Axillary Node Status Research Articles

Evaluation of Sensitivity and Specificity of Ultrasound-Guided FNA of Suspicious Axillary Lymph Nodes in Patients with Breast Cancer

Background: The axillary lymph node status in breast cancer is a major prognostic factor in survival and establishing a personalized treatment scheme. The ultrasound-guided fine needle aspiration (US-FNA) is a method for taking a lymph node sample. It allows physicians to decide how to manage the axilla. Objectives: This study was conducted to investigate the sensitivity of the US-FNA technique on suspicious axillary lymph nodes with a thickness of 3 to 6 mm in breast cancer patients. Methods: In a cross-sectional study, all the patients were subjected to preoperative ultrasound evaluation of the axilla to determine the presence of lymph nodes suspicious of malignancy. In cases where the suspicious lymph node cortex size was between 3 and 6 mm, US-FNA was performed. After surgery, the frozen section of the biopsy sample was examined histologically and compared with fine needle aspiration (FNA) cytology results. Results: A total of 102 patients were examined in the study. FNA test results indicated that 46 subjects had axillary malignant tissue, and benign cases summed 56. Also, the final results of frozen section surgical histopathology identified 46.1% of patients with involved lymph nodes. The sensitivity and specificity of FNA were 93.62% and 96.36%, respectively. Also, the overall diagnostic accuracy was 95.1%. Conclusions: This study showed that the sensitivity, specificity, and accuracy were more than 90% for the ultrasound-guided FNA test in identifying involved lymph nodes in patients with breast cancer. Therefore, the results of this test can be considered clinically reliable. However, there is still a need to examine the sensitivity and specificity of this method in identifying lymph node involvement.

Breast edema score at breast MRI: its value in prediction of molecular subtype of breast cancer and its impact on axillary LN metastasis

BackgroundSince many newly diagnosed breast cancer patients have breast MRI, the value of preoperative breast magnetic resonance imaging would improve if molecular subtypes could be consistently identified, and prognostic information provided in addition to diagnostic imaging. Breast edema may improve the ability to predict molecular subtypes and clinical and pathological outcomes in invasive breast cancer patients. The prognosis for breast cancer prognosis based on the findings of breast edema by magnetic resonance imaging will be useful in both pretreatment planning and prognosis. Breast edema on T2-weighted images and STIR was scored on a scale of 1 to 4, as follows: (a) breast edema score (BES) 1, no edema; (b) BES 2, peritumoral edema; (c) BES 3, pre pectoral edema; and (d) BES 4, subcutaneous edema (suspicious for occult inflammatory breast cancer “IBC”). Axillary lymph node status and number were also evaluated in T2 and STIR and after contrast administration. The aim of this work was to assess the role of tumour-related breast edema MRI features in distinguishing molecular subtypes of breast cancer and its effect on pathological axillary lymph nodes in patients with breast cancer.ResultsThere was a highly significant difference between BES with respect to the molecular subtypes of breast cancer, size of the mass, Ki-67 expression, LN status, and LN number (p < 0.0001, 0.045, < 0.0001, < 0.0001, and < 0.0001 respectively). However, there was no significant difference between BES and histopathological grade in studied masses, such as p-value = 0.49.ConclusionsTumour-related breast edema MRI characteristics may be useful in distinguishing molecular subtypes of breast cancer and could be used as a promising feature to improve the predictive performance of pathological axillary lymph nodes in patients with breast cancer, contributing to preoperative treatment planning and prognostic outcome.

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.

Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients. In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing. For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05). Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

Determination of axillary lymph node status by fine-needle aspiration: preoperative axillary staging vs. postoperative surveillance for breast cancers.

