Deep Learning Prediction of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Clinical Implication-Applied Preprocessed CT Images

Background: Accurate detection of axillary lymph node (ALN) metastases in breast cancer is crucial for clinical staging and treatment planning. This study aims to develop a deep learning model using clinical implication-applied preprocessed computed tomography (CT) images to enhance the prediction of ALN metastasis in breast cancer patients. Methods: A total of 1128 axial CT images of ALN (538 malignant and 590 benign lymph nodes) were collected from 523 breast cancer patients who underwent preoperative CT scans between January 2012 and July 2022 at Hallym University Medical Center. To develop an optimal deep learning model for distinguishing metastatic ALN from benign ALN, a CT image preprocessing protocol with clinical implications and two different cropping methods (fixed size crop [FSC] method and adjustable square crop [ASC] method) were employed. The images were analyzed using three different convolutional neural network (CNN) architectures (ResNet, DenseNet, and EfficientNet). Ensemble methods involving and combining the selection of the two best-performing CNN architectures from each cropping method were applied to generate the final result. Results: For the two different cropping methods, DenseNet consistently outperformed ResNet and EfficientNet. The area under the receiver operating characteristic curve (AUROC) for DenseNet, using the FSC and ASC methods, was 0.934 and 0.939, respectively. The ensemble model, which combines the performance of the DenseNet121 architecture for both cropping methods, delivered outstanding results with an AUROC of 0.968, an accuracy of 0.938, a sensitivity of 0.980, and a specificity of 0.903. Furthermore, distinct trends observed in gradient-weighted class activation mapping images with the two cropping methods suggest that our deep learning model not only evaluates the lymph node itself, but also distinguishes subtler changes in lymph node margin and adjacent soft tissue, which often elude human interpretation. Conclusions: This research demonstrates the promising performance of a deep learning model in accurately detecting malignant ALNs in breast cancer patients using CT images. The integration of clinical considerations into image processing and the utilization of ensemble methods further improved diagnostic precision.

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.

Association and predictive value of contrast‑enhanced ultrasound features with axillary lymph node metastasis in primary breast cancer

Primary breast cancer is the most common malignant tumor in women worldwide, and axillary lymph node metastasis (ALNM) is an important marker of disease progression in patients with breast cancer. The objective of the present study was to analyze the association between contrast-enhanced ultrasound (CEUS) features and ALNM in primary breast cancer and its predictive value. A total of 120 patients with breast cancer were assigned to the non-metastatic group (n=70) and metastatic group (n=50). The factors influencing ALNM were explored by multivariate logistic regression analysis. The consistency of CEUS, ordinary ultrasonography and pathological examination in the diagnosis of the ALNM of breast cancer was evaluated by consistency testing. The sensitivity, specificity and consistency rate of CEUS features and ordinary ultrasonography were analyzed by receiver operating characteristic curve and four-fold table analyses. High enhancement amplitude, centripetal enhancement sequence, increased maximum cortical thickness, high peak intensity and a larger area under the curve of lymph nodes were more commonly found in the metastatic group than in the non-metastatic group. The lymph node aspect ratio and time to peak were lower in the metastatic group than the non-metastatic group. The time to peak was a protective factor for ALNM in patients with breast cancer. The sensitivity, specificity and coincidence rate with pathological examination of CEUS in the diagnosis of ALNM were 92.00, 90.00 and 90.83%, while these of ordinary ultrasonography were 76.00, 80.00 and 78.33%, respectively. The consistency test indicated that CEUS and pathological examination were consistent in the diagnosis of ALNM in patients with breast cancer, with a κ value of 0.816, indicating a good consistency. The κ value of ordinary ultrasonography and pathological examination was 0.763, also indicating a good consistency. However, these results indicate that CEUS is more valuable than ordinary ultrasonography in the diagnosis of ALNM in cases of breast cancer. In conclusion, the present study indicates that CEUS features were influencing factors associated with ALNM in patients with breast cancer and may serve as an important reference for the preoperative prediction of ALNM in breast cancer.

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model

BACKGROUND

Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear.

PURPOSE

To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status.

STUDY TYPE

Retrospective.

SUBJECTS

1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively.

FIELD STRENGTH/SEQUENCE

1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging.

ASSESSMENT

Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated.

STATISTICAL TESTS

Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant.

RESULTS

The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827.

DATA CONCLUSION

A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Prediction of axillary lymph node metastasis using tumor volume to breast volume ratio: retrospective cohort study

Background

Tumour size appear to be a risk factor of axillary lymph node metastasis in breast cancer. Recent evidence shows that higher the T staging is associated with higher rate of axillary lymph node metastasis. However, no studies shows that in the same T staging or tumour size but different breast size or breast volume the incidence of axillary lymph node metastasis differ or not .

Objectives

This Study aimed to investigate the association between tumour to breast ratio in breast cancer as a predictive factor of axillary lymph node metastasis.

Methods

This study included 200 consecutive patients diagnosed with breast cancer between January 2012 to march 2022. The authors retrospectively reviewed medical data pathologic report and Ultrasonography and mammography of breast. Tumour diameter reported in pathologic report was used to calculate tumour volume using formula for ellipse. Breast volume was calculate using formula referencing from study of Jack W. Rostas et all by formula Breast Volume=1/3׶×Radius2ccview×Heightccview by measuring from mammography of patient. Tumour volume to breast volume ratio was calculated and analyzed.

Result

Of 200 patient included in this study, 84 patient (42%) was in lymph node positive group and 116 patient (58%) was in lymph node-negative group. Median for tumour and breast volume ratio in node positive group was higher [median 0.0093 (interquartile range=0.0047-0.023)] than in node-negative group [median 0.0065 (interquartile range (0.0028-0.0199)]. P=0.0414 receiver operating characteristic curve for tumour to breast ratio showed AUC of 0.7389 (95% CI 0.67993-0.82335) Which seems to be a significance as predictive factors for Axillary lymph node metastasis.

Conclusion

Higher tumour volume to breast volume ratio tends to be a significance predictive factors for axillary lymph node metastasis in breast cancer patients.

Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study

BACKGROUND

The high false negative rate (FNR) associated with sentinel lymph node biopsy often leads to unnecessary axillary lymph node dissection following neoadjuvant chemotherapy (NAC) in breast cancer. The authors aimed to develop a multifactor artificial intelligence (AI) model to aid in axillary lymph node surgery.

MATERIALS AND METHODS

A total of 1038 patients were enrolled, comprising 234 patients in the primary cohort, 723 patients in three external validation cohorts, and 81 patients in the prospective cohort. For predicting axillary lymph node response to NAC, robust longitudinal radiomics features were extracted from pre-NAC and post-NAC magnetic resonance images. The U test, the least absolute shrinkage and selection operator, and the spearman analysis were used to select the most significant features. A machine learning stacking model was constructed to detect ALN metastasis after NAC. By integrating the significant predictors, we developed a multifactor AI-assisted surgery pipeline and compared its performance and false negative rate with that of sentinel lymph node biopsy alone.

RESULTS

The machine learning stacking model achieved excellent performance in detecting ALN metastasis, with an area under the curve (AUC) of 0.958 in the primary cohort, 0.881 in the external validation cohorts, and 0.882 in the prospective cohort. Furthermore, the introduction of AI-assisted surgery reduced the FNRs from 14.88 (18/121) to 4.13% (5/121) in the primary cohort, from 16.55 (49/296) to 4.05% (12/296) in the external validation cohorts, and from 13.64 (3/22) to 4.55% (1/22) in the prospective cohort. Notably, when more than two SLNs were removed, the FNRs further decreased to 2.78% (2/72) in the primary cohort, 2.38% (4/168) in the external validation cohorts, and 0% (0/15) in the prospective cohort.

CONCLUSION

Our study highlights the potential of AI-assisted surgery as a valuable tool for evaluating ALN response to NAC, leading to a reduction in unnecessary axillary lymph node dissection procedures.

