Ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases

Development and Validation of a Nomogram for Axillary Lymph Node Metastasis Risk in Breast Cancer

Purpose: Preoperative assessment of axillary lymph node (ALN) status is essential for breast cancer treatment planning. This study prospectively analyzed risk factors for ALN metastasis by comparing high-resolution computed tomography (HRCT) imaging with pathology and developed a nomogram to aid in diagnosis. Methods: From April 2023 to May 2024, breast cancer patients confirmed by pathology participated in the study. All had chest HRCT before surgery, and ALN samples were anatomically matched to HRCT imaging and pathology. The least absolute shrinkage and selection operator (LASSO) regression helped refine metastasis features, and a nomogram was constructed using the final selected features determined by multivariate logistic regression. The nomogram's performance was evaluated with concordance index (C-index), calibration plot, and decision curve analysis, with internal validation through bootstrapping. Results: A total of 302 ALN from 98 patients were included in this study. The predictors included in the nomogram encompassed the mean CT value, short diameter, border, and shape of ALN, as well as the Ki-67 status and histological grade of the primary tumor. The model exhibited satisfactory discrimination, with a C-index of 0.869 (95% CI: 0.826-0.912) and an AUC of 0.862 (95% CI, 0.815-0.909). The calibration curve demonstrated a high degree of concordance between the predicted and actual probabilities. The decision curve analysis demonstrated that the nomogram was clinically useful when the threshold for intervention was set at the metastasis possibility range of 1% to 86%. Conclusion: The nomogram combined with preoperative pathology and HRCT imaging have the potential to improve the evaluation of ALN status.

Artificial intelligence-based pathological application to predict regional lymph node metastasis in Papillary Thyroid Cancer

In this study, a model for predicting lymph node metastasis in papillary thyroid cancer was trained using pathology images from the TCGA(The Cancer Genome Atlas) public dataset of papillary thyroid cancer, and a front-end inference model was trained using our center's dataset based on the concept of probabilistic propagation of nodes in graph neural networks. Effectively predicting whether a tumor will spread to regional lymph nodes using a single pathological image is the capacity of the model described above. This study demonstrates that regional lymph nodes in papillary thyroid cancer are a common and predictable occurrence, providing valuable ideas for future research. Now we publish the above research process and code for further study by other researchers, and we also make the above inference algorithm public at the URL: http:// thyroid-diseases-research.com/, with the hope that other researchers will validate it and provide us with ideas or datasets for further study.

Value of Contrast-Enhanced Ultrasound Combined with Immune-Inflammatory Markers in Predicting Axillary Lymph Node Metastasis of Breast Cancer

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with immune-inflammatory markers in predicting axillary lymph node metastasis (ALNM) in breast cancer patients.

METHODS

From January 2020 to June 2023, the clinicopathological data and ultrasound features of 401 breast cancer patients who underwent biopsy or surgery were recorded. Patients were randomly divided into a training set (321 patients) and a validation set (80 patients). The risk factors for ALNM were determined using univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, and prediction models were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess their diagnostic performance.

RESULTS

Logistic regression analysis demonstrated that systemic immunoinflammatory index (SII), CA125, Ki67, pathological type, lesion size, enhancement pattern and Breast Imaging Reporting and Data System (BI-RADS) category were significant risk factors for ALNM. Three different models were constructed, and the combined model yielded an AUC of 0.903, which was superior to the clinical model (AUC=0.790) and ultrasound model (AUC=0.781). A nomogram was constructed based on the combined model, calibration curves and DCA demonstrated its satisfactory performance in predicting ALNM.

