Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

A Validated Ultrasound-Based Scoring System to Stratify Risk of Axillary Metastasis in Breast Cancer: AX-RADS (Axillary Imaging Reporting and Data System)

INTRODUCTION

Ultrasound is the imaging modality of choice for evaluation of axillary involvement in breast cancer, but is associated with variable sensitivity and specificity. Understanding the risk of axillary lymph node metastasis (ALNM) based on ultrasonographic and clinical features will inform treatment decisions. Our group aimed to create a scoring system to quantify the risk of ALNM based on ultrasound characteristics in breast cancer patients. We validated the model and tested it among different Memorial Sloan Kettering Breast Cancer Sentinel Lymph Node Metastasis Nomogram (MSK) subgroups.

METHODS

The ultrasound score was developed using data collected at a single institution from 2019 to 2021 by allocating points based on the regression coefficients of variables found to significantly predict ALNM. We validated the test statistics of our score at an outside institution. The index and validation cohorts were combined: 358 pooled patients were stratified by predicted ALNM positivity according to a validated nomogram based on primary tumor characteristics.

RESULTS

Between 2019 and 2021, in the validation cohort, the NPV for low risk (0-1) scores was 87%, while the PPV for high-risk (5 +) scores was 71%. Overall, in the combined cohort, 241 (67%) patients had low-risk (0-1) axillary ultrasound scores and 33 (9%) had high risk (5 +) scores. In this combined cohort, NPV was 84% (203/241 low-risk score patients were node negative), while PPV for high-risk scores was 85% (28/33 high-risk score patients were node positive). When stratified via the Memorial Sloan Kettering Breast Cancer Nomogram: Sentinel Lymph Node Metastasis predicted ALNM rates, the NPV of low-risk scores was 87%-89% for patients with < 50% predicted ALNM positivity. For patients with > 50% predicted ALNM positivity, the PPV of high-risk scores was 82%.

CONCLUSIONS

A scoring system to predict ALNM among biopsy-proven breast cancer patients undergoing upfront surgery was successfully developed from a multivariate model based on axillary ultrasound characteristics. Combining the axillary US scoring system with an additional validated nomogram based on primary tumor and patient characteristics may help foster better communication about ALNM risk to inform treatment decisions.

Development and validation of a multivariable risk model based on clinicopathological characteristics, mammography, and MRI imaging features for predicting axillary lymph node metastasis in patients with upgraded ductal carcinoma in situ

Background

Axillary surgical staging is required for patients with upgraded ductal carcinoma in situ (DCIS) (DCIS is diagnosed on core biopsy with invasive cancer found on pathology after complete surgical excision), which may lead to complications in axillary surgery. At present, there is no reliable and accurate method for predicting axillary lymph node metastasis (ALNM) in patients with upgraded DCIS; however, such a method could prevent unnecessary axillary surgical interventions from being performed. In this study, we aimed to construct a non-invasive model for predicting ALNM in DCIS patients based on clinicopathological characteristics, mammography (MG) features, and magnetic resonance imaging (MRI) features.

Methods

Between February 2018 and June 2020, 326 patients with upgraded DCIS were enrolled in this retrospective analysis. These patients were randomly divided into the training cohort (80%) and validation cohort (20%). Univariate and multivariable regression analyses were conducted to identify the candidate pathological features, which then used to develop a clinicopathological model. The features of the 2-mm, 4-mm, and 6-mm intratumoral and peritumoral regions (T-PTR) were extracted to develop the MRI radiomics model, and two deep learning classification models were developed based on the medial-lateral oblique (MLO) and craniocaudal (CC) views of the MG. A fusion model was then established that combined these sub-models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), and other indicators were used to evaluate the performance of these models.

Results

The clinicopathological characteristics of the two cohorts were basically balanced. The AUC values of the clinicopathological model were 0.675 and 0.690 in the training and validation cohorts, respectively. The model based on the T-PTR of MRI showed promising predictive ability. Among the three MRI models, the T-PTR (4 mm) model showed the best predictivity both in the training (AUC =0.885) and validation cohorts (AUC =0.843). The AUC values for the deep learning models of the MG CC and MLO positions all exceeded 0.7, indicating reliable predictive performance. The fusion model that combined the three methods significantly improved the accuracy and robustness of ALNM prediction. In both the training (AUC =0.975) and validation (AUC =0.877) cohorts, the fusion model showed excellent performance.

Conclusions

We developed a fusion model that combined clinicopathological characteristics, MRI T-PTR (4 mm) radiomics, and MG-based deep learning. Our combined model showed promising performance in predicting ALNM in patients with upgraded DCIS.

