Wei-Jun Zhou1,2, Yi-Dan Zhang3, Wen-Tao Kong3, Chao-Xue Zhang2, Bing Zhang1,4. 1. Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China. 2. Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China. 3. Department of Ultrasound, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China. 4. Institute of Brain Science, Nanjing University, Nanjing, China.
Abstract
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 gray-scale 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. 2021 Gland Surgery. All rights reserved.
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 gray-scale 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. 2021 Gland Surgery. All rights reserved.
Entities:
Keywords:
Radiomics; axillary lymph node (ALN); breast cancer (BC); gray-scale; ultrasound
Authors: Filipa Ferreira da Silva; Maria de Lurdes Orvalho; Augusto Gaspar; Paulina Viana Lopes; João Leal de Faria; Ana Catarino; Mónica Nave; José Luís Passos-Coelho Journal: Breast J Date: 2019-07-23 Impact factor: 2.431
Authors: Patricia Akissue de Camargo Teixeira; Luciano F Chala; Carlos Shimizu; José R Filassi; Jonathan Y Maesaka; Nestor de Barros Journal: Ultrasound Med Biol Date: 2017-06-17 Impact factor: 2.998