Axillary Lymph Node Metastasis from Ovarian Carcinoma: A Systematic Review of the Literature

BACKGROUND

Axillary lymph node metastasis is a rare stage IV ovarian carcinoma manifestation. This manuscript aims to systematically review the literature regarding axillary lymph node metastasis from ovarian carcinoma.

METHODS

We searched three medical internet databases (PubMed, Scopus, and Web of Science) for relevant articles published until 22 July 2023. Cases describing supraclavicular or intramammary lymph node metastases and concurrent metastasis to the breast were excluded.

RESULTS

After applying eligibility/inclusion and exclusion criteria, twenty-one manuscripts describing twenty-five cases were included from the English literature. Data were collected and analyzed regarding demographic, clinical, laboratory, radiological, histopathological, and oncological characteristics.

CONCLUSIONS

We analyzed the clinical and oncological characteristics of patients with axillary lymph node metastasis from ovarian carcinoma, presented either as an initial diagnosis of the disease or as a recurrent disease. The analysis we performed showed a significant difference only in the serum CA-125 level (p = 0.004) between the two groups. There was no observed difference in womens' survival.

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.

Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning

Objective

To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients.

Methods

A total of 271 US videos from 271 early breast cancer patients collected from Xiang'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models.

Results

Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers' review.

Conclusion

A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.

Multimodal radiomics and nomogram-based prediction of axillary lymph node metastasis in breast cancer: An analysis considering optimal peritumoral region

PURPOSE

To explore the optimal peri-tumoral regions on ultrasound (US) images and investigate the performance of multimodal radiomics for predicting axillary lymph node metastasis (ALNM).

METHODS

This retrospective study included 326 patients (training cohort: n = 162, internal validation cohort: n = 74, external validation cohort: n = 90). Intra-tumoral region of interests (ROIs) were delineated on US and digital mammography (DM) images. Peri-tumoral ROI (PTR) on US images were gained by dilating actual 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 3.5 mm radius surrounding the tumor. Support vector machine (SVM) method was used to calculate the importance of radiomics features and to pick the 10 most important. Recursive feature elimination-SVM was used to evaluate the efficacy of models with different feature numbers used.

RESULTS

The PTR0.5mm yielded a maximum AUC of 0.802 (95% confidence interval (CI): 0.676-0.901) within the validation cohort using SVM classifier. The multimodal radiomics (intra-tumoral US and DM and US-based PTR0.5mm radiomics model) achieved the highest predictive ability (AUC = 0.888/0.844/0.835 and 95% CI = 0.829-0.936/0.741-0.929/0.752-0.896 for training/internal validation/external validation cohort, respectively).

CONCLUSION

The PTR0.5mm could be the optimal area for predicting ALNM. A favorable predictive accuracy for predicting ALNM was achieved using multimodal radiomics and its based nomogram.

Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer

Aim: To develop a deep learning-based multiomics integration model. Materials & methods: Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model via a deep neural network, incorporating an attention mechanism that adaptively considers the weights of multiomics features. Results: Compared with other methods, the deep learning-based multiomics integration model achieved remarkable results, with an area under the curve of 0.89 (95% CI: 0.863-0.910). Conclusion: The deep learning-based multiomics integration model achieved promising results and is an effective method for predicting axillary lymph node metastasis in breast cancer.

Combining primary tumor features derived from conventional and contrast-enhanced ultrasound facilitates the prediction of positive axillary lymph nodes in Breast Imaging Reporting and Data System category 4 malignant breast lesions

PURPOSE

To determine whether the primary tumor features derived from conventional ultrasound (US) and contrast-enhanced US (CEUS) facilitate the prediction of positive axillary lymph nodes (ALNs) in breast cancer diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4.

METHODS

A total of 240 women with breast cancer who underwent preoperative conventional US, strain elastography, and CEUS between September 2016 and December 2019 were included. The multiple parameters of the primary tumor were obtained, and univariate and multivariate analyses were performed to predict positive ALNs. Then three prediction models (conventional US features, CEUS features, and the combined features) were developed, and the diagnostic performance was evaluated with receiver operating characteristic curves.

RESULTS

On conventional US, the traits of large size and the non-circumscribed margin of the primary tumor were marked as two independent predictors. On CEUS, the features of vessel perforation or distortion and the enhanced range of the primary tumor were marked as two independent predictors for positive ALNs. Three prediction models were then developed: model A (conventional US features), model B (CEUS features), and model C (model A plus B). Model C yielded the highest area under the curve (AUC) of 0.82 [95% confidence interval (CI), 0.75-0.88] compared with model A (AUC 0.74; 95% CI, 0.68-0.81; P = 0.008) and model B (AUC 0.72; 95% CI, 0.65-0.80; P < 0.001) as per the DeLong test.

CONCLUSION

CEUS, as a non-invasive examination technique, can be used to predict ALN metastasis. Combining conventional US and CEUS may produce favorable predictive accuracy for positive ALNs in BI-RADS category 4 breast cancer.

Optimal treatment strategy for hormone receptor-positive human epidermal growth factor receptor 2-negative breast cancer patients with 1-2 suspicious axillary lymph node metastases on breast magnetic resonance imaging: upfront surgery vs. neoadjuvant chemotherapy

Background

It is unclear whether upfront surgery or neoadjuvant chemotherapy is appropriate for first treatment in hormone receptor (HR)-positive human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients with 1-2 suspicious axillary lymph node (ALN) metastases on preoperative breast magnetic resonance imaging (MRI).

Method

We identified 282 patients with HR+HER2- breast cancer and 1-2 suspicious ALN metastases on baseline breast MRI (147 received upfront surgery; 135 received neoadjuvant chemotherapy). We evaluated the predictive clinicopathological factors for pN2-3 in the adjuvant setting and axillary pathologic complete response (pCR) in the neoadjuvant setting.

Results

Lymphovascular invasion (LVI)-positive and clinical tumors >3 cm were significantly associated with pN2-3 in patients who received upfront surgery. The pN2-3 rate was 9.3% in patients with a clinical tumor ≤ 3 cm and LVI-negative versus 34.7% in the others (p < 0.001). The pN2-3 rate in patients with a clinical tumor ≤ 3 cm and LVI-negative and in the others were 9.3% versus 34.7% in all patients (p < 0.001), 10.7% versus 40.0% (p = 0.033) in patients aged < 50 years, and 8.5% versus 31.0% in patients aged ≥ 50 years (p < 0.001), respectively. In the neoadjuvant setting, patients with tumor-infiltrating lymphocytes (TILs) ≥ 20% had a higher axillary pCR than those with TILs < 20% (46.7% vs. 15.3%, p < 0.001). A similar significant finding was also observed in patients < 50 years.

Conclusions

Upfront surgery may be preferable for patients aged ≥ 50 years with a clinical tumor < 3 cm and LVI-negative, while neoadjuvant chemotherapy may be preferable for those aged < 50 years with TILs ≥ 20%.

Pure Tubular Breast Carcinoma: Clinicopathological Characteristics and Clinical Outcomes

Objective:

Tubular breast carcinoma (TBC) is a rare subtype of breast carcinoma (BC) with a good prognosis. In this study, we aimed to assess the clinicopathological characteristics of pure TBC (PTBC), analyze factors that may influence long-term prognosis, examine the frequency of axillary lymph node metastasis (ALNM), and discuss the need for axillary surgery in PTBC.

Materials and Methods:

Fifty-four Patients diagnosed with PTBC between January 2003 and December 2020 at Istanbul Faculty of Medicine were included. Clinicopathological, surgical, treatment, and overall survival (OS) data were analyzed.

