{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T02:04:53Z","timestamp":1792807493974,"version":"4.5.0"},"reference-count":63,"publisher":"Elsevier BV","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC2414200"],"award-info":[{"award-number":["2023YFC2414200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0714002"],"award-info":[{"award-number":["2020YFA0714002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82460344"],"award-info":[{"award-number":["82460344"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82560341"],"award-info":[{"award-number":["82560341"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012433","name":"Key Project of Application Development Plan of Chongqing City","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012433","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.acra.2026.01.004","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T12:39:08Z","timestamp":1768912748000},"page":"2270-2283","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"title":["Habitat-Based Radiomics on Dual-Energy CT for Preoperative Assessment of Invasiveness in Lung Adenocarcinoma with Ground-Glass Nodules"],"prefix":"10.1016","volume":"33","author":[{"given":"Daoyu","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaolei","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fajin","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xunran","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenliang","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0857-3834","authenticated-orcid":false,"given":"Xianchun","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2026.01.004_bib1","first-page":"7","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA Cancer J Clin"},{"key":"10.1016\/j.acra.2026.01.004_bib2","doi-asserted-by":"crossref","DOI":"10.1038\/nrdp.2015.9","article-title":"Non-small-cell lung cancer","volume":"1","author":"Gridelli","year":"2015","journal-title":"Nat Rev Dis Primers"},{"key":"10.1016\/j.acra.2026.01.004_bib3","first-page":"40","article-title":"Lung cancer immunotherapy: progress, pitfalls, and promises","volume":"22","author":"Lahiri","year":"2023","journal-title":"Mol\u00a0Cancer"},{"key":"10.1016\/j.acra.2026.01.004_bib4","first-page":"108","article-title":"Overdiagnosis and overtreatment of ground-glass nodule-like lung cancer","volume":"21","author":"Liang","year":"2025","journal-title":"Asia-Pacific J\u00a0Clin\u00a0Oncol"},{"key":"10.1016\/j.acra.2026.01.004_bib5","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.jtho.2021.11.003","article-title":"The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015","volume":"17","author":"Nicholson","year":"2022","journal-title":"J\u00a0Thoracic Oncol"},{"key":"10.1016\/j.acra.2026.01.004_bib6","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.jtho.2021.04.007","article-title":"Long-term prognosis of patients with resected adenocarcinoma in situ and minimally invasive adenocarcinoma of the lung","volume":"16","author":"Yotsukura","year":"2021","journal-title":"J\u00a0Thoracic Oncol"},{"key":"10.1016\/j.acra.2026.01.004_bib7","article-title":"Deep learning and radiomics fusion for predicting the invasiveness of lung adenocarcinoma within ground glass nodules","volume":"15","author":"Sun","year":"2025","journal-title":"Sci\u00a0Rep"},{"key":"10.1016\/j.acra.2026.01.004_bib8","first-page":"791","article-title":"Clinical impact of a small component of ground-glass opacity in solid-dominant clinical stage IA non-small cell lung cancer","volume":"163","author":"Watanabe","year":"2022","journal-title":"J\u00a0Thoracic\u00a0Cardiovasc\u00a0Surg"},{"key":"10.1016\/j.acra.2026.01.004_bib9","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1038\/s41388-019-1127-5","article-title":"The microcosmos of intratumor heterogeneity: the space-time of cancer evolution","volume":"39","author":"Janiszewska","year":"2020","journal-title":"Oncogene"},{"key":"10.1016\/j.acra.2026.01.004_bib10","doi-asserted-by":"crossref","first-page":"3650","DOI":"10.1007\/s00330-020-06776-y","article-title":"Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction","volume":"30","author":"Sun","year":"2020","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2026.01.004_bib11","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13244-022-01363-9","article-title":"Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas","volume":"14","author":"Feng","year":"2023","journal-title":"Insights