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Methods (2): This retrospective study included 98 internal and 55 external AAA patients undergoing [18F]FDG PET-CT. RFs were extracted from manual segmentations of AAAs using PyRadiomics. Recursive feature elimination (RFE) reduced features for model optimisation. A multi-layer perceptron (MLP) was developed for AAA growth prediction and compared against Random Forest (RF), XGBoost, and Support Vector Machine (SVM). Accuracy was evaluated via cross-validation, with uncertainty quantified using dropout (MLP), standard deviation (RF), and 95% prediction intervals (XGBoost). External validation used independent data from two centres. Ground truth growth rates were calculated from serial ultrasound (US) measurements or CT volumes. Results (3): From 93 initial RFs, 29 remained after RFE. The MLP model achieved an MAE \u00b1 SEM of 1.35 \u00b1 3.2e\u22124 mm\/year with the full feature set and 1.35 \u00b1 2.5e\u22124 mm\/year with RFE. External validation yielded 1.8 \u00b1 8.9e\u22128 mm\/year. RF, XGBoost, and SVM models produced comparable accuracies internally (1.4\u20131.5 mm\/year) but showed higher errors during external validation (1.9\u20131.97 mm\/year). The MLP model demonstrated reduced uncertainty with the full feature set across all datasets. Conclusions (4): An MLP model leveraging [18F]FDG PET-CT radiomics accurately predicted AAA growth rates and generalised well to external data. In the future, more sophisticated stratification could guide individualised patient care, facilitating risk-tailored management of AAAs.<\/jats:p>","DOI":"10.3390\/a18020086","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T10:09:52Z","timestamp":1738750192000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development and External Validation of [18F]FDG PET-CT-Derived Radiomic Models for Prediction of Abdominal Aortic Aneurysm Growth Rate"],"prefix":"10.3390","volume":"18","author":[{"given":"Simran Singh","family":"Dhesi","sequence":"first","affiliation":[{"name":"Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1567-9795","authenticated-orcid":false,"given":"Pratik","family":"Adusumilli","sequence":"additional","affiliation":[{"name":"Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0134-107X","authenticated-orcid":false,"given":"Nishant","family":"Ravikumar","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Leeds, Leeds LS2 9JT, UK"}]},{"given":"Mohammed A.","family":"Waduud","sequence":"additional","affiliation":[{"name":"Leeds Institute of Cardiovascular & Metabolic Medicine, School of Medicine, University of Leeds, Leeds LS2 9JT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2681-9922","authenticated-orcid":false,"given":"Russell","family":"Frood","sequence":"additional","affiliation":[{"name":"Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK"}]},{"given":"Alejandro F.","family":"Frangi","sequence":"additional","affiliation":[{"name":"Christabel Pankhurst Institute for Health Technology Research and Innovation, University of Manchester, Manchester M13 9PS, UK"}]},{"given":"Garry","family":"McDermott","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK"}]},{"given":"James H. F.","family":"Rudd","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Cambridge, Cambridge CB2 0SP, UK"}]},{"given":"Yuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Cambridge, Cambridge CB2 0SP, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3990-3341","authenticated-orcid":false,"given":"Jonathan R.","family":"Boyle","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK"}]},{"given":"Maysoon","family":"Elkhawad","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK"}]},{"given":"David E.","family":"Newby","sequence":"additional","affiliation":[{"name":"British Heart Foundation Centre of Research Excellence, University of Edinburgh, Edinburgh EH16 4SA, UK"}]},{"given":"Nikhil","family":"Joshi","sequence":"additional","affiliation":[{"name":"British Heart Foundation Centre of Research Excellence, University of Edinburgh, Edinburgh EH16 4SA, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1116-3115","authenticated-orcid":false,"given":"Jing Yi","family":"Kwan","sequence":"additional","affiliation":[{"name":"Leeds Institute of Cardiovascular & Metabolic Medicine, School of Medicine, University of Leeds, Leeds LS2 9JT, UK"},{"name":"Leeds Vascular Institute, Leeds Teaching Hospitals NHS Trust, Great George Street, Leeds LS1 3EX, UK"}]},{"given":"Patrick","family":"Coughlin","sequence":"additional","affiliation":[{"name":"Leeds Vascular Institute, Leeds Teaching Hospitals NHS Trust, Great George Street, Leeds LS1 3EX, UK"}]},{"given":"Marc A.","family":"Bailey","sequence":"additional","affiliation":[{"name":"Leeds Institute of Cardiovascular & Metabolic Medicine, School of Medicine, University of Leeds, Leeds LS2 9JT, UK"},{"name":"Leeds Vascular Institute, Leeds