Results:

A total of 54 patients with a mean age of 52.2 years were assessed. The mean size of the tumor was 10.6 mm. Four (7.4%) patients had not undergone axillary surgery, while thirty-eight (70.4%) had undergone sentinel lymph node biopsy and twelve (22.2%) had undergone axillary lymph node dissection (ALND). Significantly, four (33.3%) of those who had undergone ALND had tumor grade 2 (p = 0.020) and eight of them (66.7%) had ALNM. Fifty percent (50%) of patients who were treated with chemotherapy had grade 2 and multifocal tumors and ALNM. Moreover, the frequency of ALNM was higher in patients with tumor diameters greater than 10 mm. Median follow-up time was 80 months (12–220). None of the patients had locoregional recurrence, but one patient had systemic metastasis. Furthermore, five-year OS was 97.9%, while ten-year OS was 93.6%.

Conclusion:

PTBC is associated with favorable prognosis, good clinical outcomes and high survival rate, with rare recurrences and metastases.

Application of artificial intelligence in predicting lymph node metastasis in breast cancer

Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.

Ultrasound and clinicopathological characteristics of breast cancer for predicting axillary lymph node metastasis

OBJECTIVES

The goal of this study was to assess the clinicopathological and ultrasound (US) features of breast cancer for predicting the risk of axillary lymph node metastasis.

METHODS

Patients with breast cancer were included in this retrospective, monocentric, observational study. Their preoperative ultrasound features, clinical data, laboratory results and postoperative pathologic results and immunophenotyping were collected. The association of these factors of breast cancer with axillary lymph node metastasis was evaluated by univariate and multivariate analysis.

RESULTS

In this study, 471 patients diagnosed with breast cancer at the First Affiliated Hospital of Xi'an Jiaotong University between July 2016 and September 2019 were collected, with a total of 471 nodules, of which 231(49.0%) had axillary lymph node metastasis, and 240(51.0%) did not. The parameters of hyperechoic halo, posterior acoustic decrease, microcalcification, carcinogenic embryonic antigen (CEA), cancer antigen-153 (CA153), CK5/6 (+), Ki67 (≥40%), AR (+) and histological grade (grade II and grade III) were significantly and independently associated with axillary lymph node metastasis (p < 0.05 for all).

CONCLUSIONS

The combination of ultrasound features, tumor markers, pathology, and immunohistochemistry can predict axillary lymph node metastasis in breast cancer patients.

Radiological Evaluation of a Malignant Gastrointestinal Stromal Tumor in a Female Patient With the Coincidental Detection of Primary Breast Cancer: A Case Report

Gastrointestinal stromal tumors (GIST) are a rare and unique group of mesenchymal tumors arising from the gastrointestinal tract, omentum, mesentery, and retroperitoneum. Though they have certain typical radiological features that can differentiate them from epithelial tumors, it is often difficult to differentiate them from other non-epithelial tumors. Their features also vary depending on their size, site of origin, etc. When differentiation from other mesenchymal tumors on histopathology is difficult, receptor tyrosine kinase (C-KIT proto-oncogene/CD117) and gastrointestinal stromal tumor (GIST-1) discovered on GIST1 (DOG-1) expression are confirmatory. The concurrent presence of other primary cancers with GISTs has been described in the literature, among which most have been of gastrointestinal origin. Few cases of primary breast cancer in GIST have been described. Lymph nodal metastasis is rarely encountered in GIST, and metastasis to the breast is even rarer. We present a case of a 39-year-old female with non-specific symptoms who was referred for ultrasonography (USG) and computed tomography (CT) that showed a small intestinal GIST along with a breast lump and axillary lymphadenopathy that were labeled as metastases from the GIST on frozen sections; however, they were later diagnosed as primary breast cancer with axillary metastases on the histopathology and immunohistochemistry of the excision biopsy specimens post-surgery. The patient underwent surgical resection and chemotherapy.

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.

Rare metastasis of gastric cancer to the axillary lymph node: A case report

Lymph node metastasis of gastric cancer is more common, metastatic lymph nodes are often around the stomach, and metastasis is carried out in a certain order, but gastric cancer metastasis to axillary lymph nodes is very rare. Due to the small number of patients with this kind of metastasis, its clinical features and treatment are not very clear. We initially thought that the enlarged axillary lymph nodes were inflammatory lesions. Axillary lymph node biopsy was later diagnosed as gastric cancer metastases to axillary lymph nodes. The patient refused further treatment and died 11 months after the second operation because of multiple systemic metastases. We believe that metastasis of gastric cancer to axillary lymph nodes is rare and the prognosis is poor. In clinical work, the possibility of metastatic lymph nodes should be considered in patients with a history of gastric cancer with enlarged axillary lymph nodes.

A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

PURPOSE

To develop a nomogram based on radiomics signature and deep-learning signature for predicting the axillary lymph node (ALN) metastasis in breast cancer.

METHODS

A total of 151 patients were assigned to a training cohort (n = 106) and a test cohort (n = 45) in this study. Radiomics features were extracted from DCE-MRI images, and deep-learning features were extracted by VGG-16 algorithm. Seven machine learning models were built using the selected features to evaluate the predictive value of radiomics or deep-learning features for the ALN metastasis in breast cancer. A nomogram was then constructed based on the multivariate logistic regression model incorporating radiomics signature, deep-learning signature, and clinical risk factors.

RESULTS

Five radiomics features and two deep-learning features were selected for machine learning model construction. In the test cohort, the AUC was above 0.80 for most of the radiomics models except DecisionTree and ExtraTrees. In addition, the K-nearest neighbor (KNN), XGBoost, and LightGBM models using deep-learning features had AUCs above 0.80 in the test cohort. The nomogram, which incorporated the radiomics signature, deep-learning signature, and MRI-reported LN status, showed good calibration and performance with the AUC of 0.90 (0.85-0.96) in the training cohort and 0.90 (0.80-0.99) in the test cohort. The DCA showed that the nomogram could offer more net benefit than radiomics signature or deep-learning signature.

CONCLUSIONS

Both radiomics and deep-learning features are diagnostic for predicting ALN metastasis in breast cancer. The nomogram incorporating radiomics and deep-learning signatures can achieve better prediction performance than every signature used alone.

Nomogram models for stratified prediction of axillary lymph node metastasis in breast cancer patients (cN0)

Objectives

To determine the predictors of axillary lymph node metastasis (ALNM), two nomogram models were constructed to accurately predict the status of axillary lymph nodes (ALNs), mainly high nodal tumour burden (HNTB, > 2 positive lymph nodes), low nodal tumour burden (LNTB, 1-2 positive lymph nodes) and negative ALNM (N0). Accordingly, more appropriate treatment strategies for breast cancer patients without clinical ALNM (cN0) could be selected.

Methods

From 2010 to 2015, a total of 6314 patients with invasive breast cancer (cN0) were diagnosed in the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and internal validation groups at a ratio of 3:1. As the external validation group, data from 503 breast cancer patients (cN0) who underwent axillary lymph node dissection (ALND) at the Second Affiliated Hospital of Chongqing Medical University between January 2011 and December 2020 were collected. The predictive factors determined by univariate and multivariate logistic regression analyses were used to construct the nomograms. Receiver operating characteristic (ROC) curves and calibration plots were used to assess the prediction models' discrimination and calibration.

Results

Univariate analysis and multivariate logistic regression analyses showed that tumour size, primary site, molecular subtype and grade were independent predictors of both ALNM and HNTB. Moreover, histologic type and age were independent predictors of ALNM and HNTB, respectively. Integrating these independent predictors, two nomograms were successfully developed to accurately predict the status of ALN. For nomogram 1 (prediction of ALNM), the areas under the receiver operating characteristic (ROC) curve in the training, internal validation and external validation groups were 0.715, 0.688 and 0.876, respectively. For nomogram 2 (prediction of HNTB), the areas under the ROC curve in the training, internal validation and external validation groups were 0.842, 0.823 and 0.862. The above results showed a satisfactory performance.

Conclusion

We established two nomogram models to predict the status of ALNs (N0, 1-2 positive ALNs or >2 positive ALNs) for breast cancer patients (cN0). They were well verified in further internal and external groups. The nomograms can help doctors make more accurate treatment plans, and avoid unnecessary surgical trauma.

