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

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

doi:10.3389/fonc.2022.989650

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

Front Oncol. 2022 Sep 13:12:989650. doi: 10.3389/fonc.2022.989650. eCollection 2022.

Authors

Shun Kawaguchi¹, Nobuko Tamura¹, Kiyo Tanaka¹, Yoko Kobayashi¹, Junichiro Sato², Keiichi Kinowaki², Masato Shiiba³, Makiko Ishihara³, Hidetaka Kawabata¹

Affiliations

¹ Breast and Endocrine Surgery, Toranomon Hospital, Tokyo, Japan.
² Pathology, Toranomon Hospital, Tokyo, Japan.
³ Diagnostic Imaging Center, Toranomon Hospital, Tokyo, Japan.

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. 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 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. 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.

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 (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.

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.

Keywords: MRI; PET/CT; PET/MRI; axillary lymph node metastasis; breast cancer; logistic regression; macrometastasis; micrometastasis.