Assessing the accuracy of conventional gadolinium-enhanced breast MRI in measuring the nodal response to neoadjuvant chemotherapy (NAC) in breast cancer.
Lisa Christine MurphyEdel Marie QuinnZeeshan RazzaqClaire BradyVicki LivingstoneLorna DuddyJosephine BarryHenry Paul RedmondMark Anthony CorriganPublished in: The breast journal (2020)
Management of the axilla in the era of neoadjuvant chemotherapy for breast cancer is evolving. The aim of this study is to determine if conventional gadolinium-enhanced breast MRI can aid in evaluation of the response to neoadjuvant chemotherapy in the axilla. A retrospective review of a prospectively maintained database of patients undergoing neoadjuvant chemotherapy for breast cancer was performed. Pre and post-neoadjuvant chemotherapy MRI reports for node-positive patients were examined in conjunction with demographic data, treatment type, and final histopathology reports. One-hundred and fourteen patients with breast cancer undergoing neoadjuvant chemotherapy were included in the study. The sensitivity of magnetic resonance imaging in detecting nodal response post-neoadjuvant chemotherapy was 33.93% and the specificity was 82.76%. Magnetic resonance imaging had a positive predictive value of 65.52% and a negative predictive value of 56.47%. MRI was found to be most specific in the detection of triple-negative cancer response. Specificity was 100% in this group and sensitivity was 75%. Magnetic resonance imaging has a relatively high specificity in detecting nodal response post-neoadjuvant chemotherapy but has a low sensitivity. Alone it cannot be relied upon to identify active axillary malignancy post-neoadjuvant chemotherapy. However, given its increased specificity among certain subgroups, it may have a role in super-selecting patients suitable for sentinel lymph node biopsy post-neoadjuvant chemotherapy.
Keyphrases
- neoadjuvant chemotherapy
- sentinel lymph node
- magnetic resonance imaging
- lymph node
- locally advanced
- contrast enhanced
- end stage renal disease
- patients undergoing
- ejection fraction
- newly diagnosed
- computed tomography
- chronic kidney disease
- squamous cell carcinoma
- radiation therapy
- prognostic factors
- emergency department
- ultrasound guided
- early stage
- deep learning
- data analysis
- big data