Abstract

Simple SummaryThe presence of axillary lymph node metastases in breast cancer patients is an essential factor in axillary surgery and possible additional treatment. This study aimed to investigate the potential of dedicated axillary MRI-based radiomics analysis for the prediction of axillary lymph node metastases. Dedicated axillary MRI examinations provide a very specific and complete field of view of the axilla. Accurate preoperative prediction of axillary lymph node metastases in breast cancer patients using radiomics analysis can aid in clinical decision-making for the type of treatment.Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51–68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41–0.74 and 0.48–0.89 in the training cohorts, respectively, and between 0.30–0.98 and 0.37–0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.

Highlights

  • In breast cancer patients, the axillary lymph node status provides essential prognostic information about the locoregional recurrence and overall survival rate [1,2,3,4]

  • This study aimed to investigate the potential of dedicated axillary magnetic resonance imaging (MRI)-based radiomics analysis for the prediction of axillary lymph node metastases

  • Fourteen of the included patients were node-positive at final pathology, with a total of 36 axillary lymph nodes with macrometastases and 58 axillary lymph nodes without metastasis

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Summary

Introduction

The axillary lymph node status provides essential prognostic information about the locoregional recurrence and overall survival rate [1,2,3,4]. The five-year survival rate decreases from 99% to 85% with the presence of lymph node metastasis in the axilla [5]. In the preoperative setting, imaging for axillary lymph node assessment is recommended in the clinical workup of invasive breast cancer patients [6]. For the evaluation of tumor extent in the breast or following neoadjuvant treatment, breast magnetic resonance imaging (MRI) is often performed, which includes the axilla in the field of view [8]. When using dedicated breast coils, the field of view of the axillary region can be limited [9]. Dedicated unenhanced T2-weighted (T2W) axillary MRI showed good diagnostic performance based on node-by-node analysis but remained insufficient to accurately exclude axillary lymph node metastasis [12]

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