Combination of Deep Learning and Radiomic Classifiers Within the Tumor and Tumor Environment for Prediction of Response to Neoadjuvant Chemotherapy (NAC) In Breast DCE-MRI
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Case Western Reserve University School of Graduate Studies / OhioLINK
2020
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=case1575235591194931 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-case15752355911949312021-08-03T07:13:26Z Combination of Deep Learning and Radiomic Classifiers Within the Tumor and Tumor Environment for Prediction of Response to Neoadjuvant Chemotherapy (NAC) In Breast DCE-MRI Eben, Jeffrey E. Computer Science Neoadjuvant chemotherapy (NAC) is the standard treatment for locally advanced breast cancer, but less than half of recipients achieve pathological complete response (pCR), necessitating a way to predict pCR prior to NAC. Previous work has shown that pCR prediction is viable via either radiomic or deep learning classification methods when applied to the tumoral region on breast MRI, with additional benefits gained from including the peritumoral region. In this work, we spatially invoke different analytic representations in different tumor compartments to create a multi-representation-based prediction of response to NAC. Deep learning and radiomic classifiers were trained separately within the tumor and the peritumoral region, with classifier predictions then being fused together via a logistic regression classifier. Best performance was achieved when fusing all four spatially-invoked classifiers, indicating that different representations invoked in different spatial regions contain unique information, and combining these representations can provide advantages over traditional approaches. 2020-01-28 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1575235591194931 http://rave.ohiolink.edu/etdc/view?acc_num=case1575235591194931 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Computer Science |
| spellingShingle |
Computer Science Eben, Jeffrey E. Combination of Deep Learning and Radiomic Classifiers Within the Tumor and Tumor Environment for Prediction of Response to Neoadjuvant Chemotherapy (NAC) In Breast DCE-MRI |
| author |
Eben, Jeffrey E. |
| author_facet |
Eben, Jeffrey E. |
| author_sort |
Eben, Jeffrey E. |
| title |
Combination of Deep Learning and Radiomic Classifiers Within the Tumor and Tumor Environment for Prediction of Response to Neoadjuvant Chemotherapy (NAC) In Breast DCE-MRI |
| title_short |
Combination of Deep Learning and Radiomic Classifiers Within the Tumor and Tumor Environment for Prediction of Response to Neoadjuvant Chemotherapy (NAC) In Breast DCE-MRI |
| title_full |
Combination of Deep Learning and Radiomic Classifiers Within the Tumor and Tumor Environment for Prediction of Response to Neoadjuvant Chemotherapy (NAC) In Breast DCE-MRI |
| title_fullStr |
Combination of Deep Learning and Radiomic Classifiers Within the Tumor and Tumor Environment for Prediction of Response to Neoadjuvant Chemotherapy (NAC) In Breast DCE-MRI |
| title_full_unstemmed |
Combination of Deep Learning and Radiomic Classifiers Within the Tumor and Tumor Environment for Prediction of Response to Neoadjuvant Chemotherapy (NAC) In Breast DCE-MRI |
| title_sort |
combination of deep learning and radiomic classifiers within the tumor and tumor environment for prediction of response to neoadjuvant chemotherapy (nac) in breast dce-mri |
| publisher |
Case Western Reserve University School of Graduate Studies / OhioLINK |
| publishDate |
2020 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=case1575235591194931 |
| work_keys_str_mv |
AT ebenjeffreye combinationofdeeplearningandradiomicclassifierswithinthetumorandtumorenvironmentforpredictionofresponsetoneoadjuvantchemotherapynacinbreastdcemri |
| _version_ |
1719456516880203776 |