Collagen signature as a novel biomarker to predict axillary lymph node metastasis in breast cancer using multiphoton microscopy

Accurate identification of axillary lymph node (ALN) status is crucial for tumor staging procedure and decision making. This retrospective study of 898 participants from two institutions was conducted. The aim of this study is to evaluate the diagnostic performance of clinical parameters combined with collagen signatures (tumor-associated collagen signatures [TACS] and the TACS corresponding microscopic features [TCMF]) in predicting the probability of ALN metastasis in patients with breast cancer. These findings suggest that TACS and TCMF in the breast tumor microenvironment are both novel and independent biomarkers for the estimation of ALN metastasis. The nomogram based on independent clinical parameters combined with TACS and TCMF yields good diagnostic performance in predicting ALN status.

Gail model and fifth edition of ultrasound BI-RADS help predict axillary lymph node metastasis in breast cancer-A multicenter prospective study

RATIONALE AND OBJECTIVES

We aim to assess the performance of the Gail model and the fifth edition of ultrasound BI-RADS (Breast Imaging Reporting and Data System) in breast cancer for predicting axillary lymph node metastasis (ALNM).

MATERIALS AND METHODS

We prospectively studied 958 female patients with breast cancer between 2018 and 2019 from 35 hospitals in China. Based on B-mode, color Doppler, and elastography, radiologists classified the degree of suspicion based on the fifth edition of BI-RADS. Individual breast cancer risk was assessed with the Gail model. The association between the US BI-RADS category and the Gail model in terms of ALNM was analyzed.

RESULTS

We found that US BI-RADS category was significantly and independently associated with ALNM (P < 0.001). The sensitivity, specificity, and accuracy of BI-RADS category 5 for predicting ALNM were 63.6%, 71.6%, and 68.6%, respectively. Combining the Gail model with the BI-RADS category showed a significantly higher sensitivity than using the BI-RADS category alone (67.8% vs. 63.6%, P < 0.001). The diagnostic accuracy of the BI-RADS category combined with the Gail model was better than that of the Gail model alone (area under the curve: 0.71 vs. 0.50, P < 0.001).

CONCLUSION

Based on the conventional ultrasound and elastography, the fifth edition of ultrasound BI-RADS category could be used to predict the ALNM of breast cancer. ALNM was likely to occur in patients with BI-RADS category 5. The Gail model could improve the diagnostic sensitivity of the US BI-RADS category for predicting ALNM in breast cancer.

Stratification of axillary lymph node metastasis risk with breast magnetic resonance imaging in breast cancer

Aims: To develop a model based on breast MRI to stratify axillary lymph node metastasis (ALNM) in breast cancer. Patients & methods: A total of 134 eligible patients were used to build a predicting model, which was validated with an independent group of 57 patients and evaluated for accuracy and sensitivity. Results: A model based on breast MRI was developed and yielded total accuracy of 82.5% and sensitivities of 94.3, 64.3 and 62.5% to predict patients with no, low and heavy ALNM burden, respectively, in the validation group. Conclusion: A noninvasive model based on breast MRI was developed to preoperatively stratify ALNM in breast cancer; its performance needs to be validated and improved in future research.

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Objectives

To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN.

Methods

A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model.

Results

The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density (p = 0.015), circumference (p = 0.009), circularity (p = 0.010), and orientation (p = 0.012).

Conclusion

Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.

Carcinomatous-like mastitis due to axillary lymphadenopathy in a case of nasopharyngeal carcinoma: A case report

Nasopharyngeal carcinoma (NPC) is a rare form of malignancy, accounting for 2% of all cancers of the head and neck in Europe. Axillary lymph node metastases are very rare in these cases. This is a case report of a 40-year-old premenopausal woman diagnosed in May 2015 with T1N2M0 stage III NPC, treated with induction chemotherapy, followed by chemo-radiotherapy. Post-therapeutic computed tomography (CT) scan showed partial response (PR) on the primary tumor and complete response (CR) on the latero-cervical lymph nodes. In 2017, our patient developed left carcinomatous-like mastitis with axillary lymphadenopathy. This raised suspicions of a carcinomatous mastitis. The pathology report with immunohistochemistry (IHC) of the third biopsy highlighted axillary metastasis of a non-keratinizing squamous cell carcinoma (NSCC). There are very few references in the literature regarding axillary metastases from squamous cell carcinoma of the head and neck (HNSCC). As far as we know, this is the first case report of mastitis due to NPC. To conclude, treatment consisted of two surgical excisions of axillary lymphadenopathy associated with local radiotherapy and chemotherapy (neo-adjuvant, adjuvant). The second surgery, performed after radiotherapy, required plastic surgery. A psychiatric evaluation was necessary, revealing a reactive anxiety disorder. This case required multidisciplinary management, where oncology, plastic surgery, pathology and psychiatric specialists collaborated in deciding the therapeutic approach.

Axillary staging should be routine in primary rhabdomyosarcoma of breast: A rare case report and review of literature

INTRODUCTION

Primary rhabdomyosarcoma (RMS) of breast is an uncommon entity and axillary lymph node (ALN) involvement is exceedingly rare.

METHODS

Herein, we are reporting a case of RMS of breast with ALN metastasis in an adolescent girl. We searched Pubmed and Cochrane databases with keywords rhabdomyosarcoma and breast. All studies published in English language literature were included. Articles describing metastatic involvement of breast with RMS were excluded.

RESULT

The initial search yielded a total of 8468 studies, out of which 03 were found to be duplicate. 8420 studies were excluded based on title and abstract as they did not fulfill inclusion criteria. Full text of the remaining 48 studies was screened. After full text screening, 26 case reports describing primary breast RMS were included. Overall 21% patients had axillary lymph node metastasis.

CONCLUSION

Axillary staging should be considered in every patient undergoing surgery for breast RMS. However, it's impact on recurrence and survival could not be determined based on current review.

Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI

BACKGROUND

The number of metastatic axillary lymph nodes (ALNs) play a crucial role in the staging, prognosis and therapy of patients with breast cancer.

PURPOSE

To predict the number of metastatic ALNs in breast cancer via radiomics.

MATERIAL AND METHODS

We enrolled 197 patients with breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A total of 3386 radiomic features were extracted from the early- and delayed-phase subtraction images. To classify the number of metastatic ALNs, logistic regression was used to develop a radiomic signature and nomogram.

RESULTS

The radiomic signature were constructed to distinguish the N₀ group from the N₊ (metastatic ALNs ≥ 1) group, which yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and test group, respectively. Based on the radiomic signature and BI-RADS category, a nomogram was further developed and showed excellent predictive performance with AUC values of 0.85 and 0.89 in the training and test groups, respectively. Another radiomic signature was constructed to distinguish the N₁ (1-3 ALNs) group from the N_2-3 (≥4 metastatic ALNs) group and showed encouraging performance with AUC values of 0.94 and 0.84 in training and test group, respectively.

CONCLUSIONS

We developed a nomogram and a radiomic signature that can be used to predict ALN metastasis and distinguish the N₁ from the N_2-3 group. Both nomogram and radiomic signature may be potential tools to assist clinicians in assessing ALN metastasis in patients with breast cancer.

