A citation-based method for searching scientific literature

Qinglin Wang, Ning Mao, Meijie Liu, Yinghong Shi, Heng Ma, Jianjun Dong, Xuexi Zhang, Shaofeng Duan, Bin Wang, Haizhu Xie. Clin Imaging 2021
Times Cited: 9







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



Times Cited
  Times     Co-cited
Similarity


Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes.
Doris Leithner, Blanca Bernard-Davila, Danny F Martinez, Joao V Horvat, Maxine S Jochelson, Maria Adele Marino, Daly Avendano, R Elena Ochoa-Albiztegui, Elizabeth J Sutton, Elizabeth A Morris,[...]. Mol Imaging Biol 2020
38
55

Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer.
Ming Fan, Hui Li, Shijian Wang, Bin Zheng, Juan Zhang, Lihua Li. PLoS One 2017
78
44

Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics.
Doris Leithner, Marius E Mayerhoefer, Danny F Martinez, Maxine S Jochelson, Elizabeth A Morris, Sunitha B Thakur, Katja Pinker. J Clin Med 2020
28
44

Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results.
Doris Leithner, Joao V Horvat, Maria Adele Marino, Blanca Bernard-Davila, Maxine S Jochelson, R Elena Ochoa-Albiztegui, Danny F Martinez, Elizabeth A Morris, Sunitha Thakur, Katja Pinker. Breast Cancer Res 2019
46
44

Radiomics: Images Are More than Pictures, They Are Data.
Robert J Gillies, Paul E Kinahan, Hedvig Hricak. Radiology 2016
33

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
33

Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation.
Jia Wu, Xiaoli Sun, Jeff Wang, Yi Cui, Fumi Kato, Hiroki Shirato, Debra M Ikeda, Ruijiang Li. J Magn Reson Imaging 2017
54
33

Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study.
Jeff Wang, Fumi Kato, Noriko Oyama-Manabe, Ruijiang Li, Yi Cui, Khin Khin Tha, Hiroko Yamashita, Kohsuke Kudo, Hiroki Shirato. PLoS One 2015
91
33


A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer.
Aydin Demircioglu, Johannes Grueneisen, Marc Ingenwerth, Oliver Hoffmann, Katja Pinker-Domenig, Elizabeth Morris, Johannes Haubold, Michael Forsting, Felix Nensa, Lale Umutlu. PLoS One 2020
20
33

Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging.
Qiyuan Hu, Heather M Whitney, Maryellen L Giger. J Med Imaging (Bellingham) 2020
14
22

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.
Alex Zwanenburg, Martin Vallières, Mahmoud A Abdalah, Hugo J W L Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Spyridon Bakas, Roelof J Beukinga, Ronald Boellaard,[...]. Radiology 2020
975
22


Fully automated detection of breast cancer in screening MRI using convolutional neural networks.
Mehmet Ufuk Dalmış, Suzan Vreemann, Thijs Kooi, Ritse M Mann, Nico Karssemeijer, Albert Gubern-Mérida. J Med Imaging (Bellingham) 2018
22
22

Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
Amirhessam Tahmassebi, Georg J Wengert, Thomas H Helbich, Zsuzsanna Bago-Horvath, Sousan Alaei, Rupert Bartsch, Peter Dubsky, Pascal Baltzer, Paola Clauser, Panagiotis Kapetas,[...]. Invest Radiol 2019
109
22


An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies.
Nina Pötsch, Matthias Dietzel, Panagiotis Kapetas, Paola Clauser, Katja Pinker, Stephan Ellmann, Michael Uder, Thomas Helbich, Pascal A T Baltzer. Eur Radiol 2021
9
22

Convolutional neural networks: an overview and application in radiology.
Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, Kaori Togashi. Insights Imaging 2018
491
22

Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.
Sebastian Bickelhaupt, Daniel Paech, Philipp Kickingereder, Franziska Steudle, Wolfgang Lederer, Heidi Daniel, Michael Götz, Nils Gählert, Diana Tichy, Manuel Wiesenfarth,[...]. J Magn Reson Imaging 2017
82
22

Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI.
Daniel Truhn, Simone Schrading, Christoph Haarburger, Hannah Schneider, Dorit Merhof, Christiane Kuhl. Radiology 2019
97
22

Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study.
Zhenyu Liu, Zhuolin Li, Jinrong Qu, Renzhi Zhang, Xuezhi Zhou, Longfei Li, Kai Sun, Zhenchao Tang, Hui Jiang, Hailiang Li,[...]. Clin Cancer Res 2019
167
22

Artificial Intelligence-Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI.
Mehmet U Dalmiş, Albert Gubern-Mérida, Suzan Vreemann, Peter Bult, Nico Karssemeijer, Ritse Mann, Jonas Teuwen. Invest Radiol 2019
57
22

Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.
Richard Ha, Christine Chin, Jenika Karcich, Michael Z Liu, Peter Chang, Simukayi Mutasa, Eduardo Pascual Van Sant, Ralph T Wynn, Eileen Connolly, Sachin Jambawalikar. J Digit Imaging 2019
42
22

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.
Wentian Guo, Hui Li, Yitan Zhu, Li Lan, Shengjie Yang, Karen Drukker, Elizabeth Morris, Elizabeth Burnside, Gary Whitman, Maryellen L Giger,[...]. J Med Imaging (Bellingham) 2015
95
22