Despite the common use of ultrasound (US)-guided fine-needle aspiration (FNA) for axillary node (AN) in breast cancer patients, only a limited number of studies are available regarding the diagnostic performance of AN-FNA according to the suspicion level based on US findings. This study compares the outcomes of US-guided AN-FNA in breast cancer patients, differentiating between those undergoing staging and surveillance. A cross-sectional retrospective study with retrospective analysis was conducted on 767 consecutive AN-FNA procedures performed in 2017 at Samsung Medical Center in Seoul, with 654 for staging and 113 for surveillance in breast cancer patients. The radiologists performed axillary US and the specific finding was prospectively classified into the AN-reporting and data system (AN-RADS) category 3-5 before FNA. The malignancy rate of each category was evaluated. The chi-square test, with or without Bonferroni correction, or Fisher's exact test was used to compare the malignancy rates between the staging and surveillance groups for each category. Among the 767 AN-FNAs, 424 (55.3%) were malignant. The malignancy rate was significantly higher in the staging group (59.5%) than in the surveillance group (31.0%, P<0.0001). The distribution of AN-RADS categories differed between the groups (P=0.015), with 4A being the most common. The malignancy rates in categories 3, 4A, 4B, 4C, and 5 were as follows: 5.6%, 36.0%, 77.4%, 87.7%, and 98.4% in the staging group, and 0.0%, 9.7%, 53.3%, 88.9%, and 100% in the surveillance group. The malignancy rate was significantly different between the two groups only in category 4A (P=0.0001). AN-FNA according to AN-RADS category appears to be an appropriate method for determination of axillary nodal status. Overall malignancy rate of AN-FNA in breast cancer patients was higher in the staging group than in the surveillance group. According to the suspicion level, the difference between two groups was significant only in category 4A.

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer.

To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer. A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA). A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models. The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

228 (PB-135) Poster - Correlation between plasma D-Dimer and Factor VIII levels with clinical stage and axillary lymph node status in patients presenting with operable breast cancer

228 (PB-135) Poster - Correlation between plasma D-Dimer and Factor VIII levels with clinical stage and axillary lymph node status in patients presenting with operable breast cancer

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.

Clinical characteristics and prognostic factors of breast cancer patients with tumor deposits in the ipsilateral axillary region

Objective: To investigate the clinicopathologic features and prognostic factors of breast cancer patients with tumor deposits in the ipsilateral axillary region. Methods: We retrospectively analyzed the clinicopathologic data and follow-up results of 155 patients with breast cancer diagnosed for the first time and complicated with tumor deposits in the ipsilateral axillary region in the Department of Thyroid-Breast-Vascular Surgery of Xijing Hospital from January 2008 to September 2018. Kaplan-Meier method was used for survival analysis. Log rank test was used for the univariate analysis of prognostic factors, and Cox regression was used for multivariate analysis. Results: The median disease free survival (DFS), median distant metastasis free survival (DMFS), and median overall survival (OS) of the 155 patients were 52.0 months, 66.6 months, and 102.2 months, respectively. The 5-year and 10-year DFS rates were 45.7% and 23.1%, the 5-year and 10-year DMFS rates were 56.9% and 28.9%, and the 5-year and 10-year OS rates were 79.3% and 46.0%, respectively. Multivariate Cox regression analysis showed that family tumor history (HR=0.362, 95% CI: 0.140-0.937), clinical T stage (T3: HR=3.508, 95% CI: 1.380-8.918; T4: HR=2.220, 95% CI: 1.076-4.580), estrogen/progesterone receptor status (HR=0.476, 95% CI: 0.261-0.866), number of tumor deposits (HR=1.965, 95% CI:1.104-3.500) and neoadjuvant chemotherapy (HR=1.961, 95% CI: 1.032-3.725) were independent influencing factors for DFS. Molecular subtype [human epidermal growth factor receptor-2(HER-2) positive and hormone receptor negative: HR=7.862, 95% CI: 3.189-19.379], number of tumor deposits (HR=2.155, 95% CI: 1.103-4.212), neoadjuvant chemotherapy (HR=5.002, 95% CI: 2.300-10.880) and radiotherapy (HR=2.316, 95% CI: 1.005-5.341) were independent influencing factors of DMFS. Histological grade (HR=4.362, 95% CI: 1.932-9.849), estrogen/progesterone receptor expression (HR=0.399, 95% CI: 0.168-0.945), HER-2 expression (HR=2.535, 95% CI: 1.114-5.768) and neoadjuvant chemotherapy (HR=4.080, 95% CI: 1.679-9.913) were independent influencing factors of OS. Conclusions: The presence of tumor deposits weakens the influence of axillary lymph node status and distant metastases on the prognosis of breast cancer patients. Therefore, a clinicopathological staging system taking into account tumor deposits should be developed. Since the number of tumor deposits affects the risk of recurrence and metastasis of breast cancer patients, we recommend that the number of tumor deposits should be reported in detail in the pathological report after breast cancer surgery.