Axillary lymph node metastasis in breast cancer: from historical axillary surgery to updated advances in the preoperative diagnosis and axillary management

Axillary lymph node status, which was routinely assessed by axillary lymph node dissection (ALND) until the 1990s, is a crucial factor in determining the stage, prognosis, and therapeutic strategy used for breast cancer patients. Axillary surgery for breast cancer patients has evolved from ALND to minimally invasive approaches. Over the decades, the application of noninvasive imaging techniques, machine learning approaches and emerging clinical prediction models for the detection of axillary lymph node metastasis greatly improves clinical diagnostic efficacy and provides optimal surgical selection. In this work, we summarize the historical axillary surgery and updated perspectives of axillary management for breast cancer patients.

Predicting Axillary Lymph Node Metastasis in Young Onset Breast Cancer: A Clinical-Radiomics Nomogram Based on DCE-MRI

Background

Young onset breast cancer, diagnosed in women under 50, is known for its aggressive nature and challenging prognosis. Precisely forecasting axillary lymph node metastasis (ALNM) is essential for customizing treatment plans and enhancing patient results.

Objective

This research sought to create and verify a clinical-radiomics nomogram that combines radiomic features from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) with standard clinical predictors to improve the accuracy of predicting ALNM in young breast cancer patients.

Methods

We performed a retrospective analysis at one facility, involving the creation and validation of a nomogram in two stages.At first, a medical model was developed utilizing conventional indicators like tumor dimensions, molecular classifications, multifocal presence, and MRI-determined ALN status.A more detailed clinical-radiomics model was subsequently developed by integrating radiomic characteristics derived from DCE-MRI images.These models were created using logistic regression analyses on a training dataset, and their effectiveness was assessed by measuring the area under the receiver operating characteristic curve (AUC) in a separate validation dataset.

Results

The clinical-radiomics nomogram surpassed the clinical-only model, recording an AUC of 0.892 in the training dataset and 0.877 in the validation dataset.Significant predictors included MRI-reported ALN status and select radiomic features, which markedly enhanced the model's predictive capacity.

Conclusion

Integrating radiomic features with clinical predictors in a nomogram significantly improves ALNM prediction in young onset breast cancer, providing a valuable tool for personalized treatment planning. This study underscores the potential of merging advanced imaging data with clinical insights to refine oncological predictive models. Future research should expand to multicentric studies and include genomic data to boost the nomogram's generalizability and precision.

Construction of a risk prediction model for axillary lymph node metastasis in breast cancer based on gray-scale ultrasound and clinical pathological features

Purpose

This study aimed to develop a model to predict the risk of axillary lymph node (ALN) metastasis in breast cancer patients, using gray-scale ultrasound and clinical pathological features.

Methods

A retrospective analysis of 212 breast cancer patients who met the inclusion criteria from January 2011 to December 2021 was carried out. Clinical and pathological characteristics, including age, tumor size, pathological type, molecular subtype, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and proliferation cell nuclear antigen (Ki-67), were examined. Preoperative ultrasound examinations were performed, and ultrasound radiomics features of breast cancer lesions were extracted using Pyradiomics software. The data was divided into training (70%) and testing (30%) sets. A predictive model for axillary lymph node metastasis (ALNM) was established by combining clinical and ultrasound features. The diagnostic performance of the model was evaluated using receiver operating characteristic (ROC) curves and five-fold cross-validation.

Results

The rate of lymph node metastasis was 41.51%. Using LASSO algorithm, 17 features linked to ALN metastasis were extracted from a comprehensive databank of 8 clinical features and 1314 ultrasound radiomic attributes. Of these, four were clinical-pathological features (tumor size, tumor type, age, and expression levels of the Ki-67 protein), and 13 were radiomic features. And the following features exhibited both high weights and correlation coefficients: tumor size (R=0.29, weight=0.071), tumor type (R=-0.24, weight=-0.048), wavelet-LH_glcm_Imc1 (R=0.28, weight=0.029363), wavelet-LH_glszm_SZNUN (R=-0.20, weight=-0.028507), and squareroot_ firstorder_ Minimum (R= -0.25, weight= -0.059). The ROC area under the curve for the model in the training and testing sets was 0.882 (95% CI: 0.830-0.935) and 0.853 (95% CI: 0.762-0.945), respectively. The predictive model demonstrated a sensitivity of 87.5% on the training set and 79.2% on the test set, with corresponding specificities of 75.0% and 77.5%, accuracy of 80.4% and 78.1%, respectively. When evaluated using 5-fold cross-validation, the model achieved an average test set area under the curve (AUC) of 0.799 and a training set AUC of 0.852.

Conclusion

The clinical-radiomic model has the potential to predict axillary lymph node metastasis in breast cancer.

Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer

PURPOSE

This study aims to develop and validate a deep learning radiomics nomogram (DLRN) for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients.

