![]() 1 for patient exclusion details and Table 1 for patient characteristics in the eligible cohort and included cohort). ![]() By providing better prediction of response, MRI can advance personalized treatment and play an important role in assessing whether to change targeted therapies or proceed directly to surgical resection.Ī total of 384 patients who had complete MRI data and pCR outcome were included in the analysis (see Fig. This study investigated whether the predictive performance of MRI can be improved over FTV or any single feature alone by using a combination of features measured on DCE-MRI. Several studies have shown the association of BPE with breast cancer risk in the screening setting, and decreased BPE has been found to be associated with pCR following neoadjuvant chemotherapy 12, 13, 14, 16, 17. ![]() Sphericity is a three-dimensional shape feature previously found to be associated with pCR in the I-SPY2 trial 11. Longest diameter is a standard clinical measurement used to assess tumor response, consistent with the Response Evaluation Criteria in Solid Tumors (RECIST) 15. These additional measures have also shown value for prediction of pCR 11, 12, 13, 14. Additional features can be derived from the same DCE-MRI data, including longest diameter, sphericity, and contralateral background parenchymal enhancement (BPE). While FTV has shown effectiveness for the prediction of pCR, there is still potential for improvement, especially in the setting of hormone-positive tumors 10. Pathologic complete response is the primary endpoint in I-SPY 2.įTV represents the active portion of tumor volume, as defined by pharmacokinetic thresholds applied to dynamic contrast-enhanced MRI (DCE-MRI) 9. Subsequently, serial measures of FTV during treatment are used in the adaptive randomization engine of the I-SPY 2 trial, designed to accelerate the evaluation of novel agents for breast cancer 8. Results from the I-SPY 1 TRIAL (CALGB 150007/ACRIN 6657) found that functional tumor volume (FTV) predicted pathologic complete response (pCR) and recurrence-free survival 6, 7. Among imaging methods, magnetic resonance imaging (MRI) is the most accurate for assessing tumor response to NAC 1, 2, 3, 4, 5. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.Īn important advantage of neoadjuvant chemotherapy (NAC) over adjuvant therapy for locally advanced breast cancer is the ability to monitor treatment response, which allows informed adjustment of the treatment plan. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. A total of 384 patients (median age: 49 y/o) were included. The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Npj Breast Cancer volume 6, Article number: 63 ( 2020)ĭynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL
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