Axillary Lymph Node Metastasis Research Articles

Enhancing detection of high-level axillary lymph node metastasis after neoadjuvant therapy in breast cancer patients with nodal involvement: a combined approach of axilla ultrasound and breast elastography.

To develop a combined approach using shear wave elastography (SWE) and conventional ultrasound (US) to determine the extent of positive axillary lymph nodes (LNs) following neoadjuvant therapy (NAT) in breast cancer patients with nodal involvement. This prospective, multicenter study was registered on the Chinese Clinical Trial Registry (ChiCTR2400085035). From October 2018 to February 2024, a total of 303 breast cancer patients with biopsy-proven positive LN were enrolled. The conventional US features of axillary LNs and SWE characteristics of breast lesions after NAT were analyzed. The diagnostic performances of axilla US, breast SWE, and their combination in detecting residual metastasis in axillary level III after NAT were assessed. Pathologically positive LN(s) in axilla level III were detected in 13.75% of cases following NAT. The kappa value for the axilla level with positive LN confirmed by surgical pathology and detected by US is 0.39 (p < 0.001). The AUC of conventional axilla US to determine the status of axilla level III LNs after NAT was 0.67, with a sensitivity of 51.52%, a specificity of 74.36%. The breast SWE displayed moderate performance for detecting residual metastasis in axilla level III following NAT, with an AUC of 0.79, sensitivity of 84.85%, and specificity of 74.36%. Compared to axilla US and breast SWE alone, the combination of axilla US with breast SWE achieved a stronger discriminatory ability (AUC, 0.86 vs 0.67 vs 0.79, p < 0.05, Delong's test) and precise calibration (X2 = 13.90, p = 0.085, HL test), with an improved sensitivity of 93.94% and a comparable specificity of 75.64%%. SWE outperformed conventional US in identifying the axilla levels with nodal metastasis following NAT in patients with initially diagnosed positive axilla. Furthermore, combining breast SWE with axilla US showed good diagnostic performance for detecting residual metastasis in axilla level III after NAT.

Effects on lymph node size, staging and primary tumor histology on diagnostic accuracy of axillary lymph node aspirate of breast cancers.

Fine-needle aspiration cytology is preferred for axillary lymph node metastasis with low costs and minimal risks. To improve diagnostic performance by incorporating clinical-radiological-pathological parameters, a large cohort pre-operative aspirates in were reviewed for parameters affecting adequacy rate and accuracy. Axillary nodal aspirates from three institutions with histologic correlation were retrieved. Case notes were reviewed for parameters pertaining to the primary tumor, nodal status, histologic and cytologic diagnoses. Totally 1361 specimens were included. The risk of malignancy for C1-C5 categories were 53.39%, 27.45%, 70.97%, 83.33% and 88.00%, increasing to 75.86%, 94.59% and 99.28% for C3/C4/C5 categories excluding cases with neoadjuvant therapy. Node size (p < 0.001) and histologic grade (p = 0.003) of primary tumor positively correlated with specimen adequacy. Presence of in situ component trended towards inadequacy (p = 0.069). Lymph node size remained a strong predictor of concordant cytologic diagnosis (p < 0.001). A higher percentage of involved node (p = 0.006) and HER2 overexpressed breast cancers (p = 0.027) increased concordance. Cases with ≥ 4 (up to ≥ 10) positive nodes were more likely to be concordant (p = 0.009- < 0.001), with improvements of 8.27%-12.37%. For size, cut-offs of ≥ 5 and ≥ 10mm were significant (p = 0.006- < 0.001). It is critical that clinical-radiological-pathological findings be interpreted together with cytology. Aspirates from smaller nodes are more likely to be non-informative, irrespective of the total number of suspicious nodes, or a high-grade primary. In axillae with less than 4 suspicious nodes and/or a target node of less than 5-10mm, the diagnostic accuracy of aspiration cytology decreases and should be interpreted cautiously.

The correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis.

