Axillary Lymph Nodes In Breast Cancer Research Articles

Abstract PO4-22-10: Prognostic impact of preoperative lymph node diagnostic tools for patients with suspicious node positive breast cancer

Abstract (Abstract) Recently, the SOUND trial demonstrated the feasibility for omission of sentinel lymph node biopsy (SNB) in cases with negative ultrasound (US)-guided fine-needle aspiration cytology (FNA) of suspicious lymph nodes (LNs). An important consideration for omission of SNB depends on a highly accurate assessment of preoperative staging for axillary LNs. (Purpose) The purpose of this study was to investigate the impact of preoperative diagnostic tools for axillary lymph nodes (LNs) staging of early breast cancer. (Materials and Methods) A total of 3088 consecutive patients with operable breast cancer were retrospectively identified at our institution between April 2013 and March 2020. Patients with suspicious axillary LN of breast cancer were assessed using preoperative US and computed tomography (CT), underwent FNA or core needle biopsy (CNB). The inclusion criteria for both FNA and CNB were a cortical thickness [3 mm or abnormal morphological characteristics. Patients with biopsy-proven metastasis underwent axillary lymph node dissection (ALND), and those with a negative FNA or CNB underwent SNB. If the SNB was positive, ALND was performed. Diagnostic accuracy for SNB was calculated for both FNA and CNB. In addition, the patients in this study were divided into two groups as follows: the cN0-FNA negative group (suspicious LN but negative FNA) and the cN0-CNB group (suspicious LN but negative CNB). Overall survival (OS)and invasive disease survival(iDFS) was estimated by using the Kaplan-Meier method and compared by using the log-rank test. (Results) A number of patients with negative or suspicious metastasis US/CT findings of LNs were 3187, with 963 undergoing FNA and 395 undergoing CNB for suspicious LNs. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 63, 99, 99, 73, and 81% in FNA, and 87, 100, 100, 91 and 95% in CNB, respectively. SNB was performed in 207 (cN0-CNB group) of 395 CNB and 581 (cN0-FNA group) of 963 FNA patients. Two hundred and seven patients from the cN0-CNB group (T1ab; 42, T1c; 72, T2;84, T3;9 patients) treated with SNB were compared to 581 from the cN0-FNA group (T1ab;86, T1c;193, T2;278, T3;24 patients) in terms of number of LN metastasis. A number of patients with micrometastases, 1,2,3 or more than 4 positive LNs were 5(2%),7(3%),3(1%)9,3(1%) and 0 (0%) in cN0-CNB group, and 23(4%),67(12%),26(4%),12(2%) and 29 (5%) in cN0-FNA group, respectively. The significant difference in 5ys iDFS and OS was observed between CNB group and FNA group (94.2% vs.91.4%, p =0.04 and 99% vs. 95%, p=0.004, respectively). Conclusions The preoperative diagnosis of axillary LNs was influenced by the diagnostic tool used. CNB is a reliable method for the preoperative diagnosis of LN metastasis. The prognosis of early breast cancer with clinically metastasis-negative lymph nodes diagnosed by CNB is better than that by FNA. Table. Number of positive lymph nodes cN0-FNA (%) cN0-CNB (%) Total 207 100 581 100 0 189 91 424 73 micrometastasis 5 2 23 4 1 7 3 67 12 2 3 1 26 4 3 3 1 12 2 4 more than 0 0 29 5 Citation Format: Rikiya Nakamura, Shouko Hayama, Rikiya Nakamura, Naohito Yamamoto. Prognostic impact of preoperative lymph node diagnostic tools for patients with suspicious node positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-22-10.

An ultrasound-based nomogram for predicting axillary node pathologic complete response after neoadjuvant chemotherapy in breast cancer: Modeling and external validation.

