Axillary Lymph Node Metastasis Research Articles

Case report: A panorama gene profile of ovarian cancer metastasized to axillary lymph node

BackgroundOvarian cancer is among the most lethal gynecologic malignancies, with a high proportion of patients diagnosed at advanced stages, leading to poor survival outcomes. Axillary lymph node metastasis from ovarian cancer is extremely rare and the mechanism is still unclear.MethodsA comprehensive set of clinical and gynecologic oncology assessments were performed, including ultrasound, mammography, MRI, transvaginal ultrasound, and tissue staining. To unravel the carcinogenesis, the next-generation sequencing (NGS) was performed.ResultsConventional examinations and imaging suggested the presence of both occult breast cancer and ovarian cancer. However, immunohistochemical staining confirmed the diagnosis of high-grade serous ovarian carcinoma. Further analysis of NGS identified two novel missense mutations, D326E in BTK (Bruton’s tyrosine kinase) at SH2 domain and D251E in EPHA5 (EPH receptor A5), along with other known cancer- associated mutations. These mutations, particularly the novel missense mutations, may lead to metastasis to the axillary lymph nodes and drug resistance. Therefore, based on these findings, the chemotherapy regimen was adjusted accordingly.ConclusionThis is the first report on the panorama gene profile of ovarian cancer metastasis to axillary lymph node and we found two novel mutations (BTK pD326E and EPHA5 pD251E). This study unraveled the potential mechanism of genetic mutation for tumor metabolism, drug resistance, and metastasis.

Solitary Axillary Lymph Node Metastasis From Ovarian Cancer on 18F-FDG PET/CT.

Solitary axillary lymph node metastasis from ovarian cancer is rare. A 74-year-old woman who had undergone hysterectomy and bilateral salpingo-oophorectomy for ovarian cancer 2 years ago presented to our hospital with enlarged axillary lymph node. 18F-FDG PET/CT revealed left axillary lymphadenopathy with an SUVmax of 8.92. The postbiopsy pathology confirmed metastasis of poorly differentiated ovarian adenocarcinoma. This case highlights the need to consider ovarian cancer metastasis in cases of solitary axillary lymphadenopathy with intense FDG uptake, especially in patients with a recent history of ovarian cancer.

Utility of systemic staging in breast cancer patients with a positive sentinel lymph node biopsy.

CT thorax, abdomen and pelvis (CT-TAP) remains the standard in the identification of metastatic disease in patients with newly diagnosed breast cancer. In patients with proven micro and macro axillary nodal metastasis, the optimal radiological technique remains controversial. A consensus on which patients with axillary nodal disease should receive radiological staging for distant disease and how this should be performed is not currently available. The aim of this study was to evaluate the yield from CT staging of the thorax, abdomen and pelvis (CT-TAP) in patients with proven nodal disease. Patients diagnosed with invasive breast cancer with a positive sentinel lymph node biopsy (SLNB) and subsequent staging CT-TAP between 2013 and 2017 were identified. Patient demographics, clinicopathological characteristics, CT-TAP findings and further imaging requirements were documented. A total of 234 patients were identified. Of these, 164 (70%) were found to have macrometastasis and 70 (30%) to have micrometastasis or isolated tumour cells on SLNB. Within the macrometastasis cohort, abnormalities were noted on staging CT-TAP for 100 (61%) patients. Eighty of the 100 received follow-up assessment of abnormalities with 3 (2%) patients being diagnosed with distant metastatic disease. Within the micrometastasis group, abnormalities on CT-TAP staging were noted in 36 (52.1%) patients. Twenty-eight (40%) patients required further investigation and follow-up. No patient was found to have metastatic disease within this group. Patients diagnosed with micrometastatic disease of the axilla following a sentinel lymph node biopsy do not require systemic staging as it fails to detect metastatic disease.