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers.
Roberto Lo Gullo, Isaac Daimiel, Carolina Rossi Saccarelli, Almir Bitencourt, Peter Gibbs, Michael J Fox, Sunitha B Thakur, Danny F Martinez, Maxine S Jochelson, Elizabeth A Morris,[...]. Eur Radiol 2020
16
22

Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer.
Nathaniel Braman, Prateek Prasanna, Jon Whitney, Salendra Singh, Niha Beig, Maryam Etesami, David D B Bates, Katherine Gallagher, B Nicolas Bloch, Manasa Vulchi,[...]. JAMA Netw Open 2019
122
22

Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps.
Yu Zhang, Yifeng Zhu, Kai Zhang, Yajie Liu, Jingjing Cui, Juan Tao, Yingzi Wang, Shaowu Wang. Radiol Med 2020
42
22

A curated mammography data set for use in computer-aided detection and diagnosis research.
Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Kanae Kawai Miyake, Mia Gorovoy, Daniel L Rubin. Sci Data 2017
90
22

Preliminary Study on Molecular Subtypes of Breast Cancer Based on Magnetic Resonance Imaging Texture Analysis.
Xinru Sun, Bing He, Xin Luo, Yuhua Li, Jinfeng Cao, Jinlan Wang, Jun Dong, Xiaoyu Sun, Guangxia Zhang. J Comput Assist Tomogr 2018
17
22

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment.
Katja Pinker, Joanne Chin, Amy N Melsaether, Elizabeth A Morris, Linda Moy. Radiology 2018
120
22

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
Hui Li, Yitan Zhu, Elizabeth S Burnside, Karen Drukker, Katherine A Hoadley, Cheng Fan, Suzanne D Conzen, Gary J Whitman, Elizabeth J Sutton, Jose M Net,[...]. Radiology 2016
281
22

MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study.
Kirsi Holli-Helenius, Annukka Salminen, Irina Rinta-Kiikka, Ilkka Koskivuo, Nina Brück, Pia Boström, Riitta Parkkola. BMC Med Imaging 2017
59
22

AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer.
Anke Meyer-Base, Lia Morra, Amirhessam Tahmassebi, Marc Lobbes, Uwe Meyer-Base, Katja Pinker. J Magn Reson Imaging 2021
10
22

DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes.
Serena Monti, Marco Aiello, Mariarosaria Incoronato, Anna Maria Grimaldi, Michela Moscarino, Peppino Mirabelli, Umberto Ferbo, Carlo Cavaliere, Marco Salvatore. Contrast Media Mol Imaging 2018
28
22

Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.
Richard Ha, Simukayi Mutasa, Jenika Karcich, Nishant Gupta, Eduardo Pascual Van Sant, John Nemer, Mary Sun, Peter Chang, Michael Z Liu, Sachin Jambawalikar. J Digit Imaging 2019
35
22

Prediction of molecular subtypes of breast cancer using BI-RADS features based on a "white box" machine learning approach in a multi-modal imaging setting.
Mingxiang Wu, Xiaoling Zhong, Quanzhou Peng, Mei Xu, Shelei Huang, Jialin Yuan, Jie Ma, Tao Tan. Eur J Radiol 2019
16
22

Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer.
Tianwen Xie, Zhe Wang, Qiufeng Zhao, Qianming Bai, Xiaoyan Zhou, Yajia Gu, Weijun Peng, He Wang. Front Oncol 2019
37
22

The association between MRI findings and breast cancer subtypes: focused on the combination patterns on diffusion-weighted and T2-weighted images.
Sachiko Yuen, Shuichi Monzawa, Seiji Yanai, Hajime Matsumoto, Yoshihiro Yata, You Ichinose, Teruyuki Deai, Takashi Hashimoto, Takashi Tashiro, Kazuhiko Yamagami. Breast Cancer 2020
12
22


Triple-negative invasive breast carcinoma: the association between the sonographic appearances with clinicopathological feature.
Jia-Wei Li, Kai Zhang, Zhao-Ting Shi, Xun Zhang, Juan Xie, Jun-Ying Liu, Cai Chang. Sci Rep 2018
18
22

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging.
Maria Adele Marino, Doris Leithner, Janice Sung, Daly Avendano, Elizabeth A Morris, Katja Pinker, Maxine S Jochelson. Diagnostics (Basel) 2020
18
22


Deep learning for identifying radiogenomic associations in breast cancer.
Zhe Zhu, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R Harowicz, Maciej A Mazurowski. Comput Biol Med 2019
52
22


Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI.
Yuhao Dong, Qianjin Feng, Wei Yang, Zixiao Lu, Chunyan Deng, Lu Zhang, Zhouyang Lian, Jing Liu, Xiaoning Luo, Shufang Pei,[...]. Eur Radiol 2018
137
22

Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review.
Seung Hak Lee, Hyunjin Park, Eun Sook Ko. Korean J Radiol 2020
35
22

Breast MRI: State of the Art.
Ritse M Mann, Nariya Cho, Linda Moy. Radiology 2019
206
22

Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.
Faranak Aghaei, Maxine Tan, Alan B Hollingsworth, Bin Zheng. J Magn Reson Imaging 2016
36
22


Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models.
Yasemin Kayadibi, Burak Kocak, Nese Ucar, Yesim Namdar Akan, Pelin Akbas, Sibel Bektas. Acad Radiol 2022
4
50


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.