4.5 Article

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Journal

BREAST CANCER RESEARCH
Volume 19, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/s13058-017-0846-1

Keywords

Imaging; MRI; Neoadjuvant chemotherapy; Treatment response; Radiomics; Personalized medicine

Categories

Funding

  1. National Cancer Institute of the National Institutes of Health [1U24CA199374-01, R01 CA202752-01A1, R01 CA208236-01A1, R21 CA179327-01, R21 CA195152-01]
  2. National Institute of Biomedical Imaging and Bioengineering [T32EB007509]
  3. National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK098503-02]
  4. National Center for Research Resources [1 C06 RR12463-01]
  5. U.S. Department of Defense (DOD) [PC120857]
  6. DOD Lung Cancer Idea Development New Investigator Award [LC130463]
  7. DOD Prostate Cancer Idea Development Award
  8. DOD Peer Reviewed Medical Research Program [W81XWH-16-1-0329]
  9. Cleveland Clinic
  10. Wallace H. Coulter Foundation at Case Western Reserve University
  11. I-Corps@Ohio Program

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Background: In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Methods: A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2(-)) and triple-negative or HER2(+) (TN/HER2(+)) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance. Results: Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 +/- 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 +/- 0.025 within the HR+, HER2(-) group using DLDA and 0.93 +/- 0.018 within the TN/HER2(+) group using a naive Bayes classifier. In HR+, HER2(-) breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2(+) tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors. Conclusions: Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.

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