Imaging"},{"key":"10.1016\/j.acra.2026.01.004_bib12","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.1038\/s41598-021-83167-3","article-title":"Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules","volume":"11","author":"Chen","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.acra.2026.01.004_bib13","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1186\/s12931-022-02016-7","article-title":"A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules","volume":"23","author":"Zhu","year":"2022","journal-title":"Respir Res"},{"key":"10.1016\/j.acra.2026.01.004_bib14","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1148\/radiol.2020192431","article-title":"Diagnosis of invasive lung adenocarcinoma based on chest CT radiomic features of part-solid pulmonary nodules: a multicenter study","volume":"297","author":"Wu","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2026.01.004_bib15","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.ebiom.2019.05.023","article-title":"Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy","volume":"44","author":"Xie","year":"2019","journal-title":"EBioMedicine"},{"key":"10.1016\/j.acra.2026.01.004_bib16","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020190168","article-title":"Tumor habitat-derived radiomic features at pretreatment MRI that are prognostic for progression-free survival in glioblastoma are associated with key morphologic attributes at histopathologic examination: a feasibility study","volume":"2","author":"Verma","year":"2020","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.acra.2026.01.004_bib17","article-title":"MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer","volume":"308","author":"Shi","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2026.01.004_bib18","article-title":"Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT","volume":"14","author":"Zhang","year":"2024","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2026.01.004_bib19","doi-asserted-by":"crossref","DOI":"10.2214\/AJR.24.31675","article-title":"Habitat imaging with tumoral and peritumoral radiomics for prediction of lung adenocarcinoma invasiveness on preoperative chest CT: a multicenter study","volume":"223","author":"Shang","year":"2024","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.acra.2026.01.004_bib20","doi-asserted-by":"crossref","first-page":"7334","DOI":"10.1007\/s00330-024-10782-9","article-title":"Electron density dual-energy CT can improve the detection of lumbar disc herniation with higher image quality than standard and virtual non-calcium images","volume":"34","author":"Jeong","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2026.01.004_bib21","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.1007\/s00330-021-08374-y","article-title":"Diagnostic performance of electron-density dual-energy CT in detection of cervical disc herniation in comparison with standard gray-scale CT and virtual non-calcium images","volume":"32","author":"Shim","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2026.01.004_bib22","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.222680","article-title":"Spinal hematoma visualized with dual-energy CT-derived electron density overlay maps","volume":"307","author":"Nelles","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2026.01.004_bib23","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.3390\/diagnostics11101841","article-title":"Diagnosis of pulmonary embolism in unenhanced dual energy CT using an electron density image","volume":"11","author":"Bae","year":"2021","journal-title":"Diagnostics"},{"key":"10.1016\/j.acra.2026.01.004_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejro.2024.100628","article-title":"Potential of spectral imaging generated by contrast-enhanced dual-energy CT for lung cancer histopathological classification - a preliminary study","volume":"14","author":"Sasaki","year":"2025","journal-title":"Eur J Radiol Open"},{"key":"10.1016\/j.acra.2026.01.004_bib25","doi-asserted-by":"crossref","first-page":"5901","DOI":"10.1007\/s00432-022-04543-8","article-title":"Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules","volume":"149","author":"Zhang","year":"2023","journal-title":"J Cancer Res Clin Oncol"},{"key":"10.1016\/j.acra.2026.01.004_bib26","first-page":"349","article-title":"Could spectral CT have a potential benefit in coronavirus disease (COVID-19)?","volume":"216","author":"Daoud","year":"2021","journal-title":"Am\u00a0J\u00a0Roentgenol"},{"key":"10.1016\/j.acra.2026.01.004_bib27","series-title":"2016 38th annual international conference of the IEEE engineering in medicine and biology society\u00a0(EMBC)","first-page":"3342","article-title":"ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images","author":"Yushkevich","year":"2016"},{"key":"10.1016\/j.acra.2026.01.004_bib28","first-page":"451","article-title":"The