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For: Choi JH, Kim HA, Kim W, Lim I, Lee I, Byun BH, Noh WC, Seong MK, Lee SS, Kim BI, Choi CW, Lim SM, Woo SK. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning. Sci Rep 2020;10:21149. [PMID: 33273490 DOI: 10.1038/s41598-020-77875-5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Kashyap R. Breast Cancer Histopathological Image Classification Using Stochastic Dilated Residual Ghost Model: . International Journal of Information Retrieval Research 2022;12:1-24. [DOI: 10.4018/ijirr.289655] [Reference Citation Analysis]
2 Moon SH, Cho YS, Choi JY. KSNM60 in Clinical Nuclear Oncology. Nucl Med Mol Imaging 2021;55:210-24. [PMID: 34721714 DOI: 10.1007/s13139-021-00711-9] [Reference Citation Analysis]
3 Rezaeijo SM, Ghorvei M, Mofid B. Predicting breast cancer response to neoadjuvant chemotherapy using ensemble deep transfer learning based on CT images. J Xray Sci Technol 2021. [PMID: 34219704 DOI: 10.3233/XST-210910] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Wehrend J, Silosky M, Xing F, Chin BB. Automated liver lesion detection in 68Ga DOTATATE PET/CT using a deep fully convolutional neural network. EJNMMI Res 2021;11:98. [PMID: 34601660 DOI: 10.1186/s13550-021-00839-x] [Reference Citation Analysis]
5 Fowler AM, Strigel RM. Clinical advances in PET–MRI for breast cancer. The Lancet Oncology 2022;23:e32-43. [DOI: 10.1016/s1470-2045(21)00577-5] [Reference Citation Analysis]
6 Moreau N, Rousseau C, Fourcade C, Santini G, Brennan A, Ferrer L, Lacombe M, Guillerminet C, Colombié M, Jézéquel P, Campone M, Normand N, Rubeaux M. Automatic Segmentation of Metastatic Breast Cancer Lesions on 18F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment. Cancers (Basel) 2021;14:101. [PMID: 35008265 DOI: 10.3390/cancers14010101] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Lee JS, Kim KM, Choi Y, Kim HJ. A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea. Nucl Med Mol Imaging 2021;55:265-84. [PMID: 34868376 DOI: 10.1007/s13139-021-00721-7] [Reference Citation Analysis]
8 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] [Reference Citation Analysis]
9 Gu J, Tong T, He C, Xu M, Yang X, Tian J, Jiang T, Wang K. Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study. Eur Radiol 2021. [PMID: 34654965 DOI: 10.1007/s00330-021-08293-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Balkenende L, Teuwen J, Mann RM. Application of Deep Learning in Breast Cancer Imaging. Semin Nucl Med 2022:S0001-2998(22)00017-4. [PMID: 35339259 DOI: 10.1053/j.semnuclmed.2022.02.003] [Reference Citation Analysis]
11 Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis. Comput Struct Biotechnol J 2021;19:5546-55. [PMID: 34712399 DOI: 10.1016/j.csbj.2021.10.006] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]