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Computational approaches to assist in the evaluation of cancer treatment response
Imaging in Medicine
◽
10.2217/iim.11.8
◽
2011
◽
Vol 3
(2)
◽
pp. 233-246
◽
Cited By ~ 2
Author(s):
Mia A Levy
◽
Daniel L Rubin
Keyword(s):
Cancer Treatment
◽
Treatment Response
◽
Computational Approaches
Download Full-text
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10.30621/jbachs.2019.708
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Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response
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10.1038/s41598-020-63810-1
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Vol 10
(1)
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A Semi-automated Toolkit for Analysis of Liver Cancer Treatment Response Using Perfusion CT
Lecture Notes in Computer Science - Abdominal Imaging. Computational and Clinical Applications
◽
10.1007/978-3-319-13692-9_3
◽
2014
◽
pp. 23-32
Author(s):
Elina Naydenova
◽
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◽
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◽
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Bladder cancer treatment response assessment using deep learning in CT with transfer learning
10.1117/12.2254977
◽
2017
◽
Author(s):
Kenny H. Cha
◽
Lubomir M. Hadjiiski
◽
Heang-Ping Chan
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Cancer Treatment
◽
Treatment Response
◽
Transfer Learning
◽
Response Assessment
◽
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Deep learning based bladder cancer treatment response assessment
Medical Imaging 2019: Computer-Aided Diagnosis
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10.1117/12.2512240
◽
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◽
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Eric Wu
◽
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Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging
Clinical Cancer Research
◽
10.1158/1078-0432.ccr-18-2495
◽
2019
◽
Vol 25
(11)
◽
pp. 3266-3275
◽
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◽
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Deep Learning
◽
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Neoadjuvant breast cancer treatment response; tumor size evaluation through different conventional imaging modalities in the NeoDense study
Acta Oncologica
◽
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◽
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◽
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◽
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◽
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◽
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◽
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◽
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◽
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◽
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◽
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◽
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ARID1A influences HDAC1/BRD4 activity, intrinsic proliferative capacity and breast cancer treatment response
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◽
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Modeling of treatment response to erythropoiesis-stimulating agents as a function of center- and patient-related variables: results from the Anemia Cancer Treatment (ACT) study
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◽
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◽
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◽
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Rapid and Quantitative Assessment of Cancer Treatment Response Using In Vivo Bioluminescence Imaging
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◽
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◽
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◽
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◽
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◽
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