This study aimed to explore the correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis. A total of 196 patients with surgically confirmed breast cancer between September 2019 and December 2023 were included. Data on preoperative B-mode ultrasound (US), color Doppler, and shear wave elastography (SWE) features of breast cancer masses were collected and analyzed to determine their correlation with axillary lymph node metastasis. The area under the receiver operating characteristic curve (AUC) of B-mode US, color Doppler, SWE, and the multi-mode predictive model for evaluating axillary lymph node metastasis were compared. Among the 196 patients, 70 had positive axillary lymph nodes, while 126 had negative axillary lymph nodes. There was no significant difference in the color features between the negative and positive axillary lymph node groups. Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were identified as independent risk factors to predict axillary lymph node metastasis according to binary logistic regression analysis. The AUC of the predictive model based on these independent risk factors was 0.803 (95% CI: 0.739-0.867), which was significantly higher than that of B-mode US or SWE alone. Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were found to be valuable for predicting axillary lymph node metastasis in patients with breast cancer. The predictive model developed in this study, combining the multi-mode ultrasonographic features of breast cancer masses, could serve as a noninvasive and convenient method to predict axillary lymph node status. This approach could aid in clinical decision-making and individualized treatment to improve the prognosis of breast cancer patients.

Are background breast parenchymal features on preoperative breast MRI associated with disease-free survival in patients with invasive breast cancer?

To evaluate whether breast parenchymal features of the contralateral breast on preoperative MRI are associated with primary breast cancer characteristics and disease-free survival (DFS) in women with invasive breast cancer. Women with newly diagnosed invasive breast cancer who underwent preoperative breast MRI followed by surgery were retrospectively evaluated. Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI and background diffusion signal (BDS) on diffusion-weighted MRI of the contralateral breast were qualitatively assessed using a four-category scale: minimal, mild, moderate, or marked. Primary breast cancer characteristics were compared based on the degree of BPE or BDS. Cox proportional hazards models were used to evaluate the association between MRI parenchymal features and DFS after adjusting for clinicopathologic features. A total of 515 women (mean age, 54years) were included. Of whom, 46 (8.9%) patients who developed disease recurrence at a median follow-up of 60months were observed. A high level (moderate/marked) of BPE or BDS was associated with younger age (≤ 45) and premenopausal status (all P < 0.05) compared to a low level (minimal/mild), but it was not associated with primary cancer characteristics such as tumor stage, grade, or subtype. Multivariable Cox proportional hazards analysis demonstrated that larger tumor size (> 2cm) (hazard ratio [HR], 3.877; P < . 001), triple-negative subtype (HR, 2.440; P = .013), and axillary node metastasis (HR, 1.823; P = .049) were associated with worse DFS. No associations were observed between background parenchymal features and disease outcomes. MRI parenchymal features, including BPE and BDS, of the contralateral breast showed no associations with primary breast cancer characteristics or DFS in women with invasive breast cancer.

Factors affecting lymphedema after neoadjuvant chemotherapy and axillary dissection in female breast cancer patients: a retrospective cohort study based on the Chinese population.

Breast cancer-related lymphedema (BCRL) is a common complication among breast cancer survivors. Most BCRL studies have focused on patients receiving adjuvant chemotherapy, with relatively little attention paid to BCRL in patients undergoing neoadjuvant chemotherapy (NAC). This study aimed to investigate the risk factors associated with BCRL in Chinese women undergoing NAC and axillary lymph node dissection (ALND). At our institution, this cohort study collected data from 336 women with breast cancer and documented axillary nodal metastasis at diagnosis, who received NAC and ALND surgery between 2015 and 2020. BCRL was assessed through both objective limb circumference measurements and subjective self-reported symptoms. Multivariate logistic regression was employed to identify risk factors for BCRL, considering clinical, demographic, and lifestyle-related characteristics. The cumulative incidence of BCRL within 2.5 years was 43.75%. Factors independently associated with BCRL included radiotherapy (versus no radiotherapy; hazard ratio (HR) = 1.611; P = 0.020), NAC duration of 105 days or shorter (versus 105-143 days; HR = 0.471; P = 0.020), removal of more than 15 lymph nodes (versus 15 or fewer lymph nodes; HR = 1.593; P = 0.036), drainage duration of 20-29 days (versus 10-19 days; HR = 1.568; P = 0.028), and sleeping biased toward the affected arm (versus sleeping biased toward the healthy arm; HR = 2.033; P = 0.019). This study identified several risk factors for BCRL in breast cancer patients following NAC and ALND. Patients presenting with one or more of these factors should be monitored closely for early detection and intervention. Further research is warranted to explore the impact of drainage duration and sleep position on the development of BCRL.