The staging and treatment of axillary nodes in breast cancer have become a focus of research. For breast cancer patients with fine-needle aspiration-or core needle biopsy-confirmed positive nodes, axillary lymph node dissection (ALND) after neoadjuvant chemotherapy (NAC) is still a standard treatment. However, some patients achieve an axillary pathologic complete response (pCR) after NAC. In this study, the authors sought to construct a model to predict axillary pCR in patients with positive axillary lymph nodes (cN+) breast cancer. Data from patients with pathologically proven cN+ breast cancer treated with NAC followed by ALND between January 2010 and April 2019 at the Peking University Cancer Hospital were reviewed. Axillary lymph node status was assessed using ultrasonography before and after NAC. The patient cohort was assigned to the construction and internal validation cohorts according to admission time. A nomogram was constructed based on the significant factors associated with axillary pCR. The predictive performance of the model was externally validated using data from Peking University First Hospital. This study included 953 and 267 patients from Peking University Cancer Hospital and Peking University First Hospital, respectively. In the construction cohort, 39.7% (238 of 600) of patients achieved axillary pCR after NAC. The result of multivariate logistic regression analysis showed that tumor grade, clinical nodal response, NAC regimen, tumor pCR, lymphovascular invasion, and tumor biologic subtype were significant independent predictors of ypN0 (p < 0.05). The areas under the receiver operating characteristic curves for the construction, validation, and independent testing cohorts were 0.87 (95% confidence interval [CI], 0.84-0.90), 0.83 (95% CI, 0.79-0.87), and 0.84 (0.79-0.89), respectively. A nomogram was constructed to predict the pCR of axillary lymph nodes after NAC for breast cancer. Validation of both the internal and external cohorts achieved good predictive performance, indicating that the model has preliminary clinical application prospects.

Is 18 F-fluorodeoxyglucose-positron emission tomography or computed tomography scan a triaging technique for axillary dissection in breast cancer?

Background: To determine if fluorodeoxyglucose-positron emission tomography/ computed tomography (FDG PET/CT) scan identifies axillary nodal disease in conjunction with ultrasound (US) guided fine needle aspiration cytology (FNAC) of suspicious axillary nodes in breast cancer. To determine if this will enable axillary node dissection upfront, avoiding sentinel lymph node biopsy (SLNB). Methods: Study was performed in Apollo main hospital, Off Greams-lane, Greams road, Chennai IRB approval was obtained from institutional ethical committee-bio medical research-Apollo hospitals, (IEC application number: AMH-C-S-029/04-23). Informed consent was obtained from all the patients who met the exclusion criteria. The 61 patients with cT1-T3, N0-N1, and M0 disease underwent 18F-FDG-PET/CT scans after mammographic evaluation and an US guided FNAC of nodes which were seen to be suspicious in the mammogram and correlated US of the Axilla. All patients underwent axillary lymph node dissection (ALND levels 1 and 2) without a SLNB. Results: Out of 61 patients, 40 (65.6%) had axillary node uptake on PET/CT scan. But, after ALND, only 32 patients were found to have metastatic nodes on the histopathological examination (HPE) of the operative specimen. Out of these 32 patients, results for axillary metastasis had been noted on PET/CT, US, and guided FNAC in 18 patients (56.3%). Discordant results were noted in 14 (43.8%) patients, 29 patients had no metastasis on HPE. Negative results for axillary metastasis were noted on PET/CT, US and guided FNAC in 26 patients (89.7%). The results were discordant in 3 (10.3%) patients. Conclusions: An expanded preoperative axillary assessment does not have sufficient reliability to serve as a reliable triaging technique in axillary management. Concordant positive results may permit directly proceeding to ALND without resorting to SLNB. Concordant negative results may similarly permit avoiding axillary surgery. But discordant results warrant SLNB for assessment of axilla.

CT Radiomics for Predicting Pathological Complete Response of Axillary Lymph Nodes in Breast Cancer After Neoadjuvant Chemotherapy: A Prospective Study.