Teaching Hospitals NHS Trust, Great George Street, Leeds LS1 3EX, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4243-032X","authenticated-orcid":false,"given":"Andrew F.","family":"Scarsbrook","sequence":"additional","affiliation":[{"name":"Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK"},{"name":"Leeds Institute of Health and Research, Faculty of Medicine, University of Leeds, Leeds LS2 9LN, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/s41572-018-0030-7","article-title":"Abdominal aortic aneurysms","volume":"4","author":"Sakalihasan","year":"2018","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, X., Zhao, G., Zhang, J., Duan, Z., and Xin, S. (2013). Prevalence and Trends of the Abdominal Aortic Aneurysms Epidemic in General Population\u2014A Meta-Analysis. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0081260"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.jvs.2008.08.012","article-title":"A population-based case-control study of the familial risk of abdominal aortic aneurysm","volume":"49","author":"Larsson","year":"2009","journal-title":"J. Vasc. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1093\/oxfordjournals.aje.a010245","article-title":"Risk factors for abdominal aortic aneurysm: Results of a case-control study","volume":"151","author":"Blanchard","year":"2000","journal-title":"Am. J. Epidemiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.jvs.2010.05.090","article-title":"Analysis of risk factors for abdominal aortic aneurysm in a cohort of more than 3 million individuals","volume":"52","author":"Kent","year":"2010","journal-title":"J. Vasc. Surg."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s10439-010-0165-5","article-title":"A framework for the automatic generation of surface topologies for abdominal aortic aneurysm models","volume":"39","author":"Shum","year":"2010","journal-title":"Ann. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3310\/hta17410","article-title":"Systematic review and meta-analysis of the growth and rupture rates of small abdominal aortic aneurysms: Implications for surveillance intervals and their cost-effectiveness","volume":"17","author":"Thompson","year":"2013","journal-title":"Health Technol. Assess"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1067\/mva.2003.119","article-title":"The risk of rupture in untreated aneurysms: The impact of size, gender, and expansion rate","volume":"37","author":"Brown","year":"2003","journal-title":"J. Vasc. Surg."},{"key":"ref_9","first-page":"11","article-title":"Abdominal aortic aneurysm: A comprehensive review","volume":"16","author":"Aggarwal","year":"2011","journal-title":"Exp. Clin. Cardiol."},{"key":"ref_10","unstructured":"GOVUK (2024, November 27). AAA Screening: Professional Guidance. GOVUK, Available online: https:\/\/www.gov.uk\/government\/collections\/aaa-screening-supporting-documents."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"US Preventive Services Task Force, Owens, D., Davidson, K., and Krist, A. (2019). Screening for Abdominal Aortic Aneurysm: US Preventive Services Task Force Recommendation Statement. JAMA, 322, 2211.","DOI":"10.1001\/jama.2019.18928"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/s13139-017-0482-9","article-title":"Correlation of [18F]FDG PET\/CT Findings with Long-Term Growth and Clinical Course of Abdominal Aortic Aneurysm","volume":"52","author":"Lee","year":"2018","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.jvs.2008.03.059","article-title":"Increased 18F-fluorodeoxyglucose uptake in abdominal aortic aneurysms in positron emission\/computed tomography is associated with inflammation, aortic wall instability, and acute symptoms","volume":"48","author":"Reeps","year":"2008","journal-title":"J. Vasc. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1007\/s12350-019-01940-4","article-title":"Iterative reconstruction incorporating background correction improves quantification of [18F]-NaF PET\/CT images of patients with abdominal aortic aneurysm","volume":"28","author":"Akerele","year":"2019","journal-title":"J. Nucl. Cardiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1007\/s00259-011-1799-8","article-title":"What is the relationship between 18F-[18F]FDG aortic aneurysm uptake on PET\/CT and future growth rate?","volume":"38","author":"Kotze","year":"2011","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.ejvs.2008.12.016","article-title":"Increased metabolic activity in abdominal aortic aneurysm detected by 18F-fluorodeoxyglucose (18F-[18F]FDG) positron emission tomography\/computed tomography (PET\/CT)","volume":"38","author":"Kotze","year":"2008","journal-title":"Eur. J. Vasc. Endovasc. Surg."