Ming Fan1, Guolin Wu2, Hu Cheng3, Juan Zhang4, Guoliang Shao5, Lihua Li6. 1. Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China. Electronic address: ming.fan@hdu.edu.cn. 2. Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China. Electronic address: 704211577@qq.com. 3. Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China. Electronic address: 604214039@qq.com. 4. Zhejiang Cancer Hospital, Hangzhou, 310010, China. Electronic address: zhangjuan_496@163.com. 5. Zhejiang Cancer Hospital, Hangzhou, 310010, China. Electronic address: Radiologistzjzlyy@gmail.com. 6. Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China. Electronic address: lilh@hdu.edu.cn.
Abstract
OBJECTIVES: To enhance the accurate prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer patients by using a quantitative analysis of dynamic enhancement magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: A dataset of 57 cancer patients with breast DCE-MR images acquired before NAC was used. Among them, 47 patients were Responders, and 10 patients were non-Responders based on the RECIST criteria. The breast regions were segmented on the MR images, and a total of 158 radiomic features were computed to represent the morphologic, dynamic, and the texture of the tumors as well as the background parenchymal features. The optimal subset of features was selected using evolutionary based Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-out cross-validation (LOOCV) method to classify Responder and non-Responder cases. The area under a receiver operating characteristic curve (AUC) was computed to assess the classifier performance. An additional independent dataset with 46 patients was also included to validate the results. RESULTS: The evolutionary algorithm (EA)-based method identified optimal subsets comprising 12 image features that were fit for classification for the main cohort. Following the same feature selection procedure, the independent validation dataset produced 11 image features, 7 of which were identical to those from the main cohort. The classifier based on the features yield a LOOCV AUC of 0.910 and 0.874 for the main and the reproducibility study cohort, respectively. If the optimal features in the main cohort were utilized to test performance on the reproducibility cohort, the classifier generated an AUC of 0.713. While the features developed in the reproducibility cohort were applied to test the main cohort, the classifier achieved an AUC of 0.683. The AUC of the averaged receiver operating characteristic (ROC) curve for the two data cohort was 0.703. CONCLUSIONS: This study demonstrated that quantitative analyses of radiomic features from pretreatment breast DCE-MRI data could be used as valuable image markers that are associated with tumor response to NAC.
OBJECTIVES: To enhance the accurate prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancerpatients by using a quantitative analysis of dynamic enhancement magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: A dataset of 57 cancerpatients with breast DCE-MR images acquired before NAC was used. Among them, 47 patients were Responders, and 10 patients were non-Responders based on the RECIST criteria. The breast regions were segmented on the MR images, and a total of 158 radiomic features were computed to represent the morphologic, dynamic, and the texture of the tumors as well as the background parenchymal features. The optimal subset of features was selected using evolutionary based Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-out cross-validation (LOOCV) method to classify Responder and non-Responder cases. The area under a receiver operating characteristic curve (AUC) was computed to assess the classifier performance. An additional independent dataset with 46 patients was also included to validate the results. RESULTS: The evolutionary algorithm (EA)-based method identified optimal subsets comprising 12 image features that were fit for classification for the main cohort. Following the same feature selection procedure, the independent validation dataset produced 11 image features, 7 of which were identical to those from the main cohort. The classifier based on the features yield a LOOCV AUC of 0.910 and 0.874 for the main and the reproducibility study cohort, respectively. If the optimal features in the main cohort were utilized to test performance on the reproducibility cohort, the classifier generated an AUC of 0.713. While the features developed in the reproducibility cohort were applied to test the main cohort, the classifier achieved an AUC of 0.683. The AUC of the averaged receiver operating characteristic (ROC) curve for the two data cohort was 0.703. CONCLUSIONS: This study demonstrated that quantitative analyses of radiomic features from pretreatment breast DCE-MRI data could be used as valuable image markers that are associated with tumor response to NAC.
Authors: Elizabeth Hope Cain; Ashirbani Saha; Michael R Harowicz; Jeffrey R Marks; P Kelly Marcom; Maciej A Mazurowski Journal: Breast Cancer Res Treat Date: 2018-10-16 Impact factor: 4.872
Authors: Geraldine J Liao; Leah C Henze Bancroft; Roberta M Strigel; Rhea D Chitalia; Despina Kontos; Linda Moy; Savannah C Partridge; Habib Rahbar Journal: J Magn Reson Imaging Date: 2019-04-19 Impact factor: 4.813