Preoperative comprehensive risk estimation for axillary lymph node metastasis in breast cancer: development and verification of a network-based prediction model

To prevent the overaggressive treatment of axillary lymph nodes (ALNs) in breast cancer, it is necessary to develop a convenient analysis method that accurately and comprehensively reflects whether ALNs are metastatic or nonmetastatic. We retrospectively analyzed data from patients who underwent surgery for breast cancer at the Weifang Hospital of Traditional Chinese Medicine between January 2019 and June 2023. Binary logistic regression analysis was used to predict the metastasis status of ALNs. The developmental data set included 531 patients (January 2019–June 2023). The validation set included separate data points (n = 178, January 2019–June 2023). Multivariate analysis revealed that positive findings on breast physical examination, ultrasound grades of ALNs, lymphovascular invasion, and Her-2 status had significant predictive value for metastatic ALNs. Based on these findings, a 5-grade risk scoring system and 3-level management recommendations were developed. The risk of metastasis ranged from 11.25 to 93.46%, which was positively correlated with an increase in risk grade. The areas under the curve of the development and validation sets were 0.895 and 0.865, respectively. Ultimately, a convenient, accurate and comprehensive web-based predictive model was constructed using various breast cancer clinical, imaging and pathological criteria to stratify ALNs according to the metastasis probability.

Prediction of axillary lymph node metastasis in breast cancer patients based on ultrasonograhic-clinicopathologic features.

Determination of axillary lymph-node status plays a pivotal role in decision making for breast cancer treatment. Biopsy is the current standard of care but hold risks of complications as well. We aimed to find out the correlation of sonographic features of lymph node and histo-pathological findings, to predict axillary lymph-node metastasis in breast cancer patients. This retrospective observational study included 176 breast cancer patients at a private tertiary care hospital from January 2019 to December 2023. The study calculated sensitivity, specificity and accuracy of ultrasound (US) in identifying ALN metastasis. Also, binary logistic regression analysis was used to demonstrate the association between suspicious findings on axillary US with pathology report. Patients who never had undergone axillary surgery or with insufficient data, were excluded from our study. In our study Axillary US was found to be 84.2% sensitive, 48.1% specific, and 67.6% accurate in identifying nodal metastases. In this context, ALN metastases was strongly and independently correlated with cortical thickness > 3 mm and the absence of a fatty hilum (P <.05). Ultrasound was found to be highly sensitive but not specific in predicting metastatic lymph nodes in patients with breast cancer.

Merits of 18F-FDG PET/CT metabolic tumor volume for prediction of occult axillary lymph node metastasis in luminal early breast cancer patients with clinical and radiologically negative axilla

Abstract Background To see if pre-treatment 18F-FDG PET/CT metabolic tumor volume (MTV) might predict occult axillary lymph node metastases (ALNM) in luminal early breast cancer (BC) with negative axilla on clinical examination, radiological studies, and conventional 18F-FDG PET/CT interpretation. Methods Out of 330 consecutive patients with early BC who were referred for preoperative staging, only 110 female patients with luminal early BC who had biopsy-proven axillary lymph node (ALN) status and negative axilla on clinical examination, radiological studies, and conventional 18F-FDG PET/CT interpretation, were eligible for analysis. Maximum standardized uptake value (SUVmax), SUVmean, MTV, total lesion glycolysis (TLG), and clinicopathological features were determined. Via ROC analysis, we determined the optimal cutoff values for SUVmax, SUVmean, MTV, and TLG of the primary tumor to predict ALNM. Univariate and multivariate regression analyses were performed to identify the most potent PET metabolic parameters to predict ALNM. Results Pathologic ALNM was identified in 29.1% of cases; 40.6% of them had lymphovascular invasion (LVI) compared to 9% of the ALN-negative group (P &lt; 0.0001). The median MTV of tumors with ALNM was significantly higher than that of the ALN-negative group (P = 0.049). The best cutoff value of MTV was &gt; 4.83. In univariate analysis, MTV (P = 0.005) and LVI (P &lt; 0.0001) were significantly associated with ALNM, and in multivariate analysis, both MTV (P = 0.002) and LVI (P &lt; 0.0001) were confirmed to be independent predictors for ALNM. Conclusions In early luminal BC with clinically and radiologically negative axilla, including negative conventional interpretation of PET/CT, the inclusion of MTV has an added value in predicting ALNM.

