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 Amirhessam; Wengert Georg J.; Helbich Thomas H.; Bago-Horvath Zsuzsanna; Alaei Sousan; Bartsch Rupert; Dubsky Peter; Balizen Pascal; Clausen Paola; Kapetas Panagiotis; Morris Elizabeth A.; Meyer-Baese Anke; Pinker Katja

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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

机译：机器学习对乳房乳房的多射磁共振成像的影响，对乳腺癌患者的新辅助化疗和生存结果的早期预测

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Purpose: The aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients.

机译：目的：本研究的目的是评估用多个磁共振成像（MPMRI）的机器学习的潜力，以便早期预测病理完全反应（PCR）对新辅助化疗（NAC）和乳腺癌患者的存活结果。

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来源
《Investigative radiology》 |2019年第2期|共8页
作者
Tahmassebi Amirhessam; Wengert Georg J.; Helbich Thomas H.; Bago-Horvath Zsuzsanna; Alaei Sousan; Bartsch Rupert; Dubsky Peter; Balizen Pascal; Clausen Paola; Kapetas Panagiotis; Morris Elizabeth A.; Meyer-Baese Anke; Pinker Katja;
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作者单位

Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA;

Med Univ Vienna Div Mol &

Gender Imaging Dept Biomed Imaging &

Image Guided Therapy Vienna;

Med Univ Vienna Div Mol &

Gender Imaging Dept Biomed Imaging &

Image Guided Therapy Vienna;

Med Univ Vienna Dept Pathol Vienna Austria;

Med Univ Vienna Dept Pathol Vienna Austria;

Med Univ Vienna Div Oncol Dept Internal Med Vienna Austria;

Med Univ Vienna Comprehens Canc Ctr Dept Surg Vienna Austria;

Med Univ Vienna Div Mol &

Gender Imaging Dept Biomed Imaging &

Image Guided Therapy Vienna;

Med Univ Vienna Div Mol &

Gender Imaging Dept Biomed Imaging &

Image Guided Therapy Vienna;

Med Univ Vienna Div Mol &

Gender Imaging Dept Biomed Imaging &

Image Guided Therapy Vienna;

Mem Sloan Kettering Canc Ctr Breast Imaging Serv Dept Radiol 300 E 66th St 7th Floor New York;

Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA;

Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA;

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原文格式 PDF
正文语种 eng
中图分类放射医学;
关键词
breast cancer; machine learning; neoadjuvant chemotherapy; residual cancer burden; multiparametric magnetic resonance imaging; dynamic contrast-enhanced MRI; diffusion-weighted imaging;

机译：乳腺癌;机器学习;Neoadjuvant化疗;残留的癌症负担;多射磁共振成像;动态对比增强MRI;扩散加权成像;

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专利

1. 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 [J] . Tahmassebi Amirhessam, Wengert Georg J., Helbich Thomas H., Investigative radiology . 2019,第2期

机译：机器学习对乳房乳房的多射磁共振成像的影响，对乳腺癌患者的新辅助化疗和生存结果的早期预测
2. Multiparametric Magnetic Resonance Imaging for Predicting Pathological Response After the First Cycle of Neoadjuvant Chemotherapy in Breast Cancer [J] . Li Xia, Abramson Richard G., Arlinghaus Lori R., Investigative radiology . 2015,第4期

机译：多参数磁共振成像预测乳腺癌新辅助化疗第一周期后的病理反应
3. Comparing the Performances of Magnetic Resonance Imaging Size vs Pharmacokinetic Parameters to Predict Response to Neoadjuvant Chemotherapy and Survival in Patients With Breast Cancer [J] . Basak E. Dogan, Qing Yuan, Rol Bassett, Current Problems in Diagnostic Radiology . 2019,第3期

机译：比较磁共振成像尺寸与药代动力学参数的性能预测乳腺癌患者对新辅助化疗和存活的反应
4. Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer [C] . Juzhong Fu, Ming Fan, Bin Zheng, Conference on PACS and imaging informatics . 2016

机译：动态对比增强磁共振成像预测乳腺癌对新辅助化疗的反应
5. Diffusion-weighted magnetic resonance imaging in monitoring treatment response in patients with locally advanced breast cancer. [D] . Singer, Lisa. 2010

机译：扩散加权磁共振成像可监测局部晚期乳腺癌患者的治疗反应。
6. Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients [O] . Shujun Chen, Zhenyu Shu, Yongfeng Li, 2020

机译：基于机器学习的辐射瘤载体使用磁共振图像来预测乳腺癌患者的Neoadjuvant化疗疗效
7. Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer. [O] . Minarikova, L, Bogner, W, Pinker, K, 2016

机译：研究乳腺癌新辅助化疗对3T的多参数磁共振成像的预测价值。

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

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