Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data

2016 ◽  
Author(s):  
Joseph B. DeGrandchamp ◽  
Jennifer G. Whisenant ◽  
Lori R. Arlinghaus ◽  
V. G. Abramson ◽  
Thomas E. Yankeelov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced Mri ◽  
Dynamic Contrast Enhanced ◽  
New Methods ◽  
Enhanced Mri ◽  
Contrast Enhanced ◽  
Contrast Enhanced Mri

Dynamic Contrast-Enhanced MRI for Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy: Initial Results

2008 ◽  
Vol 191 (5) ◽  
pp. 1331-1338 ◽  
Author(s):  
Claudette E. Loo ◽  
H. Jelle Teertstra ◽  
Sjoerd Rodenhuis ◽  
Marc J. van de Vijver ◽  
Juliane Hannemann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Dynamic Contrast Enhanced Mri ◽  
Dynamic Contrast Enhanced ◽  
Enhanced Mri ◽  
Contrast Enhanced ◽  
Initial Results ◽  
Response To Neoadjuvant Chemotherapy ◽  
Contrast Enhanced Mri

scholarly journals Contrast-enhanced mammography in comparison with dynamic contrast-enhanced MRI: which modality is appropriate for whom?

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Rasha Kamal ◽  
Sahar Mansour ◽  
Amr Farouk ◽  
Mennatallah Hanafy ◽  
Ahmed Elhatw ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Successful Implementation ◽  
Main Body ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced Mri ◽  
Dynamic Contrast Enhanced ◽  
Enhanced Mri ◽  
Contrast Enhanced ◽  
Tumor Boards ◽  
Contrast Enhanced Mri

Abstract Background Dynamic contrast-enhanced MRI (DCE-MRI) is a revolution regarding screening and diagnosis of breast cancer. Yet, sometimes it is not the appropriate choice of imaging since the examination needs to be scheduled and may take place in another department. Contrast-enhanced mammography (CEM) is contrast-based digital mammogram, and consequently, it has emerged as a potential and promising replacer to DCE-MRI. Main body of the abstract There is a frequently asked question during the multidisciplinary breast cancer tumor boards is: which modality is more appropriate to be used in each clinical scenario? This article provided a detailed understanding of these two modalities in order to achieve a successful implementation of them into the clinical practice. Which modality to start with, in the context of the detection (screening) followed by characterization or diagnosis of the identified lesions? What is the appropriate application of both modalities in local staging and follow-up? All of these issues would be discussed in this article. Short conclusion MRI is a safe tool for breast imaging and has a superior diagnostic performance compared to CEM. However, CEM is getting close: this lies in its accessibility, short-time procedure, requirement of less training and feasibility to standardize.

scholarly journals Coregistration of Dynamic Contrast Enhanced MRI and Broadband Diffuse Optical Spectroscopy for Characterizing Breast Cancer

2005 ◽  
Vol 4 (5) ◽  
pp. 549-558 ◽  
Author(s):  
David Hsiang ◽  
Natasha Shah ◽  
Natasha Yu ◽  
Min-Ying Su ◽  
Albert Cerussi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Optical Spectroscopy ◽  
Morphological Data ◽  
Diffuse Optical Spectroscopy ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced Mri ◽  
Dynamic Contrast Enhanced ◽  
Enhanced Mri ◽  
Contrast Enhanced ◽  
Contrast Enhanced Mri

A handheld scanning probe based on broadband Diffuse Optical Spectroscopy (DOS) was used in combination with dynamic contrast enhanced MRI (DCE-MRI) to quantitatively characterize locally-advanced breast cancers in six patients. Measurements were performed sequentially using external fiducial markers for co-registration. Tumor patterns were categorized according to MRI morphological data, and 3D DCE-MRI slices were converted into a volumetric matrix with isotropic voxels to generate views that coincided with the DOS scanning plane. Tumor volume and depth at each DOS measurement site were determined, and a tissue optical index (TOI) that reflects both angiogenic and stromal characteristics was derived from broadband DOS data. In all six cases, optical scans showed significant TOI contrast corresponding to MRI morphological information. Sharp TOI peaks were recovered for well-circumscribed masses. A reduction in TOI was found inside a tumor with a necrotic center. A broadened peak was observed for a diffuse tumor pattern, and an inflammatory septal case provided two TOI peaks that correlated qualitatively with MRI enhancement. These results provide qualitative confirmation of the common signal origin and complementary information content that can be achieved by combining optical and MR imaging for breast cancer detection and clinical management.

scholarly journals Machine Learning-Based Radiomics Nomogram With Dynamic Contrast-Enhanced MRI of the Osteosarcoma for Evaluation of Efficacy of Neoadjuvant Chemotherapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Lu Zhang ◽  
Yinghui Ge ◽  
Qiuru Gao ◽  
Fei Zhao ◽  
Tianming Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced Mri ◽  
Dynamic Contrast Enhanced ◽  
Quantitative Parameters ◽  
Enhanced Mri ◽  
Contrast Enhanced ◽  
Contrast Enhanced Mri

ObjectivesThis study aims to evaluate the value of machine learning-based dynamic contrast-enhanced MRI (DCE-MRI) radiomics nomogram in prediction treatment response of neoadjuvant chemotherapy (NAC) in patients with osteosarcoma.MethodsA total of 102 patients with osteosarcoma and who underwent NAC were enrolled in this study. All patients received a DCE-MRI scan before NAC. The Response Evaluation Criteria in Solid Tumors was used as the standard to evaluate the NAC response with complete remission and partial remission in the effective group, stable disease, and progressive disease in the ineffective group. The following semi-quantitative parameters of DCE-MRI were calculated: early dynamic enhancement wash-in slope (Slope), time to peak (TTP), and enhancement rate (R). The acquired data is randomly divided into 70% for training and 30% for testing. Variance threshold, univariate feature selection, and least absolute shrinkage and selection operator were used to select the optimal features. Three classifiers (K-nearest neighbor, KNN; support vector machine, SVM; and logistic regression, LR) were implemented for model establishment. The performance of different classifiers and conventional semi-quantitative parameters was evaluated by confusion matrix and receiver operating characteristic curves. Furthermore, clinically relevant risk factors including age, tumor size and site, pathological fracture, and surgical staging were collected to evaluate their predictive values for the efficacy of NAC. The selected clinical features and imaging features were combined to establish the model and the nomogram, and then the predictive efficacy was evaluated.ResultsThe clinical relevance risk factor analysis demonstrates that only surgical stage was an independent predictor of NAC. A total of seven radiomic features were selected, and three machine learning models (KNN, SVM, and LR) were established based on such features. The prediction accuracy (ACC) of these three models was 0.89, 0.84, and 0.84, respectively. The area under the subject curve (AUC) of these three models was 0.86, 0.92, and 0.93, respectively. As for Slope, TTP, and R parameters, the prediction ACC was 0.91, 0.89, and 0.81, respectively, while the AUC was 0.87, 0.85, and 0.83, respectively. In both the training and testing sets, the ACC and AUC of the combined model were higher than those of the radiomics models (ACC = 0.91 and AUC = 0.95), which indicate an outstanding performance of our proposed model.ConclusionsThe radiomics nomogram demonstrates satisfactory predictive results for the treatment response of patients with osteosarcoma before NAC. This finding may provide a new decision basis to improve the treatment plan.

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