Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Dooman Arefan"'
Publikováno v:
Breast Cancer Research, Vol 26, Iss 1, Pp 1-8 (2024)
Abstract Background Patients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70–80% of the biopsies are negative/benign. In this study, we developed a deep learning classification
Externí odkaz:
https://doaj.org/article/76812f57d12d478a992794590b57d41e
Publikováno v:
BMC Medical Imaging, Vol 22, Iss 1, Pp 1-9 (2022)
Abstract Background Renal cell carcinoma (RCC) is a heterogeneous group of kidney cancers. Renal capsule invasion is an essential factor for RCC staging. To develop radiomics models from CT images for the preoperative prediction of capsule invasion i
Externí odkaz:
https://doaj.org/article/9863e5d424f54e76b2c27fdf34efce3e
Autor:
Qianwei Zhou, Margarita Zuley, Yuan Guo, Lu Yang, Bronwyn Nair, Adrienne Vargo, Suzanne Ghannam, Dooman Arefan, Shandong Wu
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
While active efforts are advancing medical AI model development and clinical translation, safety issues of medical AI models have emerged. Here, the authors investigate the effects on an AI model and on human experts of potential fake/adversarial ima
Externí odkaz:
https://doaj.org/article/d1e77e72c53f49be976580eec2ad8f1e
Publikováno v:
BMC Cancer, Vol 21, Iss 1, Pp 1-9 (2021)
Abstract Background The abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological im
Externí odkaz:
https://doaj.org/article/98040f8e13ba4178a895e104e1a1f4c7
Autor:
Degan Hao, Qiong Li, Qiu-Xia Feng, Liang Qi, Xi-Sheng Liu, Dooman Arefan, Yu-Dong Zhang, Shandong Wu
Publikováno v:
Frontiers in Oncology, Vol 11 (2022)
BackgroundGastric cancer is one of the leading causes of cancer death in the world. Improving gastric cancer survival prediction can enhance patient prognostication and treatment planning.MethodsIn this study, we performed gastric cancer survival pre
Externí odkaz:
https://doaj.org/article/04e835e8fff04596964665e7a97fa7b7
Autor:
Ziwei Lin, Wenhuan Feng, Yanjun Liu, Chiye Ma, Dooman Arefan, Donglei Zhou, Xiaoyun Cheng, Jiahui Yu, Long Gao, Lei Du, Hui You, Jiangfan Zhu, Dalong Zhu, Shandong Wu, Shen Qu
Publikováno v:
Frontiers in Endocrinology, Vol 12 (2021)
Background and objectiveClinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise cl
Externí odkaz:
https://doaj.org/article/2c511794d801412798daae70722c3280
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
ObjectivesTo evaluate the effectiveness of radiomic features on classifying histological subtypes of central lung cancer in contrast-enhanced CT (CECT) images.Materials and MethodsA total of 200 patients with radiologically defined central lung cance
Externí odkaz:
https://doaj.org/article/5d41ab65172c4c4283bf56e56203c2bc
Autor:
Aneta Kowalski, Dooman Arefan, Marie A Ganott, Kimberly Harnist, Amy E Kelly, Amy Lu, Bronwyn E Nair, Jules H Sumkin, Adrienne Vargo, Wendie A Berg, Margarita L Zuley
Publikováno v:
Journal of Breast Imaging. 5:148-158
ObjectiveEvaluate lesion visibility and radiologist confidence during contrast-enhanced mammography (CEM)-guided biopsy.MethodsWomen with BI-RADS ≥4A enhancing breast lesions were prospectively recruited for 9-g vacuum-assisted CEM-guided biopsy. B
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e9361709e5939ede12f449c3ac7a5d0b
https://doi.org/10.1093/jbi/wbac096
https://doi.org/10.1093/jbi/wbac096
Autor:
Matthew, Pease, Dooman, Arefan, Jason, Barber, Esther, Yuh, Ava, Puccio, Kerri, Hochberger, Enyinna, Nwachuku, Souvik, Roy, Stephanie, Casillo, Nancy, Temkin, David O, Okonkwo, Shandong, Wu, John K, Yue
Publikováno v:
Radiology. 304:385-394
Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep lear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44bd0661a47fb85feff97ce1f568d90c
https://doi.org/10.1148/radiol.212181
https://doi.org/10.1148/radiol.212181
Autor:
Saba Dadsetan, Marcio Albers, Allison Weinstock, Volker Musahl, Gene Kitamura, Dooman Arefan, Shandong Wu
Publikováno v:
Medical Imaging 2023: Computer-Aided Diagnosis.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d35be3dd0b91acda1891962785e39897
https://doi.org/10.1117/12.2655278
https://doi.org/10.1117/12.2655278