Effect of the COVID-19 Pandemic on the Management of Breast Cancer Patients.

Background: The COVID-19 pandemic has significantly affected breast cancer patients by causing delays in diagnosis and treatment processes. This study aims to investigate the effects of the pandemic on the treatment process and short-term outcomes of breast cancer patients. Methods: This retrospective, cross-sectional, single-center study included 414 patients who underwent surgery for breast cancer at the Inonu University General Surgery Clinic between March 2018 and June 2021. The patients were divided into two groups: pre-pandemic (Pre-COVID-19; n = 240) and pandemic (COVID-19 Era; n = 174) periods. The groups were compared in terms of demographic, clinical, and histopathological variables. Results: During the pandemic period, the use of neoadjuvant therapy (from 21.3% to 34.5%) and preoperative PET-CT imaging (from 80.4% to 90.8%) rates increased, while breast-conserving surgery (from 27.9% to 19.0%) and the presence of comorbid diseases (from 45.0% to 29.9%) decreased significantly. While there was no significant difference between the groups in terms of the time from diagnosis to surgery (25 vs. 28.5 days, p = 0.121), the time to report the pathology result after surgery decreased during the pandemic period (28 vs. 23 days, p < 0.001). There was no significant difference between the groups in terms of immunohistochemical (ER, PR, Ki-67, E-cadherin, and c-erbB2), histopathological (lymphovascular invasion, perineural invasion, comedo necrosis, modified Bloom-Richardson grade, and TNM classification), and clinical (recurrence, metastasis, and axillary lymph node metastasis) features of the tumor. The mortality rate in the Pre-COVID-19 group (7.1%) was significantly higher than in the COVID-19 Era group (2.3%) (p = 0.049). Finally, in terms of the survival analysis, a statistically significant difference was found between the Pre-COVID-19 and COVID-19 Era groups in terms of the mean follow-up duration of the patients (p = 0.044). Conclusions: The study results show that the use of neoadjuvant therapy and radical surgery preference increased in breast cancer treatment during the pandemic period, but there was no significant change in tumor biology and histopathological features. Breast-conserving surgery rates, comorbidity rates, and pathology reporting times were significantly shortened. Long-term follow-up periods of 3 and 5 years are needed to see the impact of the pandemic on breast cancer patients.

Hidradenocarcinoma of the Chest With Axillary Lymph Node Metastasis: Report of 2 Cases and Systematic Literature Review of a Breast Cancer Mimicker.

Hidradenocarcinoma (HAC) is a rare malignant neoplasm originating from eccrine sweat glands, often presenting diagnostic challenges because of its resemblance to other malignancies, particularly breast cancer when occurring in the chest region. This report describes 2 cases of HAC with axillary lymph node metastasis, both initially misinterpreted clinically. The first case involved a 63-year-old woman with a sternal mass, near the right breast, initially suspected to be a sebaceous cyst. Histologic examination revealed a solid-cystic epithelial tumor with features suggestive of HAC, confirmed by immunohistochemical analysis. The second case concerned an 81-year-old woman with a subcutaneous growth in the sternal area, also diagnosed as HAC after histopathologic and immunohistochemical assessment. Both cases demonstrated strong estrogen receptor positivity, leading to the recommendation of hormonal therapy. A systematic review of the literature identified 21 similar cases of HAC in the chest wall, highlighting the diagnostic complexities and the potential for these tumors to mimic breast carcinoma. This review underscores the need for careful histologic and immunohistochemical evaluation to differentiate HAC from other malignancies, particularly in the breast region. Given the rare and the potential aggressive nature of HAC, early and accurate diagnosis is crucial for guiding appropriate therapeutic strategies and improving patient outcomes.

Artificial Intelligence for Breast Ultrasound: AJR Expert Panel Narrative Review.