Determining the axillary nodal status with four current imaging modalities including 18F-FDG PET/MRI in newly diagnosed breast cancer: A comparative study using histopathology as reference standard

Purpose: To compare breast magnetic resonance imaging (MRI), thoracal MRI, thoracal 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)/MRI and axillary sonography for the detection of axillary lymph node metastases in women with newly diagnosed breast cancer. Materials and Methods: This prospective double-center study included patients with newly diagnosed breast cancer between March 2018 and December 2019. Patients underwent thoracal (18F-FDG PET/)MRI, axillary sonography, and dedicated prone breast MRI. Datasets were evaluated separately regarding nodal status (nodal+ vs. nodal-). Histopathology served as reference standard in all patients. The diagnostic performance of breast MRI, thoracal MRI, thoracal PET/MRI and axillary sonography in detecting nodal positive patients was tested by creating receiver-operating-characteristic curves (ROC) with a calculated area under the curve (AUC). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for all four modalities. A McNemar test was used to assess differences. Results: 112 female patients (mean age 53.04 ± 12.6 years) were evaluated. Thoracal PET/MRI showed the highest ROC-AUC with a value of 0.892. The AUC for breast MRI, thoracal MRI and sonography were 0.782, 0.814 and 0.834, respectively. Differences between thoracal PET/MRI and axillary sonography, thoracal MRI and breast MRI were statistically significant (PET/MRI vs. axillary sonography, P = 0.01; PET/MRI vs. thoracal MRI, P = 0.02; PET/MRI vs. breast MRI, P = 0.03). PET/MRI showed the highest sensitivity (81.8%, 36/44) (95%-CI: 67.29-91.81%) while axillary sonography had the highest specificity (98.5%, 65/66), 95%-CI: 91.84-99.96%). Conclusion: 18F-FDG PET/MRI outperforms axillary sonography, breast MRI and thoracal MRI in determining the axillary lymph node status. In a clinical setting, the combination of 18F-FDG PET/MRI and axillary sonography might be considered to provide even more accuracy in diagnosis.

Metastasis of primary breast angiosarcoma to axillary and supraclavicular lymph nodes: a rare case diagnosed using imaging data

Primary breast angiosarcoma (PBA) is a rare malignant tumor. PBA usually undergoes hematogenous metastasis; lymph node metastasis is very rare in such patients, and metastasis of PBA to the supraclavicular lymph nodes has not previously been reported. Here, we describe a rare case of PBA manifested by a diffuse enlargement of the left breast, with metastasis to the left axillary and bilateral supraclavicular lymph nodes. Contrast-enhanced ultrasound and positron emission tomography findings indicated a malignant lesion, whereas magnetic resonance imaging suggested a benign lesion. Core needle biopsy identified the lesion as a lymphangioma, and the histological characteristics suggested a high-grade angiosarcoma. Multimodal imaging and perfusion patterns obtained using various contrast agents can thus help to diagnose PBA.

TGF-β1: is it related to the stiffness of breast lesions and can it predict axillary lymph node metastasis?

Background

This study aimed to explore whether transforming growth factor β1 (TGF-β1) is correlated with the stiffness of breast lesions and if it can predict axillary lymph node (ALN) metastasis.

Methods

A retrospective analysis was performed in our hospital. A total of 135 breast lesions in 130 patients who were to undergo vacuum-assisted excisional biopsy (VAEB) or surgery were enrolled between April 2018 and October 2018. Ultrasound (US) and shear wave elastography (SWE) examinations were performed for every lesion before VAEB or surgery. Pathology results obtained by VAEB or surgery were regarded as gold criteria. The elastic parameters and TGF-β1 expression level of malignant breast lesions were compared with those of benign lesions; the relationship between TGF-β1 expression level in breast lesions and the elastic parameters was analyzed; the TGF-β1 expression level in breast lesions with or without ALN metastasis were compared; and the efficacy of TGF-β1 expression level in predicting ALN metastasis was analyzed.

Results

The malignant breast lesions were different from benign lesions in the maximum and mean elasticity (Emax, Emean), standard deviation of elasticity (ESD), elastic ratio of the lesions to the peripheral tissue (Eratio), and the occurrence rate of "stiff rim sign" (P<0.001). The expression level of TGF-β1 in benign breast lesions was significantly lower than that in malignant lesions (P<0.001), and the TGF-β1 expression level was positively correlated with Emax, Emean, ESD, and Eratio (r=0.869, 0.840, 0.834, and 0.734, respectively). The expression level of TGF-β1 in breast lesions with or without "stiff rim sign" was significantly different (P<0.001), and the TGF-β1 expression level in malignant breast lesions with ALN metastasis was significantly higher than that in malignant lesions without ALN metastasis (P=0.0009). When TGF-β1 expression level >0.3138 was taken as the cut-off value, its efficacy in predicting ALN metastasis was 0.853, with a sensitivity of 86.67%, and a specificity 83.33%.

Conclusions

The expression level of TGF-β1 was positively correlated with the elastic parameters of breast lesions, and it could be useful for predicting ALN metastasis, especially for negative ALN diagnosis clinically.

Clinicopathological Follow-up of Breast DCIS Diagnosed on Biopsies: A Single Institutional Study of 575 Patients

Stratifying ductal carcinoma in situ (DCIS) patients into different upgrading risk groups is important in exploiting more precise therapeutic options. Evaluation of estrogen receptor/progesterone receptor/human epidermal growth factor receptor 2 (ER/PR/HER2) status and axillary lymph node metastatic status for DCIS and their upgraded invasive counterparts can also provide diagnostic and therapeutic implications. We retrospectively studied 575 patients with first-time diagnosis of DCIS on biopsies, and followed up their final diagnosis, ER/PR/HER2 status, and axillary lymph node involvement on excisions. As a result, biopsy-diagnosed DCIS had an overall 19.1% risk to be upgraded on subsequent excisions, with 4.7% being upgraded to microinvasive carcinoma (pT1mi) and 14.4% to overt invasive carcinoma (⩾pT1a). Factors significantly associated with higher upgrading risk on multivariate analysis include biopsy guidance by ultrasound (P <.001), DCIS with suspicious microinvasion (P < .001), and DCIS diagnosed in left breast (P = .026). DCIS diagnosed in younger patients (⩽40 years old) or DCIS with high nuclear grade showed higher upgrading risk only on univariate analysis. About 80% ER + /PR + and ER-/PR- DCIS remained the same ER/PR status after being upgraded, and ER + /PR -  DCIS had the highest risk (63.6%) of having HER2 amplification in upgraded invasive carcinoma. For upgraded DCIS, microinvasive carcinoma was more likely to have HER2 amplification (50%) than overt invasive carcinoma (29.5%). Besides, pure DCIS had a low risk of axillary lymph node macrometastasis (0.74%), while the risk increased in DCIS with microinvasion (4.4%) and was highest in overt invasive carcinoma (14.7%). The findings of this study are clinically relevant with respect to criteria that might be used in selecting patients for de-escalation trials.

Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51-68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41-0.74 and 0.48-0.89 in the training cohorts, respectively, and between 0.30-0.98 and 0.37-0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.

Distance Between the Tumour and Nipple as a Predictor of Axillary Lymph Node Involvement in Breast Cancer

Purpose

The possibility of axillary node metastasis via the lymphatics might be related to a cancer’s location within the breast. Previous studies of this topic had small sample sizes, inaccuracies because of subjective differences, and the inability to depict the entire three-dimensional structure of the breast. Here, we aimed to improve upon these existing drawbacks by retrospectively analysing whether tumour location (quadrants) and tumour–nipple distance can predict axillary node positivity.

Patients and Methods

We identified 961 patients with invasive breast cancer between January 2000 and April 2016. The tumour–nipple distance was objectively measured intraoperatively and clinicopathological information was extracted from hospital database. The distance was measured radially from the nipple to the epicentre rather than the edge of tumour to obviate confounders resulting from tumour size variations.

Results

A total of 847 breast cancers (839 patients) met the eligibility criteria and were included in the statistical analysis. The tumour–nipple distance was smaller in node-positive patients (n = 307; 2.76 ± 2.07 cm) than in node-negative patients (n = 297; 3.41 ± 2.18 cm) (p < 0.001). Tumour–nipple distance was an independent predictor of axillary involvement on logistic regression analysis. However, no statistically significant relationship was detected between node positivity and breast quadrant tumour location.

Conclusion

Tumour–nipple distance can be used to predict axillary lymph node metastasis and assist in surgical decision-making and therapy planning. However, exploratory studies are required to increase our understanding of the mechanism.

