{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:00:39Z","timestamp":1767322839370,"version":"3.48.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032113160","type":"print"},{"value":"9783032113177","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-11317-7_19","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:58:12Z","timestamp":1767322692000},"page":"222-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiomic Analysis of\u00a0Mouse Magnetic Resonance Images and\u00a0Correlation of\u00a0Features Extracted to\u00a0Vascular Defects in\u00a0Sickle Cell Disease: Preclinical Applications"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3629-355X","authenticated-orcid":false,"given":"Viviana","family":"Benfante","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5048-6713","authenticated-orcid":false,"given":"Liana","family":"Hatoum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1360-2860","authenticated-orcid":false,"given":"Hannah Song","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edward A.","family":"Botchwey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6390-4320","authenticated-orcid":false,"given":"Manu O.","family":"Platt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9290-6103","authenticated-orcid":false,"given":"Albert","family":"Comelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","unstructured":"Ali, M., et al.: Prostate cancer detection: performance of radiomics analysis in multiparametric MRI. In: International Conference on Image Analysis and Processing (2024). https:\/\/doi.org\/10.1007\/978-3-031-51026-7_8","DOI":"10.1007\/978-3-031-51026-7_8"},{"key":"19_CR2","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1002\/asmb.2642","volume":"37","author":"S Barone","year":"2021","unstructured":"Barone, S., et al.: Hybrid descriptive-inferential method for key feature selection in prostate cancer radiomics. Appl. Stoch. Models Bus Ind. 37, 961\u2013972 (2021). https:\/\/doi.org\/10.1002\/asmb.2642","journal-title":"Appl. Stoch. Models Bus Ind."},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Basirinia, G., et al.: Theranostic approaches for gastric cancer: an overview of in vitro and in vivo investigations. Cancers (Basel) 16, 3323 (2024). https:\/\/doi.org\/10.3390\/cancers16193323","DOI":"10.3390\/cancers16193323"},{"key":"19_CR4","doi-asserted-by":"publisher","unstructured":"Benfante, V., et al.: Grading and staging of bladder tumors using radiomics analysis in magnetic resonance imaging. In: International Conference on Image Analysis and Processing (2024). https:\/\/doi.org\/10.1007\/978-3-031-51026-7_9","DOI":"10.1007\/978-3-031-51026-7_9"},{"key":"19_CR5","doi-asserted-by":"publisher","unstructured":"Cairone, L., et al.: Robustness of radiomics features to varying segmentation algorithms in magnetic resonance images. In: International Conference on Image Analysis and Processing (2022). https:\/\/doi.org\/10.1007\/978-3-031-13321-3_41","DOI":"10.1007\/978-3-031-13321-3_41"},{"key":"19_CR6","doi-asserted-by":"publisher","unstructured":"Canfora, I., et al.: A predictive system to classify preoperative grading of rectal cancer using radiomics features. In: International Conference on Image Analysis and Processing (2022). https:\/\/doi.org\/10.1007\/978-3-031-13321-3_38","DOI":"10.1007\/978-3-031-13321-3_38"},{"key":"19_CR7","doi-asserted-by":"publisher","unstructured":"Giaccone, P., Benfante, V., Stefano, A., Cammarata, F., Russo, G., Comelli, A.: Pet images atlas-based segmentation performed in native and in template space: a radiomics repeatability study in mouse models. In: International Conference on Image Analysis and Processing (2022). https:\/\/doi.org\/10.1007\/978-3-031-13321-3_31","DOI":"10.1007\/978-3-031-13321-3_31"},{"key":"19_CR8","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"J van Griethuysen","year":"2017","unstructured":"van Griethuysen, J., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104\u2013e107 (2017). https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0339","journal-title":"Cancer Res."