Breast ultrasound is used in a wide variety of clinical scenarios, including both diagnostic and screening applications. Limitations of ultrasound, however, include its low specificity and, for automated breast ultrasound screening, the time necessary to review whole-breast ultrasound images. As of this writing, four AI tools that are approved or cleared by the FDA address these limitations. Current tools, which are intended to provide decision support for lesion classification and/or detection, have been shown to increase specificity among nonspecialists and to decrease interpretation times. Potential future applications include triage of patients with palpable masses in low-resource settings, preoperative prediction of axillary lymph node metastasis, and preoperative prediction of neoadjuvant chemotherapy response. Challenges in the development and clinical deployment of AI for ultrasound include the limited availability of curated training datasets compared with mammography, the high variability in ultrasound image acquisition due to equipment- and operator-related factors (which may limit algorithm generalizability), and the lack of postimplementation evaluation studies. Furthermore, current AI tools for lesion classification were developed based on 2D data, but diagnostic accuracy could potentially be improved if multimodal ultrasound data were used, such as color Doppler, elastography, cine clips, and 3D imaging.

Diagnostic performance of DCE-MRI radiomics in predicting axillary lymph node metastasis in breast cancer patients: A meta-analysis.

Radiomics offers a novel strategy for the differential diagnosis, prognosis evaluation, and prediction of treatment responses in breast cancer. Studies have explored radiomic signatures from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM), but the diagnostic accuracy varies widely. To evaluate this performance, we conducted a meta-analysis performing a comprehensive literature search across databases including PubMed, EMBASE, SCOPUS, Web of Science (WOS), Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, and the Chinese BioMedical Literature Database (CBM) until March 31, 2024. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC) were calculated. Twenty-four eligible studies encompassing 5588 breast cancer patients were included in the meta-analysis. The meta-analysis yielded a pooled sensitivity of 0.81 (95% confidence interval [CI]: 0.77-0.84), specificity of 0.85 (95%CI: 0.81-0.87), PLR of 5.24 (95%CI: 4.32-6.34), NLR of 0.23 (95%CI: 0.19-0.27), DOR of 23.16 (95%CI: 17.20-31.19), and AUC of 0.90 (95%CI: 0.87-0.92), indicating good diagnostic performance. Significant heterogeneity was observed in analyses of sensitivity (I2 = 74.64%) and specificity (I2 = 83.18%). Spearman's correlation coefficient suggested no significant threshold effect (P = 0.538). Meta-regression and subgroup analyses identified several potential heterogeneity sources, including data source, integration of clinical factors and peritumor features, MRI equipment, magnetic field strength, lesion segmentation, and modeling methods. In conclusion, DCE-MRI radiomic models exhibit good diagnostic performance in predicting ALNM and SLNM in breast cancer. This non-invasive and effective tool holds potential for the preoperative diagnosis of lymph node metastasis in breast cancer patients.

Predictive value of 18F-fluorodeoxyglucose uptake for axillary lymph node metastasis in operable breast cancer: impact of molecular subtypes.

To evaluate the predictive value of standardized uptake value (SUV) in both primary tumors and axillary lymph nodes (ALNs) using FDG PET/CT for lymph node metastasis in breast cancer patients, and to assess the influence of molecular subtypes on this predictive performance. This retrospective study included 287 patients with invasive ductal carcinoma (IDC) who underwent FDG PET/CT prior to surgery between September 2016 and December 2019. The maximum standardized uptake value (SUVmax)of primary tumors (SUV-B) and ALNs (SUV-LN) were analyzed. Molecular subtypes were classified as hormone receptor-positive, HER2-positive, and triple-negative breast cancer (TNBC). Receiver operating characteristic (ROC) curve analysis was performed to assess and compare the diagnostic performance of SUV-B and SUV-LN for predicting ALN metastasis. Among the 287 patients, 62 (21.6%) had confirmed ALN metastasis. The median SUV-LN was significantly higher in patients with metastasis compared to those without metastasis(1.5 vs. 0.9; P < 0.001). SUV-LN demonstrated good discriminative performance for ALN metastasis (AUC: 0.796), whereas SUV-B did not show significant predictive value (AUC: 0.536). The SUV_LN demonstrated significantly lower predictive performance for ALN metastasis in the hormone-positive group (AUC: 0.796) compared to the excellent discriminative performance in the HER2-positive (AUC: 0.923, P = 0.018) and TNBC (AUC: 0.940, P = 0.004) groups. Hormone receptor-positive tumors also exhibited lower FDG uptake in metastatic lymph nodes compared to HER2-positive and TNBC subtypes (P = 0.031). FDG PET/CT SUV-LN effectively predicts ALN metastasis in HER2-positive and TNBC subtypes. Hormone receptor-positive breast cancers show lower FDG uptake in metastatic ALNs, reducing diagnostic accuracy. This finding may aid in selecting the most appropriate diagnostic modality based on tumor characteristics in the era of personalized medicine.