The diagnostic effectiveness of traditional imaging techniques is insufficient to assess the response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC), especially for pathological complete response (pCR). A radiomics model based on computed tomography (CT) could be helpful. Prospective consecutive breast cancer patients with positive axillary LNs initially were enrolled, who received NAC prior to surgery. Chest contrast-enhanced thin-slice CT scan was performed both before and after the NAC (recorded as the first and the second CT respectively), and on both of them, the target metastatic axillary LN was identified and demarcated layer by layer. Using pyradiomics-based software that was independently created, radiomics features were retrieved. A pairwise machine learning workflow based on Sklearn (https://scikit-learn.org/) and FeAture Explorer was created to increase diagnostic effectiveness. An effective pairwise auto encoder model was developed by the improvement of data normalization, dimensionality reduction, and features screening scheme as well as the comparison of the prediction effectiveness of the various classifiers. A total of 138 patients were enrolled, and 77 (58.7%) in the overall group achieved pCR of LN after NAC. Nine radiomics features were finally chosen for modeling. The AUCs of the training group, validation group, and test group were 0.944 (0.919-0.965), 0.962 (0.937-0.985), and 1.000 (1.000-1.000), respectively, and the corresponding accuracies were 0.891, 0.912, and 1.000. The pCR of axillary LNs in breast cancer following NAC can be precisely predicted using thin-sliced enhanced chest CT-based radiomics.

Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer

Accurate identification of axillary lymph node (ALN) status in breast cancer patients is important for determining treatment options and avoiding axillary overtreatments. Our study aims to comprehensively compare the performance of the traditional radiomics model, deep learning radiomics model, and the fusion models in evaluating breast cancer ALN status based on dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI) images. The handcrafted radiomics features and deep features were extracted from 3062 DCE-MRI images. The feature selection was performed by applying mutual information and feature recursive elimination algorithms. The traditional radiomics model and deep learning radiomics model were built using the optimal features and machine learning classifiers, respectively. The fusion models for distinguishing axillary lymph node status were constructed using two fusion strategies. The performance of the models with MRI-reported lymphadenopathy or suspicious nodes to evaluate axillary lymph node status was also compared. The decision fusion model, with the integration of the radiomics features and deep learning features at the decision level, achieved an area under the curve (AUC) of 0.91 (95% confidence interval (CI): 0.879-0.937), which was higher than that of the traditional radiomics model and deep learning radiomics model. The results of the decision fusion model with clinical characteristic yielded an AUC of 0.93 (95% CI: 0.899-0.951), which was also superior to other models incorporating clinical characteristic. This study demonstrates the effectiveness of the fusion models for predicting axillary lymph node metastasis in breast cancer.

Identification of the lymph node metastasis-related automated breast volume scanning features for predicting axillary lymph node tumor burden of invasive breast cancer via a clinical prediction model.

Breast cancer has become the malignant tumor with the highest incidence in women. Axillary lymph node dissection (ALND) is an effective method of maintaining regional control; however, it is associated with a significant risk of complications. Meanwhile, whether the patients need ALND or not is according to sentinel lymph node biopsy (SLNB). However, the false-negative results of SLNB had been reported. Automated breast volume scanning (ABVS) is a routine examination in breast cancer. A real-world cohort consisting of 245 breast cancer patients who underwent ABVS examination were enrolled, including 251 tumor lesions. The ABVS manifestations were analyzed with the SLNB results, and the ALND results for selecting the lymph node metastasis were related to ABVS features. Finally, a nomogram was used to construct a breast cancer axillary lymph node tumor burden prediction model. Breast cancer patients with a molecular subtype of luminal B type, a maximum lesion diameter of ≥5 cm, tumor invasion of the Cooper’s ligament, and tumor invasion of the nipple had heavy lymph node tumor burden. Molecular classification, tumor size, and Cooper’s ligament status were used to construct a clinical prediction model of axillary lymph node tumor burden. The consistency indexes (or AUC) of the training cohort and the validation cohort were 0.743 and 0.711, respectively, which was close to SLNB (0.768). The best cutoff value of the ABVS nomogram was 81.146 points. After combination with ABVS features and SLNB, the AUC of the prediction model was 0.889, and the best cutoff value was 178.965 points. The calibration curve showed that the constructed nomogram clinical prediction model and the real results were highly consistent. The clinical prediction model constructed using molecular classification, tumor size, and Cooper’s ligament status can effectively predict the probability of heavy axillary lymph node tumor burden, which can be the significant supplement to the SLNB. Therefore, this model may be used for individual decision-making in the diagnosis and treatments of breast cancer.