The Efficacy of MRI-Based ADC Measurements in Detecting Axillary Lymph Node Metastasis: Evaluation of a Prospective Study.

Objective: This study aimed to evaluate the efficacy of MRI-based Apparent Diffusion Coefficient (ADC) measurements in detecting axillary lymph node metastasis in breast cancer patients. By comparing preoperative MRI findings with intraoperative sentinel lymph node biopsy (SLNB) and postoperative pathological results, we sought to explore the potential of ADC values as a non-invasive alternative to axillary interventions. Methods: A total of 104 female patients diagnosed with breast cancer between 2019 and 2021 were included in this prospective study. ADC values of axillary lymph nodes, tumors, and muscle tissues were measured using a 3T MRI system. The correlation between these measurements and pathological outcomes was analyzed. Statistical analyses, including t-tests, ANOVA, and ROC curve analysis, were employed to assess the diagnostic performance of ADC values. Results: The results indicated that, while the mean ADC values of metastatic lymph nodes were lower than those of benign nodes, the sensitivity and specificity of MRI-based ADC measurements were inferior to the expected standards. The tumor ADC value and the tumor-to-lymph node ADC ratio were found to be more reliable indicators of metastasis than the lymph node ADC value alone. The diagnostic power of the tumor ADC value was significant, with a sensitivity of 75% and a specificity of 73%. Conclusions: MRI-based ADC measurements, particularly the tumor ADC value and the tumor-to-lymph node ADC ratio, show promise as potential non-invasive markers for axillary lymph node metastasis in breast cancer patients. However, the current results suggest that ADC measurements cannot yet replace SLNB in clinical practice.

Locoregional event or dyssynchronous distant metastasis: clinicopathological and molecular analysis of contralateral axillary lymph node metastasis in breast cancer patients.

Contralateral axillary lymph node metastasis (CAM) is a rare clinical condition in patients with breast cancer (BC). CAM can be either a locoregional event or a distant metastasis. Molecular application for clonal evolution in BC has not been reported in CAM cases. We studied six patients with CAM with clinical, pathological and/or molecular evidence of distant metastasis; those patients had poor outcomes. Two cases with molecular analysis of paired primary and CAM established clonal evolution of the CAM with its corresponding primary with additional molecular alteration, increased tumour mutation burden, and copy number variations (CNVs) in the CAMs. Four cases containing alterations from genes potentially modulate chromatin organization, supporting chromatin and subsequent transcriptional signature changes are essential in CAM. Molecular analysis is critical to establish the connection between CAM and its primary counterpart. Distant CAM shows clonal evolution compared with its corresponding primary with additional molecular alterations, increased mutation burden and/or copy number variations. CAM should be evaluated individually and handled in a personalized fashion. Evidence of a true metastatic CAM can be supported by distant metastasis to other organs, specific morphological features and/or clonal evolution.

Upstaging of Invasive Lobular Cancer With FES PET/CT.

A 78-year-old woman diagnosed with left breast invasive lobular carcinoma with left axillary nodal metastasis underwent 18 F-fluoroestradiol (FES) PET/CT imaging for further evaluation of indeterminate right axillary lymph nodes seen on staging 18 F-FDG PET/CT. 18 F-FES PET/CT revealed abnormal 18 F-FES-avid right axillary and bilateral cervical nodes, subsequently biopsy-proven metastases, upstaging the patient from stage II to IV and greatly changing patient management. This case demonstrates the value of 18 F-FES PET/CT in accurately staging metastatic invasive lobular carcinoma at diagnosis, an indication for which 18 F-FES PET/CT "may be appropriate" per current Society of Nuclear Medicine and Molecular Imaging guidelines.

Expression of histone methyltransferase WHSC1 in invasive breast cancer and its correlation with clinical and pathological data

