Axillary Node Research Articles

848: Treatment laterality and incidental dose to internal mammary nodes and axilla in breast radiotherapy

848: Treatment laterality and incidental dose to internal mammary nodes and axilla in breast radiotherapy

3399: DBCG IMN2: Internal mammary node irradiation in 4541 high-risk breast cancer patients treated 2007-2014

3399: DBCG IMN2: Internal mammary node irradiation in 4541 high-risk breast cancer patients treated 2007-2014

685: Incidental dose distribution to contralateral internal mammary nodes in breast cancer patients

685: Incidental dose distribution to contralateral internal mammary nodes in breast cancer patients

Retrieval of the Clipped Axillary Lymph Node and Its Impact on Treatment Decisions.

We examined clinically node-positive (cN+) breast cancer patients undergoing neoadjuvant chemotherapy and clipped lymph node (CLN) localization to determine the rate of CLN = non-sentinel lymph node (SLN), the factors associated with cN+ to pN0 conversion, and the treatment impact. We conducted a single institution review of cN+ patients receiving NAC from 2016 to 2022 with preoperative CLN localization (N = 81). Demographics, hormone receptor (HR) and HER2 status, time to surgery, staging, chemotherapy regimen, localization method, pathology, and adjuvant therapy were analyzed. Pathologic complete response (pCR) of the CLN was observed in 41 patients (50.6%): 18.8% HR+/HER2-, 75% HR+/HER2+, 75% HR-/HER2+, and 62.5% triple-negative breast cancer (p-value = 0.006). CLN = SLN in 68 (84%) patients, while CLN = non-SLN in 13 (16%). In 14 (17.3%) patients, the final treatment was altered based on +CLN status: 11 patients underwent axillary lymph node dissection (ALND), and 3 had systemic treatment changes. pCR rates varied, with the highest conversion rates observed in HER2+ disease and the lowest in HR+/HER2- disease. In 2 (2.5%) patients, adjuvant therapy changes were made based on a non-sentinel CLN, while in 97.5% of patients, a SLN biopsy alone represented the status of the axilla. This demonstrates that a +CLN often alters final plans and that, despite also being a SLN in most cases, a subset of patients will be undertreated by SLN biopsy alone.

Viability of Whole-Slide Imaging for Intraoperative Touch Imprint Cytological Diagnosis of Axillary Sentinel Lymph Nodes in Breast Cancer Patients.

Whole-slide imaging (WSI) is a promising tool in pathology. However, the use of WSI in cytopathology has lagged behind that in histology. We aimed to evaluate the utility of WSI for the intraoperative touch imprint cytological diagnosis of axillary sentinel lymph nodes (SLNs) in breast cancer patients. Glass slides from touch imprint cytology of 480 axillary SLNs were scanned using two different WSI scanners. The intra- and interobserver concordance, accuracy, possible reasons for misdiagnosis, scanning time, and review time for three cytopathologists were compared between WSI and light microscopy (LM). A total of 4320 diagnoses were obtained. There was substantial to strong intraobserver concordance when comparing reads among paired LM slides and WSI digital slides (κ coefficient ranged from 0.63 to 0.88, and concordance rates ranged from 94.58% to 98.33%). Substantial to strong interobserver agreement was also observed among the three cytopathologists (κ coefficient ranged from 0.67 to 0.85, and concordance rates ranged from 95.42% to 97.92%). The accuracy of LM was slightly higher (average of 98.06%) than that of WSI (averages of 96.81% and 97.78%). The majority of misdiagnoses were false negative diagnoses due to the following top three causes: few cancer cells, confusing cancer cells with histiocytes, and confusing cancer cells with lymphocytes. This study is the first to address the feasibility of WSI in touch imprint cytology. The use of WSI for intraoperative touch imprint cytological diagnosis of SLNs is a practical option when experienced staff are not available on-site.

Surgical Management and Its Impact on Adjuvant Treatment in Recurrent Ipsilateral Breast Cancer: A Retrospective Cohort Study.