CONCLUSION

The nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status

Axillary staging is commonly performed via sentinel lymph node biopsy for patients with early breast cancer (EBC) presenting with clinically negative axillary lymph nodes (cN0). The present study aimed to investigate the association between axillary lymph node metastasis (ALNM), clinicopathological characteristics of tumors and results from axillary ultrasound (US) scanning. Moreover, a nomogram model was developed to predict the risk for ALNM based on relevant factors. Data from 998 patients who met the inclusion criteria were retrospectively reviewed. These patients were then randomly divided into a training and validation group in a 7:3 ratio. In the training group, receiver operating characteristic curve analysis was used to identify the cutoff values for continuous measurement data. R software was used to identify independent ALNM risk variables in the training group using univariate and multivariate logistic regression analysis. The selected independent risk factors were incorporated into a nomogram. The model differentiation was assessed using the area under the curve (AUC), while calibration was evaluated through calibration charts and the Hosmer-Lemeshow test. To assess clinical applicability, a decision curve analysis (DCA) was conducted. Internal verification was performed via 1000 rounds of bootstrap resampling. Among the 998 patients with EBC, 228 (22.84%) developed ALNM. Multivariate logistic analysis identified lymphovascular invasion, axillary US findings, maximum diameter and molecular subtype as independent risk factors for ALNM. The Akaike Information Criterion served as the basis for both nomogram development and model selection. Robust differentiation was shown by the AUC values of 0.855 (95% CI, 0.817-0.892) and 0.793 (95% CI, 0.725-0.857) for the training and validation groups, respectively. The Hosmer-Lemeshow test yielded P-values of 0.869 and 0.847 for the training and validation groups, respectively, and the calibration chart aligned closely with the ideal curve, affirming excellent calibration. DCA showed that the net benefit from the nomogram significantly outweighed both the 'no intervention' and the 'full intervention' approaches, falling within the threshold probability interval of 12-97% for the training group and 17-82% for the validation group. This underscores the robust clinical utility of the model. A nomogram model was successfully constructed and validated to predict the risk of ALNM in patients with EBC and cN0 status. The model demonstrated favorable differentiation, calibration and clinical applicability, offering valuable guidance for assessing axillary lymph node status in this population.

New Insight for Axillary De-Escalation in Breast Cancer Surgery: "SoFT Study" Retrospective Analysis

Background: The SOUND study demonstrated that an axillary de-escalation may be sufficient in locoregional and distant disease control in selected early breast cancer (EBC) patients. To establish any preoperative variables that may drive sentinel lymph node biopsy (SLNB) omission, a study named sentinel omission risk factor (SOFT) 1.23 was planned. Methods: A single-center retrospective study from a prospectively maintained database was designed, aiming at underlying preoperative prognostic factors involved in sentinel lymph node (SLN) metastasis (lymph node involvement (LN+) vs. negative lymph node (LN-) group). Secondary outcomes included surgical room occupancy analysis for SLNB in patients fulfilling the SOUND study inclusion criteria. The institutional ethical committee Area Territoriale Lazio 2 approved the study (n° 122/23). Results: Between 1 January 2022 and 30 June 2023, 160 patients were included in the study and 26 (%) were included in the LN+ group. Multifocality, higher cT stage, and larger tumor diameter were reported in the LN+ group (p = 0.020, p = 0.014, and 0.016, respectively). Tumor biology, including estrogen and progesterone receptors, and molecular subtypes showed association with the LN+ group (p < 0.001; p = 0.001; and p = 0.001, respectively). A total of 117 (73.6%) patients were eligible for the SOUND study and the potential operating room time saved was 2696.81 min. Conclusions: De-escalating strategies may rationalize healthcare activities. Multifactorial risk stratification may further refine the selection of patients who could benefit from SLNB omission.

A cutting-edge deep learning-and-radiomics-based ultrasound nomogram for precise prediction of axillary lymph node metastasis in breast cancer patients ≥ 75 years

Objective

The objective of this study was to develop a deep learning-and-radiomics-based ultrasound nomogram for the evaluation of axillary lymph node (ALN) metastasis risk in breast cancer patients ≥ 75 years.

Methods

The study enrolled breast cancer patients ≥ 75 years who underwent either sentinel lymph node biopsy or ALN dissection at Fudan University Shanghai Cancer Center. DenseNet-201 was employed as the base model, and it was trained using the Adam optimizer and cross-entropy loss function to extract deep learning (DL) features from ultrasound images. Additionally, radiomics features were extracted from ultrasound images utilizing the Pyradiomics tool, and a Rad-Score (RS) was calculated employing the Lasso regression algorithm. A stepwise multivariable logistic regression analysis was conducted in the training set to establish a prediction model for lymph node metastasis, which was subsequently validated in the validation set. Evaluation metrics included area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. The calibration of the model's performance and its clinical prediction accuracy were assessed using calibration curves and decision curves respectively. Furthermore, integrated discrimination improvement and net reclassification improvement were utilized to quantify enhancements in RS.

