Clinical prediction model based on 18F-FDG PET/CT plus contrast-enhanced MRI for axillary lymph node macrometastasis

Abstract

PurposePositron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) are useful for detecting axillary lymph node (ALN) metastasis in invasive ductal breast cancer (IDC); however, there is limited clinical evidence to demonstrate the effectiveness of the combination of PET/CT plus MRI. Further axillary surgery is not recommended against ALN micrometastasis (lesion ≤2 mm) seen in sentinel lymph nodes, especially for patients who received proper adjuvant therapy. We aimed to evaluate the efficacy of a prediction model based on PET/CT plus MRI for ALN macrometastasis (lesion >2 mm) and explore the possibility of risk stratification of patients using the preoperative PET/CT plus MRI and biopsy findings.Materials and methodsWe retrospectively investigated 361 female patients (370 axillae; mean age, 56 years ± 12 [standard deviation]) who underwent surgery for primary IDC at a single center between April 2017 and March 2020. We constructed a prediction model with logistic regression. Patients were divided into low-risk and high-risk groups using a simple integer risk score, and the false negative rate for ALN macrometastasis was calculated to assess the validity. Internal validation was also achieved using a 5-fold cross-validation.ResultsThe PET/CT plus MRI model included five predictor variables: maximum standardized uptake value of primary tumor and ALN, primary tumor size, ALN cortical thickness, and histological grade. In the derivation (296 axillae) and validation (74 axillae) cohorts, 54% and 61% of patients, respectively, were classified as low-risk, with a false-negative rate of 11%. Five-fold cross-validation yielded an accuracy of 0.875.ConclusionsOur findings demonstrate the validity of the PET/CT plus MRI prediction model for ALN macrometastases. This model may aid the preoperative identification of low-risk patients for ALN macrometastasis and provide helpful information for PET/MRI interpretation.

High-accuracy prediction of axillary lymph node metastasis in invasive lobular carcinoma using focal cortical thickening on magnetic resonance imaging

“…Furthermore, the cut-off value of the morphological features of metastatic ALNs was not well-established in the previous literature [ 11 , 13 , 18 ]. In our experience [ 19 ], the presence of LOH caused by metastasis cannot be judged when ALNs are smaller than 4 mm in long-axis diameter. Moreover, as most normal ALNs smaller than 4 mm show a round shape, we measured all ALNs that were at least 3 mm in long-axis diameter, and tried to interpret the cortex structure as closely as possible.…”

Identification of Sentinel Lymph Node Macrometastasis in Breast Cancer by Deep Learning Based on Clinicopathological Characteristics

“…When assessing the outcomes of patients with cN0 breast cancer randomized to SLNB or no axillary intervention, SLNB was associated with significantly increased arm and breast morbidity. 11 Considering the low incidence of clinically relevant macro-SLNMs in contemporary breast cancer populations, [12][13][14] the utility of routine SLNB in all patients with primary invasive breast cancer has been questioned, and there is a current trend toward deescalation of axillary surgery. 15 Axillary ultrasonography (AUS) enables noninvasive axillary staging and is currently included in the diagnostic work-up for primary breast cancer; however, it has not replaced SLNB because of low accuracy.…”