Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration
| Autor: | Despina Kontos, Susan P. Weinstein, David C. Newitt, Christos Davatzikos, Eric Cohen, Meng-Kang Hsieh, Lauren Pantalone, Nola M. Hylton, Nariman Jahani |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Oncology medicine.medical_treatment Logistic regression Tumour biomarkers Breast cancer Computer-Assisted 0302 clinical medicine Longitudinal Studies skin and connective tissue diseases Neoadjuvant therapy Cancer Multidisciplinary medicine.diagnostic_test Area under the curve Middle Aged Prognosis Magnetic Resonance Imaging Neoadjuvant Therapy 3. Good health Biomedical Imaging Medicine Female Biomedical engineering medicine.medical_specialty Science Clinical Trials and Supportive Activities Image registration Breast Neoplasms Predictive markers Article Disease-Free Survival 03 medical and health sciences Clinical Research Internal medicine Image Interpretation Computer-Assisted Breast Cancer medicine Humans Image Interpretation Proportional hazards model business.industry Magnetic resonance imaging medicine.disease Clinical trial 030104 developmental biology Cancer imaging business 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports, Vol 9, Iss 1, Pp 1-12 (2019) Scientific reports, vol 9, iss 1 Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p |
| Databáze: | OpenAIRE |
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