3D reconstruction based novel methods are more effective than traditional clinical assessment in breast cancer axillary lymph node metastasis prediction

The status of axillary lymph node metastases determines the treatment and overall survival of breast cancer (BC) patients. Three-dimensional (3D) assessment methods have advantages for spatial localization and are more responsive to morphological changes in lymph nodes than two-dimensional (2D) assessment methods, and we speculate that methods developed using 3D reconstruction systems have high diagnostic efficacy. This exploratory study included 43 patients with histologically confirmed BC diagnosed at Second Xiangya Hospital of Central South University between July 2017 and August 2020, all of whom underwent preoperative CT scans. Patients were divided into a training cohort to train the model and a validation cohort to validate the model. A 3D axillary lymph node atlas was constructed on a 3D reconstruction system to create various methods of assessing lymph node metastases for a comparison of diagnostic efficacy. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic values of these methods. A total of 43 patients (mean [SD] age, 47 [10] years) met the eligibility criteria and completed 3D reconstruction. An axillary lymph node atlas was established, and a correlation between lymph node sphericity and lymph node metastasis was revealed. By continuously fitting the size and characteristics of axillary lymph nodes on the 3D reconstruction system, formulas and models were established to determine the presence or absence of lymph node metastasis, and the 3D method had better sensitivity for axillary lymph node assessment than the 2D method, with a statistically significant difference in the correct classification rate. The combined diagnostic method was superior to a single diagnostic method, with a 92.3% correct classification rate for the 3D method combined with ultrasound. In addition, in patients who received neoadjuvant chemotherapy (NAC), the correct classification rate of the 3D method (72.7%) was significantly higher than that of ultrasound (45.5%) and CT (54.5%). By establishing an axillary lymph node atlas, the sphericity formula and model developed with the 3D reconstruction system achieve a high correct classification rate when combined with ultrasound or CT and can also be applied to patients receiving NAC.

Chyle Leak After Axillary Node Clearance in Breast Cancer Surgery—A Rare Complication and a Proposed Management Strategy from the British and Italian Experience

Chyle leak after axillary surgery is a rare complication that lacks consensus and management guidelines. This study aimed to present the experiences of two centers in chyle leak management after axillary node clearance for breast cancer. Furthermore the authors provide a review of its pathophysiology and clinical diagnostic methods. They compare approaches for management of the axilla with those for other locations. A multicentre case series descriptive analysis of chyle leak after breast cancer axillary node clearance between 1 January 2013 and 31 May 2020 was performed. The center in the United Kingdom and the center in Italy performed 655 and 4969 axillary node clearances, respectively. Four patients experienced chyle leaks. All the leaks had left-sided surgery (3 patients had level 3 clearances with mastectomy and implant-based breast reconstructions; 1 patient had level 2 clearance with therapeutic mammoplasty). All the leaks appeared within 3 days after surgery. Leak duration was between 11 and 29 days. The maximum daily output was 600 mL. All the leaks were treated conservatively with nutritional team support together with close drainage monitoring. The management strategy included a low-fat diet, a high-protein diet, total parenteral nutrition, and medium chain triglyceride supplements, as well as other elements. No negative effects on oncoplastic and reconstructive breast surgery wound-healing and no delays in adjuvant treatment were observed. The incidence of chyle leak after breast cancer axillary node clearance was 0.07%. Early diagnosis and close monitoring together with conservative management involving nutritional team support can result in successful treatment of chyle leak without negative sequalae of breast cancer oncologic treatment.