global k-means clustering algorithm","volume":"36","author":"Likas","year":"2003","journal-title":"Pattern\u00a0Recognit"},{"key":"10.1016\/j.acra.2026.01.004_bib29","first-page":"609","article-title":"A comprehensive review of markov random field and conditional random field approaches in pathology image analysis","volume":"29","author":"Li","year":"2022","journal-title":"Arch\u00a0Computat\u00a0Methods\u00a0Eng"},{"key":"10.1016\/j.acra.2026.01.004_bib30","series-title":"2020 IEEE 7th international conference on data science and advanced analytics (DSAA)","first-page":"747","article-title":"Cluster quality analysis using silhouette score","author":"Shahapure","year":"2020"},{"key":"10.1016\/j.acra.2026.01.004_bib31","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J\u00a0Mach\u00a0Learn\u00a0Res"},{"key":"10.1016\/j.acra.2026.01.004_bib32","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"Van Griethuysen","year":"2017","journal-title":"Cancer Res"},{"key":"10.1016\/j.acra.2026.01.004_bib33","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2026.01.004_bib34","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","article-title":"Harmonization of cortical thickness measurements across scanners and sites","volume":"167","author":"Fortin","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.acra.2026.01.004_bib35","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.21105\/joss.01026","article-title":"Pingouin: statistics in Python","volume":"3","author":"Vallat","year":"2018","journal-title":"J Open Source Softw"},{"key":"10.1016\/j.acra.2026.01.004_bib36","first-page":"88","article-title":"Sensitivity of radiomic features to inter-observer variability and image pre-processing in apparent diffusion coefficient (ADC) maps of cervix cancer patients","volume":"143","author":"Traverso","year":"2020","journal-title":"Radiother\u00a0Oncol"},{"key":"10.1016\/j.acra.2026.01.004_bib37","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s13244-023-01582-8","article-title":"Intratumoral and peritumoral CT radiomics in predicting prognosis in patients with chondrosarcoma: a multicenter study","volume":"15","author":"Li","year":"2024","journal-title":"Insights into Imaging"},{"key":"10.1016\/j.acra.2026.01.004_bib38","first-page":"71","article-title":"CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency","volume":"8","author":"Bortolotto","year":"2024","journal-title":"Eur\u00a0Radiol\u00a0Exp"},{"key":"10.1016\/j.acra.2026.01.004_bib39","first-page":"1","article-title":"Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning","volume":"18","author":"Lema\u00c3\u017dtre","year":"2017","journal-title":"J\u00a0Mach\u00a0Learn\u00a0Res"},{"key":"10.1016\/j.acra.2026.01.004_bib40","series-title":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","first-page":"785","article-title":"Xgboost: a scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.acra.2026.01.004_bib41","first-page":"5","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach\u00a0Learn"},{"key":"10.1016\/j.acra.2026.01.004_bib42","first-page":"1189","article-title":"Greedy function approximation: a gradient boosting machine","author":"Friedman","year":"2001","journal-title":"Ann\u00a0Statist"},{"key":"10.1016\/j.acra.2026.01.004_bib43","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell\u00a0Syst\u00a0Their Appl"},{"key":"10.1016\/j.acra.2026.01.004_bib44","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1161\/CIRCULATIONAHA.106.682658","article-title":"Logistic regression","volume":"117","author":"LaValley","year":"2008","journal-title":"Circulation"},{"key":"10.1016\/j.acra.2026.01.004_bib45","doi-asserted-by":"crossref","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"10.1016\/j.acra.2026.01.004_bib46","first-page":"71","article-title":"Learning decision tree classifiers","volume":"28","author":"Quinlan","year":"1996","journal-title":"ACM Comput\u00a0Surv"},{"key":"10.1016\/j.acra.2026.01.004_bib47","series-title":"2017 Artificial intelligence and signal processing conference","first-page":"209","article-title":"A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features","author":"Jahromi","year":"2017"},{"key":"10.1016\/j.acra.2026.01.004_bib48","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0925-2312(91)90023-5","article-title":"Multilayer perceptrons for classification and regression","volume":"2","author":"Murtagh","year":"1991","journal-title":"Neurocomputing"},{"key":"10.1016\/j.acra.2026.01.004_bib49","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nature