Results

Histological grade, axillary ultrasound, and RS were identified as independent risk factors for predicting lymph node metastasis. The integration of the RS into the clinical prediction model significantly improved its predictive performance, with an AUC of 0.937 in the training set, surpassing both the clinical model and the RS model alone. In the validation set, the integrated model also outperformed other models with AUCs of 0.906, 0.744, and 0.890 for the integrated model, clinical model, and RS model respectively. Experimental results demonstrated that this study's integrated prediction model could enhance both accuracy and generalizability.

Conclusion

The DL and radiomics-based model exhibited remarkable accuracy and reliability in predicting ALN status among breast cancer patients ≥ 75 years, thereby contributing to the enhancement of personalized treatment strategies' efficacy and improvement of patients' quality of life.

Establishment of a logistic regression model nomogram for clinicopathological characteristics and risk factors with axillary lymph node metastasis in T1 locally advanced breast cancer: a retrospective study

Background

Although the research reports on locally advanced breast cancer (LABC) are increasing year by year, there are few reports on T1 LABC axillary lymph node metastasis (ALNM). By establishing a prediction model for T1 LABC ALNM, this study provides a reference value for the probability of ALNM of related patients, which helps clinicians to develop a more effective and individualized treatment plan for LABC.

Methods

Cases with pathologically confirmed T1 breast cancer (BC) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were identified. Logistic regression was used to analyze the correlation between LABC lymph node metastasis and every factor, and the odds ratio (OR) and 95% confidence interval (CI) were used to identify any influencing factors. A nomogram was drawn after incorporating meaningful factors identified in multivariate logistic regression into the model. The receiver operating characteristic (ROC) curve of the model was drawn, and the area under the curve (AUC) and its 95% CI were calculated. Hosmer-Lemeshow goodness-of-fit test and clinical decision curve analysis (DCA) were performed. The results were validated in the validation group.

Results

A total of 200,933 female T1 BC patients were included in this study. Univariate and multivariate logistic regression analysis of T1 BC showed that progesterone receptor (PR)-negative, race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and larger tumor diameter increased the probability of ALNM in T1 LABC. A predictive nomogram was established using the above predictors, the AUC of the modeling group was 0.739 (95% CI: 0.732-0.747), and when the AUC cut-off value was 0.026, the specificity and sensitivity of the model were 65.78% and 69.99%, respectively. Validation of the model showed that the AUC of the validation group (n=60,280) was 0.741. When all the risk factors were met, the predicted probability of N2-N3 was 50.40%.

Conclusions

In this study, it was found that PR-negative, Black race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and tumor diameter increased the probability of large lymph node metastasis in T1 LABC small tumors.

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.

Factors Associated With Node-Positive Disease in Estrogen Receptor-Positive Breast Cancer Patients

INTRODUCTION

Larger tumor size and shorter tumor-to-nipple distance at diagnosis are associated with greater risk of lymph node involvement in breast cancer. However, the relationship between receptor subtype status and lymph node metastasis remains unclear. Our objective was to examine the association between primary tumor size, location, and nodal metastasis across estrogen receptor (ER)+/ progesterone receptor (PR)+/ human epidermal growth factor receptor 2 (HER2)-, ER+/PR-/HER2-, ER+/PR+/HER2+, and ER+/PR-/HER2+ tumors.

METHODS

A single-institution retrospective chart review was conducted of breast cancer patients diagnosed between 1998 and 2019 who underwent nodal evaluation during primary surgery. Neoadjuvant chemotherapy, pure ductal carcinoma in situ, inflammatory, recurrent, metastatic, bilateral, multicentric, and multifocal disease were excluded. Descriptive statistics (proportions and frequencies for categorical variables and medians [Q1-Q3] for continuous variables) were used to summarize patient characteristics. Kruskal-Wallis test was applied to test the association of outcome variables and continuous variables. Chi-square test or Fisher exact test was applied to test the association of outcome variables and categorical variables.