Background: Breast cancer (BC) recurrence, defined as the reappearance of cancer in the ipsilateral breast after primary treatment, poses significant challenges in clinical management. Despite advances in treatment, recurrence rates persist, ranging from 0.6 to 1.5% annually, reaching 10-15% at 20 years. This study aims to analyze the surgical and oncological characteristics of patients with BC recurrence. Methods: This retrospective study includes 56 patients diagnosed with recurrent BC between October 2018 and April 2022. Data were collected from a prospectively maintained surgical database. A descriptive analysis was performed on the initial BC, and the recurrence, including surgical complications, was classified using the Clavien-Dindo system. The success rates of selective sentinel lymph node (SLN) biopsies and aberrant drainages were assessed based on previous surgeries. Results: The cohort included 55 females and 1 male, with a median age of 65.3 years. The mean time to BC recurrence was 11.5 years. Among them, 26.8% underwent breast-conserving surgery, 41.1% had a mastectomy, 21.4% had a mastectomy with reconstruction, and 10.7% had an excision over a previous mastectomy. An SLN biopsy was performed in 78.6% of cases, with higher success rates in those without a previous axillary lymph node dissection (85.7% vs. 63.2%). Aberrant drainage was more frequent in patients with a previous ALND (44.4% vs. 20%). The median follow-up was 41.3 months, with 10.7% experiencing a second recurrence. Conclusions: Repeat breast-conserving surgery with re-irradiation for ipsilateral recurrence is feasible and does not significantly increase complications. SLN biopsy is valuable for restaging and tailoring adjuvant therapies, with ALND not being necessary if re-SLN biopsy shows no drainage. The management of aberrant drainage remains controversial.

Postoperative Radiotherapy Using VMAT for RNI Including IMN in Breast Cancer: Report of Short-term Follow-up.

The application of volumetric-modulated arc therapy (VMAT) in postoperative radiotherapy for breast cancer has recently garnered considerable interest, especially when regional lymph node irradiation (RNI), is extended to include the internal mammary node (IMN) region, along with whole-breast or chest wall irradiation. This study aimed to assess both acute and late toxicities, as well as breast cancer outcomes, during the observation period. This retrospective study aimed to assess the safety and feasibility of VMAT in postoperative radiotherapy, including the IMN region, for patients with breast cancer at our hospital. We included 33 eligible patients who received postoperative VMAT radiotherapy, including the IMN region, between November 2018 and July 2021. The primary indications for implementing VMAT were the presence of IMN metastases before primary systemic therapy or more than four axillary lymph node metastases. The median prescribed dose for the chest wall or conserved breast and regional lymph nodes was 50 Gy in 25 fractions. In 16 cases, a simultaneous boost (median, 10 Gy) was administered to residual lymph node metastases. VMAT facilitated a reduction in the mid- or high-dose range to organs at risk, exemplified by V20Gy and V40Gy of the ipsilateral lung, which were 24.10% and 6.84%, respectively, while ensuring adequate target dose irradiation, V45Gy=95.23%. No serious adverse events, including symptomatic radiation pneumonitis, occurred during the 15-month median observation period. Our findings demonstrate the safety and effectiveness of postoperative radiotherapy for breast cancer, including the IMN region, when using VMAT.

Utilizing multiclassifier radiomics analysis of ultrasound to predict high axillary lymph node tumour burden in node-positive breast cancer patients: a multicentre study

Background The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis. Objective This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer. Methods A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model. Results During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model. Conclusions The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.

Breast cancer-related lymphedema: A comprehensive analysis of risk factors.

Breast cancer-related lymphedema is a devastating condition that negatively affects the quality of life of breast cancer survivors. We sought to identify risk factors that predicted the timing and development of lymphedema. Women with breast cancer that underwent sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND) at our institution between 2007 and 2022 were identified and sociodemographic and clinical information was extracted. We used logistic regression analysis to identify risk factors for lymphedema and performed cox-regression analysis to predict the timing of lymphedema presentation after surgery. We identified 1,223 patients, of which 161 (13.2%) developed lymphedema within 1.8 (mean, SD = 2.5) years postoperatively. Patients with SLNB had significantly lower odds for lymphedema development (vs. ALND, OR = 0.29 [0.14-0.57]). Patients between 40 and 49 years of age, and 50-59 (vs. <40 years, OR = 2.14 [1.00-4.60]; OR = 2.42, [1.13-5.16] respectively), African American patients (vs. Caucasian, OR = 1.86 [1.12-3.09]), patients with stage II, III, and IV disease (vs. stage 0, OR = 3.75 [1.36-10.33]; OR = 6.62 [2.14-20.51]; OR = 9.36 [2.94-29.81]), and patients with Medicaid (vs. private insurance, OR = 3.56 [1.73-7.28]) had higher rates of lymphedema. Cox-regression analysis showed that African American (HR = 1.71 [1.08-2.70]), higher BMI (HR = 1.03 [1.00-1.06]), higher stage (stage II, HR = 2.22 [1.05-7.09]; stage III, HR = 5.26 [1.86-14.88]; stage IV, HR = 6.13 [2.12-17.75]), and Medicaid patients (HR = 2.15 [1.12-3.80]) had higher hazards for lymphedema. Patients with SLNB had lower hazards for lymphedema (HR = 0.43 [0.87-2.11]). Lymphedema has identifiable risk factors that can reliably be used to predict the chances of lymphedema development and enable clinicians to educate patients better and formulate treatment plans accordingly. III (Retrospective study).

Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: multicentre study

The accurate evaluation of axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer holds great value. This study aimed to develop an artificial intelligence system utilising multiregional dynamic contrast-enhanced MRI (DCE-MRI) and clinicopathological characteristics to predict axillary pathological complete response (pCR) after NAC in breast cancer. This study included retrospective and prospective datasets from six medical centres in China between May 2018 and December 2023. A fully automated integrated system based on deep learning (FAIS-DL) was built to perform tumour and ALN segmentation and axillary pCR prediction sequentially. The predictive performance of FAIS-DL was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RNA sequencing analysis were conducted on 45 patients to explore the biological basis of FAIS-DL. 1145 patients (mean age, 50 years±10 [SD]) were evaluated. Among these patients, 506 were in the training and validation sets (axillary pCR rate of 40.3%), 127 in the internal test set (axillary pCR rate of 37.8%), 414 in the pooled external test set (axillary pCR rate of 48.8%), and 98 in the prospective test set (axillary pCR rate of 43.9%). For predicting axillary pCR, FAIS-DL achieved AUCs of 0.95, 0.93, and 0.94 in the internal test set, pooled external test set, and prospective test set, respectively, which were also significantly higher than those of the clinical model and deep learning models based on single-regional DCE-MRI (all P<0.05, DeLong test). In the pooled external and prospective test sets, the FAIS-DL decreased the unnecessary axillary lymph node dissection rate from 47.9% to 6.8%, and increased the benefit rate from 52.2% to 86.5%. RNA sequencing analysis revealed that high FAIS-DL scores were associated with the upregulation of immune-mediated genes and pathways. FAIS-DL has demonstrated satisfactory performance in predicting axillary pCR, which may guide the formulation of personalised treatment regimens for patients with breast cancer in clinical practice. This study was supported by the National Natural Science Foundation of China (82371933), National Natural Science Foundation of Shandong Province of China (ZR2021MH120), Mount Taishan Scholars and Young Experts Program (tsqn202211378), Key Projects of China Medicine Education Association (2022KTM030), China Postdoctoral Science Foundation (314730), and Beijing Postdoctoral Research Foundation (2023-zz-012).

Predicting Pathological Characteristics of HER2-Positive Breast Cancer from Ultrasound Images: a Deep Ensemble Approach.

The objective is to evaluate the feasibility of utilizing ultrasound images in identifying critical prognostic biomarkers for HER2-positive breast cancer (HER2 + BC). This study enrolled 512 female patients diagnosed with HER2-positive breast cancer through pathological validation at our institution from January 2016 to December 2021. Five distinct deep convolutional neural networks (DCNNs) and a deep ensemble (DE) approach were trained to classify axillary lymph node involvement (ALNM), lymphovascular invasion (LVI), and histological grade (HG). The efficacy of the models was evaluated based on accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curves, areas under the ROC curve (AUCs), and heat maps. DeLong test was applied to compare differences in AUC among different models. The deep ensemble approach, as the most effective model, demonstrated AUCs and accuracy of 0.869 (95% CI: 0.802-0.936) and 69.7% in LVI, 0.973 (95% CI: 0.949-0.998) and 73.8% in HG, thus providing superior classification performance in the context of imbalanced data (p < 0.05 by the DeLong test). On ALNM, AUC and accuracy were 0.780 (95% CI: 0.688-0.873) and 77.5%, which were comparable to other single models. The pretreatment US-based DE model could hold promise as a clinical guidance for predicting pathological characteristics of patients with HER2-positive breast cancer, thereby providing benefit of facilitating timely adjustments in treatment strategies.

