Predicting Axillary Lymph Node Status With a Nomogram Based on Breast Lesion Ultrasound Features: Performance in N1 Breast Cancer Patients

Luo, Yanwen; Zhao, Chenyang; Gao, Yuanjing; Xiao, Mengsu; Li, Wenbo; Zhang, Jing; Ma, Li; Qin, Jing; Jiang, Yuxin; Zhu, Qingli

doi:10.3389/fonc.2020.581321

“…At present, many studies evaluated the relationship between the axillary lymph node ultrasound characteristics and the properties of axillary lymph nodes. Some research reported that the L/S of axillary lymph nodes is of great significance for judging metastasis of axillary lymph nodes [ 12 , 13 ]. Some researchers believe that disappearance of lymph node hilum is helpful for judging metastasis of lymph nodes.…”

Section: Discussionmentioning

confidence: 99%

“…Du et al [ 15 ] reported that the morphology and blood flow grading of axillary lymph nodes in ultrasound signs were independent influencing factors for predicting lymph node metastasis. Luo et al [ 12 ] constructed an ROC curve for predicting axillary lymph node status using ultrasound signs (lymph node size, lesion boundary). Its AUC reached 0.75 (95% CI: 0.70-0.79) and 0.91 (95% CI: 0.84-0.97), indicating a reliable predictive ability.…”

Section: Discussionmentioning

confidence: 99%

Clinical Value of Preoperative Ultrasound Signs in Evaluating Axillary Lymph Node Status in Triple-Negative Breast Cancer

Wang

¹

,

Liu

²

,

Zheng

³

et al. 2022

Journal of Oncology

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Purpose. To explore the clinical value of preoperative ultrasound signs in evaluating axillary lymph node status in triple-negative breast cancer (TNBC). Methods. A retrospective study was conducted on 162 patients with TNBC who were admitted to our hospital from January 2017 to June 2021. A total of 62 patients with axillary lymph node metastasis and 100 patients with normal axillary lymph nodes were included. Univariate and logistic regression was used to analyze the correlation between clinicopathological parameters, ultrasound features, and axillary lymph node metastasis between these two groups. The receiver operating characteristic (ROC) curve of each index was drawn to predict positive axillary lymph node. Results. The lymph node positive rate was higher in patients with tumor size ( 2 mm < T ≤ 5 mm ) and tumor stage III, and the difference between these two groups was statistically significant ( P < 0.05 ). The patients with cortical thickness ≥ 3 , blood flow grades II-III, aspect ratio L / S ≥ 2 , and RI ≥ 0.7 had higher lymph node positive rate, and the difference between these two groups was statistically significant ( P < 0.05 ). Other index shows no correlation with ancillary lymph node positive rate, or the correlation was not statistically significant ( P > 0.05 ). Further regression analysis indicated that the blood flow grade and L/S of axillary lymph nodes were independent influencing factors of axillary lymph node metastasis in TNBC patients ( P < 0.05 ). Relevant receiver operating characteristic (ROC) curves were constructed, and the AUC of axillary lymph node blood flow grade and L/S for predicting axillary lymph node status was 0.6329 and 0.6498, respectively. The AUC for the joint prediction of the two indicators is 0.6898. Conclusion. Ultrasound sign combined with clinicopathological characteristics can predict the axillary lymph nodes metastasis in TNBC, which could guide clinical decision of axillary lymph node surgery.

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“…Li et al (26) found that breast cancer with calcification had a higher rate of lymph node metastasis. Luo et al (27,28) found that the tumor pathological type, tumor burr sign, and calcification characteristics were related to axillary lymph node metastasis. These studies are consistent with the findings of the present study.…”

Section: Figurementioning

confidence: 99%

A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP

Zhang

¹

,

Shi

²

,

Yin

³

et al. 2022

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BackgroundThis study aimed to determine an optimal machine learning (ML) model for evaluating the preoperative diagnostic value of ultrasound signs of breast cancer lesions for sentinel lymph node (SLN) status.MethodThis study retrospectively analyzed the ultrasound images and postoperative pathological findings of lesions in 952 breast cancer patients. Firstly, the univariate analysis of the relationship between the ultrasonographic features of breast cancer morphological features and SLN metastasis. Then, based on the ultrasound signs of breast cancer lesions, we screened ten ML models: support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), linear discriminant analysis (LDA), logistic regression (LR), naive bayesian model (NB), k-nearest neighbors (KNN), multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN). The diagnostic performance of the model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), Kappa value, accuracy, F1-score, sensitivity, and specificity. Then we constructed a clinical prediction model which was based on the ML algorithm with the best diagnostic performance. Finally, we used SHapley Additive exPlanation (SHAP) to visualize and analyze the diagnostic process of the ML model.ResultsOf 952 patients with breast cancer, 394 (41.4%) had SLN metastasis, and 558 (58.6%) had no metastasis. Univariate analysis found that the shape, orientation, margin, posterior features, calculations, architectural distortion, duct changes and suspicious lymph node of breast cancer lesions in ultrasound signs were associated with SLN metastasis. Among the 10 ML algorithms, XGBoost had the best comprehensive diagnostic performance for SLN metastasis, with Average-AUC of 0.952, Average-Kappa of 0.763, and Average-Accuracy of 0.891. The AUC of the XGBoost model in the validation cohort was 0.916, the accuracy was 0.846, the sensitivity was 0.870, the specificity was 0.862, and the F1-score was 0.826. The diagnostic performance of the XGBoost model was significantly higher than that of experienced radiologists in some cases (P<0.001). Using SHAP to visualize the interpretation of the ML model screen, it was found that the ultrasonic detection of suspicious lymph nodes, microcalcifications in the primary tumor, burrs on the edge of the primary tumor, and distortion of the tissue structure around the lesion contributed greatly to the diagnostic performance of the XGBoost model.ConclusionsThe XGBoost model based on the ultrasound signs of the primary breast tumor and its surrounding tissues and lymph nodes has a high diagnostic performance for predicting SLN metastasis. Visual explanation using SHAP made it an effective tool for guiding clinical courses preoperatively.

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“…A multivariate analysis is performed in [ 40 ] to diagnose breast cancer to get the confidence interval of 95% of the area under the curve (AUC), which is shown for the primary cohort as 0.75 and 0.91 for the external cohort. This study achieved 88% sensitivity.…”

Section: Related Workmentioning

confidence: 99%

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

Irfan

¹

,

Almazroi

²

,

Rauf

³

et al. 2021

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Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

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Predicting Axillary Lymph Node Status With a Nomogram Based on Breast Lesion Ultrasound Features: Performance in N1 Breast Cancer Patients

Cited by 13 publications

References 23 publications

Clinical Value of Preoperative Ultrasound Signs in Evaluating Axillary Lymph Node Status in Triple-Negative Breast Cancer

Clinical Value of Preoperative Ultrasound Signs in Evaluating Axillary Lymph Node Status in Triple-Negative Breast Cancer

A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

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