Comparative Analysis of Nomogram and Machine Learning Models for Predicting Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Study on Clinically and Ultrasound-Negative Axillary Cases Across Two Centers

ObjectiveEarly and accurate prediction of axillary lymph node metastasis (ALNM) is crucial in determining appropriate treatment strategies for patients with early-stage breast cancer. The aim of this study was to evaluate the efficacy of radiomic features extracted from ultrasound (US) images combined with machine learning (ML) methods in predicting ALNM to improve diagnostic accuracy and patient prognosis. MethodsIn this retrospective study, data of 282 early-stage breast cancer patients from two centers were analyzed. We considered clinicopathological characteristics, conventional US features, contrast-enhanced ultrasound (CEUS) characteristics, and radiomics features. Radiomics features were extracted from US images, and using least absolute shrinkage and selection operator (LASSO) regression, 12 key features were selected to compute a Radiomics score (Rad-score). A nomogram was developed based on these features, alongside five ML models: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using metrics such as the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), negative predictive value (NPV), and positive predictive value (PPV). ResultsBoth the nomogram and ML models, including the Rad-score combined with histologic type, significantly predicted ALNM. Among all models, the XGBoost model showed the best performance with an AUC of 0.810 and an accuracy of 84.1% in the external test set, surpassing the nomogram and other ML models. SHapley Additive exPlanations (SHAP) analysis further provided insights into the influence of individual radiomics features on ALNM prediction. ConclusionsWhile the nomogram provides a useful traditional statistical approach, integrating radiomics features with ML, particularly the XGBoost model enhanced by SHAP interpretability, offers superior predictive accuracy for ALNM in early-stage breast cancer patients.

Prediction of axillary nodal burden using preoperative magnetic resonance imaging scoring in patients with clinically node-negative breast cancer: a retrospective cohort study.

Axillary lymph node metastasis (ALNM) is a significant predictor of overall patient survival; thus, precise evaluation of ALNM is essential for staging breast cancer, informing multimodal treatment strategies, and ensuring optimal patient care. This study aimed to establish a magnetic resonance imaging (MRI) scoring system for predicting extensive axillary nodal metastasis in patients with clinically node-negative breast cancer derived from preoperative breast and axillary MRI. This study included 226 patients with clinically node-negative breast cancer who underwent preoperative breast and axillary MRI between January 1, 2010 and December 31, 2020 at King Chulalongkorn Memorial Hospital. Their clinical, radiological, and pathological features were retrospectively reviewed. MRI characteristics of breast tumors and axillary lymph nodes (LNs) were assessed. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Cohen's Kappa coefficient of the scoring system were evaluated. The receiver operating characteristic curve was used to determine the cutoff value for the MRI scoring system to differentiate extensive ALNM from nonextensive ALNM. Of the 226 patients, 144 had cancer-free axilla, 51 had 1-2 positive metastatic LNs, and 31 had ≥3 positive metastatic LNs. Moreover, only 60 could be evaluated for the apparent diffusion coefficient (ADC) value of LNs because of size limitations. The cutoff value for the MRI scoring system with ADC was 14 (NPV =87.1% with moderately acceptable discrimination), and the cutoff value without ADC was 8 (sensitivity =77.4%; specificity =81%; PPV =39.3%; NPV =95.8% with moderately acceptable discrimination). The MRI scoring system using breast and axillary LN characteristics from preoperative MRI may help predict extensive ALNM and aid axillary nodal treatment selection.