RESULTS

Six hundred eighteen ER + patients had a median tumor size of 1.7 cm (1.1-2.5 cm). Two hundred ninety six out of 618 (47.9%) were node-positive and 188/618 (30.4%) had axillary dissection. Eighty four point three percent of patients were ER+/PR+/HER2-, 6.31% were ER+/PR-/HER2-, 6.96% were ER+/PR+/HER2+, and 1.13% were ER+/PR-/HER2+. Median tumor size was significantly larger in node-positive cases compared to node-negative cases in ER+/PR+/HER2-, ER+/PR+/HER2+, and ER+/PR-/HER2- subgroups. In ER+/PR+/HER2-patients, median tumor-nipple distance was significantly shorter in node-positive patients compared to node-negative patients. Upper outer quadrant location was significantly associated with nodal positivity in ER+/PR-/HER2- patients.

CONCLUSIONS

Across ER + patients, the significance between tumor size, location, and lymph node positivity varied significantly when differentiating by PR and HER2 status.

Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early‑stage invasive breast cancer

Axillary lymph node (ALN) status is a key prognostic factor in patients with early-stage invasive breast cancer (IBC). The present study aimed to develop and validate a nomogram based on multimodal ultrasonographic (MMUS) features for early prediction of axillary lymph node metastasis (ALNM). A total of 342 patients with early-stage IBC (240 in the training cohort and 102 in the validation cohort) who underwent preoperative conventional ultrasound (US), strain elastography, shear wave elastography and contrast-enhanced US examination were included between August 2021 and March 2022. Pathological ALN status was used as the reference standard. The clinicopathological factors and MMUS features were analyzed with uni- and multivariate logistic regression to construct a clinicopathological and conventional US model and a MMUS-based nomogram. The MMUS nomogram was validated with respect to discrimination, calibration, reclassification and clinical usefulness. US features of tumor size, echogenicity, stiff rim sign, perfusion defect, radial vessel and US Breast Imaging Reporting and Data System category 5 were independent risk predictors for ALNM. MMUS nomogram based on these factors demonstrated an improved calibration and favorable performance [area under the receiver operator characteristic curve (AUC), 0.927 and 0.922 in the training and validation cohorts, respectively] compared with the clinicopathological model (AUC, 0.681 and 0.670, respectively), US-depicted ALN status (AUC, 0.710 and 0.716, respectively) and the conventional US model (AUC, 0.867 and 0.894, respectively). MMUS nomogram improved the reclassification ability of the conventional US model for ALNM prediction (net reclassification improvement, 0.296 and 0.288 in the training and validation cohorts, respectively; both P<0.001). Taken together, the findings of the present study suggested that the MMUS nomogram may be a promising, non-invasive and reliable approach for predicting ALNM.

Diagnostic value of applying preoperative breast ultrasound and clinicopathologic features to predict axillary lymph node burden in early invasive breast cancer: a study of 1247 patients

BACKGROUND

Since the Z0011 trial, the assessment of axillary lymph node status has been redirected from the previous assessment of the occurrence of lymph node metastasis alone to the assessment of the degree of lymph node loading. Our aim was to apply preoperative breast ultrasound and clinicopathological features to predict the diagnostic value of axillary lymph node load in early invasive breast cancer.

METHODS

The 1247 lesions were divided into a high lymph node burden group and a limited lymph node burden group according to axillary lymph node status. Univariate and multifactorial analyses were used to predict the differences in clinicopathological characteristics and breast ultrasound characteristics between the two groups with high and limited lymph node burden. Pathological findings were used as the gold standard.

RESULTS

Univariate analysis showed significant differences in ki-67, maximum diameter (MD), lesion distance from the nipple, lesion distance from the skin, MS, and some characteristic ultrasound features (P < 0.05). In multifactorial analysis, the ultrasound features of breast tumors that were associated with a high lymph node burden at the axilla included MD (odds ratio [OR], 1.043; P < 0.001), shape (OR, 2.422; P = 0.0018), hyperechoic halo (OR, 2.546; P < 0.001), shadowing in posterior features (OR, 2.155; P = 0.007), and suspicious lymph nodes on axillary ultrasound (OR, 1.418; P = 0.031). The five risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.702.