Arm symptom pattern among breast cancer survivors with and without lymphedema: a contemporaneous network analysis.

Arm symptoms commonly endure in post-breast cancer period and persist into long-term survivorship. However, a knowledge gap existed regarding the interactions among these symptoms. This study aimed to construct symptom networks and visualize the interrelationships among arm symptoms in breast cancer survivors (BCS) both with and without lymphedema (LE). We conducted a secondary analysis of 3 cross-sectional studies. All participants underwent arm circumference measurements and symptom assessment. We analyzed 17 symptoms with a prevalence >15%, identifying clusters and covariates through exploratory factor and linear regression analysis. Contemporaneous networks were constructed with centrality indices calculated. Network comparison tests were performed. 1116 cases without missing data were analyzed, revealing a 29.84% prevalence of LE. Axillary lymph node dissection [ALND] (vs sentinel lymph node biopsy [SLNB]), longer post-surgery duration, and radiotherapy significantly impacted overall symptom severity (P < .001). "Lymphatic Stasis," "Nerve Injury," and "Movement Limitation" symptom clusters were identified. Core symptoms varied: tightness for total sample network, firmness for non-LE network, and tightness for LE network. LE survivors reported more prevalent and severe arm symptoms with stronger network connections than non-LE group (P = .010). No significant differences were observed among different subgroups of covariates (P > .05). Network structures were significantly different between ALND and SLNB groups. Our study revealed arm symptoms pattern and interrelationships in BCS. Targeting core symptoms in assessment and intervention might be efficient for arm symptoms management. Future research is warranted to construct dynamic symptom networks in longitudinal data and investigate causal relationships among symptoms.

Comparison of the Performance of [18F]-FDG PET/CT and [18F]-FDG PET/MRI for Lymph Node Metastasis in Breast Cancer: A Systematic Review and Meta- Analysis.

The primary objective of this study was to conduct a comparative analysis of the diagnostic efficacy of [18F]-FDG PET/CT and [18F]-FDG PET/MRI in the detection of breast cancer lymph node metastasis. We conducted a comprehensive search on PubMed, Embase, and Web of Science databases, encompassing eligible articles until March 2023. The pooled sensitivity and specificity for [18F]-FDG PET/CT and [18F]-FDG PET/MRI have been reported as estimates with 95% Confidence Intervals (CIs) using a bivariate random-effect model. Utilizing the I square (I2) statistic, heterogeneity among pooled studies was evaluated. The quality assessment of the included studies was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) methodology. We included 18 studies (2057 patients). The sensitivity, specificity, and AUC (Area Under the Curve) values of [18F]-FDG PET/CT for overall lymph node metastasis in breast cancer have been found to be 0.58 (0.39 - 0.75), 0.83 (0.69-0.92), and 0.79 (0.75-0.82), respectively. Correspondingly, the values for [18F]-FDG PET/MRI were found to be 0.76 (0.60-0.88), 0.85 (0.77-0.91), and 0.89 (0.86-0.91), respectively. The sensitivity, specificity, and AUC values of [18F]-FDG PET/CT for axillary lymph node metastasis in breast cancer were 0.52 (0.37-0.67), 0.84 (0.68-0.92), and 0.73 (0.69-0.76), respectively. Correspondingly, the values for [18F]-FDG PET/MRI were 0.84 (0.76-0.89), 0.87 (0.75-0.94), and 0.86 (0.83-0.89), respectively. This study has suggested [18F]-FDG PET/MRI to have greater diagnostic power than [18F]-FDG PET/CT in detecting lymph node metastasis in breast cancer. However, the [18F]-FDG PET/MRI results have been obtained from a small sample size study, and more and larger prospective studies are needed for further confirmation on this issue.</p>.

The feasibility of targeted axillary dissection for breast cancer axillary surgery de-escalation after neoadjuvant therapy: A prospective cohort study.