Methods"},{"key":"10.1016\/j.acra.2026.01.004_bib50","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.acra.2026.01.004_bib51","first-page":"1","article-title":"Pandas: a foundational Python library for data analysis and statistics","volume":"14","author":"McKinney","year":"2011","journal-title":"Python\u00a0High Performance\u00a0Sci\u00a0Comput"},{"key":"10.1016\/j.acra.2026.01.004_bib52","first-page":"4913","article-title":"Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics","volume":"15","author":"Du","year":"2025","journal-title":"Sci\u00a0Rep"},{"key":"10.1016\/j.acra.2026.01.004_bib53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40644-020-00376-1","article-title":"Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact","volume":"21","author":"Jiang","year":"2021","journal-title":"Cancer Imaging"},{"key":"10.1016\/j.acra.2026.01.004_bib54","doi-asserted-by":"crossref","first-page":"5121","DOI":"10.1007\/s00330-018-5509-9","article-title":"The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules","volume":"28","author":"She","year":"2018","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2026.01.004_bib55","article-title":"A radiological-radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: a two-center retrospective study","volume":"176","author":"Dong","year":"2024","journal-title":"Eur\u00a0J\u00a0Radiol"},{"key":"10.1016\/j.acra.2026.01.004_bib56","doi-asserted-by":"crossref","DOI":"10.1016\/j.lungcan.2025.108674","article-title":"Prognostic impact of lepidic growth in intermediate and high-grade lung adenocarcinoma","volume":"206","author":"Niedermaier","year":"2025","journal-title":"Lung Cancer"},{"key":"10.1016\/j.acra.2026.01.004_bib57","first-page":"399","article-title":"The fibroinflammatory response in cancer","volume":"25","author":"Xiao","year":"2025","journal-title":"Nature Rev\u00a0Cancer"},{"key":"10.1016\/j.acra.2026.01.004_bib58","doi-asserted-by":"crossref","first-page":"5423","DOI":"10.1007\/s00330-019-06149-0","article-title":"Morphological classification of pre-invasive lesions and early-stage lung adenocarcinoma based on CT images","volume":"29","author":"Gao","year":"2019","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2026.01.004_bib59","doi-asserted-by":"crossref","first-page":"4037","DOI":"10.1007\/s00330-017-4829-5","article-title":"CT and histopathologic characteristics of lung adenocarcinoma with pure ground-glass nodules 10 mm or less in diameter","volume":"27","author":"Wu","year":"2017","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2026.01.004_bib60","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1002\/ijc.34238","article-title":"Comprehensive characterization of genomic and radiologic features reveals distinct driver patterns of RTK\/RAS pathway in ground-glass opacity pulmonary nodules","volume":"151","author":"Yu","year":"2022","journal-title":"Int J Cancer"},{"key":"10.1016\/j.acra.2026.01.004_bib61","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000011670","article-title":"Improved differentiation between high- and low-grade gliomas by combining dual-energy CT analysis and perfusion CT","volume":"97","author":"Kaichi","year":"2018","journal-title":"Medicine"},{"key":"10.1016\/j.acra.2026.01.004_bib62","first-page":"575","article-title":"Usefulness of electron density imaging from dual-energy CT for detecting pulmonary thromboembolism in unenhanced CT","volume":"36","author":"Kim","year":"2026","journal-title":"Eur\u00a0Radiol"},{"key":"10.1016\/j.acra.2026.01.004_bib63","first-page":"54","article-title":"Extracellular matrix stiffness: mechanisms in tumor progression and therapeutic potential in cancer","volume":"14","author":"Zhang","year":"2025","journal-title":"Exp\u00a0Hematol Oncol"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226000012?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226000012?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T10:02:50Z","timestamp":1778839370000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633226000012"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":63,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["S1076633226000012"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2026.01.004","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Habitat-Based Radiomics on Dual-Energy CT for Preoperative Assessment of Invasiveness in Lung Adenocarcinoma with Ground-Glass Nodules","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2026.01.004","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Association of Academic Radiology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}