Do Habitat MRI and Fractal Analysis Help Distinguish Triple-Negative Breast Cancer From Non-Triple-Negative Breast Carcinoma.

Purpose: To determine whether multiparametric MRI-based spatial habitats and fractal analysis can help distinguish triple-negative breast cancer (TNBC) from non-TNBC. Method: Multiparametric DWI and DCE-MRI at 3T were obtained from 142 biopsy- and surgery-proven breast cancer with 148 breast lesions (TNBC = 26 and non-TNBC = 122). The contrast-enhancing lesions were divided into 3 spatial habitats based on perfusion and diffusion patterns using K-means clustering. The fractal dimension (FD) of the tumour subregions was calculated. The accuracy of the habitat segmentation was measured using the Dice index. Inter- and intra-reader reliability were evaluated with the intraclass correlation coefficient (ICC). The ability to predict TNBC status was assessed using the receiver operating characteristic curve. Results: The Dice index for the whole tumour was 0.81 for inter-reader and 0.88 for intra-reader reliability. The inter- and intra-reader reliability were excellent for all 3 tumour habitats and fractal features (ICC > 0.9). TNBC had a lower hypervascular cellular habitat and higher FD 1 compared to non-TNBC (all P < .001). Multivariate analysis confirmed that hypervascular cellular habitat (OR = 0.88) and FD 1 (OR = 1.35) were independently associated with TNBC (all P < .001) after adjusting for rim enhancement, axillary lymph nodes status, and histological grade. The diagnostic model combining hypervascular cellular habitat and FD 1 showed excellent discriminatory ability for TNBC, with an AUC of 0.951 and an accuracy of 91.9%. Conclusions: The fraction of hypervascular cellular habitat and its FD may serve as useful imaging biomarkers for predicting TNBC status.

Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer.

We aimed at assessing the predictive ability of ultrasound-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions. A total of 755 patients (527 in the primary cohort and 228 in the external validation cohort) were enrolled in this study. Ultrasound images for all patients were acquired and radiomics analysis performed for intratumoral and different peritumoral regions. The MRMR and LASSO regression analyses were performed on extracted features from the primary cohort to construct a radiomics signature formula combined with clinical characteristics. Pearson's coefficient and the variance inflation factor (VIF) were performed to check the correlation and the multicollinearity among the final predictors. The best performing model was selected to develop a nomogram, which was established by performing binary logistic regression and acquiring cut-off values based on the corresponding nomogram scores of the masses. Among all the radiomics models, the "Mass + Margin3mm" model exhibited the best performance. The areas under the curves (AUC) of the nomogram in the primary and external validation cohorts were 0.906 (95% confidence intervals [CI] 0.882-0.930) and 0.922 (95% CI 0.894-0.960), respectively. They both showed good calibrations. The nomogram exhibited a good ability to discriminate between positive and negative lymph nodes (AUC: 0.853 (95% CI 0.816-0.889) in primary cohort, 0.870 (95% CI 0.818-0.922) in validation cohort), and between low-volume and high-volume lymph nodes (AUC: 0.832 (95% CI 0.781-0.884) in primary cohort, 0.911 (95% CI 0.858-0.964) in validation cohort). The established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment.

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

Since the Z0011 trial, the assessment of axillary lymph node status has been redirected from the previous assessment of the occurrence of lymph node metastasis alone to the assessment of the degree of lymph node loading. Our aim was to apply preoperative breast ultrasound and clinicopathological features to predict the diagnostic value of axillary lymph node load in early invasive breast cancer. The 1247 lesions were divided into a high lymph node burden group and a limited lymph node burden group according to axillary lymph node status. Univariate and multifactorial analyses were used to predict the differences in clinicopathological characteristics and breast ultrasound characteristics between the two groups with high and limited lymph node burden. Pathological findings were used as the gold standard. Univariate analysis showed significant differences in ki-67, maximum diameter (MD), lesion distance from the nipple, lesion distance from the skin, MS, and some characteristic ultrasound features (P < 0.05). In multifactorial analysis, the ultrasound features of breast tumors that were associated with a high lymph node burden at the axilla included MD (odds ratio [OR], 1.043; P < 0.001), shape (OR, 2.422; P = 0.0018), hyperechoic halo (OR, 2.546; P < 0.001), shadowing in posterior features (OR, 2.155; P = 0.007), and suspicious lymph nodes on axillary ultrasound (OR, 1.418; P = 0.031). The five risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.702. Breast ultrasound features and clinicopathological features are better predictors of high lymph node burden in early invasive breast cancer, and this prediction helps to develop more effective treatment plans.

Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer

PurposeTo investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC). MethodsFrom January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student’s t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated. ResultsThe combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722. ConclusionsWhole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.

Retrospective validation study of an artificial neural network-based preoperative decision-support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN14341750)

BackgroundSurgical sentinel lymph node biopsy (SLNB) is routinely used to reliably stage axillary lymph nodes in early breast cancer (BC). However, SLNB may be associated with postoperative arm morbidities. For most patients with BC undergoing SLNB, the findings are benign, and the procedure is currently questioned. A decision-support tool for the prediction of benign sentinel lymph nodes based on preoperatively available data has been developed using artificial neural network modelling.MethodsThis was a retrospective geographical and temporal validation study of the noninvasive lymph node staging (NILS) model, based on preoperatively available data from 586 women consecutively diagnosed with primary BC at two sites. Ten preoperative clinicopathological characteristics from each patient were entered into the web-based calculator, and the probability of benign lymph nodes was predicted. The performance of the NILS model was assessed in terms of discrimination with the area under the receiver operating characteristic curve (AUC) and calibration, that is, comparison of the observed and predicted event rates of benign axillary nodal status (N0) using calibration slope and intercept. The primary endpoint was axillary nodal status (discrimination, benign [N0] vs. metastatic axillary nodal status [N+]) determined by the NILS model compared to nodal status by definitive pathology.ResultsThe mean age of the women in the cohort was 65 years, and most of them (93%) had luminal cancers. Approximately three-fourths of the patients had no metastases in SLNB (N0 74% and 73%, respectively). The AUC for the predicted probabilities for the whole cohort was 0.6741 (95% confidence interval: 0.6255–0.7227). More than one in four patients (n = 151, 26%) were identified as candidates for SLNB omission when applying the predefined cut-off for lymph node-negative status from the development cohort. The NILS model showed the best calibration in patients with a predicted high probability of healthy axilla.ConclusionThe performance of the NILS model was satisfactory. In approximately every fourth patient, SLNB could potentially be omitted. Considering the shift from postoperatively to preoperatively available predictors in this validation study, we have demonstrated the robustness of the NILS model. The clinical usability of the web interface will be evaluated before its clinical implementation.Trial registrationRegistered in the ISRCTN registry with study ID ISRCTN14341750.Date of registration 23/11/2018.

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

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. 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. Retrospective. 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. 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. 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. 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. A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. 3 TECHNICAL EFFICACY: Stage 2.

Intramammary Tumor Location and Ipsilateral Lymphatic Spread in Early Breast Cancer Patients Using One‐Step Nucleic Acid Amplification (OSNA) Assay

Establishing an accurate prognosis for women diagnosed with breast cancer (BC) is extremely challenging. Axillary lymph node (ALN) evaluation is considered of major prognostic value. The one‐step nucleic acid amplification (OSNA) assay is currently used for assessing axillary sentinel lymph node (SLN) status in BC. Additionally, total tumor load (TTL) may help predict further metastatic axillary involvement beyond the SLN. The prognostic value of primary BC location remains controversial due to lack of consensus on the biological differences among tumors at various sites. Evidence suggests that tumors in the internal quadrants (INLs) have worse prognosis compared to those in the external quadrants. Furthermore, ALN involvement is believed to be mainly associated with external quadrant tumors, mainly due to the lymphatic drainage system of the breast. This pilot observational study, despite lacking a control group and having a relatively small sample size, is the first to evaluate the potential relationship between primary BC location and ALN metastasis using the OSNA assay. A sample of consecutive BC patients undergoing axillary staging with the OSNA assay were included. Tumors were categorized into three groups based on primary location: external quadrants and axillary tail (EXL), INLs, and nipple and areola location (NAL). Although not statistically significant, the INL group exhibited a higher mean TTL. Additionally, no significant differences were observed between groups concerning SLN detection techniques, SLN status, number of metastatic SLN, or mean TTL. These findings support the use of the innovative tracer superparamagnetic iron oxide regardless of tumor site. This study underscores the importance of understanding the relationship between BC location and ALN status, which may improve prognostic stratification and targeted therapies based on tumor site. If these observations are confirmed in larger, multicentric studies, the potential conclusions may shift the paradigm of INL tumor treatment, significantly impacting clinical practice and research.

