.007_bib33","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.ccr.2012.02.022","article-title":"Accessories to the crime: functions of cells recruited to the tumor microenvironment.","volume":"21","author":"Hanahan","year":"2012","journal-title":"Cancer Cell"},{"issue":"5","key":"10.1016\/j.acra.2023.11.007_bib34","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.3892\/or.2016.4660","article-title":"Role of the tumor microenvironment in tumor progression and the clinical applications (Review).","volume":"35","author":"Yuan","year":"2016","journal-title":"Oncol Rep"},{"issue":"1","key":"10.1016\/j.acra.2023.11.007_bib35","doi-asserted-by":"crossref","first-page":"8738","DOI":"10.1038\/s41598-017-09315-w","article-title":"Bladder cancer treatment response assessment in CT using radiomics with deep-learning.","volume":"7","author":"Cha","year":"2017","journal-title":"Sci Rep"},{"key":"10.1016\/j.acra.2023.11.007_bib36","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s41698-019-0096-z","article-title":"A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer","volume":"3","author":"Nasief","year":"2019","journal-title":"NPJ Precis Oncol"},{"key":"10.1016\/j.acra.2023.11.007_bib37","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1186\/s12885-017-3847-7","article-title":"Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy","volume":"17","author":"Cozzi","year":"2017","journal-title":"BMC Cancer"},{"issue":"15","key":"10.1016\/j.acra.2023.11.007_bib38","first-page":"e15086","article-title":"Radiomics features on CT scans to predict response to HER2-targeted therapy of hepatic metastases from colorectal cancer.","volume":"37","author":"Giannini","year":"2019","journal-title":"J\u00a0Clin\u00a0Oncol"},{"key":"10.1016\/j.acra.2023.11.007_bib39","article-title":"Value of CT-based radiomics in predicating the efficacy of anti-HER2 therapy for patients with liver metastases from breast cancer.","volume":"12","author":"He","year":"2022","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2023.11.007_bib40","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0207362","article-title":"Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients","volume":"13","author":"Klaassen","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.acra.2023.11.007_bib41","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0166550","article-title":"Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings.","volume":"11","author":"Lu","year":"2016","journal-title":"PLoS One"},{"issue":"21","key":"10.1016\/j.acra.2023.11.007_bib42","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ab489f","article-title":"A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis.","volume":"64","author":"Xu","year":"2019","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.acra.2023.11.007_bib43","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0217536","article-title":"Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET\/CT radiomics.","volume":"14","author":"van Timmeren","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.acra.2023.11.007_bib44","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0166550","article-title":"Assessing agreement between radiomic features computed for multiple CT imaging settings.","volume":"11","author":"Lu","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.acra.2023.11.007_bib45","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/8790694","article-title":"The effect of CT scan parameters on the measurement of CT radiomic features: a lung nodule phantom study.","volume":"2019","author":"Kim","year":"2019","journal-title":"Comput Math Methods Med"},{"key":"10.1016\/j.acra.2023.11.007_bib46","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1097\/RTI.0000000000000389","article-title":"Effect of reconstruction parameters on the quantitative analysis of chest computed tomography.","volume":"34","author":"Kim","year":"2019","journal-title":"J Thorac Imaging"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633223006256?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633223006256?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T21:23:06Z","timestamp":1725052986000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633223006256"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":46,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["S1076633223006256"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2023.11.007","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Correlation of Radiomics with Treatment Response in Liver Metastases","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2023.11.007","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}