Predictive value of intravoxel incoherent motion combined with diffusion kurtosis imaging for breast cancer axillary lymph node metastasis: a retrospective study.

Non-invasive imaging technologies for assessing axillary lymph node (ALN) metastasis of breast cancer are needed in clinical practice. To explore the clinical value of intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) for predicting ALN metastasis of breast cancer. A total of 194 patients with pathologically confirmed breast cancer who underwent IVIM and DKI examination were reviewed retrospectively. The IVIM derived parameters of D, D*, and f and DKI-derived parameters of MD and MK were measured. The independent samples t-test was used to compare the parameters between the ALN metastasis and non-ALN metastasis groups. Receiver operating characteristic (ROC) curve analysis was also performed. The D and MD in the ALN metastasis group were significantly lower than those in the non-ALN metastasis group (P < 0.001, P < 0.001). The D*, f, and MK were higher in the ALN metastasis group than in the non-ALN metastasis group (P = 0.015, P = 0.014, and P = 0.001, respectively). The area under the ROC curve (AUC) of D (0.768) was highest. In addition, the diagnostic efficiency of both IVIM and DKI were higher than that of the conventional MRI (P = 0.002, P = 0.048). The diagnostic efficiency of IVIM + DKI were higher than that of the IVIM or DKI alone (P = 0.021, P = 0.004). IVIM and DKI can be used for predicting breast cancer ALN metastasis with D as the most meaningful parameter.

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

ObjectiveTo explore the diagnostic value of shear wave elastography examination (SWE) on axillary node metastasis (ANM) in breast cancer, this study aimed to evaluate the correlation between the SWE features and different molecular types of breast cancer, and to check the elastic modulus differences among the molecular types.MethodsBreast cancer patients from November 2020 to December 2021 were subjected to both conventional ultrasonic examination (CUE) and SWE before ultrasound-guided percutaneous biopsy or axillary lymph node dissection (ALND). We used the pathological results as the gold standard to draw the receiver operating characteristic (ROC) curve.ResultsSWE outperforms CUE, but their conjunctive use is the best option. No significant correlation was found between the elastic modulus values and the molecular types of breast cancer.ConclusionSWE can be used as an routine auxiliary method of CUE for ANM.

Decreased Expression of circ_0000160 in Breast Cancer With Axillary Lymph Node Metastasis.

Background: Circular RNAs (circRNAs) have been shown to play important roles in the development and progression of human cancers. Emerging evidence shows that circRNAs have the potential to be promising biomarkers for cancer diagnosis and prognosis. However, the roles of circRNAs in breast cancer axillary lymph node metastasis (ALNM) remain to be determined. Methods: Transcriptome sequencing was utilized to screen the differentially expressed circRNAs in three breast cancer tissues with ALNM and three without ALNM. Differentially expressed circRNAs were further verified by quantitative real-time PCR. Moreover, receiver operating characteristic (ROC) curve analysis was performed to calculate the value of circRNAs to distinguish breast cancer tissues with ALNM and those without ALNM. To explore the potential mechanism of the circRNAs, a circRNA–miRNA–mRNA network was constructed based on the CircInteractome, circBank, and mirDIP online software. Results: In total, 31 differentially expressed circRNAs were identified by transcriptome sequencing; among them, 21 were upregulated and 10 were downregulated in breast cancer with ALNM compared to those without ALNM. Circ_0000160 was validated to be downregulated in breast cancer tissues with ALNM compared with those without ALNM. The ROC curve showed the ability of circ_0000160 to distinguish breast cancer tissues with ALNM and those without ALNM, with an area under the curve of 0.7435. Furthermore, bioinformatics analysis revealed that the predicted mRNAs for circ_0000160 may be related to lymph node metastasis. The predicted mRNAs for circ_0000160 may be involved in many cancer-related pathways. Conclusion: A decreased expression of circ_0000160 was found in breast cancer with axillary lymph node metastasis. Circ_0000160 may have the potential to distinguish breast cancer with axillary lymph node metastasis from those without axillary lymph node metastasis.

