Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL

Abstract

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Circulating tumor DNA and magnetic resonance imaging to predict neoadjuvant chemotherapy response and recurrence risk

“…We have previously established subtype specific FTV-based prediction models trained using MRI data from 990 I-SPY 2 patients 12 . In this study, multivariable logistic regression analysis was performed to assess the additive value of ctDNA to pCR prediction models based on FTV alone.…”

“…Clinicopathologic and response data have also been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO SuperSeries accession number https://identifiers.org/geo:GSE150576 39 .The full MRI data will be deposited in The Cancer Imaging Archive (TCIA) and the accession ID is anticipated to be released in mid-2021. When the MRI data become available, the metadata record associated with the group's previous article (https://doi.org/10.6084/m9.figshare.12912191) 40 will be updated to include the TCIA data DOI. Prior to release, MRI data queries can be directed to the corresponding authors (W.L.…”

“…Compared to clinical exam, mammography, and ultrasound, magnetic resonance imaging (MRI) is the most accurate imaging tool in monitoring tumor response to treatment in NAC 9 . Previous studies showed that MRI-based functional tumor volume (FTV) can predict pCR and recurrence-free survival for patients with invasive breast cancer undergoing NAC [10][11][12] . In the I-SPY 2 TRIAL, serial MRI exams has been implemented to monitor treatment response and patient randomization 13 .…”

Editorial for “Functional Tumor Volume by Fast Dynamic Contrast‐Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple‐Negative Breast Cancer”

“…The use of magnetic resonance imaging (MRI) to discriminate responders from nonresponders has been an active area of research, and one of the goals of trials such as I-SPY and I-SPY2. 1,2 In recent years, "fast"-or "ultrafast"dynamic contrast-enhanced MRI (DCE-MRI) has been introduced for breast imaging, where conventional high-spatial/ low-temporal resolution sequences are substituted for hightemporal resolution imaging, especially during the initial phase of contrast media wash-in. [3][4][5] The use of these sequences has shown that parameters descriptive of initial tumor kinetics can be useful in the diagnosis and characterization of breast lesions.…”

“…Identifying breast cancer patients who are unlikely to achieve a pathologically complete response (pCR) from neoadjuvant systemic therapy (NAST) early in their treatment could spare nonresponders toxicity from ineffective therapies and direct them toward alternative treatments that could result in better outcomes. The use of magnetic resonance imaging (MRI) to discriminate responders from nonresponders has been an active area of research, and one of the goals of trials such as I‐SPY and I‐SPY2 1,2 . In recent years, “fast”—or “ultrafast”—dynamic contrast‐enhanced MRI (DCE‐MRI) has been introduced for breast imaging, where conventional high‐spatial/low‐temporal resolution sequences are substituted for high‐temporal resolution imaging, especially during the initial phase of contrast media wash‐in 3–5 .…”