AUTHOR=Kawaguchi Shun , Tamura Nobuko , Tanaka Kiyo , Kobayashi Yoko , Sato Junichiro , Kinowaki Keiichi , Shiiba Masato , Ishihara Makiko , Kawabata Hidetaka 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.989650

DOI=10.3389/fonc.2022.989650

ISSN=2234-943X

ABSTRACT=Purpose: Positron 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. The treatment efficacy against ALN micrometastasis (lesion ≤2 mm) is poor. 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 patients’ risk stratification using the PET/CT plus MRI model.
Materials and Methods: We 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. Using a simple integer risk score, patients were divided into low-risk and high-risk groups, and internal validation was achieved in a split-sample design. 
Results: The 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 (333 axillae) and validation (37 axillae) cohorts, 55% and 65% of patients, respectively, were classified as low-risk, with negative predictive values of 97% and 100%, respectively. 
Conclusions: Our 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.