Imaging Predictors for Nonsentinel Lymph Node Metastases in Breast Cancer Patients

Developing and Evaluating a Nomogram Model Predicting Axillary Lymph Node Metastasis of Triple-Negative Breast Cancer Based on Multimodal Imaging Characteristics

RATIONALE AND OBJECTIVES

Breast cancer is the most frequently diagnosed cancer among women worldwide, with axillary lymph nodes being common sites of metastasis, particularly triple-negative breast cancer (TNBC), which is the subtype with the poorest prognosis. This study aimed to develop a nomogram model to predict axillary lymph node metastasis (ALNM) in TNBC patients based on mammography (MG), multimodal ultrasound (US), and clinical pathological characteristics.

PATIENTS AND METHODS

A retrospective study was performed on 291 patients diagnosed with TNBC from two centers. Patients from the Center 1 were randomly divided into a training cohort (n = 159) and a internal test cohort (n = 68) using a 7:3 ratio, while patients from the Center 2 served as an external test cohort. Each group was further divided into an ALNM group and a non-ALNM group based on the presence or absence of ALNM. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic analysis. The predictive performance of the nomogram model was evaluated by the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

Notable predictors included MG_reported_margin, MG_reported_suspicious malignant calcifications, MG_reported_abnormal ALN, elastography score, and US_reported_abnormal ALN. The area under the receiver operating characteristics curve (AUC) value of the nomogram model was 0.931 (95%CI: 0.890-0.973) for the training cohort, AUC=0.929 (95%CI: 0.871-0.986) for the internal test cohort and AUC=0.891 (95%CI: 0.794-0.987) for the external test cohort. Calibration curves and DCA both suggested that the nomogram exhibited favorable calibration and clinical utility.

CONCLUSION

The predictive model combined with multimodal US and MG characteristics developed in this study is highly accurate, serves as a powerful tool for clinical assessment, and shows promise for predicting ALNM in patients with TNBC.

Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration

IntroductionThe study aims to evaluate the performance of an interpretable machine learning model in predicting preoperative axillary lymph node metastasis using primary breast cancer and lymph node features derived from contrast-enhanced mammography (CEM) and ultrasound (US) breast imaging reporting and data systems (BI-RADS).MethodsThis retrospective study included patients diagnosed with primary breast cancer. Two experienced radiologists extracted the BI-RADS features from the largest cross-section of the lesions and axillary lymph nodes based on CEM and US images, creating three datasets. Each dataset will train six base models to predict axillary lymph nodes, with pathological results serving as the gold standard. The top three models were used to train the five ensemble models. Additionally, SHapley Additive exPlanations (SHAP) was used to interpret the optimal model. The receiver-operating characteristic curve (ROC) and AUC were used to evaluate model performance.ResultsThis study involved 292 female patients, of whom 99 had axillary lymph node metastasis and 193 did not. The combination of CEM and ultrasound BI-RADS demonstrated the best performance in predicting axillary lymph node metastasis. Among these, the LightGBM achieved the highest AUC (0.762) and specificity (86.67%, while the ensemble model using RF as the meta-model had an AUC (0.754) and specificity (83.33%. The most important variables identified by SHAP were the long diameters of the lymph nodes in the CEM recombined image, along with their complete morphology in the low-energy image.ConclusionThe machine learning model using CEM and US BI-RADS features accurately predicted axillary lymph node metastasis before surgery, thereby serving as a valuable tool for clinical decision-making in patients with breast cancer.

Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer

OBJECTIVE

To develop a nomogram for predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer.

METHODS

We included 307 patients with clinicopathologically confirmed invasive breast cancer. The cohort was divided into a training group (n=215) and a validation group (n=92). Ultrasound images were used to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) algorithm helped select pertinent features, from which Radiomics Scores (Radscores) were calculated using the LASSO regression equation. We developed three logistic regression models based on Radscores and 2D image features, and assessed the models' performance in the validation group. A nomogram was created from the best-performing model.

RESULTS

In the training set, the area under the curve (AUC) for the Radscore model, 2D feature model, and combined model were 0.76, 0.85, and 0.88, respectively. In the validation set, the AUCs were 0.71, 0.78, and 0.83, respectively. The combined model demonstrated good calibration and promising clinical utility.

