Preoperative axillary lymph node staging in early breast cancer: Diagnostic efficacy and histopathology correlation

Silva, Filipa Ferreira da; Orvalho, Maria de Lurdes; Gaspar, Augusto; Lopes, Paulina; Faria, João Leal de; Catarino, Ana; Nave, Mónica; Passos‐Coelho, José Luís

doi:10.1111/tbj.13471

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Axillary US can evaluate nodal morphology real-time, guide fine-needle biopsies, and was useful for predicting pN2 or higher ALN disease in BC patients (8). While traditional US mainly obtains visual image information and focuses on the qualitative analysis of tumors (2), the diagnostic performances of US to detect ALN involvement varies between studies (8)(9)(10)(11). Given the limited ability of traditional US to evaluate ALN involvement, developing an applicable non-invasive method for detecting ALN metastasis has been a challenge.…”

Section: Original Articlementioning

confidence: 99%

Preoperative prediction of axillary lymph node metastasis in patients with breast cancer based on radiomics of gray-scale ultrasonography

et al. 2021

View full text Add to dashboard Cite

Background: To investigate the performance of a radiomics model based on gray-scale ultrasonography (US) for the preoperative non-invasive prediction of ipsilateral axillary lymph node (ALN) metastasis in patients with breast cancer (BC).Methods: A total of 192 pathologically confirmed BC patients were included in this study. The training set was comprised of 132 patients from hospital 1 and the test set was comprised of 60 patients from hospital 2.All patients underwent US before percutaneous core biopsy and the results of ALN status reported by a radiologist with 12 years of experience were recorded. Radiomic features were extracted from the grayscale US images. Max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) were used for data dimension reduction and feature selection. A radiomics model was constructed using LASSO and was validated using the leave group out cross-validation (LGOCV) method.The performance of the model was validated with receiver operating characteristic (ROC), calibration curve, and decision curve analysis.Results: A total of 860 features were extracted from the gray-scale US images of each breast lesion, and 9 radiomic features were selected for model construction. The area under the curve (AUC), sensitivity, and specificity of the model for predicting ALN metastasis were 0.85, 78.9%, and 77.3% in the training set and 0.65, 68.0%, and 79.4% in the test set, respectively. The prediction performance of the model was significantly higher than that of the radiologist (AUC: 0.85 vs. 0.59, P<0.01) in the training set and was slightly higher than that of the radiologist (AUC: 0.65 vs. 0.63, P>0.05) in the test set. Conclusions:The non-invasive radiomics model has the ability to predict ALN metastasis for patients with breast cancer and may outperform US-reported ALN status performed by the radiologist.

show abstract