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Journal Abstract Search

207 related items for PubMed ID: 38745321

  • 21. 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
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  • 22. 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; 69():103460. PubMed ID: 34233259
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  • 23. A preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy.
    Zhou J, Bai Y, Zhang Y, Wang Z, Sun S, Lin L, Gu Y, You C.
    Cancer Imaging; 2024 Jul 30; 24(1):98. PubMed ID: 39080809
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  • 29. 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 30; 173(2):455-463. PubMed ID: 30328048
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  • 30. Identifying Associations between DCE-MRI Radiomic Features and Expression Heterogeneity of Hallmark Pathways in Breast Cancer: A Multi-Center Radiogenomic Study.
    Ming W, Zhu Y, Li F, Bai Y, Gu W, Liu Y, Sun X, Liu X, Liu H.
    Genes (Basel); 2022 Dec 22; 14(1):. PubMed ID: 36672769
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  • 31. 3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients.
    Militello C, Rundo L, Dimarco M, Orlando A, Woitek R, D'Angelo I, Russo G, Bartolotta TV.
    Acad Radiol; 2022 Jun 22; 29(6):830-840. PubMed ID: 34600805
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  • 35. MRI radiomics and biological correlations for predicting axillary lymph node burden in early-stage breast cancer.
    Hong M, Fan S, Xu Z, Fang Z, Ling K, Lai P, Han C, Chen Z, Hou J, Liang Y, Zhou C, Wang J, Chen X, Huang Y, Xu M.
    J Transl Med; 2024 Sep 06; 22(1):826. PubMed ID: 39243024
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  • 36. Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma.
    Fan M, Liu Z, Xie S, Xu M, Wang S, Gao X, Li L.
    Phys Med Biol; 2019 Oct 23; 64(21):215001. PubMed ID: 31470420
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  • 37. Evaluation of Multiparametric MRI Radiomics-Based Nomogram in Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Two-Center study.
    Wang X, Hua H, Han J, Zhong X, Liu J, Chen J.
    Clin Breast Cancer; 2023 Aug 23; 23(6):e331-e344. PubMed ID: 37321954
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  • 38. DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers.
    Fan M, Cheng H, Zhang P, Gao X, Zhang J, Shao G, Li L.
    J Magn Reson Imaging; 2018 Jul 23; 48(1):237-247. PubMed ID: 29219225
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  • 39. Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI for Predicting Lymphovascular Invasion in Invasive Breast Cancer.
    Zheng H, Jian L, Li L, Liu W, Chen W.
    Acad Radiol; 2024 May 23; 31(5):1762-1772. PubMed ID: 38092588
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  • 40. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer.
    Li ZY, Wang XD, Li M, Liu XJ, Ye Z, Song B, Yuan F, Yuan Y, Xia CC, Zhang X, Li Q.
    World J Gastroenterol; 2020 May 21; 26(19):2388-2402. PubMed ID: 32476800
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