CONCLUSION

Our ultrasound-based radiomics nomogram can accurately and non-invasively predict ALNM in breast cancer, suggesting potential clinical applications to optimize surgical and medical strategies.

Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer

RATIONALE AND OBJECTIVES

This study aimed to create a nomogram model that combines clinical factors with radiomics analysis of both intra- and peritumoral regions extracted from preoperative digital breast tomosynthesis (DBT) images, in order to develop a reliable method for predicting the lymphovascular invasion (LVI) status in invasive breast cancer (IBC) patients.

MATERIALS AND METHODS

A total of 178 patients were randomly split into a training dataset (N = 124) and a validation dataset (N = 54). Comprehensive clinical data, encompassing DBT features, were gathered for all cases. Radiomics features were extracted and selected from intra- and peritumoral region to establish radiomics signature (Radscore). To construct the clinical model and nomogram model, univariate and multivariate logistic regression analyses were utilized to identify independent risk factors. To assess and validate these models, various analytical methods were employed, including receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI).

RESULTS

The clinical model is constructed based on two independent risk factors: tumor margin and the DBT-reported lymph node metastasis (DBT_reported_LNM). Incorporating Radscore_Combine (utilizing both intra- and peritumoral radiomics features), tumor margin, and DBT_reported_LNM into the nomogram achieved a reliable predictive performance, with area under the curve (AUC) values of 0.906 and 0.905 in both datasets, respectively. The significant improvement demonstrated by the NRI and IDI indicates that the Radscore_Combine could be a valuable biomarker for effectively predicting the status of LVI.

CONCLUSION

The nomogram demonstrated a reliable ability to predict LVI in IBC patients.

Radiomics nomogram based on digital breast tomosynthesis: preoperative evaluation of axillary lymph node metastasis in breast carcinoma

PURPOSE

This study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma.

METHODS

The data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted.

RESULTS

The clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status.

CONCLUSION

The radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer.

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning

BACKGROUND

Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients.

METHODS

From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared.

RESULTS

NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706).

CONCLUSIONS

The ML model XGBoost can well predict NSLNM in breast cancer patients.

Contrast-Enhanced Spectral Mammography-Based Prediction of Non-Sentinel Lymph Node Metastasis and Axillary Tumor Burden in Patients With Breast Cancer

Purpose

To establish and evaluate non-invasive models for estimating the risk of non-sentinel lymph node (NSLN) metastasis and axillary tumor burden among breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs).

Materials and Methods

Breast cancer patients with 1–2 positive SLNs who underwent axillary lymph node dissection (ALND) and contrast-enhanced spectral mammography (CESM) examination were enrolled between 2018 and 2021. CESM-based radiomics and deep learning features of tumors were extracted. The correlation analysis, least absolute shrinkage and selection operator (LASSO), and analysis of variance (ANOVA) were used for further feature selection. Models based on the selected features and clinical risk factors were constructed with multivariate logistic regression. Finally, two radiomics nomograms were proposed for predicting NSLN metastasis and the probability of high axillary tumor burden.

Results

A total of 182 patients [53.13 years ± 10.03 (standard deviation)] were included. For predicting the NSLN metastasis status, the radiomics nomogram built by 5 selected radiomics features and 3 clinical risk factors including the number of positive SLNs, ratio of positive SLNs, and lymphovascular invasion (LVI), achieved the area under the receiver operating characteristic curve (AUC) of 0.85 [95% confidence interval (CI): 0.71–0.99] in the testing set and 0.82 (95% CI: 0.67–0.97) in the temporal validation cohort. For predicting the high axillary tumor burden, the AUC values of the developed radiomics nomogram are 0.82 (95% CI: 0.66–0.97) in the testing set and 0.77 (95% CI: 0.62–0.93) in the temporal validation cohort.

Discussion

CESM images contain useful information for predicting NSLN metastasis and axillary tumor burden of breast cancer patients. Radiomics can inspire the potential of CESM images to identify lymph node metastasis and improve predictive performance.