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For: Leithner D, Mayerhoefer ME, Martinez DF, Jochelson MS, Morris EA, Thakur SB, Pinker K. Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics. J Clin Med 2020;9:E1853. [PMID: 32545851 DOI: 10.3390/jcm9061853] [Cited by in Crossref: 25] [Cited by in F6Publishing: 28] [Article Influence: 8.3] [Reference Citation Analysis]
Number Citing Articles
1 Romeo V, Pinker K, Helbich TH. Breast imaging. Clinical PET/MRI 2023. [DOI: 10.1016/b978-0-323-88537-9.00008-8] [Reference Citation Analysis]
2 Pinker K, Gullo RL, Eskreis-winkler S, Bitencourt A, Gibbs P, Thakur SB. Artificial Intelligence—Enhanced Breast MRI and DWI: Current Status and Future Applications. Diffusion MRI of the Breast 2023. [DOI: 10.1016/b978-0-323-79702-3.00010-1] [Reference Citation Analysis]
3 Guo Y, Wu J, Wang Y, Jin Y. Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma. Diagnostics (Basel) 2022;12. [PMID: 36553137 DOI: 10.3390/diagnostics12123130] [Reference Citation Analysis]
4 Chen D, Liu X, Hu C, Hao R, Wang O, Xiao Y. Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis. Future Oncology 2022. [DOI: 10.2217/fon-2022-0333] [Reference Citation Analysis]
5 Zhang Y, Li G, Bian W, Bai Y, He S, Liu Y, Liu H, Liu J. Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis. Ann Transl Med 2022;10:1394. [PMID: 36660694 DOI: 10.21037/atm-22-5986] [Reference Citation Analysis]
6 Wu J, Ge L, Jin Y, Wang Y, Hu L, Xu D, Wang Z. Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma. Front Oncol 2022;12. [DOI: 10.3389/fonc.2022.993466] [Reference Citation Analysis]
7 Zhou C, Xie H, Zhu F, Yan W, Yu R, Wang Y. Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters. Clin Exp Med 2022. [DOI: 10.1007/s10238-022-00944-8] [Reference Citation Analysis]
8 Zhong S, Wang F, Wang Z, Zhou M, Li C, Yin J. Multiregional Radiomic Signatures Based on Functional Parametric Maps from DCE-MRI for Preoperative Identification of Estrogen Receptor and Progesterone Receptor Status in Breast Cancer. Diagnostics 2022;12:2558. [DOI: 10.3390/diagnostics12102558] [Reference Citation Analysis]
9 Zhou C, Xie H, Zhu F, Yan W, Yu R, Wang Y. Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters.. [DOI: 10.21203/rs.3.rs-2040401/v1] [Reference Citation Analysis]
10 Romeo V, Kapetas P, Clauser P, Baltzer PAT, Rasul S, Gibbs P, Hacker M, Woitek R, Pinker K, Helbich TH. A Simultaneous Multiparametric 18F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer. Cancers 2022;14:3944. [DOI: 10.3390/cancers14163944] [Reference Citation Analysis]
11 Zhang S, Wang X, Yang Z, Zhu Y, Zhao N, Li Y, He J, Sun H, Xie Z. Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study. Front Oncol 2022;12:905551. [DOI: 10.3389/fonc.2022.905551] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Xu A, Chu X, Zhang S, Zheng J, Shi D, Lv S, Li F, Weng X. Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study. Front Oncol 2022;12:799232. [DOI: 10.3389/fonc.2022.799232] [Reference Citation Analysis]
13 Sha Y, Chen J. MRI-based radiomics for the diagnosis of triple-negative breast cancer: a meta-analysis. Clinical Radiology 2022. [DOI: 10.1016/j.crad.2022.04.015] [Reference Citation Analysis]
14 Ljubic B, Pavlovski M, Gillespie A, Rubin D, Collier G, Obradovic Z. Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions.. [DOI: 10.1101/2022.04.22.22274183] [Reference Citation Analysis]
15 Umutlu L, Kirchner J, Bruckmann NM, Morawitz J, Antoch G, Ting S, Bittner AK, Hoffmann O, Häberle L, Ruckhäberle E, Catalano OA, Chodyla M, Grueneisen J, Quick HH, Fendler WP, Rischpler C, Herrmann K, Gibbs P, Pinker K. Multiparametric 18F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2022;14:1727. [PMID: 35406499 DOI: 10.3390/cancers14071727] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
16 Zhang Y, Liu F, Zhang H, Ma H, Sun J, Zhang R, Song L, Shi H. Diagnostic Value of Radiomics Analysis in Contrast-Enhanced Spectral Mammography for Identifying Triple-Negative Breast Cancer. Front Oncol 2021;11:773196. [PMID: 35004294 DOI: 10.3389/fonc.2021.773196] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Cè M, Caloro E, Pellegrino ME, Basile M, Sorce A, Fazzini D, Oliva G, Cellina M. Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review. Explor Target Antitumor Ther 2022;3:795-816. [PMID: 36654817 DOI: 10.37349/etat.2022.00113] [Reference Citation Analysis]