Diagnostic Performance of Quantitative and Qualitative Elastography for Axillary Lymph Node Metastasis in Breast Cancer: A Systematic Review and Meta-Analysis

Background: Studies have shown inconsistent results regarding the diagnostic performance of ultrasound elastography for axillary lymph node metastasis (ALNM) in breast cancer. This meta-analysis aimed to estimate the diagnostic performance of ultrasound elastography (divided into quantitative and qualitative elastography) for ALNM in patients with breast cancer. Methods: The PubMed and Embase databases were searched for eligible studies exploring the diagnostic performance of ultrasound elastography for ALNM in patients with breast cancer. The included studies were divided into quantitative and qualitative elastography groups to perform separate meta-analyses. The diagnostic performance was investigated with pooled sensitivity and specificity and diagnostic odds ratio (DOR) using a bivariate mixed-effects regression model. A summary receiver operating characteristic curve was constructed, and the area under the curve (AUC) was calculated. Results: Seven and 11 studies were included in the quantitative and qualitative elastography meta-analyses, respectively. The pooled sensitivity and specificity, DOR, and AUC with their corresponding 95% confidence intervals were 0.82 (0.75, 0.87), 0.88 (0.78, 0.93), 33 (13, 83), and 0.89 (0.86, 0.91), respectively, for quantitative elastography and 0.81 (0.69, 0.89), 0.92 (0.79, 0.97), 46 (12, 181), and 0.92 (0.89, 0.94), respectively, for qualitative elastography. No significant publication bias existed. Fagan plots demonstrated good clinical utility. However, substantial heterogeneity existed among studies. Study design, measurement, and reference standard served as potential sources of heterogeneity for quantitative studies, which were measurement and reference standard for qualitative studies. Conclusions: Both quantitative and qualitative elastography seem to be feasible, non-invasive diagnostic tools for ALNM in breast cancer. Nevertheless, the results must be interpreted carefully, paying attention to heterogeneity issues, especially for quantitative elastography studies.

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

Objective

To develop a nomogram for predicting axillary lymph node (ALN) metastases using the breast imaging reporting and data system (BI-RADS) ultrasound lexicon.

Methods

A total of 703 patients from July 2015 to January 2018 were included in this study as a primary cohort for model construction. Moreover, 109 patients including 51 pathologically confirmed N1 patients (TNM staging) and 58 non-metastatic patients were recruited as an external validation cohort from March 2018 to August 2019. Ultrasound images and clinical information of these patients were retrospectively reviewed. The ultrasonic features based on the BI-RADS lexicon were extracted by two radiologists. The features extracted from the primary cohort were used to develop a nomogram using multivariate analysis. Internal and external validations were performed to evaluate the predictive efficacy of the nomogram.

Results

The nomogram was based on two features (size, lesion boundary) and showed an area under the curve of 0.75 (95% confidence interval [CI], 0.70–0.79) in the primary cohort and 0.91 (95% CI, 0.84–0.97) in the external validation cohort; it achieved an 88% sensitivity in N1 patients.

Conclusion

The nomogram based on BI-RADS ultrasonic features can predict breast cancer ALN status with relatively high accuracy. It has potential clinical value in improving the sensitivity and accuracy of the preoperative diagnosis of ALN metastases, especially for N1 patients.

A Model to Predict the Risk of Lymph Node Metastasis in Breast Cancer Based on Clinicopathological Characteristics

Background

Sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) may cause lymphatic and nervous system side effects in patients with breast cancer. It is imperative to develop a model to evaluate the risk of sentinel lymph node metastasis to avoid unnecessary operation.

Patients and Methods

A total of 2705 cases of female breast cancer patients enrolled in this retrospective study. We divided into the training group (SLNB group) and the validation group (ALND group) to analyze the relathionship between lymph node metastasis and clinical-pathological factors. Logistic regression analysis was performed to verify the variables which involved in ALN metastasis and established a prediction model. ROC curves were employed to evaluate the predictive ability of the model.

Results

In the SLNB group, 9 variables, including pathological type, histological grade, tumor size, hormone receptor, HER-2, Ki-67, multifocality, and molecular subtypes, were related to breast cancer ALN metastasis. Clinically negative lymph nodes, favorable histologic type, tumor size <2 cm, and Ki-67 <15% were at very low risk for lymph node metastasis. The AUC of the validation group was 0.786.

Conclusion

We successfully establish a mathematics model to predict lymph node metastasis of breast cancer. Axillary surgery should be individual with preoperative clinical characteristics, especially for patients with a longer life expectancy.

Could Ultrasound-Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer?

OBJECTIVES

This work aimed to investigate whether quantitative radiomics imaging features extracted from ultrasound (US) can noninvasively predict breast cancer (BC) metastasis to axillary lymph nodes (ALNs).

METHODS

Presurgical B-mode US data of 196 patients with BC were retrospectively studied. The cases were divided into the training and validation cohorts (n = 141 versus 55). The elastic net regression technique was used for selecting features and building a signature in the training cohort. A linear combination of the selected features weighted by their respective coefficients produced a radiomics signature for each individual. A radiomics nomogram was established based on the radiomics signature and US-reported ALN status. In a receiver operating characteristic curve analysis, areas under the curves (AUCs) were determined for assessing the accuracy of the prediction model in predicting ALN metastasis in both cohorts. The clinical value was assessed by a decision curve analysis.

RESULTS

In all, 843 radiomics features per case were obtained from expert-delineated lesions on US imaging in this study. Through radiomics feature selection, 21 features were selected to constitute the radiomics signature for predicting ALN metastasis. Area under the curve values of 0.778 and 0.725 were obtained in the training and validation cohorts, respectively, indicating moderate predictive ability. The radiomics nomogram comprising the radiomics signature and US-reported ALN status showed the best performance for ALN detection in the training cohort (AUC, 0.816) but moderate performance in the validation cohort (AUC, 0.759). The decision curve showed that both the radiomics signature and nomogram displayed good clinical utility.

CONCLUSIONS

This pilot radiomics study provided a noninvasive method for predicting presurgical ALN metastasis status in BC.

Predicting Axillary Lymph Node Metastasis in Patients With Breast Invasive Ductal Carcinoma With Negative Axillary Ultrasound Results Using Conventional Ultrasound and Contrast-Enhanced Ultrasound

OBJECTIVES

The purpose of this study was to establish a scoring system for predicting axillary lymph node metastasis (ALNM) in patients with breast invasive ductal carcinoma with negative axillary ultrasound (US) results.

METHODS

In this retrospective study, 156 breast invasive ductal carcinoma lesions from 156 women were retrospectively enrolled. The features of conventional US and contrast-enhanced ultrasound (CEUS) qualitative enhancement patterns and quantitative enhancement parameters were analyzed. Subsequently, a scoring system was created by a multivariate logistic regression analysis.

RESULTS

The results found that 60 patients (38%) showed ALNM. A scoring system was defined as risk score = 1.75 × (if lesion size ≥20 mm) + 1.93 × (if uncircumscribed margin shown on conventional US) + 1.77 × (if coarse or twisting penetrating vessels shown on CEUS). When the risk scores were less than 1.75, 1.75 to 1.93, 1.94 to 3.70, and 3.70 or higher, the risk rates of ALNM were 0% (0 of 9), 10.7% (5 of 46), 29.2% (14 of 48) and 77.4% (41 of 53), respectively. In comparison with conventional US alone, the scoring system using the combination of conventional US and CEUS showed better discrimination ability in terms of the area under the curve (0.830 versus 0.777; P = .037).

CONCLUSIONS

A scoring system based on conventional US and CEUS may improve the prediction of ALNM.

Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction.

Methods: We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models.

Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models.

Conclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

Risk factors for axillary lymph node metastases in clinical stage T1-2N0M0 breast cancer patients

Axillary lymph node metastasis (ALNM) is commonly the earliest detectable clinical manifestation of breast cancer when distant metastasis emerges. This study aimed to explore the influencing factors of ALNM and develop models that can predict its occurrence preoperatively.Cases of sonographically visible clinical stage T1-2N0M0 breast cancers treated with breast and axillary surgery at West China Hospital were retrospectively reviewed. Univariate and multivariate logistic regression analyses were performed to evaluate associations between ALNM and variables. Decision tree analyses were performed to construct predictive models using the C5.0 packages.Of the 1671 tumors, 541 (32.9%) showed axillary lymph node positivity on final surgical histopathologic analysis. In multivariate logistic regression analysis, tumor size (P < .001), infiltration of subcutaneous adipose tissue (P < .001), infiltration of the interstitial adipose tissue (P = .031), and tumor quadrant locations (P < .001) were significantly correlated with ALNM. Furthermore, the accuracy in the decision tree model was 69.52%, and the false-negative rate (FNR) was 74.18%. By using the error-cost matrix algorithm, the FNR significantly decreased to 14.75%, particularly for nodes 5, 8, and 13 (FNR: 11.4%, 9.09%, and 14.29% in the training set and 18.1%,14.71%, and 20% in the test set, respectively).In summary, our study demonstrated that tumor lesion boundary, tumor size, and tumor quadrant locations were the most important factors affecting ALNM in cT1-2N0M0 stage breast cancer. The decision tree built using these variables reached a slightly higher FNR than sentinel lymph node dissection in predicting ALNM in some selected patients.

Correlation Between Raf/MEK/ERK Signaling Pathway and Clinicopathological Features and Prognosis for Patients With Breast Cancer Having Axillary Lymph Node Metastasis

Objective:

This study aims to investigate the correlations between rapidly accelerated fibrosarcoma/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase signaling pathway and clinicopathological features and prognosis for patients with breast cancer having axillary lymph node metastasis.

Methods:

A total of 118 breast cancer tissues with axillary lymph node metastasis (axillary lymph node metastasis group), 150 breast cancer tissues with non-axillary lymph node metastasis (non-axillary lymph node metastasis group), and 216 normal breast tissues (normal group) were enrolled in this study. The messenger RNA and protein expressions of rapidly accelerated fibrosarcoma, MEK, extracellular signal-regulated kinase, and their phosphorylated (p-) proteins were examined by reverse transcriptase quantitative polymerase chain reaction and immunohistochemistry, respectively. All patients received a 1-year follow-up, and the clinical follow-up data were collected. The multiple factors on the prognosis of patients with breast cancer having axillary lymph node metastasis were tested by Cox regression analysis.

Results:

The messenger RNA expressions of rapidly accelerated fibrosarcoma, MEK, and extracellular signal-regulated kinase and positive rates of rapidly accelerated fibrosarcoma, MEK, phosphorylated MEK, extracellular signal-regulated kinase, and p-extracellular signal-regulated kinase in the axillary lymph node metastasis group were higher than in the non-axillary lymph node metastasis and normal groups (all P < .05). The protein expressions of rapidly accelerated fibrosarcoma, MEK, phosphorylated MEK, extracellular signal-regulated kinase, and p-extracellular signal-regulated kinase were associated with tumor size, clinical stage, and axillary lymph node metastasis number (all P < .05). Rapidly accelerated fibrosarcoma, MEK, and extracellular signal-regulated kinase expressions were significantly correlated with the prognosis of patients with breast cancer (all P < .05). Patients with BC having positive rapidly accelerated fibrosarcoma, MEK, phosphorylated MEK, extracellular signal-regulated kinase, and phosphorylated ERK expressions had a higher survival rate than patients with BC having the negative ones (all P < .05). Rapidly accelerated fibrosarcoma and extracellular signal-regulated kinase protein expressions, clinical stage, pathological grade, and axillary lymph node metastasis number were independent prognostic factors in patients with breast cancer having axillary lymph node metastasis (all P < .05).

Conclusion:

Our study proved that rapidly accelerated fibrosarcoma/MEK/extracellular signal-regulated kinase signaling pathway is significantly correlated with the clinicopathological features and prognosis for patients with BC having axillary lymph node metastasis. Rapidly accelerated fibrosarcoma and extracellular signal-regulated kinase protein expressions are independent prognostic factors for patients with breast cancer having axillary lymph node metastasis.

Predictive Value of Preoperative Multidetector-Row Computed Tomography for Axillary Lymph Nodes Metastasis in Patients With Breast Cancer

Introduction: Axillary lymph nodes (ALN) status is an essential component in tumor staging and treatment planning for patients with breast cancer. The aim of present study was to evaluate the predictive value of preoperative multidetector-row computed tomography (MDCT) for ALN metastasis in breast cancer patients.

Methods: A total of 148 cases underwent preoperative MDCT examination and ALN surgery were eligible for the study. Logistic regression analysis of MDCT variates was used to estimate independent predictive factors for ALN metastasis. The prediction of ALN metastasis was determined with MDCT variates through receiver operating characteristic (ROC) analysis.

Results: Among the 148 cases, 61 (41.2%) cases had ALN metastasis. The cortical thickness in metastatic ALN was significantly thicker than that in non-metastatic ALN (7.5 ± 5.0 mm vs. 2.6 ± 2.8 mm, P < 0.001). Multi-logistic regression analysis indicated that cortical thickness of >3 mm (OR: 12.32, 95% CI: 4.50–33.75, P < 0.001) and non-fatty hilum (OR: 5.38, 95% CI: 1.51–19.19, P = 0.009) were independent predictors for ALN metastasis. The sensitivity, specificity and AUC of MDCT for ALN metastasis prediction based on combined-variated analysis were 85.3%, 87.4%, and 0.893 (95% CI: 0.832–0.938, P < 0.001), respectively.

Conclusions: Cortical thickness (>3 mm) and non-fatty hilum of MDCT were independent predictors for ALN metastasis. MDCT is a potent imaging tool for predicting ALN metastasis in breast cancer. Future prospective study on the value of contrast enhanced MDCT in preoperative ALN evaluation is warranted.

Axillary Lymph Node Metastasis in Gallbladder Carcinoma with Incidentally Detected Coexistence of Aberrant Right Subclavian Artery with Left-Sided Superior Vena Cava

The sequential development of port site recurrence, followed by recurrence in the axillary lymph node in gallbladder carcinoma is very infrequently reported in the literature. The representing 18F-fluorodeoxyglucose positron emission tomography-computed tomography image shows a metastatic right axillary lymph node in a case of gallbladder cancer developed following surgical removal of port site recurrence and six cycles of chemotherapy. The image also shows coexistence of two incidentally detected vascular anomalies, i.e., aberrant right subclavian artery and left-sided superior vena cava. Coexistence of both the vascular anomalies is rare among the general population and have their own clinical implications as described.

A nomogram to predict the probability of axillary lymph node metastasis in female patients with breast cancer in China: A nationwide, multicenter, 10-year epidemiological study

Axillary lymph node dissection (ALND) or sentinel lymph node biopsy (SLNB) alone may lead to postoperative complications. Among patients with positive ALN in the preoperative examination, approximately 40% patients do not have SLN metastasis. Herein, we aimed to develop a model to predict the probability of ALN metastasis as a preoperative tool to support clinical decision-making. We retrospectively analyzed the clinicopathological features of 4211 female patients with breast cancer who were diagnosed in seven breast cancer centers representing entire China, over 10 years (1999-2008). The patients were randomly categorized into a training cohort or validation cohort (3:1 ratio). Multivariate logistic regression analysis was performed for 1869 patients with complete information on the study variables. Age at diagnosis, tumor size, tumor quadrant, clinical nodal status, local invasion status, pathological type, and molecular subtypes were the independent predictors of ALN metastasis. The nomogram was then developed using the seven variables. Further, it was subsequently validated in 642 patients with complete data on variables in the validation cohort. Coefficient of determination (R²) and the area under the receiver-operating characteristic (ROC) curve (AUC) were calculated to be 0.979 and 0.7007, showing good calibration and discrimination of the model, respectively. The false-negative rates of the nomogram were 0 and 6.9% for the predicted risk cut-off values of 14.03% and 20%, respectively. Therefore, when the predicted risk is less than 20%, SLNB may be avoided. After further validation in various patient populations, this model may support increasingly limited axillary surgery in breast cancer.