Assessment of Tissue Eosinophilic Infiltration in Invasive Mammary Carcinoma.

Background: Stromal inflammatory cells in malignant tissue have recently gained increasing interest. Unlike the extensive research on tumor-infiltrating lymphocytes, published data about tumor-infiltrating eosinophils in breast cancer are scarce. Furthermore, similar studies have yet to be conducted in Iraq. Aims: The objective of this study is to examine the presence of eosinophilic infiltration by direct visualization using light microscopy and to analyze its relationship with other histological parameters in a group of Iraqi women diagnosed with invasive mammary cancer. Methods and material: A retrospective study enrolled 90 histological samples of invasive mammary carcinoma provided by core needle biopsy from a single center, together with their immunohistochemical results for ER and HER2-NEU. Data reviewing, direct morphological visualizations, and counting eosinophilic infiltration in tissue sections were done by two independent pathologists using light microscopy. The results were statistically correlated with the grade, ER, HER2-NEU, calcification, and axillary lymph node status at presentation. Results: Out of the entire sample size (90), 40 (44%) showed the presence of eosinophilic infiltration in the tissue, both intratumoral and stromal. Further analysis revealed that most eosinophilic infiltrates had an intermediate score (4-19) per 10 consecutive high-power fields. A strong and meaningful statistical relationship was seen between tissue eosinophilic infiltration and HER2/NEU status. A statistically insignificant correlation was seen between tissue eosinophilic infiltration and histological grade, ER receptor status, calcification, and axillary lymph node status at presentation. Conclusions: Eosinophils are tumor-infiltrating cells in breast cancer, both intratumoral and stromal. The presence of tissue eosinophilic infiltration can predict HER2/NEU negativity in breast cancer.

Does F-18 FDG-PET/CT Have an Additional Impact on Axillary Approach in Early-Stage Breast Cancer?

Breast cancer (BC) is a significant health concern and one of the most diagnosed cancers in women, both in Turkey and globally. Despite advances in the management of BC, axillary lymph node involvement remains a significant consideration for treatment planning, local recurrence, and prognosis. We aimed to evaluate the contribution of F-18 fluorodeoxyglucose-positron emission tomography/computed tomography (F-18 FDG-PET/CT) in detecting axillary lymph node metastasis compared to ultrasound (US). Eighty patients who were diagnosed with stage I and II BC and underwent US and F-18 FDG-PET/CT scans before surgery were enrolled in this study. Those who did not undergo F-18 FDG-PET/CT imaging, patients with distant metastases at the time of diagnosis and patients with micrometastases in the axilla were excluded from the analysis. Imaging results of the status of axillary lymph nodes were verified with the final pathology report of axillary lymph nodes. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of F-18 FDG-PET/CT for the detection of ipsilateral axillary lymph node metastases were 75%, 77.27%, 72.97%, 79.07%, and 76.25%. The corresponding values for US were 72.22%, 81.82%, 76.47%, 78.26%, and 77.50%, respectively. When US finding is negative or suspicious in axillary lymph node evaluation, the accuracy of F-18 FDG-PET/CT for the detection of ipsilateral axillary lymph node metastases were 65.38%, 83.33%, 70.83%, and 79.55%, respectively. This study found that F-18 FDG-PET/CT does not provide an additional advantage over US in assessing the axilla in early-stage disease.