Development and validation of a dynamic contrast-enhanced magnetic resonance imaging-based habitat and peritumoral radiomic model to predict axillary lymph node metastasis in patients with breast cancer: a retrospective study.

The heterogeneity within breast cancer and its microenvironment are associated with metastasis. Analyzing distinct tumor subregions using habitat analysis and characterizing the tumor microenvironment through radiomics may be valuable for predicting axillary lymph node metastasis (ALNM) in breast cancer. This study aimed to develop and validate a nomogram for predicting ALNM in breast cancer patients by integrating clinicopathological, intra- or peri-tumoral radiomic, and habitat signatures based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and determine the optimal peritumoral region size for accurate prediction. Four hundred and twenty-six breast cancer patients who underwent preoperative DCE-MRI at Shenzhen People's Hospital between June 2019 and August 2021 were retrospectively enrolled (338 in training set, 88 in test set). The clinicopathological data were analyzed by univariable and multivariable analyses. Peritumoral regions were generated with thicknesses of 2, 4, 6, and 8 mm. Habitat analysis clustered three sub-regions within the tumor area. Intratumoral, peritumoral, and habitat features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) regression method. The prediction models were constructed, including (I) Clinical model, (II) Intra model, (III) Peri2mm model, (IV) Peri4mm model, (V) Peri6mm model, (VI) Peri8mm model, (VII) Habitat model, and (VIII) Fusion nomogram model. Models were evaluated using the receiver operating characteristic (ROC) curve analysis, calibration curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). The Clinical model showed relatively low predictive performance with an area under the curve (AUC) of 0.667 in the test set, while the Intra model demonstrated moderate predictive performance with an AUC of 0.745 in the test set. The 4 mm was identified as the optimal peritumoral region size for ALNM prediction, with AUCs of 0.871 and 0.773 in training and test sets, respectively. The Habitat model exhibited outstanding predictive performance, achieving AUCs of 0.973 and 0.854 in the training and test set, respectively. The Fusion nomogram model, incorporating clinicopathological signatures, Peri4mm radiomic signatures, and habitat signatures, achieved the highest AUCs (0.977 and 0.873 in training and test sets). This model was well-calibrated with significant clinical benefit, outperforming individual signatures according to calibration curve, NRI, IDI, and DCA. The optimal peritumoral region size based on DCE-MRI radiomics for predicting ALNM in breast cancer patients was 4 mm. The nomogram combining clinicopathological factors, Peri4mm radiomics, and habitat signatures derived from DCE-MRI demonstrates robust performance in predicting ALNM and may aid clinical decision-making.

Expression of the Hopeful Therapeutic Target CD15 in Women with Breast Cancer

Background: CD15 is emerging as a recent prognostic marker with potential as a future therapeutic target in human malignancies. Its biological role in human cancer involves facilitating tumor cell adhesion through interaction with E-, L-, and P-selectins. This interaction allows adhesion to endothelial cells, enhancing metastatic potential. Objective: to study CD15 expression and its relationship to some pathologic parameters to provide the first Iraqi data-driven analysis of this growing concept in women with breast cancer. Subjects and Methods: A retrospective study was carried out utilizing tissue samples obtained from 85 patients with invasive breast cancer. Materials included one formalin-fixed paraffin-embedded tissue block, one hematoxylin and eosin-stained histological section, and two immunohistochemical-stained ER and HER2/NEU sections for each patient. The samples were obtained through core needle biopsy under ultrasound guidance. Data review and immunohistochemical staining with CD15 were conducted, followed by statistical analysis using the chi-square test to explore correlations. Results: CD15 expression was observed in 55% of breast cancer samples. There was a highly significant statistical relationship between CD15 expression and the presence of axillary lymph node metastasis (proven by fine needle aspiration cytology). However, there was no significant statistical relationship between CD15 expression and the histological grade, ER receptor, HER2-NEU status, and tissue calcification. Conclusion: CD15 expression was detected in breast cancer cells, with a highly significant association observed between CD15 expression and axillary lymph node metastasis at presentation. CD15 could serve as a prognostic marker as association with axillary lymph nodes reflects negative impact on prognosis and at the same time a promising therapeutic target for patients with breast cancer as blockage of CD15 antigen’s function will result in reduction of the metastatic potential and it could be an immunotherapeutic target.

