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List of co-cited articles
430 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Radiomics: Images Are More than Pictures, They Are Data.
Robert J Gillies, Paul E Kinahan, Hedvig Hricak. Radiology 2016
45

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.
Ceyda Turan Bektas, Burak Kocak, Aytul Hande Yardimci, Mehmet Hamza Turkcanoglu, Ugur Yucetas, Sevim Baykal Koca, Cagri Erdim, Ozgur Kilickesmez. Eur Radiol 2019
76
29


Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations.
Christoph A Karlo, Pier Luigi Di Paolo, Joshua Chaim, A Ari Hakimi, Irina Ostrovnaya, Paul Russo, Hedvig Hricak, Robert Motzer, James J Hsieh, Oguz Akin. Radiology 2014
168
29

Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.
Burak Kocak, Aytul Hande Yardimci, Ceyda Turan Bektas, Mehmet Hamza Turkcanoglu, Cagri Erdim, Ugur Yucetas, Sevim Baykal Koca, Ozgur Kilickesmez. Eur J Radiol 2018
59
27

The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.
Kenneth Clark, Bruce Vendt, Kirk Smith, John Freymann, Justin Kirby, Paul Koppel, Stephen Moore, Stanley Phillips, David Maffitt, Michael Pringle,[...]. J Digit Imaging 2013
23

Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features.
Payel Ghosh, Pheroze Tamboli, Raghu Vikram, Arvind Rao. J Med Imaging (Bellingham) 2015
26
50

CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges.
Meghan G Lubner, Andrew D Smith, Kumar Sandrasegaran, Dushyant V Sahani, Perry J Pickhardt. Radiographics 2017
422
21

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?
Taryn Hodgdon, Matthew D F McInnes, Nicola Schieda, Trevor A Flood, Leslie Lamb, Rebecca E Thornhill. Radiology 2015
186
20

Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective.
Zhi-Cheng Li, Guangtao Zhai, Jinheng Zhang, Zhongqiu Wang, Guiqin Liu, Guang-Yu Wu, Dong Liang, Hairong Zheng. Eur Radiol 2019
47
23

Texture analysis as a radiomic marker for differentiating renal tumors.
HeiShun Yu, Jonathan Scalera, Maria Khalid, Anne-Sophie Touret, Nicolas Bloch, Baojun Li, Muhammad M Qureshi, Jorge A Soto, Stephan W Anderson. Abdom Radiol (NY) 2017
85
20

Radiogenomics: bridging imaging and genomics.
Zuhir Bodalal, Stefano Trebeschi, Thi Dan Linh Nguyen-Kim, Winnie Schats, Regina Beets-Tan. Abdom Radiol (NY) 2019
99
20

Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas.
Burak Kocak, Emine Sebnem Durmaz, Ozlem Korkmaz Kaya, Ozgur Kilickesmez. Acta Radiol 2020
15
73

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters.
Roberto Berenguer, María Del Rosario Pastor-Juan, Jesús Canales-Vázquez, Miguel Castro-García, María Victoria Villas, Francisco Mansilla Legorburo, Sebastià Sabater. Radiology 2018
295
18


CT-based radiomic model predicts high grade of clear cell renal cell carcinoma.
Jiule Ding, Zhaoyu Xing, Zhenxing Jiang, Jie Chen, Liang Pan, Jianguo Qiu, Wei Xing. Eur J Radiol 2018
80
18

Radiomics: extracting more information from medical images using advanced feature analysis.
Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar, Sara Carvalho, Ruud G P M van Stiphout, Patrick Granton, Catharina M L Zegers, Robert Gillies, Ronald Boellard, André Dekker,[...]. Eur J Cancer 2012
18

Radiomics: the bridge between medical imaging and personalized medicine.
Philippe Lambin, Ralph T H Leijenaar, Timo M Deist, Jurgen Peerlings, Evelyn E C de Jong, Janita van Timmeren, Sebastian Sanduleanu, Ruben T H M Larue, Aniek J G Even, Arthur Jochems,[...]. Nat Rev Clin Oncol 2017
18

Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies.
Burak Kocak, Emine Sebnem Durmaz, Cagri Erdim, Ece Ates, Ozlem Korkmaz Kaya, Ozgur Kilickesmez. AJR Am J Roentgenol 2020
29
34

Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group.
Atul B Shinagare, Raghu Vikram, Carl Jaffe, Oguz Akin, Justin Kirby, Erich Huang, John Freymann, Nisha I Sainani, Cheryl A Sadow, Tharakeswara K Bathala,[...]. Abdom Imaging 2015
62
18

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
Heidi Coy, Kevin Hsieh, Willie Wu, Mahesh B Nagarajan, Jonathan R Young, Michael L Douek, Matthew S Brown, Fabien Scalzo, Steven S Raman. Abdom Radiol (NY) 2019
38
23

Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning-Based Study for Predicting Histopathologic Nuclear Grade.
Burak Kocak, Emine Sebnem Durmaz, Ece Ates, Ozlem Korkmaz Kaya, Ozgur Kilickesmez. AJR Am J Roentgenol 2019
43
20