Male occult breast cancer with axillary lymph node metastasis as the first manifestation: A case report and literature review

RATIONALE

Occult breast cancer (OBC) is extremely rare in males with neither symptoms in the breast nor abnormalities upon imaging examination.

PATIENT CONCERNS

This current case report presents a young male patient who was diagnosed with male OBC first manifesting as axillary lymph node metastasis. The physical and imaging examination showed no primary lesions in either breasts or in other organs.

DIAGNOSES

The pathological results revealed infiltrating ductal carcinoma in the axillary lymph nodes. Immunohistochemical (IHC) staining was negative for estrogen receptor (ER), progesterone receptor (PR), cytokeratin (CK)20 and thyroid transcription factor-1 (TTF-1), positive for CK7, gross cystic disease fluid protein-15 (GCDFP-15), epithelial membrane antigen (EMA) and carcinoembryonic antigen (CEA), and suspicious positive for human epidermal receptor-2 (Her-2). On basis of IHC markers, particularly such as CK7, CK20 and GCDFP-15, and eliminating other malignancies, male OBC was identified in spite of negativity for hormone receptors.

INTERVENTIONS

The patient underwent left axillary lymph node dissection (ALND) but not mastectomy. After the surgery, the patient subsequently underwent chemotherapy and radiotherapy.

OUTCOMES

The patient is currently being followed up without any signs of recurrence.

LESSONS

Carefully imaging examination and pathological analysis were particularly essential in the diagnosis of male OBC. The guidelines for managing male OBC default to those of female OBC and male breast cancer.

Volume-based metabolic parameter of breast cancer on preoperative 18F-FDG PET/CT could predict axillary lymph node metastasis

The purpose of our study was to evaluate the association between metabolic parameters on FDG PET/CT and axillary lymph node metastasis (ALNM) in patients with invasive breast cancer.From January 2012 to December 2012, we analyzed 173 patients with invasive ductal carcinoma (IDC) who underwent both initial breast magnetic resonance imaging (MRI) and F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) examinations. All metabolic parameters were measured from the tumor volume segmented by a gradient-based method. Once the primary target lesion was segmented, maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated automatically by the MIMvista software.Mean age of 173 patients was 49 years. Of 173 patients, 45 (26%) showed ALNM. On univariate analysis, larger tumor size (>2.2 cm; P = .002), presence of lymphovascular invasion (P < .001), higher SUVmax (>2.82; P = .038), higher SUVmean (>1.2; P = .027), higher MTV (>2.38; P < .001), and higher TLG (>3.98; P = .007) were associated with a higher probability of ALNM. On multivariate analysis, presence of lymphovascular invasion (adjusted odds ratio [OR], 11.053; 95% CI, 4.403-27.751; P < .001) and higher MTV (>2.38) (adjusted OR, 2.696; 95% CI, 1.079-6.739; P = .034) maintained independent significance in predicting ALNM. In subgroup analysis of T2/T3 breast cancer, lymphovascular invasion (adjusted OR, 20.976; 95% CI, 5.431-81.010; P < .001) and higher MTV (>2.38) (adjusted OR, 4.906; 95% CI, 1.616-14.896; P = .005) were independent predictors of ALNM. However in T1 breast cancer, lymphovascular invasion (adjusted OR, 16.096; 95% CI, 2.517-102.939; P = .003) and larger SUV mean (>1.2) (adjusted OR, 13.275; 95% CI, 1.233-142.908; P = .033) were independent predictors while MTV was not.MTV may be associated with ALNM in patients with invasive breast cancer, particularly T2 and T3 stages. In T1 breast cancer, SUVmean was associated with ALNM.

Diagnostic performance of breast ultrasonography and MRI in the prediction of lymph node status after neoadjuvant chemotherapy for breast cancer

Background Neoadjuvant chemotherapy (NAC) is widely used to treat breast cancer. Sentinel lymph node biopsy has replaced axillary lymph node dissection in patients who convert to node-negative status after NAC. However, few studies have evaluated the diagnostic performance of ultrasonography and magnetic resonance imaging (MRI) in determining axillary lymph node status after NAC. Purpose To evaluate the diagnostic performance of breast ultrasonography and MRI in determining residual metastatic axillary lymph node status after NAC for breast cancer and to identify histopathological factors affecting radiological performance. Material and Methods This study included 157 patients who underwent initial and follow-up preoperative breast ultrasonography and MRI before NAC between January and December 2010. The sensitivity, specificity, negative and positive predictive values, and accuracy of ultrasonography, MRI, and their combinations were evaluated. Results The sensitivity of ultrasonography, MRI, and their combination in post-NAC axillary imaging was 60.00%, 57.33%, and 65.33%, respectively; the specificity was 60.47%, 72.09%, and 60.47%, respectively. The positive predictive value was highest with MRI (78.18%). On univariate analysis, positive estrogen receptor status was associated with misdiagnosis by ultrasonography ( P = 0.002), MRI ( P = 0.002), and their combination ( P = 0.001). When residual metastatic lymph nodes were present, lymph nodes with macrometastasis (>2.0 mm) were associated with correct ultrasonography-based diagnosis ( P = 0.0027). Conclusion Imaging assists in predicting axillary lymph node status in patients undergoing NAC; however, is imprudent to omit sentinel lymph node biopsy or axillary lymph node dissection for staging in women determined to be node-positive.

A prediction model for the presence of axillary lymph node involvement in women with invasive breast cancer: a focus on older women

Axillary lymph node (ALN) status at diagnosis is the most powerful prognostic indicator for patients with breast cancer. Our aim is to examine the contribution of variables that lead to ALN metastases in a large dataset with a high proportion of patients greater than 70 years old. Using the data from two multicenter prospective studies, a retrospective review was performed on 2,812 patients diagnosed with clinically node-negative invasive breast cancer from 1996 to 2005 and who underwent ALN sampling. Univariate and multivariate logistic regression were used to identify variables that were strongly associated with axillary metastases, and an equation was developed to estimate risk of ALN metastases. Of the 2,812 patients with invasive breast cancer, 18% had ALN metastases at diagnosis. Based on univariate analysis, tumor size, lymphovascular invasion (LVI), tumor grade, age at diagnosis, menopausal status, race, tumor location, tumor type, and estrogen and progesterone receptor status were statistically significant. The relationship between age and involvement of axillary metastases was nonlinear. In multivariate analysis, LVI, tumor size and menopausal status were the most significant factors associated with ALN metastases. Age, however, was not a significant contributing factor for axillary metastases. Tumor size, LVI, and menopausal status are strongly associated with ALN metastases. We believe that age may have been a strong factor in previous analyses because there was not an adequate representation of women in older age groups and because of the violation of the assumption of linearity in their multivariate analyses.

Mucinous carcinoma with axillary lymph node metastasis in a male breast: A case report

CONTEXT

Pure mucinous carcinoma of the male breast is an extremely rare neoplasm. It is characterized by a lower incidence of metastatic nodal involvement and a higher survival rate than invasive ductal carcinomas.

CASE REPORT

We report the case of a 75-year-old male who presented with a retroareolar mass of the right breast. The patient underwent radical mastectomy including right axillary lymph node dissection. The tumor was well demarcated and had a friable consistency with a gelatinous appearance. Histologically, the diagnostic of pure mucinous carcinoma with lymph node metastasis was performed. After surgery, the patient received chemotherapy, radiotherapy, and hormonotherapy (Tamoxifen). The patient remained free of disease for 36 months after surgery.

CONCLUSION

Pure mucinous carcinoma of the male breast is a very rare tumor; in which axillary nodal disease is exceptional.