Post-biopsy to Surgery Interval Tends to Increase Axillary Nodal Metastasis, Especially in Early Breast Cancer Patients

Objective: Delays between diagnosis and breast cancer surgery may raise concerns about tumor progression. Tumors of the same size should exhibit same proportions of N staging. We aimed to evaluate the impact of time to surgery (TTS) on the proportion of metastatic axillary lymph nodes (N-Staging), controlled by tumor size. Materials and Methods: A cross-sectional study of primary breast cancer patients treated between October 2021 - December 2022 at the Division of Head Neck and Breast Surgery, Siriraj Hospital, Thailand examined the association between lymph node staging and TTS, stratified by primary tumor size. Patients with neoadjuvant therapy, DCIS, or underwent excisional biopsy were excluded. Results: Of 424 patients, mean age 60.95 years, had an average tumor size 17±13.38 mm, and 20.8% LNs metastasis. The mean TTS was 7 ± 3.11 weeks. The proportion of + LNs patients stratified by tumor size was 10.6% for T1, 34.56% for T2, and 43.75% for T3 lesions. There was no significant difference between TTS and the proportion ofN-staging for all T2 and T3 tumors. In contrast, a significant finding was observed among T1 tumors. Axillary nodal metastasis became more advanced as TTS increased (p-value = 0.022); and increased N2 and N3 nodal staging was noted in patients with delayed surgery. No significant additional differences were found concerning breast cancer subtype, pathological grading, or lympho-vascular/perineural invasion. Conclusion: Increasing TTS was significantly associated with more advanced N staging. This finding highlights the need for timely intervention in early breast cancer, particularly in T1 tumors.

Triple Negative Breast Cancer: Experience From A Tertiary Care Institute In Kannur

Aims &amp; Objectives The incidence of breast cancer in Kerala is on the rise, and it is now the commonest cancer affecting women in the state. The present study was designed to investigate the prevalence of triple negative breast cancer (TNBC) in our population and also to compare the various clinicopathological characteristics of TNBC with non-TNBC in these patients. Methods A cross sectional study was conducted among 221cases of female breast cancer who reported to the Department of General surgery, Government Medical College, Kannur from 1st October 2020 to 31st September 2022 Results The mean age at presentation was 52 years (range 26-82 years). The prevalence of triple negative breast cancer was 19.9% . Most of the subjects were belongs to the age group 40-60 years. Majority were with grade 2 and 3 disease. 50.7% were ER positive and 43% were PR positive. 43% were Her2-neu positive. Conclusion Triple negative breast cancer represented 19.9%, which is in concordance with the literature. TNBC are associated with large tumor size, high-grade tumors, and a higher rate of axillary lymph node metastasis

The role of EZH2 expression in predicting axillary lymph node metastasis in invasive ductal carcinoma of the breast

The role of EZH2 expression in predicting axillary lymph node metastasis in invasive ductal carcinoma of the breast

Prediction model for ocular metastasis of breast cancer: machine learning model development and interpretation study

