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Cited by in CrossRef
For: Hayano K, Fujishiro T, Sahani DV, Satoh A, Aoyagi T, Ohira G, Tochigi T, Matsubara H, Shuto K. Computed tomography perfusion imaging as a potential imaging biomarker of colorectal cancer. World J Gastroenterol 2014; 20(46): 17345-17351 [PMID: 25516645 DOI: 10.3748/wjg.v20.i46.17345]
URL: https://www.wjgnet.com/1007-9327/full/v20/i46/17345.htm
Number Citing Articles
1
Tianye Niu, Pengfei Yang, Xiaonan Sun, Tingyu Mao, Lei Xu, Ning Yue, Yu Kuang, Liming Shi, Ke Nie. Variations of quantitative perfusion measurement on dynamic contrast enhanced CT for colorectal cancer: implication of standardized image protocolPhysics in Medicine & Biology 2018; 63(16) doi: 10.1088/1361-6560/aacb99
2
Stefano Badia, Simona Picchia, Davide Bellini, Riccardo Ferrari, Damiano Caruso, Pasquale Paolantonio, Iacopo Carbone, Andrea Laghi, Marco Rengo. The Role of Contrast-Enhanced Imaging for Colorectal Cancer ManagementCurrent Colorectal Cancer Reports 2019; 15(6) doi: 10.1007/s11888-019-00443-1
3
Yusuf Tukur, Abubakar M. Bello, Usman M. Aliyu, Oladapo B. Campbell, Aisha Mustapha, Abubakar Ibrahim Diggi, Yusuf Dantani, Shehu Salihu Umar. RECURRENT RECTAL ADENOCARCINOMA WITH KRAS MUTATION POST-MFOLFOX6/BEVACIZUMAB REMISSION: A CASE REPORTNigerian Journal of Oncology 2026; 2(1) doi: 10.67579/njo.2026.1e4nz1ph
4
Roberto García-Figueiras, Sandra Baleato-González, Anwar R. Padhani, Antonio Luna-Alcalá, Ana Marhuenda, Joan C. Vilanova, Iria Osorio-Vázquez, Anxo Martínez-de-Alegría, Antonio Gómez-Caamaño. Advanced Imaging Techniques in Evaluation of Colorectal CancerRadioGraphics 2018; 38(3) doi: 10.1148/rg.2018170044
5
Kiyohiko Shuto, Mikito Mori, Chihiro Kosugi, Kazuo Narushima, Satoko Nakabayashi, Takeshi Fujisiro, Asami Sato, Koichi Hayano, Hiroaki Shimizu, Keiji Koda. Hepatic blood flow by perfusion computed tomography as an imaging biomarker for patients with gastric cancerOncology Letters 2019;  doi: 10.3892/ol.2019.9969
6
Herman P. Marquez, Gilbert Puippe, Rishi Philip Mathew, Hatem Alkadhi, Thomas Pfammatter, Michael A. Fischer. CT Perfusion for Early Response Evaluation of Radiofrequency Ablation of Focal Liver Lesions: First ExperienceCardioVascular and Interventional Radiology 2017; 40(1) doi: 10.1007/s00270-016-1444-9
7
Ryoya Mizumachi, Koichi Hayano, Atsushi Hirata, Gaku Ohira, Shunsuke Imanishi, Toru Tochigi, Tetsuro Isozaki, Yoshihiro Kurata, Yuko Ikeda, Ryoma Urahama, Takeshi Toyozumi, Kentaro Murakami, Masaya Uesato, Hisahiro Matsubara. Development of imaging biomarker for esophageal cancer using intravoxel incoherent motion MRIEsophagus 2021; 18(4) doi: 10.1007/s10388-021-00851-z
8
Vicky Goh, Susan Mallett, Manuel Rodriguez-Justo, Victor Boulter, Rob Glynne-Jones, Saif Khan, Sarah Lessels, Dominic Patel, Davide Prezzi, Stuart Taylor, Steve Halligan. Evaluation of prognostic models to improve prediction of metastasis in patients following potentially curative treatment for primary colorectal cancer: the PROSPECT trialHealth Technology Assessment 2025;  doi: 10.3310/BTMT7049
9
Alessandro Bevilacqua, Domenico Barone, Silvia Malavasi, Giampaolo Gavelli. Automatic detection of misleading blood flow values in CT perfusion studies of lung cancerBiomedical Signal Processing and Control 2016; 26 doi: 10.1016/j.bspc.2016.01.004
10
Tom Perik, Natália Alves, John J. Hermans, Henkjan Huisman. Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal AdenocarcinomaCancers 2024; 16(3) doi: 10.3390/cancers16030577
11
Pier Paolo Mainenti, Arnaldo Stanzione, Salvatore Guarino, Valeria Romeo, Lorenzo Ugga, Federica Romano, Giovanni Storto, Simone Maurea, Arturo Brunetti. Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imagingWorld Journal of Gastroenterology 2019; 25(35): 5233-5256 doi: 10.3748/wjg.v25.i35.5233
12
Roberto García-Figueiras, Sandra Baleato-González, Anwar R. Padhani, Ana Marhuenda, Antonio Luna, Lidia Alcalá, Ana Carballo-Castro, Ana Álvarez-Castro. Advanced imaging of colorectal cancer: From anatomy to molecular imagingInsights into Imaging 2016; 7(3) doi: 10.1007/s13244-016-0465-x