Triple evaluation of positive axillary nodes in breast cancer after neoadjuvant chemotherapy

Background: After neoadjuvant chemotherapy for node positive breast cancer, pathological complete lymph node response rates between 40-75% have been described, but the selection of patients in whom axillary lymphadenectomy can be omitted remains challenging. Using a double method of lymph node marking, removing more than 1-2 sentinel lymph nodes, and removing the previously marked metastatic lymph node decreases the false negative rate of the sentinel node biopsy to <10% in this setting.

Diagnostic performance of a novel high-resolution dedicated axillary PET system in the assessment of regional nodal spread of disease in early breast cancer.

In early breast cancer, a non-invasive method with higher sensitivity and negative predictive value (NPV) is needed to identify and recognize more indolent axillary lymph nodes (ALNs). This study aimed to assess whether a novel high-resolution dedicated ALN positron emission tomography (LymphPET) system could improve sensitivity in detecting early breast cancer (clinical N0-N1 stage). A total of 103 patients with clinical stage T1-2N0-1M0 breast cancer were evaluated by 18F-fluorodeoxyglucose (18F-FDG) LymphPET. The maximum single-voxel PET uptake value of ALNs (maxLUV) and the tumor-to-background ratio (TBR) for fat (TBR1) and muscle (TBR2) tissue were calculated. Then, 78 patients with cN0 stage breast cancer received sentinel lymph node biopsy alone or combined with axillary lymph node dissection (ALND), and 25 patients with cN1 stage breast cancer underwent fine-needle aspiration. A total of 99 invasive breast carcinoma cases were included in this study. The diagnostic sensitivity of LymphPET was 88%, specificity was 79%, false-negative rate was 12%, the false-positive rate was 21%, positive predictive value was 75%, NPV was 90%, and accuracy was 83%. The maxLUV was superior to TBR1 and TBR2 in detecting ALNs, with 0.27 being the most optimal cutoff value. The 18F-FDG LymphPET system can be used to identify and recognize more indolent ALNs of breast cancer due to greater sensitivity and a much higher NPV.

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE-MRI Radiomics Nomogram.

Objective To investigate the value of preoperative prediction of breast cancer axillary lymph node metastasis based on intratumoral and peritumoral dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) radiomics nomogram. Material and Methods. In this study, a radiomics model was developed based on a training cohort involving 250 patients with breast cancer (BC) who had undergone axillary lymph node (ALN) dissection between June 2019 and January 2021. The intratumoral and peritumoral radiomics features were extracted from the second postcontrast images of DCE-MRI. Based on filtered radiomics features, the radiomics signature was built by using the least absolute shrinkage and selection operator method. The Support Vector Machines (SVM) learning algorithm was used to construct intratumoral, periatumoral, and intratumoral combined periatumoral models for predicting axillary lymph node metastasis (ALNM) in BC. Nomogram performance was determined by its discrimination, calibration, and clinical value. Multivariable logistic regression was adopted to establish a radiomics nomogram. Results The intratumoral combined peritumoral radiomics signature, which was composed of fifteen ALN status-related features, showed the best predictive performance and was associated with ALNM in both the training and validation cohorts (P < 0.001). The prediction efficiency of the intratumoral combined peritumoral radiomics model was higher than that of the intratumoral radiomics model and the peritumoral radiomics model. The AUCs of the training and verification cohorts were 0.867 and 0.785, respectively. The radiomics nomogram, which incorporated the radiomics signature, MR-reported ALN status, and MR-reported maximum diameter of the lesion, showed good calibration and discrimination in the training (AUC = 0.872) and validation cohorts (AUC = 0.863). Conclusion The intratumoral combined peritumoral radiomics model derived from DCE-MRI showed great predictive value for ALNM and may help to improve clinical decision-making for BC.

MRI Diagnosis and Pathological Examination of Axillary Lymph Node Metastasis in Breast Cancer Patients.