BackgroundThe WHSC1 protein facilitates specific dimethylation of histone H3 at the K36 position, enhancing gene transcription and expression. Studies have confirmed its high expression in diverse malignant tumors. We aimed to identify novel molecular markers to assess the biological behavior of breast cancer cells. MethodsWe conducted a comprehensive analysis of mRNA expression in breast cancer and adjacent tissues based on TCGA data. We enrolled 141 breast cancer patients treated at the First Affiliated Hospital of Fujian Medical University between 2012 and 2016. Patient clinical information and pathological specimens were obtained. We utilized tissue microarray (TMA) technology. We employed the chi-square test for between-group comparisons, with p < 0.05 indicating statistical significance. Furthermore, we analyzed the associations between WHSC1 expression and clinical or pathological data. ResultsWHSC1 mRNA expression was significantly higher in breast cancer tissues than in adjacent tissues (p < 0.001). Moreover, high WHSC1 protein expression in breast cancer was associated with several important clinical parameters, such as pathological type (p = 0.007), high Ki67 expression(Ki67＞20 %) (p < 0.001), lymph node metastasis (p < 0.001), T stage (p = 0.011), N stage (p < 0.001), postoperative pathological stage (p < 0.001), premenopausal status (p = 0.004), and positive HER2 status (p < 0.001). Multivariate regression analysis showed that high WHSC1 expression, elevated Ki67 levels, and positive HER2 status were independent risk factors for axillary lymph node metastasis in breast cancer patients. ConclusionWHSC1 protein expression is upregulated in breast cancer patients and represents an independent risk factor influencing axillary lymph node metastasis, highlighting its potential significance as a strong candidate biomarker.

Comparison of Sentinel Lymph Node Sampling with Positron Emission Tomography in the Evaluation of Axillary Lymph Node Involvement in Breast Cancer Patients

Objectives Axillary lymph node status is the most important prognostic factor in breast cancer patients.: This study was carried out to evaluate the diagnostic accuracy of FDG-PET/CT as a noninvasive technique and intraoperative frozen biopsy of sentinel lymphadenectomy (SLNB) in detecting axillary lymph node metastasis. Materials and Methods This study was carried out retrospectively on 44 patients diagnosed with breast cancer, who underwent preoperative FDGPET/ CT imaging and intraoperative SLNB, at the General Surgery Clinic of Istanbul Medeniyet University Göztepe Training and Research Hospital. The axilla was clinically negative in all patients. Preopative FDG-PET/CT imaging and intraoperative SLNB were performed. FDG-PET/CT results were compared with the histopathological results of SLNB and axillary lymph node dissection (ALND). Results According to the pathology results of axillary dissection, metastatic nodes were detected in 22 of 44 cases, and FDG-PET/ CT imaging gave false-negative results in 10. The number of false negative cases of SLNB was 3; Axillary involvement was detected as a result of pathology in one of them, while the others were evaluated as skip metastases. The sensitivity of SLNB and FDG-PET/CT imaging was measured as 86.3% and 54.5%. The specificity values were 95.4% and 100%. FDG-PET/CT imaging has low sensitivity; specificity and positive predictive value were at acceptable levels Conclusion: The sensitivity of FDG-PET/CT imaging is low in detecting axillary involvement, and SLNB is needed in those with negative axillary involvement in FDG-PET/ CT imaging. In our study, SLNB examination was superior to FDG-PET/CT imaging in detecting axillary nodal status. Bangladesh Journal of Medical Science Vol. 23 No. 04 October’24 Page : 1083-1094

Role of Ultrasonographic and Clinicopathological Characteristics in Assessing Stromal Tumor-Infiltrating Lymphocyte Density in Triple-Negative Breast Cancer

A high density of stromal tumor-infiltrating lymphocytes (sTILs) is positively correlated with the pathological complete response rate and favorable survival in patients with triple-negative breast cancer (TNBC). Heterogeneity in stromal lymphocyte distribution and limited tumor sampling may affect the accuracy of sTIL quantification in biopsy samples from patients receiving neoadjuvant therapy. Thus, identifying additional biomarkers to complement sTIL evaluation is essential. To identify biomarkers that could be used to complement sTILs evaluation. Retrospective cohort study. A total of 162 patients with invasive TNBC were enrolled in the study. The following data were gathered: sTIL density, Ki67, nuclear grades of cancer cells, lymphovascular invasion status, the American Joint Committee on Cancer stage, axillary lymph node metastasis status, and ultrasonographic parameters of the tumor (size, shape, orientation, margin, internal echo pattern, posterior feature, and vascularity). The relationship between sTIL density and ultrasonographic or clinicopathological characteristics was investigated using both continuous and categorical analyses. Posterior features of the primary tumors was associated with sTIL density (p = 0.038). Additionally, the Ki67 levels and nuclear grades were also associated with sTIL density (p < 0.001 and p = 0.024, respectively). When stratified according to a 20% cut-off of sTIL density, the posterior features of primary tumors, Ki67 levels, and nuclear grades significantly differed between the high and low sTIL density tumors. Tumors with high Ki67 levels were more likely to exhibit high sTIL density than low sTIL density [odds ratio (OR): 2.75, p = 0.021]. Furthermore, nuclear grade III tumors demonstrated significantly higher sTIL density than nuclear grade I-II tumors (OR: 2.49, p = 0.014). Additionally, tumors with posterior enhancement or no posterior features were more likely to exhibit high sTIL density than tumors with acoustic shadows (OR: 2.91, p = 0.028; OR: 2.74, p = 0.022, respectively). Low sTIL density is frequently observed in tumors exhibiting acoustic shadows on ultrasound. However, high sTIL density is more common in tumors with posterior enhancement or no posterior features. Furthermore, high Ki67 levels (> 40%) and high nuclear grades are positively correlated with high sTIL density. This study findings highlight the need for closer surveillance of these biomarkers to complement sTIL evaluation in TNBC.