Diagnostic Accuracy of Unenhanced CT Analysis to Differentiate Low-Grade From High-Grade Chromophobe Renal Cell Carcinoma.
Nicola Schieda, Robert S Lim, Satheesh Krishna, Matthew D F McInnes, Trevor A Flood, Rebecca E Thornhill. AJR Am J Roentgenol 2018
29
27


Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images.
Lifen Yan, Zaiyi Liu, Guangyi Wang, Yanqi Huang, Yubao Liu, Yuanxin Yu, Changhong Liang. Acad Radiol 2015
70
14

Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings.
Zhan Feng, Lixia Zhang, Zhong Qi, Qijun Shen, Zhengyu Hu, Feng Chen. Front Oncol 2020
13
61

Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model.
Xi Chen, Zhiguo Zhou, Raquibul Hannan, Kimberly Thomas, Ivan Pedrosa, Payal Kapur, James Brugarolas, Xuanqin Mou, Jing Wang. Phys Med Biol 2018
21
38

Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma?
G-M-Y Zhang, B Shi, H-D Xue, B Ganeshan, H Sun, Z-Y Jin. Clin Radiol 2019
26
26



A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors.
Leilei Zhou, Zuoheng Zhang, Yu-Chen Chen, Zhen-Yu Zhao, Xin-Dao Yin, Hong-Bing Jiang. Transl Oncol 2019
49
14

Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade.
Jun Shu, Yongqiang Tang, Jingjing Cui, Ruwu Yang, Xiaoli Meng, Zhengting Cai, Jingsong Zhang, Wanni Xu, Didi Wen, Hong Yin. Eur J Radiol 2018
63
12

Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images.
Xiaoqing Sun, Lin Liu, Kai Xu, Wenhui Li, Ziqi Huo, Heng Liu, Tongxu Shen, Feng Pan, Yuqing Jiang, Mengchao Zhang. Medicine (Baltimore) 2019
23
30


Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features.
En-Ming Cui, Fan Lin, Qing Li, Rong-Gang Li, Xiang-Meng Chen, Zhuang-Sheng Liu, Wan-Sheng Long. Acta Radiol 2019
23
30

Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
Maciej A Mazurowski, Jing Zhang, Lars J Grimm, Sora C Yoon, James I Silber. Radiology 2014
144
12

Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.
Jun Shu, Didi Wen, Yibin Xi, Yuwei Xia, Zhengting Cai, Wanni Xu, Xiaoli Meng, Bao Liu, Hong Yin. Eur J Radiol 2019
36
19


Computational Radiomics System to Decode the Radiographic Phenotype.
Joost J M van Griethuysen, Andriy Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, Vivek Narayan, Regina G H Beets-Tan, Jean-Christophe Fillion-Robin, Steve Pieper, Hugo J W L Aerts. Cancer Res 2017
10

Systematic Review and Meta-analysis of Diagnostic Accuracy of Percutaneous Renal Tumour Biopsy.
Lorenzo Marconi, Saeed Dabestani, Thomas B Lam, Fabian Hofmann, Fiona Stewart, John Norrie, Axel Bex, Karim Bensalah, Steven E Canfield, Milan Hora,[...]. Eur Urol 2016
290
10


CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology.
Siva P Raman, Yifei Chen, James L Schroeder, Peng Huang, Elliot K Fishman. Acad Radiol 2014
108
10

Texture Analysis of Imaging: What Radiologists Need to Know.
Bino A Varghese, Steven Y Cen, Darryl H Hwang, Vinay A Duddalwar. AJR Am J Roentgenol 2019
99
10

Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis.
Nicola Schieda, Rebecca E Thornhill, Maali Al-Subhi, Matthew D F McInnes, Wael M Shabana, Christian B van der Pol, Trevor A Flood. AJR Am J Roentgenol 2015
77
10

Renal cell carcinoma: predicting RUNX3 methylation level and its consequences on survival with CT features.
Dongzhi Cen, Li Xu, Siwei Zhang, Zhiguang Chen, Yan Huang, Ziqi Li, Bo Liang. Eur Radiol 2019
9
66

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
Hugo J W L Aerts, Emmanuel Rios Velazquez, Ralph T H Leijenaar, Chintan Parmar, Patrick Grossmann, Sara Carvalho, Johan Bussink, René Monshouwer, Benjamin Haibe-Kains, Derek Rietveld,[...]. Nat Commun 2014
10

Background, current role, and potential applications of radiogenomics.
Katja Pinker, Fuki Shitano, Evis Sala, Richard K Do, Robert J Young, Andreas G Wibmer, Hedvig Hricak, Elizabeth J Sutton, Elizabeth A Morris. J Magn Reson Imaging 2018
86
10


Radiogenomics in renal cell carcinoma.
Francesco Alessandrino, Atul B Shinagare, Dominick Bossé, Toni K Choueiri, Katherine M Krajewski. Abdom Radiol (NY) 2019
21
28


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.