Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Samuel H. Hawkins"'
Autor:
Asim Mazin, Samuel H. Hawkins, Olya Stringfield, Jasreman Dhillon, Brandon J. Manley, Daniel K. Jeong, Natarajan Raghunand
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biop
Externí odkaz:
https://doaj.org/article/2fe9e616bc764151986167520d42be11
Autor:
Samuel H. Hawkins, John N. Korecki, Yoganand Balagurunathan, Yuhua Gu, Virendra Kumar, Satrajit Basu, Lawrence O. Hall, Dmitry B. Goldgof, Robert A. Gatenby, Robert J. Gillies
Publikováno v:
IEEE Access, Vol 2, Pp 1418-1426 (2014)
Nonsmall cell lung cancer is a prevalent disease. It is diagnosed and treated with the help of computed tomography (CT) scans. In this paper, we apply radiomics to select 3-D features from CT images of the lung toward providing prognostic information
Externí odkaz:
https://doaj.org/article/99f387dd1c2f4bbb929129a89909dc2f
Autor:
Brandon J. Manley, Jasreman Dhillon, Samuel H. Hawkins, Natarajan Raghunand, Olya Stringfield, Daniel K. Jeong, Asim Mazin
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Scientific Reports
Scientific Reports
Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for
Autor:
Ying Liu, Samuel H. Hawkins, Zhaoxiang Ye, Olya Stringfield, Fangyuan Qu, Matthew B. Schabath, Robert J. Gillies, Yoganand Balagurunathan, Qian Li, Alberto Garcia, Jongphil Kim, Shichang Liu
Publikováno v:
Medical Physics. 45:2518-2526
Purpose The purpose of this study was to investigate the potential of computed tomography (CT) based radiomic features of primary tumors to predict pathological nodal involvement in clinically node-negative (N0) peripheral lung adenocarcinomas. Metho
Autor:
Alberto Garcia, Hua Wang, Henry Krewer, Lawrence O. Hall, Robert A. Gatenby, Qian Li, Ying Liu, Yoganand Balagurunathan, Matthew B. Schabath, Dmitry Cherezov, Dmitry B. Goldgof, Olya Stringfield, Samuel H. Hawkins, Robert J. Gillies
Publikováno v:
Journal of Thoracic Oncology. 11:2120-2128
Objectives The aim of this study was to determine whether quantitative analyses ("radiomics") of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer. Methods Public data from the National L
Autor:
Yoganand Balagurunathan, Lawrence O. Hall, Matthew B. Schabath, Dmitry B. Goldgof, Robert J. Gillies, Dmitry Cherezov, Samuel H. Hawkins
Publikováno v:
SMC
Computed tomography (CT) is widely used during diagnosis and treatment of Non-Small Cell Lung Cancer (NSCLC). Current computer-aided diagnosis (CAD) models, designed for the classification of malignant and benign nodules, use image features, selected
Autor:
Dmitry B. Goldgof, Ying Liu, Robert J. Gillies, Dmitry Cherezov, Qian Li, Lawrence O. Hall, Matthew B. Schabath, Yoganand Balagurunathan, Samuel H. Hawkins
Publikováno v:
Cancer Medicine
Background Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow‐up screens and performed size‐specific analyses to predict lung cancer
Autor:
Samuel H. Hawkins, Rahul Paul, Lawrence O. Hall, Dmitry B. Goldgof, Robert J. Gillies, Matthew B. Schabath
Publikováno v:
Journal of medical imaging (Bellingham, Wash.). 5(1)
Lung cancer has a high incidence and mortality rate. Early detection and diagnosis of lung cancers is best achieved with low-dose computed tomography (CT). Classical radiomics features extracted from lung CT images have been shown as able to predict
Autor:
Young-Chul Kim, Kujtim Latifi, B.A. Altazi, Puja Venkat, Samuel H. Hawkins, Eduardo G. Moros, D.C. Fernandez, Geoffrey Zhang, Matthew C. Biagioli, Syeda Mahrukh Hussnain Naqvi, Dylan Hunt
Publikováno v:
Phys Med
Quantitative image features, also known as radiomic features, have shown potential for predicting treatment outcomes in several body sites. We quantitatively analyzed (18)Fluorine–fluorodeoxyglucose ((18)F-FDG) Positron Emission Tomography (PET) up
Autor:
Satrajit Basu, Robert A. Gatenby, Samuel H. Hawkins, John N. Korecki, Yoganand Balagurunathan, Yuhua Gu, Lawrence O. Hall, Robert J. Gillies, Virendra Kumar, Dmitry B. Goldgof
Publikováno v:
IEEE Access, Vol 2, Pp 1418-1426 (2014)
Nonsmall cell lung cancer is a prevalent disease. It is diagnosed and treated with the help of computed tomography (CT) scans. In this paper, we apply radiomics to select 3-D features from CT images of the lung toward providing prognostic information