CONCLUSION

Breast ultrasound features and clinicopathological features are better predictors of high lymph node burden in early invasive breast cancer, and this prediction helps to develop more effective treatment plans.

Polo-like kinase 1 as a biomarker predicts the prognosis and immunotherapy of breast invasive carcinoma patients

Background

Invasive breast carcinoma (BRCA) is associated with poor prognosis and high risk of mortality. Therefore, it is critical to identify novel biomarkers for the prognostic assessment of BRCA.

Methods

The expression data of polo-like kinase 1 (PLK1) in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases. PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Single sample gene set enrichment analysis (ssGSEA) was performed to evaluate immune infiltration in the BRCA microenvironment, and the random forest (RF) and support vector machine (SVM) algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore (IPS). The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration. Finally, a prognostic nomogram was constructed with the risk score and pathological stage, and its clinical potential was evaluated by plotting calibration charts and DCA curves. The application of the nomogram was further validated in an immunotherapy cohort.

Results

PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort. Furthermore, PLK1 expression level, age and stage were identified as independent prognostic factors of BRCA. While the IPS was unaffected by PLK1 expression, the TMB and MATH scores were higher in the PLK1-high group, and the TIDE scores were higher for the PLK1-low patients. We also identified 6 immune cell types with high infiltration, along with 11 immune cell types with low infiltration in the PLK1-high tumors. A risk score was devised using PLK1 expression and hub immune cells, which predicted the prognosis of BRCA patients. In addition, a nomogram was constructed based on the risk score and pathological staging, and showed good predictive performance.

Conclusions

PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.

The Ongoing Necessity of Sentinel Lymph Node Biopsy for cT1-2N0 Breast Cancer Patients

Background

Recent clinical trials attempt to determine whether it is appropriate to omit axillary lymph node surgery in patients with cT1-2N0 breast cancer. The study aimed to investigate the true extent of axillary node disease in patients with clinically negative nodes and explore the differences between negative axillary ultrasound (AUS-cN0) and suspicious axillary ultrasound with negative fine-needle aspiration (FNA-cN0).

Methods

Pathologically identified T1-2 invasive breast cancer patients with clinically negative nodes were retrospectively analyzed at our center between January 2019 and December 2022. Patients who received any systematic treatment before surgery were excluded from this study.

Results

A total of 538 patients were enrolled in this study. 134 (24.9%) patients had pathologically positive nodes, and 404 (75.1%) patients had negative nodes. Univariate analysis revealed that tumor size, T stage, Ki67 level, and vascular invasion (VI) were strongly associated with pathological axillary lymph node positivity. In multivariate analysis, VI was the only independent risk factor for node positivity in patients with cT1-2N0 disease (OR: 3.723, confidence interval [CI]: 2.380-5.824, p < 0.001). Otherwise, pathological node positivity was not significantly different between AUS-cN0 and FNA-cN0 groups (23.4% vs. 28.8%, p = 0.193). However, the rate of high nodal burden (≥3 positive nodes) was significantly higher in FNA-cN0 group. Further investigation revealed that FNA-cN0 and VI were independently associated with a high nodal burden (OR: 2.650, CI: 1.081-6.496, p = 0.033; OR: 3.521, CI: 1.249-9.931, p = 0.017, respectively).

Conclusions

cT1-2 breast cancer patients with clinically negative axillary lymph nodes may have pathologically positive lymph nodes and even a high nodal burden. False negatives in AUS and AUS-guided FNA should not be ignored, and sentinel lymph node biopsy remains an ongoing necessity for cT1-2N0 breast cancer patients.

Incidence and risk factors of lymph node metastasis in breast cancer patients without preoperative chemoradiotherapy and neoadjuvant therapy: analysis of SEER data

Background

Breast cancer (BC) is the leading cause of death in the female reproductive system, often linked to lymph node involvement, indicating poor prognosis. This study investigated lymph node metastasis incidence and risk factors in M0 stage BC patients who hadn't received preoperative chemoradiotherapy or neoadjuvant therapy. We explored the influence of various factors on lymph node metastasis.