Prediction of High Nodal Burden in Patients With Sentinel Node–Positive Luminal ERBB2-Negative Breast Cancer

In patients with clinically node-negative (cN0) breast cancer and 1 or 2 sentinel lymph node (SLN) macrometastases, omitting completion axillary lymph node dissection (CALND) is standard. High nodal burden (≥4 axillary nodal metastases) is an indication for intensified treatment in luminal breast cancer; hence, abstaining from CALND may result in undertreatment. To develop a prediction model for high nodal burden in luminal ERBB2-negative breast cancer (all histologic types and lobular breast cancer separately) without CALND. The prospective Sentinel Node Biopsy in Breast Cancer: Omission of Axillary Clearance After Macrometastases (SENOMAC) trial randomized patients 1:1 to CALND or its omission from January 2015 to December 2021 among adult patients with cN0 T1-T3 breast cancer and 1 or 2 SLN macrometastases across 5 European countries. The cohort was randomly split into training (80%) and test (20%) sets, with equal proportions of high nodal burden. Prediction models were developed by multivariable logistic regression in the complete luminal ERBB2-negative cohort and a lobular breast cancer subgroup. Nomograms were constructed. The present diagnostic/prognostic study presents the results of a prespecified secondary analysis of the SENOMAC trial. Herein, only patients with luminal ERBB2-negative tumors assigned to CALND were selected. Data analysis for this article took place from June 2023 to April 2024. Predictors of high nodal burden. High nodal burden was defined as ≥4 axillary nodal metastases. The luminal prediction model was evaluated regarding discrimination and calibration. Of 1010 patients (median [range] age, 61 [34-90] years; 1006 [99.6%] female and 4 [0.4%] male), 138 (13.7%) had a high nodal burden and 212 (21.0%) had lobular breast cancer. The model in the training set (n = 804) included number of SLN macrometastases, presence of SLN micrometastases, SLN ratio, presence of SLN extracapsular extension, and tumor size (not included in lobular subgroup). Upon validation in the test set (n = 201), the area under the receiver operating characteristic curve (AUC) was 0.74 (95% CI, 0.62-0.85) and the calibration was satisfactory. At a sensitivity threshold of ≥80%, all but 5 low-risk patients were correctly classified corresponding to a negative predictive value of 94%. The prediction model for the lobular subgroup reached an AUC of 0.74 (95% CI, 0.66-0.83). The predictive models and nomograms may facilitate systemic treatment decisions without exposing patients to the risk of arm morbidity due to CALND. External validation is needed. ClinicalTrials.gov Identifier: NCT02240472.

Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor

BackgroundThis study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences.MethodsThis study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists.ResultsThe single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists.ConclusionsThe combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.

Secretory Carcinoma of the Breast: Radiologic-Pathologic Correlation.

