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Journal Abstract Search

283 related items for PubMed ID: 35633290

  • 1. Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI.
    Caballo M, Sanderink WBG, Han L, Gao Y, Athanasiou A, Mann RM.
    J Magn Reson Imaging; 2023 Jan; 57(1):97-110. PubMed ID: 35633290
    [Abstract] [Full Text] [Related]

  • 2. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.
    Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A.
    Breast Cancer Res; 2017 May 18; 19(1):57. PubMed ID: 28521821
    [Abstract] [Full Text] [Related]

  • 3. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI.
    Yoshida K, Kawashima H, Kannon T, Tajima A, Ohno N, Terada K, Takamatsu A, Adachi H, Ohno M, Miyati T, Ishikawa S, Ikeda H, Gabata T.
    Magn Reson Imaging; 2022 Oct 18; 92():19-25. PubMed ID: 35636571
    [Abstract] [Full Text] [Related]

  • 4. 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.
    Tahmassebi A, Wengert GJ, Helbich TH, Bago-Horvath Z, Alaei S, Bartsch R, Dubsky P, Baltzer P, Clauser P, Kapetas P, Morris EA, Meyer-Baese A, Pinker K.
    Invest Radiol; 2019 Feb 18; 54(2):110-117. PubMed ID: 30358693
    [Abstract] [Full Text] [Related]

  • 5. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.
    Cain EH, Saha A, Harowicz MR, Marks JR, Marcom PK, Mazurowski MA.
    Breast Cancer Res Treat; 2019 Jan 18; 173(2):455-463. PubMed ID: 30328048
    [Abstract] [Full Text] [Related]

  • 6. 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 Jan 18; 12():e17683. PubMed ID: 39026540
    [Abstract] [Full Text] [Related]

  • 7. Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer.
    Eun NL, Kang D, Son EJ, Park JS, Youk JH, Kim JA, Gweon HM.
    Radiology; 2020 Jan 18; 294(1):31-41. PubMed ID: 31769740
    [No Abstract] [Full Text] [Related]

  • 8. Radiomic Features of Primary Rectal Cancers on Baseline T2 -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study.
    Antunes JT, Ofshteyn A, Bera K, Wang EY, Brady JT, Willis JE, Friedman KA, Marderstein EL, Kalady MF, Stein SL, Purysko AS, Paspulati R, Gollamudi J, Madabhushi A, Viswanath SE.
    J Magn Reson Imaging; 2020 Nov 18; 52(5):1531-1541. PubMed ID: 32216127
    [Abstract] [Full Text] [Related]

  • 9. Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.
    Liu HQ, Lin SY, Song YD, Mai SY, Yang YD, Chen K, Wu Z, Zhao HY.
    Eur Radiol; 2023 Apr 18; 33(4):2965-2974. PubMed ID: 36418622
    [Abstract] [Full Text] [Related]

  • 10. Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.
    Zhang X, Teng X, Zhang J, Lai Q, Cai J.
    Breast Cancer Res; 2024 May 14; 26(1):77. PubMed ID: 38745321
    [Abstract] [Full Text] [Related]

  • 11. 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
    [Abstract] [Full Text] [Related]

  • 12. Functional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer.
    Musall BC, Abdelhafez AH, Adrada BE, Candelaria RP, Mohamed RMM, Boge M, Le-Petross H, Arribas E, Lane DL, Spak DA, Leung JWT, Hwang KP, Son JB, Elshafeey NA, Mahmoud HS, Wei P, Sun J, Zhang S, White JB, Ravenberg EE, Litton JK, Damodaran S, Thompson AM, Moulder SL, Yang WT, Pagel MD, Rauch GM, Ma J.
    J Magn Reson Imaging; 2021 Jul 28; 54(1):251-260. PubMed ID: 33586845
    [Abstract] [Full Text] [Related]

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  • 14. Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.
    Liu J, Li X, Wang G, Zeng W, Zeng H, Wen C, Xu W, He Z, Qin G, Chen W.
    J Magn Reson Imaging; 2025 Jan 28; 61(1):184-197. PubMed ID: 38850180
    [Abstract] [Full Text] [Related]

  • 15. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response.
    Hussain L, Huang P, Nguyen T, Lone KJ, Ali A, Khan MS, Li H, Suh DY, Duong TQ.
    Biomed Eng Online; 2021 Jun 28; 20(1):63. PubMed ID: 34183038
    [Abstract] [Full Text] [Related]

  • 16. Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer.
    Li Y, Chen Y, Zhao R, Ji Y, Li J, Zhang Y, Lu H.
    Eur Radiol; 2022 Mar 28; 32(3):1676-1687. PubMed ID: 34767068
    [Abstract] [Full Text] [Related]

  • 17. Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.
    Wu J, Gong G, Cui Y, Li R.
    J Magn Reson Imaging; 2016 Nov 28; 44(5):1107-1115. PubMed ID: 27080586
    [Abstract] [Full Text] [Related]

  • 18. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
    Yu Y, He Z, Ouyang J, Tan Y, Chen Y, Gu Y, Mao L, Ren W, Wang J, Lin L, Wu Z, Liu J, Ou Q, Hu Q, Li A, Chen K, Li C, Lu N, Li X, Su F, Liu Q, Xie C, Yao H.
    EBioMedicine; 2021 Jul 28; 69():103460. PubMed ID: 34233259
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