Assessment of Clinicopathological Profile and Receptor Status of Breast Cancer Patients at a Tertiary Care Centre in Uttarakhand, India: A Prospective Cohort Study

Introduction: Breast carcinoma has become a major public health problem among both urban and rural populations in India. The management of breast carcinoma depends on hormone receptor status and the pathological properties of the tissue, which serve as important prognostic markers. Aim: The aim of this study was to analyse the clinicopathological parameters and receptor status in breast cancer patients in the Garhwal Region of Uttarakhand. Materials and Methods: This prospective cohort study involved 51 patients who underwent modified radical mastectomy for breast carcinoma at the Department of General Surgery, Government Doon Medical College, Dehradun, Uttarakhand, India. The study was conducted over a period of two years and seven months, from May 2018 to December 2020. Various parameters such as age, side and quadrant of breast involvement, TNM stage, histological grade, receptor status, molecular subtypes, and axillary lymph node status were evaluated. The data was tabulated using Microsoft Excel 2007. The distribution of various receptors in tumour specimens was compared with clinicopathological variables and expressed as percentages. Results: The mean age of the study participants was 52.54±13.24 years, with a male to female ratio of 2:98. In this study, the majority of females (62.74%) belonged to the age group of 40 to 60 years. Left breast carcinoma showed a slightly higher prevalence (52.94%) than right breast carcinoma (47.05%), and the upper outer quadrant was the most commonly involved (50.98%). Infiltrating ductal carcinoma, not otherwise specified, was the most common histological type observed in all patients. The majority of patients (84.31%) had tumour sizes less than 5 cm. Grade-II tumours were the most frequent grade, observed in 34 cases (66.66%). Among the tumour stages, T2 stage accounted for the highest frequency (67%), followed by T3. The authors found that the highest frequency of tumours belonged to N0 stage (29.41%), followed by N1, N2, and N3, respectively. The corresponding rates of luminal A, luminal B, triple-negative, and Human Epidermal Growth Factor Receptor 2 (HER-2)/neu enriched subtypes were 5 (14.7%), 3 (8.82%), 11 (32.5%), and 3 (8.82%), respectively. The cases negative for Oestrogen Receptor (ER) and Progesterone Receptor (PR) were found to have high-grade disease on histological evaluation. Conclusion: The higher percentage of Grade-II and III tumours observed in this study highlights the need to increase health awareness in this area in order to decrease morbidity and mortality. The study recommends that Immunohistochemistry (IHC) classification serves as an important prognostic factor in breast cancer patients.

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

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

The value of chromosome instability detected by low-pass whole-genome sequencing in preoperative prediction of sentinel lymph node metastasis in breast cancer.

Breast cancer is a malignancy characterized by chromosomal instability (CIN). This study aimed to examine the potential diagnostic value of chromosomal instability, detected by low-pass whole-genome sequencing (LPWGS), in the preoperative evaluation of sentinel lymph node metastasis (SLNM) in breast cancer. A retrospective investigation of clinical records from 29 patients with breast cancer revealed two distinct groups based on sentinel lymph node biopsy (SLNB) results: the SLN metastasis group (24 cases) and the SLN non-metastasis group (five cases). CIN and CIN scores were evaluated using LPWGS. An analysis of univariate data and binary logistic regression was employed to identify factors influencing SLNM, and a curve with receiver operating characteristics (ROC) was constructed to assess the diagnostic utility of CIN in predicting SLNM. A significant association between the SLNM and CIN high groups was observed in breast cancer (P=0.011). The CIN score in the metastasis group (17,665.055 ± 8,630.691) was higher than that in the non-metastasis group (9,247.973 ± 3,692.873), demonstrating a significant difference (P=0.044). Univariate binary logistic regression analysis indicated that CIN was a significant predictor for SLNM (odds ratio: 4.036, 95% CI: 1.015-16.047, P=0.048). The AUC of CIN for preoperative diagnosis of SLNM was 0.808 (95%CI: 0.635-0.982, P=0.033), with a sensitivity value of 67.0% and specificity of 100.0% at a threshold of 13,563. Detecting CIN through LPWGS demonstrates diagnostic potential in predicting SLNM in patients with breast cancer before surgery. This approach offers a novel method for assessing axillary lymph node status in clinical practice.