Secretory carcinoma is a rare, low-grade, special histological type of invasive breast carcinoma. Although it is the most common primary breast cancer in the pediatric population, most cases are diagnosed in adults, with a median age of 48 years (range 3 to 91 years). It most often presents as a painless and slowly growing palpable lump. Imaging findings are nonspecific. Secretory carcinomas have abundant periodic acid-Schiff positive intracytoplasmic and extracellular secretions on histopathology. Nearly all secretory carcinomas have mild to moderate nuclear pleomorphism with low mitotic activity. Over 80% (86/102) of secretory carcinomas display the translocation of t(12;15)(p13;q25), resulting in ETV6::NTRK3 gene fusion. Secretory carcinoma generally has an indolent course and has a better prognosis and overall survival than invasive breast carcinoma of no special type. A good prognosis is associated with age <20 years, tumor size <2 cm, and ≤3 axillary lymph node metastases. Metastases beyond the ipsilateral axillary lymph nodes are rare, with the most common sites involving the lung and liver. Except for the potential addition of targeted drug therapy for NTRK fusion-positive tumors, the treatment approach is otherwise similar to invasive breast carcinomas of similar receptor status.

Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

To explore the clinical value of the nomogram based on spectral Doppler ultrasound combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer. We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively. Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93). Axillary ultrasound combined with Ki-67 and spectral ultrasound parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

Development and Assessment of a Predictive Model for Ki-67 Expression Using Ultrasound Indicators and Non-Morphological Magnetic Resonance Imaging Parameters Before Breast Cancer Therapy.

To formulate a predictive model for assessing Ki-67 expression in breast cancer by integrating pre-treatment ultrasound features with non-morphological magnetic resonance imaging (MRI) parameters, encompassing functional and hemodynamic indicators. A retrospective study was conducted on 167 patients. All patients underwent a breast mass biopsy for histopathological and Ki-67 analysis prior to neoadjuvant chemotherapy (NAC) treatment. Additionally, all patients underwent ultrasonography and MRI examinations prior to the biopsy. The recorded variables were Ki-67, apparent diffusion coefficient (ADC) values, Max Slope, time to peak (TTP), signal enhancement ratio (SER), early enhancement rate (EER), time-signal intensity curve (TIC), tumor maximum diameter, tumor margins and boundaries, aspect ratio, microcalcification, color Doppler flow imaging grading, resistance index (RI), and axillary lymph node metastasis. Statistical analysis was performed using the R software package. Normally distributed continuous data are presented as mean ± standard deviation (SD), skewed continuous data as median, and categorical variables as frequency or percentage. The dataset was randomly divided into a modeling group and a validation group following a 7:3 ratio, employing a predetermined random seed. The selection of variables was conducted using the random forest algorithm. Specifically, in the initial analysis, we trained a random forest model using all available variables. By evaluating the Gini importance scores of each variable, we identified those that contributed the most to predicting Ki-67 expression. The predictive model for Ki-67 expression was constructed using selected variables: Maximum Diameter, ADC value, SER value, Max Slope value, TTP value, and EER value. Within the validation group, the evaluation metrics demonstrated an Area under the curve of 0.961 with a 95% confidence interval ranging from 0.865 to 0.995. The model achieved a kappa score of 1.00, precision of 0.949, recall of 1, an F1 score of 0.974, sensitivity of 100%, specificity of 85.71%, a positive predictive value of 94.87%, and a negative predictive value of 100%. The combination of non-morphological MRI parameters and pre-treatment ultrasound features in a breast cancer prediction model powered by RF machine learning demonstrated favorable clinical outcomes and improved diagnostic performance.

Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis.

The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status. To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer. We prospectively gathered clinicopathologic and ultrasonic data from patients diagnosed with breast cancer. Various machine-learning (ML) models were developed using all available features to determine the most efficient diagnostic model. Subsequently, distinct prediction models were created using the optimal ML model, and their diagnostic performances were evaluated and compared. The study encompassed 240 patients, of whom 88 had lymph node metastases. A leave-one-out cross-validation (LOOCV) method was used to split the entire dataset into training and testing subsets. The random forest ML model outperformed the other algorithms, with an area under the curve (AUC) of 0.92. Prediction models based on clinical, ultrasonic, URF parameters, clinical + ultrasonic, clinical + URF, and ultrasonic + URF parameters had AUCs of 0.56, 0.79, 0.78, 0.90, 0.80, and 0.84, respectively, in the testing set. The comprehensive diagnostic model (clinical + ultrasonic + URF parameters) demonstrated strong diagnostic capability, with an AUC of 0.94 in the testing set, exceeding any single prediction model. The combined model (clinical + ultrasonic + URF parameters) could be used preoperatively to predict lymph node status, offering valuable input for the design of individualized surgical approaches.

An unsupervised learning model based on CT radiomics features accurately predicts axillary lymph node metastasis in breast cancer patients: diagnostic study.

