Prostate Cancer Detection using Deep Convolutional Neural Networks von Yoo, Sunghwan, Gujrathi, Isha, Haider, Masoom A., Khalvati, Farzad

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Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer von Zhang, Yucheng, Oikonomou, Anastasia, Wong, Alexander, Haider, Masoom A., Khalvati, Farzad

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It's Time for a Standardized MRI Assessment Scheme for Prostate Cancer Recurrence von Haider, Masoom A

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Deep learning-based artificial intelligence applications in prostate MRI: brief summary von Turkbey, Baris, Haider, Masoom A

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Artificial Intelligence for Automated Cancer Detection on Prostate MRI: Opportunities and Ongoing Challenges, From the AJR Special Series on AI Applications von Turkbey, Baris, Haider, Masoom A

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MRI-guided Minimally Invasive Focal Therapies for Prostate Cancer von Alabousi, Mostafa, Ghai, Sangeet, Haider, Masoom A

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The Emerging Role of MRI in Prostate Cancer Active Surveillance and Ongoing Challenges von Barrett, Tristan, Haider, Masoom A

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Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2 von Turkbey, Baris, Rosenkrantz, Andrew B., Haider, Masoom A., Padhani, Anwar R., Villeirs, Geert, Macura, Katarzyna J., Tempany, Clare M., Choyke, Peter L., Cornud, Francois, Margolis, Daniel J., Thoeny, Harriet C., Verma, Sadhna, Barentsz, Jelle, Weinreb, Jeffrey C.

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PI-RADS Steering Committee: The PI-RADS Multiparametric MRI and MRI-directed Biopsy Pathway von Padhani, Anwar R, Barentsz, Jelle, Villeirs, Geert, Rosenkrantz, Andrew B, Margolis, Daniel J, Turkbey, Baris, Thoeny, Harriet C, Cornud, François, Haider, Masoom A, Macura, Katarzyna J, Tempany, Clare M, Verma, Sadhna, Weinreb, Jeffrey C

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Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century von El Naqa, Issam, Haider, Masoom A, Giger, Maryellen L, Ten Haken, Randall K

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Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models von Khalvati, Farzad, Wong, Alexander, Haider, Masoom A

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Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform op... von Liu, Xiaoyang, Khalvati, Farzad, Namdar, Khashayar, Fischer, Sandra, Lewis, Sara, Taouli, Bachir, Haider, Masoom A., Jhaveri, Kartik S.

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Beyond the AJR : "Population-Based Prostate Cancer Screening With Magnetic Resonance Imaging or Ultrasonography: The IP1-PROSTAGRAM Study" von Deniffel, Dominik, Haider, Masoom A

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Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images von Zhang, Yucheng, Lobo-Mueller, Edrise M., Karanicolas, Paul, Gallinger, Steven, Haider, Masoom A., Khalvati, Farzad

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Overview of Dynamic Contrast-Enhanced MRI in Prostate Cancer Diagnosis and Management von VERMA, Sadhna, TURKBEY, Baris, MURADYAN, Naira, RAJESH, Arumugam, CORNUD, Francois, HAIDER, Masoom A, CHOYKE, Peter L, HARISINGHANI, Mukesh

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Prognostic Value of CT Radiomic Features in Resectable Pancreatic Ductal Adenocarcinoma von Khalvati, Farzad, Zhang, Yucheng, Baig, Sameer, Lobo-Mueller, Edrise M., Karanicolas, Paul, Gallinger, Steven, Haider, Masoom A.

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CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis von Eilaghi, Armin, Baig, Sameer, Zhang, Yucheng, Zhang, Junjie, Karanicolas, Paul, Gallinger, Steven, Khalvati, Farzad, Haider, Masoom A

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Synthetic correlated diffusion imaging hyperintensity delineates clinically significant prostate cancer von Wong, Alexander, Gunraj, Hayden, Sivan, Vignesh, Haider, Masoom A.

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Prostate Magnetic Resonance Imaging and Magnetic Resonance Imaging Targeted Biopsy in Patients with a Prior Negative Biopsy: A Consensus Statement by AUA and SAR von Rosenkrantz, Andrew B, Verma, Sadhna, Choyke, Peter, Eberhardt, Steven C, Eggener, Scott E, Gaitonde, Krishnanath, Haider, Masoom A, Margolis, Daniel J, Marks, Leonard S, Pinto, Peter, Sonn, Geoffrey A, Taneja, Samir S

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Synopsis of the PI-RADS v2 Guidelines for Multiparametric Prostate Magnetic Resonance Imaging and Recommendations for Use von Barentsz, Jelle O, Weinreb, Jeffrey C, Verma, Sadhna, Thoeny, Harriet C, Tempany, Clare M, Shtern, Faina, Padhani, Anwar R, Margolis, Daniel, Macura, Katarzyna J, Haider, Masoom A, Cornud, Francois, Choyke, Peter L

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