MATERIALS AND METHODS

We retrospectively enrolled 196 patients with non-specific invasive breast cancer confirmed by pathology, radiomics and deep learning features were extracted from unenhanced and biphasic (arterial and venous phase) contrast-enhanced CT, and the non-linear support vector machine was used to construct the radiomics signature and the deep learning signature, respectively. Next, a DLRN was developed with independent predictors and evaluated the performance of models in terms of discrimination and clinical utility.

RESULTS

Multivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and clinical n stage were independent predictors. The DLRN accurately predicted ALNM and yielded an area under the receiver operator characteristic curve of 0.893 (95% confidence interval, 0.814-0.972) in the validation set, with good calibration. Decision curve analysis confirmed that the DLRN had higher clinical utility than other predictors.

CONCLUSIONS

The DLRN had good predictive value for ALNM in breast cancer patients and provide valuable information for individual treatment.

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography

OBJECTIVE

To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer.

MATERIALS AND METHODS

This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance.

RESULTS

Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively.

CONCLUSION

CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

Development and validation of radiomics machine learning model based on contrast-enhanced computed tomography to predict axillary lymph node metastasis in breast cancer

Preoperative identification of axillary lymph node metastasis can play an important role in treatment selection strategy and prognosis evaluation. This study aimed to establish a clinical nomogram based on lymph node images to predict lymph node metastasis in breast cancer patients. A total of 193 patients with non-specific invasive breast cancer were divided into training (n = 135) and validation set (n = 58). Radiomics features were extracted from lymph node images instead of tumor region, and the least absolute shrinkage and selection operator logistic algorithm was used to select the extracted features and generate radiomics score. Then, the important clinical factors and radiomics score were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram, and the clinical benefit of using the nomogram was evaluated by decision curve analysis. We found that clinical N stage and radiomics score were independent clinical predictors. Besides, the nomogram accurately predicted axillary lymph node metastasis, yielding an area under the receiver operating characteristic curve of 0.95 (95% confidence interval 0.93-0.98) in the validation set, indicating satisfactory calibration. Decision curve analysis confirmed that the nomogram had higher clinical utility than clinical N stage or radiomics score alone. Overall, the nomogram based on radiomics features and clinical factors can help radiologists to predict axillary lymph node metastasis preoperatively and provide valuable information for individual treatment.

Radiomics model based on features of axillary lymphatic nodes to predict axillary lymphatic node metastasis in breast cancer

BACKGROUND

Breast cancer (BC) is among the most common cancers worldwide. Machine learning-based radiomics model could predict axillary lymph node metastasis (ALNM) of BC accurately.

PURPOSE

The purpose is to develop a machine learning model to predict ALNM of BC by focusing on the radiomics features of axillary lymphatic node (ALN).

METHODS

A group of 398 BC patients with 800 ALNs were retrospectively collected. A set of patient characteristics were obtained to form clinical factors. Three hundred and twenty-six radiomics features were extracted from each region of interest for ALN in contrast-enhanced computed tomography (CECT) image. A framework composed of four feature selection methods and 14 machine learning classification algorithms was systematically applied. A clinical model, a radiomics model, and a combined model were developed using a cross-validation approach and compared. Metrics of the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the performance of these models in the prediction of ALNM in BC.

RESULTS

Among the 800 cases of ALNs, there were 388 cases of positive metastasis (48.50%) and 412 cases of negative metastasis (51.50%). The baseline clinical model achieved the performance with an AUC = 0.8998 (95% CI [0.8540, 0.9457]). The radiomics model achieved an AUC = 0.9081 (95% CI [0.8640, 0.9523]). The combined model using the clinical factors and radiomics features achieved the best results with an AUC = 0.9305 (95% CI [0.8928, 0.9682]).

CONCLUSIONS

Combinations of feature selection methods and machine learning-based classification algorithms can develop promising predictive models to predict ALNM in BC using CECT features. The combined model of clinical factors and radiomics features outperforms both the clinical model and the radiomic model.

Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis

BACKGROUND

This study aimed to perform a meta-analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM) in breast cancer.

MATERIALS AND METHODS

Multiple electronic databases were systematically searched to identify relevant studies published before April 29, 2022: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The overall diagnostic odds ratio (DOR), sensitivity, specificity, and area under the curve (AUC) were calculated to evaluate the diagnostic performance of radiomic features for lymph node metastasis (LNM) in patients with breast cancer. Spearman's correlation coefficient was determined to assess the threshold effect, and meta-regression and subgroup analyses were performed to explore the possible causes of heterogeneity.