},{"key":"19_CR9","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1001\/jamaneurol.2018.0571","volume":"75","author":"H Hyacinth","year":"2018","unstructured":"Hyacinth, H., et al.: Association of sickle cell trait with ischemic stroke among African Americans. JAMA Neurol. 75, 802 (2018). https:\/\/doi.org\/10.1001\/jamaneurol.2018.0571","journal-title":"JAMA Neurol."},{"key":"19_CR10","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1001\/jama.2022.10233","volume":"328","author":"P Kavanagh","year":"2022","unstructured":"Kavanagh, P., Fasipe, T., Wun, T.: Sickle cell disease. JAMA 328, 57 (2022). https:\/\/doi.org\/10.1001\/jama.2022.10233","journal-title":"JAMA"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1159\/000540149","volume":"51","author":"J Kunz","year":"2024","unstructured":"Kunz, J., Tagliaferri, L.: Sickle cell disease. Transfus. Med. Hemother. 51, 332\u2013344 (2024). https:\/\/doi.org\/10.1159\/000540149","journal-title":"Transfus. Med. Hemother."},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin, P., et al.: Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14, 749\u2013762 (2017). https:\/\/doi.org\/10.1038\/nrclinonc.2017.141","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"19_CR13","doi-asserted-by":"publisher","first-page":"279","DOI":"10.2147\/JBM.S383472","volume":"14","author":"J Light","year":"2023","unstructured":"Light, J., Boucher, M., Baskin-Miller, J., Winstead, M.: Managing the cerebrovascular complications of sickle cell disease: current perspectives. J Blood Med 14, 279\u2013293 (2023). https:\/\/doi.org\/10.2147\/JBM.S383472","journal-title":"J Blood Med"},{"key":"19_CR14","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1016\/j.jcmg.2021.11.016","volume":"15","author":"A Lin","year":"2022","unstructured":"Lin, A., et al.: Radiomics-based precision phenotyping identifies unstable coronary plaques from computed tomography angiography. J. Am. Coll. Cardiol. Cardiovasc. Imaging 15, 859\u2013871 (2022). https:\/\/doi.org\/10.1016\/j.jcmg.2021.11.016","journal-title":"J. Am. Coll. Cardiol. Cardiovasc. Imaging"},{"key":"19_CR15","doi-asserted-by":"publisher","first-page":"3709","DOI":"10.1038\/s41598-023-30678-w","volume":"13","author":"J Lyu","year":"2023","unstructured":"Lyu, J., Xu, Z., Sun, H., Zhai, F., Qu, X.: Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage. Sci. Rep. 13, 3709 (2023). https:\/\/doi.org\/10.1038\/s41598-023-30678-w","journal-title":"Sci. Rep."},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1093\/bjr\/tqae057","volume":"97","author":"Z Meddings","year":"2024","unstructured":"Meddings, Z., Rundo, L., Sadat, U., Zhao, X., Teng, Z., Graves, M.: Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability. Br. J. Radiol. 97, 1118\u20131124 (2024). https:\/\/doi.org\/10.1093\/bjr\/tqae057","journal-title":"Br. J. Radiol."},{"key":"19_CR17","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.1161\/ATVBAHA.120.314045","volume":"40","author":"H Song","year":"2020","unstructured":"Song, H., et al.: Sickle cell anemia mediates carotid artery expansive remodeling that can be prevented by inhibition of JNK (c-jun N-terminal kinase). Arterioscler. Thromb. Vasc. Biol. 40, 1220\u20131230 (2020). https:\/\/doi.org\/10.1161\/ATVBAHA.120.314045","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"19_CR18","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.3390\/diagnostics13162627","volume":"13","author":"B Yang","year":"2023","unstructured":"Yang, B., et al.: Comparison of ruptured intracranial aneurysms identification using different machine learning algorithms and radiomics. Diagnostics 13, 2627 (2023). https:\/\/doi.org\/10.3390\/diagnostics13162627","journal-title":"Diagnostics"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing - ICIAP 2025 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-11317-7_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:58:12Z","timestamp":1767322692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-11317-7_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032113160","9783032113177"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-11317-7_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap.org\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}