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1007\/s12350-021-02616-8","article-title":"Prospect of positron emission tomography for abdominal aortic aneurysm risk stratification","volume":"28","author":"Gandhi","year":"2021","journal-title":"J. Nucl. Cardiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: The bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5677","DOI":"10.1002\/mp.13844","article-title":"Technical Note: Ontology-guided radiomics analysis workflow (O-RAW)","volume":"46","author":"Shi","year":"2019","journal-title":"Med. Phys."},{"key":"ref_20","first-page":"197","article-title":"Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype","volume":"46","author":"Price","year":"2020","journal-title":"Lung Cancer"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4271","DOI":"10.1158\/1078-0432.CCR-18-3065","article-title":"Prognostic Value of Deep Learning PET\/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma","volume":"25","author":"Peng","year":"2019","journal-title":"CliN. Cancer Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Carvalho, S., Leijenaar, R.T.H., Troost, E.G.C., van Timmeren, J.E., Oberije, C., van Elmpt, W., de Geus-Oei, L.-F., Bussink, J., and Lambin, P. (2018). 18F-fluorodeoxyglucose positron-emission tomography ([18F]FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC)\u2014A prospective externally validated study. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192859"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1007\/s11912-019-0815-1","article-title":"Radiomics: An Introductory Guide to What It May Foretell","volume":"21","author":"Nougaret","year":"2019","journal-title":"Curr. Oncol. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1007\/s13139-017-0500-y","article-title":"Radiomics in Oncological PET\/CT: Clinical Applications","volume":"52","author":"Lee","year":"2018","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"g7594","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Leijenaar, R.T., Nalbantov, G., Carvalho, S., Van Elmpt, W.J., Troost, E.G., Boellaard, R., and Lambin, P. (2015). The effect of SUV discretization in quantitative [18F]FDG-PET Radiomics: The need for standardized methodology in tumor texture analysis. Sci. Rep., 5.","DOI":"10.1038\/srep11075"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.3109\/0284186X.2013.812798","article-title":"Stability of [18F]FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability","volume":"52","author":"Leijenaar","year":"2013","journal-title":"Acta Oncol."},{"key":"ref_28","unstructured":"Pyradiomics (2024, November 27). Frequently Asked Questions\u2014Pyradiomics v3.0.post5+gf06ac1d Documentation. Pyradiomics., Available online: https:\/\/pyradiomics.readthedocs.io\/en\/latest\/faq.html."},{"key":"ref_29","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":"ref_30","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1007\/s00259-016-3599-7","article-title":"Textural features of 18F-fluorodeoxyglucose positron emission tomography scanning in diagnosing aortic prosthetic graft infection","volume":"44","author":"Saleem","year":"2017","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_31","unstructured":"Github (2024, November 27). GitHub\u2014Scikit-Learn\/Scikit-Learn: Scikit-Learn: Machine Learning in Python. Available online: https:\/\/github.com\/scikit-learn\/scikit-learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_33","unstructured":"Keras (2024, November 27). Keras: The Python Deep Learning API. Available online: https:\/\/keras.io\/."},{"key":"ref_34","first-page":"1958","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","unstructured":"Team, K. (2024, November 27). Keras Documentation: RMSprop. Available online: https:\/\/keras.io\/api\/optimizers\/rmsprop\/."},{"key":"ref_36","first-page":"1050","article-title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","volume":"48","author":"Gal","year":"2016","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_37","first-page":"225","article-title":"Linear model selection and regularization","volume":"2021","author":"James","year":"2021","journal-title":"Introd. Stat. Learn. Appl. R"},{"key":"ref_38","first-page":"384","article-title":"Evaluation of clinical prediction models (part 1): From development to external validation","volume":"8","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_39","first-page":"384","article-title":"Evaluation of clinical prediction models (part 2): How to undertake an external validation study","volume":"15","author":"Riley","year":"2024","journal-title":"BMJ"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1097\/00006231-200008000-00002","article-title":"FDG PET in the evaluation of the aggressiveness of pulmonary adenocarcinoma: Correlation with histopathological features","volume":"21","author":"Higashi","year":"2000","journal-title":"Nucl. Med. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s12013-012-9395-5","article-title":"18 [F] FDG-PET\/CT is a useful molecular marker in evaluating tumour aggressiveness: A revised understanding of an in-vivo FDG-PET imaging that alludes the alteration of cancer biology","volume":"66","author":"Fathinul","year":"2013","journal-title":"Cell Biochem. Biophys."