The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this study, the authors propose the use of radiomic technology and three-dimensional (3D) visualization technology to develop an unsupervised learning model for predicting axillary lymph node metastasis in patients with breast cancer (BC), aiming to provide a new method for clinical axillary lymph node assessment in patients with this disease. In this study, we retrospectively analyzed the data of 350 patients with invasive BC who underwent lung-enhanced computed tomography (CT) and axillary lymph node dissection surgery at the Department of Breast Surgery of the Second Xiangya Hospital of Central South University. The authors used 3D visualization technology to create a 3D atlas of ALNs and identified the region of interest for the lymph nodes. Radiomic features were subsequently extracted and selected, and a prediction model for ALNs was constructed using the K-means unsupervised algorithm. To validate the model, the authors prospectively collected data from 128 BC patients who were clinically evaluated as negative at our center. Using 3D visualization technology, we extracted and selected a total of 36 CT radiomics features. The unsupervised learning model categorized 1737 unlabeled lymph nodes into two groups, and the analysis of the radiomic features between these groups indicated potential differences in lymph node status. Further validation with 1397 labeled lymph nodes demonstrated that the model had good predictive ability for axillary lymph node status, with an area under the curve of 0.847 (0.825-0.869). Additionally, the model's excellent predictive performance was confirmed in the 128 axillary clinical assessment negative cohort (cN0) and the 350 clinical assessment positive (cN+) cohort, for which the correct classification rates were 86.72 and 87.43%, respectively, which were significantly greater than those of clinical assessment methods. The authors created an unsupervised learning model that accurately predicts the status of ALNs. This approach offers a novel solution for the precise assessment of ALNs in patients with BC.

Photoacoustic/ultrasound dual-modality imaging of Sentinel lymph node with carbon nanoparticles

Abstract Sentinel lymph node (SLN) biopsy is a widely used method for identifying axillary lymph node metastasis in breast cancer patients, offering an avoidance of unnecessary axillarydissections and a lower incidence of lymphedema and other complications. However, the nuclide localization has added complexity to the procedure due to its accessibility. Methylene blue and other small molecule dyes have been widely used due to their convenience, but they have been found to have lower specificity and sensitivity. Carbon nanoparticles (CNPs), as a new type of tracer, are safe and show improved specificity. However, the significant optical absorption in human tissues still renders the detection of deep lymph nodes challenging. To overcome these challenges, we developed a high-sensitivity ultrasound probe optimized for photoacoustic imaging. Based on the new probe, we constructed a high-sensitivity photoacoustic/ultrasound dual-modality imaging system. A pilot clinical study has been conducted on two patients, preliminarily verifying the system’s ability to locate SLN when combined with the CNPs.

Can negative axillary ultrasound reliably predict pathologically negative axillary lymph node status in breast cancer patients with cT ≤3 cm, cN0, and HER2-positive?-a retrospective, single-institution study.

Breast cancer (BC) is the leading cancer in women globally, with human epidermal growth factor receptor 2 (HER2)-positive subtype accounting for 15-20% of cases and exhibiting aggressive behavior. The standard of care for operable BC has evolved to include neoadjuvant systemic therapy, which can guide treatment decisions and improve outcomes, particularly in HER2+ BC. This study aims to investigate whether axillary ultrasound has a good negative predictive value (NPV) for early HER2 BC patients and to identify clinicopathological factors that can impact the axillary lymph node metastasis. This retrospective, single-center study evaluated the medical records of 135 patients with HER2+ BC, cT ≤3 cm, and clinically negative axillary lymph nodes from 2018 to 2020. The study aimed to determine the NPV of axillary ultrasound for pathologically negative axillary lymph node status and to identify factors associated with axillary lymph node metastasis. The NPV of axillary ultrasound was 78.5%, increasing to 89.6% and 93.3% when considering 0-1 and 0-2 metastatic lymph nodes, respectively. Lymphovascular invasion (LVI) was significantly associated with axillary lymph node metastasis, with a 2.2-fold increased risk. Axillary ultrasound shows good predictive value for axillary lymph node negativity in HER2+ BC patients with small tumors. However, the presence of LVI increases the risk of metastasis, suggesting a need for neoadjuvant chemotherapy. These findings contribute to personalized treatment strategies for early HER2+ BC, emphasizing the role of axillary ultrasound in clinical decision-making.