BackgroundBreast cancer (BC) is caused by the uncontrolled proliferation of breast epithelial cells followed by malignant transformation, and it has the highest incidence among female malignant tumors. The metastasis of BC occurs through direct and lymphatic spread. Although ocular metastasis is relatively rare, it is a good indicator of a worse prognosis. We used machine learning (ML) to establish a model to analyze the risk factors of BC eye metastasis.MethodsThe clinical data of 2225 patients with BC from 2003 to 2019 were collected and randomly classified into the training and test sets using a ratio of 7:3. Based on the presence or absence of eye metastasis, the patients with BC were classified into the ocular metastasis (OM) and non-ocular metastasis (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator (LASSO) were conducted. We used six ML algorithms to establish a predictive BC model and used 10-fold cross-validation for internal verification. The area under the receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model. In addition, we established a web hazard calculator depending on the best-performing model to facilitate its clinical application. Shapley additive interpretation (SHAP) was used to determine the risk factors and the interpretability of the black box model.ResultsUnivariate logistic regression analysis showed that histopathology (other types), axillary lymph node metastasis (ALNM) (> 4), Ca2+, total cholesterol (TC), low-density lipoprotein (LDL), apolipoprotein A (ApoA), carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 125, CA153, CA199, alkaline phosphatase (ALP), and hemoglobin (Hb) were risk factors for BC eye metastasis. Multivariate logistic regression analysis showed that CA153, ApoA, and LDL were hazardous components for BC eye metastasis. LASSO showed that ALNM, LDL, CA125, Hb, ALP, and CA199 were the first six key variables that were useful for the diagnosis of ocular metastasis in breast cancer. Bootstrapped aggregation (BAG) demonstrated the discriminative ability (area under ROC curve [AUC] = 0.992, accuracy = 0.953, sensitivity = 0.987). Based on this, we applied the BAG machine learning model to build an online web computing system to help clinicians assist in determining the risk of BC eye metastasis. In addition, two typical cases are analyzed to determine the interpretability of the model.ConclusionWe used ML to establish a risk prediction model for BC ocular metastasis, and BAG showed the greatest performance. The model can predict the risk of OM in patients with BC, facilitate early and timely diagnosis and treatment, and reduce the burden on society.

Computer-assisted diagnosis for axillary lymph node metastasis of early breast cancer based on transformer with dual-modal adaptive mid-term fusion using ultrasound elastography

Accurate preoperative qualitative assessment of axillary lymph node metastasis (ALNM) in early breast cancer patients is crucial for precise clinical staging and selection of axillary treatment strategies. Although previous studies have introduced artificial intelligence (AI) to enhance the assessment performance of ALNM, they all focus on the prediction performances of their AI models and neglect the clinical assistance to the radiologists, which brings some issues to the clinical practice. To this end, we propose a human-AI collaboration strategy for ALNM diagnosis of early breast cancer, in which a novel deep learning framework, termed DAMF-former, is designed to assist radiologists in evaluating ALNM. Specifically, the DAMF-former focuses on the axillary region rather than the primary tumor area in previous studies. To mimic the radiologists’ alternative integration of the UE images of the target axillary lymph nodes for comprehensive analysis, adaptive mid-term fusion is proposed to alternatively extract and adaptively fuse the high-level features from the dual-modal UE images (i.e., B-mode ultrasound and Shear Wave Elastography). To further improve the diagnostic outcome of the DAMF-former, an adaptive Youden index scheme is proposed to deal with the fully fused dual-modal UE image features at the end of the framework, which can balance the diagnostic performance in terms of sensitivity and specificity. The clinical experiment indicates that the designed DAMF-former can assist and improve the diagnostic abilities of less-experienced radiologists for ALNM. Especially, the junior radiologists can significantly improve the diagnostic outcome from 0.807 AUC [95% CI: 0.781, 0.830] to 0.883 AUC [95% CI: 0.861, 0.902] (P-value <0.0001). Moreover, there are great agreements among radiologists of different levels when assisted by the DAMF-former (Kappa value ranging from 0.805 to 0.895; P-value <0.0001), suggesting that less-experienced radiologists can potentially achieve a diagnostic level similar to that of experienced radiologists through human-AI collaboration. This study explores a potential solution to human-AI collaboration for ALNM diagnosis based on UE images.