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s00259-002-0978-z","article-title":"FDG-PET for prediction of tumour aggressiveness and response to intra-arterial chemotherapy and radiotherapy in head and neck cancer","volume":"30","author":"Kitagawa","year":"2003","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.ijrobp.2014.02.031","article-title":"Baseline metabolic tumor volume and total lesion glycolysis are associated with survival outcomes in patients with locally advanced pancreatic cancer receiving stereotactic body radiation therapy","volume":"89","author":"Dholakia","year":"2014","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.3390\/tomography10090108","article-title":"A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease","volume":"10","author":"Badesha","year":"2024","journal-title":"Tomography"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S1078-5884(96)80112-2","article-title":"Interobserver variability in measuring the dimensions of the abdominal aorta: Comparison of ultrasound and computed tomography","volume":"12","author":"Jaakkola","year":"1996","journal-title":"Eur. J. Vasc. Endovasc. Surg."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1053\/ejvs.2002.1856","article-title":"Intra- and interobserver variability in the measurements of abdominal aortic and common iliac artery diameter with computed tomography. The Troms\u00f8 study","volume":"25","author":"Singh","year":"2003","journal-title":"Eur. J. Vasc. Endovasc. Surg."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/0741-5214(91)90249-T","article-title":"Determination of the expansion rate and incidence of rupture of abdominal aortic aneurysms","volume":"14","author":"Limet","year":"1991","journal-title":"J. Vasc. Surg."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1002\/bjs.7465","article-title":"Systematic review and meta-analysis of growth rates of small abdominal aortic aneurysms","volume":"98","author":"Powell","year":"2011","journal-title":"J. Br. Surg."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1097\/RCT.0000000000000958","article-title":"Machine Learning to Predict the Rapid Growth of Small Abdominal Aortic Aneurysm","volume":"44","author":"Hirata","year":"2020","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ejvssr.2018.03.004","article-title":"Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans","volume":"39","author":"Lee","year":"2018","journal-title":"EJVES Short Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1007\/s10439-020-02461-9","article-title":"A Comparative Classification Analysis of Abdominal Aortic Aneurysms by Machine Learning Algorithms","volume":"48","author":"Rengarajan","year":"2020","journal-title":"Ann. Biomed. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e175","DOI":"10.1097\/SLA.0000000000004711","article-title":"Prediction of abdominal aortic aneurysm growth using geometric assessment of computerized tomography images acquired during the aneurysm surveillance period","volume":"277","author":"Chandrashekar","year":"2023","journal-title":"Ann. Surg."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1186\/s13244-024-01804-7","article-title":"Assessing abdominal aortic aneurysm growth using radiomic features of perivascular adipose tissue after endovascular repair","volume":"15","author":"Lv","year":"2024","journal-title":"Insights Into Imaging"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4270","DOI":"10.1111\/bph.15634","article-title":"Perivascular fat imaging by computed tomography (CT): A virtual guide","volume":"178","author":"Kotanidis","year":"2021","journal-title":"Br. J. Pharmacol. Nov."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1093\/eurheartj\/ehz592","article-title":"A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography","volume":"40","author":"Oikonomou","year":"2019","journal-title":"Eur. Heart J."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ding, N., Hao, Y., Wang, Z., Xuan, X., Kong, L., Xue, H., and Jin, Z. (2020). CT texture analysis predicts abdominal aortic aneurysm post-endovascular aortic aneurysm repair progression. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-69226-1"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s12265-023-10404-7","article-title":"Radiomic-based textural analysis of intraluminal thrombus in aortic abdominal aneurysms: A demonstration of automated workflow","volume":"16","author":"Rezaeitaleshmahalleh","year":"2020","journal-title":"J. Cardiovasc. Trans Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1093\/aje\/kwk052","article-title":"Relaxing the rule of ten events per variable in logistic and Cox regression","volume":"165","author":"Vittinghoff","year":"2007","journal-title":"Am. J. 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