In order to explore the characteristics and diagnostic value of magnetic resonance imaging (MRI) in axillary lymph node metastasis of breast cancer, a total of 200 breast cancer patients diagnosed and treated from January 2021 to January 2022 are selected as the study subjects, and 200 patients are divided into an axillary lymph node metastasis group and a simple breast cancer group according to pathological results. The pathological results are used as the gold standard to determine the accuracy and diagnostic efficacy of MRI results. A multivariate logistic regression method is used to analyze the influencing factors of MRI image characteristics of breast cancer axillary lymph node metastasis. The experimental results show that MRI has high application values in diagnosing axillary lymph node metastasis of breast cancer, which is worthy of clinical promotion and application.

Sentinel Lymph Node Biopsy Efficacy in Breast Cancer: A 4-year Experience Report

Sentinel lymph node biopsy has been one of the most important advances in oncologic surgery, especially in breast cancer, since it decreases the high morbidity associated with radical axillary dissection, and allows treatment personalization. The staging of axillary nodes in breast cancer is fundamental as it is a prognostic indicator and determines the need for adjuvant therapy. Sentinel lymph node is detected by injecting a radioisotope and dye through the periareolar area that consequently travels through mammary glands to axillary lymph nodes. We conducted a retrospective study that included 57 women with a histopathological diagnosis of breast cancer, and underwent sentinel node biopsy. Transoperative pathology analysis reported metastases in 24% of the patients, requiring radical axillary dissection. Sensitivity was 97%, and specificity was 85%, reflecting high efficacy of the sentinel lymph node biopsy, and thus benefitting patients by decreasing morbidity. Sentinel node biopsy is the standard procedure for correct staging of patients with early breast cancer, and no clinical evidence of lymph node involvement. Further, it decreases morbidity in these patients by preventing radical axillary dissection, a previously standard approach, without compromising the diagnosis and hence, the prognosis of the patients. We demonstrated that with the correct technique for sentinel lymph node biopsy, the prognosis for patients is much better than for those patients that undergo radical axillary dissection.

Application of ultrasound-guided placement of markers for locating axillary lymph nodes of breast cancer.

With the continuous improvement of pathological complete response (pCR) rate after neoadjuvant therapy (NAT), it is necessary to locate the tumor bed and axillary lymph nodes (ALNs) for subsequent surgery. Therefore, breast tissue markers emerge. This study aims to evaluate the feasibility and accuracy of ultrasound (US)-guided placement of markers for locating ALNs of breast cancer. A total of 285 patients who received US-guided placement of markers for locating ALNs in our hospital were selected. Among these patients, 87 patients were in the early breast cancer (EBC) group with negative ALNs and 198 ones were in the NAT group with positive ALNs. Data including the basic information of patients, position and size of ALN, process of US-guided marker placement, placement success rate, complications, detection rate of marker by imaging, and shift rate were recorded. All patients were successfully undergone US-guided marker placement. And the average operation time was 2 minutes with no adverse reactions. All the patients underwent surgery successfully. US, computer tomography (CT) and magnetic resonance imaging (MRI) were used to detect the marker. The detection rate of markers by US and CT/MRI were 100% (87/87) in EBC group, and 98.5% (195/198) and 100% (198/198) by US and CT/MRI, respectively, in NAT group. The postoperative marker shift rate was 2.1% (6/285), including 3.4% (3/87) marker shift rate in EBC group and 1.5% (3/198) in NAT group, with no statistically significant difference between them. US-guided marker placement in ALNs of breast cancer is simple and safe, with firm positioning and low shift rate, which is convenient for clinical promotion.

Artificial Intelligence Algorithm-Based Ultrasound Image Segmentation Technology in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis.