Targeted axillary dissection (TAD) after neoadjuvant therapy (NAT) includes removing of marked and sentinel lymph nodes (SLNs). The aim was to investigate the optimization of TAD localization techniques after NAT among breast cancer patients. From November 2020 to 2022, we prospectively enrolled 107 lymph node-positive breast cancer patients in XX Hospital and received complete cycles of NAT. Patients were randomly divided into the following 3 groups before treatment: group A, marked node with clip (n=34); group B, marked node with 125I seed (n=32); and group C, marked node with clip and 125I seed (n=41). Dual tracers were used to search for SLNs after NAT. The main endpoint was the detection rate of marked nodes and false-negative rate (FNR). The detection rates using the TAD localization technique were 82.6% (28/34), 100% (32/32), and 100% (41/41) for groups A, B, and C, respectively (P>0.05). The FNR rates were 15.8%, 5.9%, and 5.6% among group A, B, and C, respectively (P>0.05). The FNR rates in cN1 patients were 5.1%, 2.7%, and 2.6%, among these three groups, respectively (P>0.05). The change in distance between 125I seeds and clips in axillary lymph nodes was <3mm. The FNR rates of TAD guided by dye tracer, radiolabeled tracer, and dual tracers were 5.4%, 5.2%, and 3.4%, respectively (P>0.05). The negative predictive values were 93.0%, 93.0%, and 95.2%, respectively (P>0.05). Considering inexpensive and detect rate of 125I seeds, it is recommended that placement of 125I seeds to localize metastatic nodes in neoadjuvant setting. The TAD guided by dye tracer is also feasible for axillary de-escalation surgery after NAT in countries or regions without radiolabeled colloid.

Assessing Axillary Lymph Node Burden and Prognosis in cT1-T2 Stage Breast Cancer Using Machine Learning Methods: A Retrospective Dual-Institutional MRI Study.

Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic approaches. To develop and validate a machine learning (ML) model based on clinicopathological and MRI characteristics for assessing pALN burden and survival in patients with cT1-T2 stage breast cancer. Retrospective. A total of 506 females (range: 24-83 years) with cT1-T2 stage breast cancer from two institutions, forming the training (N = 340), internal validation (N = 85), and external validation cohorts (N = 81), respectively. This study used 1.5-T, axial fat-suppressed T2-weighted turbo spin-echo sequence and axial three-dimensional dynamic contrast-enhanced fat-suppressed T1-weighted gradient echo sequence. Four ML methods (eXtreme Gradient Boosting [XGBoost], Support Vector Machine, k-Nearest Neighbor, Classification and Regression Tree) were employed to develop models based on clinicopathological and MRI characteristics. The performance of these models was evaluated by their discriminative ability. The best-performing model was further analyzed to establish interpretability and used to calculate the pALN score. The relationships between the pALN score and disease-free survival (DFS) were examined. Chi-squared test, Fisher's exact test, univariable logistic regression, area under the curve (AUC), Delong test, net reclassification improvement, integrated discrimination improvement, Hosmer-Lemeshow test, log-rank, Cox regression analyses, and intraclass correlation coefficient were performed. A P-value <0.05 was considered statistically significant. The XGB II model, developed based on the XGBoost algorithm, outperformed the other models with AUCs of 0.805, 0.803, and 0.818 in the three cohorts. The Shapley additive explanation plot indicated that the top variable in the XGB II model was the Node Reporting and Data System score. In multivariable Cox regression analysis, the pALN score was significantly associated with DFS (hazard ratio: 4.013, 95% confidence interval: 1.059-15.207). The XGB II model may allow to evaluate pALN burden and could provide prognostic information in cT1-T2 stage breast cancer patients. 3 TECHNICAL EFFICACY: Stage 2.

AtRA Attenuates High Salt-Driven EAE Mainly Through Suppressing Th17-Like Regulatory T Cell Response Mediated by the Inhibition of IL-23R Signaling Pathway.