18 Marino MA, Avendano D, Helbich T, Pinker K. Breast MRI: Multiparametric and Advanced Techniques. Breast Imaging 2022. [DOI: 10.1007/978-3-030-94918-1_11] [Reference Citation Analysis]
19 Zhou BY, Wang LF, Yin HH, Wu TF, Ren TT, Peng C, Li DX, Shi H, Sun LP, Zhao CK, Xu HX. Decoding the molecular subtypes of breast cancer seen on multimodal ultrasound images using an assembled convolutional neural network model: A prospective and multicentre study. EBioMedicine 2021;74:103684. [PMID: 34773890 DOI: 10.1016/j.ebiom.2021.103684] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
20 Davey MG, Davey MS, Boland MR, Ryan ÉJ, Lowery AJ, Kerin MJ. Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis. Eur J Radiol 2021;144:109996. [PMID: 34624649 DOI: 10.1016/j.ejrad.2021.109996] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
21 Yang Z, Chen X, Zhang T, Cheng F, Liao Y, Chen X, Dai Z, Fan W. Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes. Front Oncol 2021;11:628824. [PMID: 34604024 DOI: 10.3389/fonc.2021.628824] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
22 Huang Y, Wei L, Hu Y, Shao N, Lin Y, He S, Shi H, Zhang X, Lin Y. Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer. Front Oncol 2021;11:706733. [PMID: 34490107 DOI: 10.3389/fonc.2021.706733] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
23 Ma M, Gan L, Jiang Y, Qin N, Li C, Zhang Y, Wang X. Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer. Comput Math Methods Med 2021;2021:2140465. [PMID: 34422088 DOI: 10.1155/2021/2140465] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
24 Bitencourt A, Daimiel Naranjo I, Lo Gullo R, Rossi Saccarelli C, Pinker K. AI-enhanced breast imaging: Where are we and where are we heading? Eur J Radiol 2021;142:109882. [PMID: 34392105 DOI: 10.1016/j.ejrad.2021.109882] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
25 Meng W, Sun Y, Qian H, Chen X, Yu Q, Abiyasi N, Yan S, Peng H, Zhang H, Zhang X. Computer-Aided Diagnosis Evaluation of the Correlation Between Magnetic Resonance Imaging With Molecular Subtypes in Breast Cancer. Front Oncol 2021;11:693339. [PMID: 34249745 DOI: 10.3389/fonc.2021.693339] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
26 Umutlu L, Kirchner J, Bruckmann NM, Morawitz J, Antoch G, Ingenwerth M, Bittner AK, Hoffmann O, Haubold J, Grueneisen J, Quick HH, Rischpler C, Herrmann K, Gibbs P, Pinker-Domenig K. Multiparametric Integrated 18F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding. Cancers (Basel) 2021;13:2928. [PMID: 34208197 DOI: 10.3390/cancers13122928] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 7.0] [Reference Citation Analysis]
27 Lenga L, Bernatz S, Martin SS, Booz C, Solbach C, Mulert-Ernst R, Vogl TJ, Leithner D. Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status. Cancers (Basel) 2021;13:2431. [PMID: 34069795 DOI: 10.3390/cancers13102431] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
28 Mori M, Fujioka T, Katsuta L, Yashima Y, Nomura K, Yamaga E, Hosoya T, Oda G, Nakagawa T, Kubota K, Tateishi U. Clinical usefulness of the fast protocol of breast diffusion-weighted imaging using 3T magnetic resonance imaging with a 16-channel breast coil. Clin Imaging 2021;78:217-22. [PMID: 34051405 DOI: 10.1016/j.clinimag.2021.04.022] [Reference Citation Analysis]
29 Beheshti M, Mottaghy FM. Special Issue: Emerging Technologies for Medical Imaging Diagnostics, Monitoring and Therapy of Cancers. J Clin Med 2021;10:1327. [PMID: 33806986 DOI: 10.3390/jcm10061327] [Reference Citation Analysis]
30 Krajnc D, Papp L, Nakuz TS, Magometschnigg HF, Grahovac M, Spielvogel CP, Ecsedi B, Bago-Horvath Z, Haug A, Karanikas G, Beyer T, Hacker M, Helbich TH, Pinker K. Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics. Cancers (Basel) 2021;13:1249. [PMID: 33809057 DOI: 10.3390/cancers13061249] [Cited by in Crossref: 15] [Cited by in F6Publishing: 17] [Article Influence: 7.5] [Reference Citation Analysis]
31 Wada N, Nakashima M, Uchiyama Y. Analysis of the Relationship between Image and Blood Examinations in an Artificial Intelligence System for the Molecular Diagnosis of Breast Cancer. OJAppS 2021;11:1016-1027. [DOI: 10.4236/ojapps.2021.119074] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
32 Ni M, Zhou X, Liu J, Yu H, Gao Y, Zhang X, Li Z. Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI. BMC Cancer 2020;20:1073. [PMID: 33167903 DOI: 10.1186/s12885-020-07557-y] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]