RESULTS

A total of 30 studies with 5611 patients were included in the meta-analysis. Pooled estimates suggesting overall diagnostic accuracy of radiomics in detecting LNM were determined: DOR, 23 (95% CI, 16-33); sensitivity, 0.86 (95% CI, 0.82-0.88); specificity, 0.79 (95% CI, 0.73-0.84); and AUC, 0.90 (95% CI, 0.87-0.92). The meta-analysis showed significant heterogeneity between sensitivity and specificity across the included studies, with no evidence for a threshold effect. Meta-regression and subgroup analyses showed that combined clinical factors, modeling method, region, and imaging modality (magnetic resonance imaging [MRI], ultrasound, computed tomography [CT], and X-ray mammography [MMG]) contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Furthermore, modeling methods, MRI, and MMG contributed to the heterogeneity in the specificity analysis (P < 0.05).

CONCLUSION

Our results show that radiomics has good diagnostic performance in predicting ALNM and SLNM in breast cancer. Thus, we propose this approach as a clinical method for the preoperative identification of LNM.

Clinical prediction model based on 18F-FDG PET/CT plus contrast-enhanced MRI for axillary lymph node macrometastasis

Purpose

Positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) are useful for detecting axillary lymph node (ALN) metastasis in invasive ductal breast cancer (IDC); however, there is limited clinical evidence to demonstrate the effectiveness of the combination of PET/CT plus MRI. Further axillary surgery is not recommended against ALN micrometastasis (lesion ≤2 mm) seen in sentinel lymph nodes, especially for patients who received proper adjuvant therapy. We aimed to evaluate the efficacy of a prediction model based on PET/CT plus MRI for ALN macrometastasis (lesion >2 mm) and explore the possibility of risk stratification of patients using the preoperative PET/CT plus MRI and biopsy findings.

Materials and methods

We retrospectively investigated 361 female patients (370 axillae; mean age, 56 years ± 12 [standard deviation]) who underwent surgery for primary IDC at a single center between April 2017 and March 2020. We constructed a prediction model with logistic regression. Patients were divided into low-risk and high-risk groups using a simple integer risk score, and the false negative rate for ALN macrometastasis was calculated to assess the validity. Internal validation was also achieved using a 5-fold cross-validation.

Results

The PET/CT plus MRI model included five predictor variables: maximum standardized uptake value of primary tumor and ALN, primary tumor size, ALN cortical thickness, and histological grade. In the derivation (296 axillae) and validation (74 axillae) cohorts, 54% and 61% of patients, respectively, were classified as low-risk, with a false-negative rate of 11%. Five-fold cross-validation yielded an accuracy of 0.875.

Conclusions

Our findings demonstrate the validity of the PET/CT plus MRI prediction model for ALN macrometastases. This model may aid the preoperative identification of low-risk patients for ALN macrometastasis and provide helpful information for PET/MRI interpretation.

Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

Medical diagnostic imaging is essential for the differential diagnosis of cervical lymphadenopathy. Here we develop an ultrasound radiomics method for accurately differentiating cervical lymph node tuberculosis (LNTB), cervical lymphoma, reactive lymph node hyperplasia, and metastatic lymph nodes especially in the multi-operator, cross-machine, multicenter context. The inter-observer and intra-observer consistency of radiomics parameters from the region of interest were 0.8245 and 0.9228, respectively. The radiomics model showed good and repeatable diagnostic performance for multiple classification diagnosis of cervical lymphadenopathy, especially in LNTB (area under the curve, AUC: 0.673, 0.662, and 0.626) and cervical lymphoma (AUC: 0.623, 0.644, and 0.602) in the whole set, training set, and test set, respectively. However, the diagnostic performance of lymphadenopathy among skilled radiologists was varied (Kappa coefficient: 0.108, *p < 0.001). The diagnostic performance of radiomics is comparable and more reproducible compared with those of skilled radiologists. Our study offers a more comprehensive method for differentiating LNTB, cervical lymphoma, reactive lymph node hyperplasia, and metastatic LN.

The Value of Shear Wave Elastography in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis and Its Correlation With Molecular Classification of Breast Masses

OBJECTIVE

To explore the diagnostic value of shear wave elastography examination (SWE) on axillary node metastasis (ANM) in breast cancer, this study aimed to evaluate the correlation between the SWE features and different molecular types of breast cancer, and to check the elastic modulus differences among the molecular types.

METHODS

Breast cancer patients from November 2020 to December 2021 were subjected to both conventional ultrasonic examination (CUE) and SWE before ultrasound-guided percutaneous biopsy or axillary lymph node dissection (ALND). We used the pathological results as the gold standard to draw the receiver operating characteristic (ROC) curve.

RESULTS

SWE outperforms CUE, but their conjunctive use is the best option. No significant correlation was found between the elastic modulus values and the molecular types of breast cancer.

CONCLUSION

SWE can be used as an routine auxiliary method of CUE for ANM.