Methods

We conducted a retrospective analysis using Surveillance, Epidemiology, and End Results data from BC patients diagnosed between 2010 and 2015. Binary logistic regression and propensity score matching (PSM) assessed significant factors in BC patients without preoperative treatment. We developed predictive nomograms and evaluated model performance using the concordance index, calibration curve, area under the curve, and decision curve analysis.

Results

Among 256,504 eligible BC patients, 25.57% had lymph node metastasis. Multivariate logistic regression revealed associations between lymph node metastasis and younger age, African-American ethnicity, central/nipple location, lobular carcinoma, human epidermal growth factor receptor 2 (HER2)-positive status, grade III classification, and T3 stage. PSM confirmed these findings. Interactions were identified between age, race, primary site, histology, breast subtype, grade, and T stage, all influencing lymph node metastasis.

Conclusions

This retrospective study identified lymph node metastasis in female BC patients with distinct clinicopathological characteristics who received no preoperative treatment. We constructed valuable nomograms, revealing that: (I) young age (<35 years), African-American race, central/nipple location, infiltrating duct carcinoma, HER2 positivity, high histological grade (grade III), and larger tumor size are risk factors for regional lymph node metastasis; (II) lymph node metastasis may not solely represent the invasive nature of triple-negative BC; (III) patients with different BC subtypes in T1c-T2 stages may benefit from individualized neoadjuvant treatment strategies.

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images

Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography images, and to evaluate how DL radiomics can be used to classify SLNM in breast cancer. Clinical and ultrasound data of 317 patients diagnosed with breast cancer at the Second Affiliated Hospital of Nanchang University were collected from January 2018 to December 2021 and randomly divided into training and internal validation cohorts at a ratio of 7:3. An external validation cohort comprising data from Nanchang Third Hospital with 42 patients collected. Three DL models, namely DL-grayscale, DL-CDFI, and DL-elastography, were proposed to predict SLNM by analyzing grayscale ultrasound, CDFI, and elastography images. Three DL models were compared and evaluated to assess diagnostic performance based on the area under the curve (AUC). The AUCs of the DL-grayscale were 0.855 and 0.788 in the internal and external validation cohorts, respectively. For the DL-CDFI model, the AUCs were 0.761 and 0.728, respectively. The diagnostic performance of DL-elastography was superior to that of the DL-grayscale and DL-CDFI. The AUC of the DL-elastography model was 0.879 in the internal validation cohort, with a classification accuracy of 86.13%, sensitivity of 91.60%, and specificity of 82.79%. The generalization capability of DL-elastography remained high in the external cohort, with an AUC of 0.876, and an accuracy of 85.00%. DL radiomics can be used to classify SLNM in breast cancer using ultrasound images. The proposed DL-elastography model based on elastography images achieved the best diagnostic performance and holds good potential for the management of patients with SLNM.

Tumor Characteristics Associated with Axillary Nodal Positivity in Triple Negative Breast Cancer

Larger-size primary tumors are correlated with axillary metastases and worse outcomes. We evaluated the relationships among tumor size, location, and distance to nipple relative to axillary node metastases in triple-negative breast cancer (TNBC) patients, as well as the predictive capacity of imaging. We conducted a single-institution, retrospective chart review of stage I-III TNBC patients diagnosed from 1998 to 2019 who underwent upfront surgery. Seventy-three patients had a mean tumor size of 20 mm (range 1-53 mm). All patients were clinically node negative. Thirty-two patients were sentinel lymph node positive, of whom 25 underwent axillary lymph node dissection. Larger tumor size was associated with positive nodes (p < 0.001): the mean tumor size was 14.30 mm in node negative patients and 27.31 mm in node positive patients. Tumor to nipple distance was shorter in node positive patients (51.0 mm) vs. node negative patients (73.3 mm) (p = 0.005). The presence of LVI was associated with nodal positivity (p < 0.001). Tumor quadrant was not associated with nodal metastasis. Ultrasound yielded the largest number of suspicious findings (21/49), with sensitivity of 0.25 and specificity of 0.40. On univariate analysis, age younger than 60 at diagnosis was also associated with nodal positivity (p < 0.002). Comparative analyses with other subtypes may identify biologic determinants.