MRI, Clinical Examination, and Mammography for Preoperative Assessment of Residual Disease and Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer: ACRIN 6657 Trial.

OBJECTIVE: The objective of our study was to determine the accuracy of preoperative measurements for detecting pathologic complete response (CR) and assessing residual disease after neoadjuvant chemotherapy (NACT) in patients with locally advanced breast cancer. SUBJECTS AND METHODS: The American College of Radiology Imaging Network 6657 Trial prospectively enrolled women with ≥ 3 cm invasive breast cancer receiving NACT. Preoperative measurements of residual disease included longest diameter by mammography, MRI, and clinical examination and functional volume on MRI. The accuracy of preoperative measurements for detecting pathologic CR and the association with final pathology size were assessed for all lesions, separately for single masses and nonmass enhancements (NMEs), multiple masses, and lesions without ductal carcinoma in situ (DCIS). RESULTS: In the 138 women with all four preoperative measures, longest diameter by MRI showed the highest accuracy for detecting pathologic CR for all lesions and NME (AUC = 0.76 and 0.84, respectively). There was little difference across preoperative measurements in the accuracy of detecting pathologic CR for single masses (AUC = 0.69-0.72). Longest diameter by MRI and longest diameter by clinical examination showed moderate ability for detecting pathologic CR for multiple masses (AUC = 0.78 and 0.74), and longest diameter by MRI and longest diameter by mammography showed moderate ability for detecting pathologic CR for tumors without DCIS (AUC = 0.74 and 0.71). In subjects with residual disease, longest diameter by MRI exhibited the strongest association with pathology size for all lesions and single masses (r = 0.33 and 0.47). Associations between preoperative measures and pathology results were not significantly influenced by tumor subtype or mammographic density. CONCLUSION: Our results indicate that measurement of longest diameter by MRI is more accurate than by mammography and clinical examination for preoperative assessment of tumor residua after NACT and may improve surgical planning.

Comparing post-operative human breast specimen radiograph and MRI in lesion margin and volume assessment.

The purpose of this research is to evaluate the potential for identifying malignant breast lesions and their margins on large specimen MRI, in comparison to specimen radiography and clinical dynamic contrast enhanced MRI (DCE-MRI). Breast specimens were imaged with an MR scanner immediately after surgery, with an IRB-approved protocol and with the patients' informed consent. Specimen sizes were at least 5 cm in diameter and approximately 1 to 4 cm thick. Coronal and axial gradient echo MR images without fat suppression were acquired over the whole specimens using a 9.4T animal scanner. Findings on specimen MRI were compared with findings on specimen radiograph, and their volumes were compared with measurements obtained from clinical DCE-MRI. The results showed that invasive ductal carcinoma (IDC) lesions were easily identified using MRI and the margins were clearly distinguishable from nearby tissue. However, ductal carcinoma in situ (DCIS) lesions were not clearly discernible and were diffused with poorly defined margins on MRI. Calcifications associated with DCIS were visualized in all specimens on specimen radiograph. There is a strong correlation between the maximum diameter of lesions as measured by radiograph and MRI (r = 0.93), as well as the maximum diameter measured by pathology and radiograph/MRI (r>0.75). The volumes of IDC measured on specimen MRI were slightly smaller than those measured on DCE-MRI. Imaging of excised human breast lumpectomy specimens with high magnetic field MRI provides promising results for improvements in lesion identification and margin localization for IDC. However, there are technical challenges in visualization of DCIS lesions. Improvements in specimen imaging are important, as they will provide additional information to standard radiographic analysis.

Computerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers.

RATIONALE AND OBJECTIVES: To conduct a preclinical evaluation of the robustness of our computerized system for breast lesion characterization on two breast magnetic resonance imaging (MRI) databases that were acquired using scanners from two different manufacturers. MATERIALS AND METHODS: Two clinical breast MRI databases were acquired from a Siemens scanner and a GE scanner, which shared similar imaging protocols and retrospectively collected under an institutional review board-approved protocol. In our computerized analysis system, after a breast lesion is identified by the radiologist, the computer performs automatic lesion segmentation and feature extraction and outputs an estimated probability of malignancy. We used a Bayesian neural network with automatic relevance determination for joint feature selection and classification. To evaluate the robustness of our classification system, we first used Database 1 for feature selection and classifier training, and Database 2 to test the trained classifier. Then, we exchanged the two datasets and repeated the process. Area under the receiver operating characteristic curve (AUC) was used as a performance figure of merit in the task of distinguishing between malignant and benign lesions. RESULTS: We obtained an AUC of 0.85 (approximate 95% confidence interval [CI] 0.79-0.91) for (a) feature selection and classifier training using Database 1 and testing on Database 2; and an AUC of 0.90 (approximate 95% CI 0.84-0.96) for (b) feature selection and classifier training using Database 2 and testing on Database 1. We failed to observe statistical significance for the difference AUC of 0.05 between the two database conditions (P = .24; 95% confidence interval -0.03, 0.1). CONCLUSION: These results demonstrate the robustness of our computerized classification system in the task of distinguishing between malignant and benign breast lesions on dynamic contrast-enhanced (DCE) MRI images from two manufacturers. Our study showed the feasibility of developing a computerized classification system that is robust across different scanners.