This paper aimed to investigate the application of ultrasound image segmentation technology based on the back propagation neural network (BPNN) artificial intelligence algorithm in the diagnosis of breast cancer axillary lymph node metastasis, thereby providing a theoretical basis for clinical diagnosis. In this study, 90 breast cancer patients with axillary lymph node metastasis were selected as the research objects and rolled randomly into an experimental group and a control group. Besides, all of them were examined by ultrasound. The BPNN algorithm for the ultrasound image segmentation diagnosis method was applied to the patiens from the experimental group, while the control group was given routine ultrasound diagnosis. Thus, the value of this algorithm in ultrasonic diagnosis was compared and explored. The results showed that when the number of hidden layer nodes based on the BPNN artificial intelligence algorithm was 2, 3, 4, 5, 6, 7, and 8, the corresponding segmentation accuracy was 97.3%, 96.5%, 94.8%, 94.8%, and 94.1% in turn. Among them, the segmentation accuracy was the highest when the number of hidden layer nodes was 2. The correlation of independent variable bubble plot analysis showed that the presence or absence of capsules, the presence of crab feet or burrs in breast cancer lesions was critical influencing factors for the occurrence of axillary lymph node metastasis, and the standardized importance was 99.7% and 70.8%, respectively. Besides, the area under the two-dimensional receiver operating characteristic (ROC) curve of the BPNN artificial intelligence algorithm model classification was always greater than the area under the curve of manual segmentation, and the segmentation accuracy was 90.31%, 94.88%, 95.48%, 95.44%, and 97.65% in sequence. In addition, the segmentation specificity of different running times was higher than that of manual segmentation. In conclusion, the BPNN artificial intelligence algorithm had high accuracy, sensitivity, and specificity for ultrasound image segmentation, with a better segmentation effect. Therefore, it had a better diagnostic effect for breast cancer axillary lymph node metastasis.

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

The number of metastatic axillary lymph nodes (ALNs) play a crucial role in the staging, prognosis and therapy of patients with breast cancer. To predict the number of metastatic ALNs in breast cancer via radiomics. 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. 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 N2-3 (≥4 metastatic ALNs) group and showed encouraging performance with AUC values of 0.94 and 0.84 in training and test group, respectively. We developed a nomogram and a radiomic signature that can be used to predict ALN metastasis and distinguish the N1 from the N2-3 group. Both nomogram and radiomic signature may be potential tools to assist clinicians in assessing ALN metastasis in patients with breast cancer.

Specimen number based diagnostic yields of suspicious axillary lymph nodes in core biopsy in breast cancer: clinical implications from a prospective exploratory study.

Ultrasound (US)-guided core needle biopsy (CNB) is widely applied in the pathological diagnosis of suspicious axillary lymph nodes (ALNs) in breast cancer. However, the number of specimens removed during biopsy is currently based on the preference of the individual radiologist. This study aims to analyze the specimen number based diagnostic yields of US guided CNB of suspicious ALNs in breast cancer. Core biopsy specimens of suspicious lymph nodes were prospectively obtained from breast cancer patients treated at our hospital between November, 2018, and July, 2019. Four specimens were obtained from each patient and labeled 1-4 in the order they were removed. Each specimen underwent pathological evaluation to determine whether metastasis had occurred. The diagnostic yields of the specimens were calculated and differences in diagnostic accuracy according to the number of specimens were evaluated by McNemar's test. A total of 167 patients were enrolled, and 139 (83.2%) cases were identified as metastasis by CNB. The diagnostic yields were: 74.2% (specimen 1), 87.8% (specimens 1-2), 91.2% (specimens 1-3), and 94.6% (specimens 1-4). The increases in diagnostic yield from specimen 1 to 1-2 and from specimens 1-2 to 1-4 were significant; however, no significant differences were detected between specimens 1-3 and the first two, or between specimens 1-4 and the first three in this sample size. The lower diagnostic abilities for the first two specimens were associated with shorter long- and short-axis lengths of lymph nodes on US. Although the second specimen contributed significant diagnostic yield of suspicious axillary lymph nodes in core biopsy in breast cancer, a minimum number cannot be determined by this study. Additional specimens may improve diagnostic yield particularly in patients with small nodes.