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531 related items for PubMed ID: 32466777

  • 1. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.
    Sutton EJ, Onishi N, Fehr DA, Dashevsky BZ, Sadinski M, Pinker K, Martinez DF, Brogi E, Braunstein L, Razavi P, El-Tamer M, Sacchini V, Deasy JO, Morris EA, Veeraraghavan H.
    Breast Cancer Res; 2020 May 28; 22(1):57. PubMed ID: 32466777
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  • 7. Prediction of axillary response by monitoring with ultrasound and MRI during and after neoadjuvant chemotherapy in breast cancer patients.
    Eun NL, Son EJ, Gweon HM, Kim JA, Youk JH.
    Eur Radiol; 2020 Mar 28; 30(3):1460-1469. PubMed ID: 31802216
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  • 9. Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer.
    Li X, Li C, Wang H, Jiang L, Chen M.
    PeerJ; 2024 Mar 28; 12():e17683. PubMed ID: 39026540
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  • 10. Predictive value of DCE-MRI for early evaluation of pathological complete response to neoadjuvant chemotherapy in resectable primary breast cancer: A single-center prospective study.
    Sun YS, He YJ, Li J, Li YL, Li XT, Lu AP, Fan ZQ, Cao K, Ouyang T.
    Breast; 2016 Dec 28; 30():80-86. PubMed ID: 27652977
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  • 19. A Multicenter Study of the Impact of Body Mass Index (BMI) on the incidence of Pathologic Complete Response (pCR) Among Saudi Patients with locally advanced Breast cancer (LABC) post Neoadjuvant Chemotherapy (NAC).
    Al-Saleh K, Abd El-Aziz N, Ali A, Abo Zeed W, Salah T, Elsamany S, Rasmy A, El Farargy O, Husain S, Al-Rikabi A, Alsaeed E, Aldiab A, Abd El-Warith A.
    Gulf J Oncolog; 2019 May 28; 1(30):33-42. PubMed ID: 31242980
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