High salt diet (HSD) is implicated in numerous disorders, which boosts Th17 cell development and weakens immunosuppressive function of regulatory T cells (Treg cells) Treg cells, leading to the exacerbation of EAE. However, little is known regarding the harness of excessive proinflammatory responses evoked by HSD. Here we show that atRA, a key vitamin A metabolite with multifaceted immunoregulatory properties has the potential in inhibiting the proinflammatory reaction of high salt. Treatment with atRA in vivo elicited the Treg generation in cervical and axillary lymph nodes (CALs), and in CNS of experimental autoimmune encephalomyelitis (EAE). Meanwhile, the proportion of Th17-like Treg cells (RORγt-positive or GM-CSF-positive Treg cells) decreased in CALs. atRA also inhibited IL-17A expression in CD4+ effector T cells. In-vitro mechanistic studies showed that atRA inhibit IL-23R but not SGK1 expression in Treg cells and this results in maintained immunosuppressive function of Treg cells even in the presence of IL-6 and high salt. Furthermore, treatment of EAE with anti-IL-23R mAb attenuated HSD-provoked EAE progress. This was associated with a reduction in the number of CNS-infiltrating Th17 cells and an increase of CAL-Treg cells. Mechanically, treatment with atRA significantly promoted LP-CD103+CD11c+ dendritic cells, a subgroup of cells most closely involved in endogenous retinoic acid metabolism, and enhanced intestinal Aldh1a1 and Rdh10 expression from HSD-fed EAE mice. Interestingly, anti-IL-23R mAb administration also reduced IL-23R expression in Treg cells, along with the increased proportion of LP-CD103+CD11c+ dendritic cells and Rdh10 mRNA expression. In conclusion, administration of atRA might be a way to combat the proinflammatory effects of HSD. Meanwhile, systematic inhibition of IL-23R also had a moderate therapeutic potential in inhibiting inflammatory effects of high salt, which may serve as a basis for the identification of novel therapeutic strategies against HSD-driven autoimmune disorders.

Significance of Shear Wave Elastography and B-Mode Ultrasound in Assessing Axillary Lymph Nodes in Patients with Breast Cancer

Significance of Shear Wave Elastography and B-Mode Ultrasound in Assessing Axillary Lymph Nodes in Patients with Breast Cancer

Sydney Reporting System for Lymph Node Fine-Needle Aspiration and Malignancy Risk Stratification: Is It of Clinical Value?

Lymphadenopathy is a common presentation of both reactive and malignant diseases, and lymph node fine-needle aspiration cytology (LN-FNAC) is an effective and inexpensive screening method. It can prevent unnecessary invasive surgery and excisional biopsy, especially in benign cases. Unfortunately, the lack of universally accepted terminology for reporting results has hindered its widespread support. The Sydney system proposal for lymph node cytopathology categorization and reporting introduced five diagnostic categories to address the lack of universally accepted terminology for reporting results in lymphadenopathy. Our study analyzed 188 lymph node fine-needle cytology (FNC) samples from King Abdulaziz University Hospital, Saudi Arabia, examining clinical follow-up data, pathology records, patient information, and final diagnosis from January 2019 to December 2022. Most specimens were from axillary lymph nodes, with 99.5% tissue correlation. The Sydney system category classification identified 56.9% of cases as malignant, while 26.1% were benign. The final surgical specimen diagnosis revealed a higher percentage of malignant diagnoses, with the highest risk of malignancy (ROM) in malignant/category V. In conclusion, our study demonstrates that LN-FNAC offers high diagnostic accuracy for lymph node (LN) aspirates, with the Sydney approach potentially aiding risk stratification and achieving consistency in cytologic diagnosis, but further multi-centric research is needed.

Nodal Response and Survival After Neoadjuvant Endocrine Therapy in Hormone Receptor-Positive Breast Cancer: 20-Year Experience from a Single Institution.

Axillary response to neoadjuvant endocrine therapy (NET) for the treatment of hormone receptor-positive breast cancer (HR+ BC) is not well-described. This study was designed to characterize nodal response after NET. Patients receiving NET followed by curative intent surgery at a comprehensive cancer center from 1998 to 2022 in a prospectively collected registry were included. Patients with distant metastasis were excluded. Primary outcome was nodal pathologic complete response (pCR). Downstaging was defined as post-NET decrease in category. We included 123 patients; the majority were cT2 (n = 59) or cT3 (n = 35), and cN0 (n = 81). Median age was 70.0 years (interquartile range 62.1-76.0). Forty-two patients (34.1%) were clinically node-positive. After NET, 73 (59.8%) underwent breast-conserving surgery. All patients underwent sentinel lymph node biopsy, and 12 (9.8%) underwent completion axillary lymph node dissection. In-breast downstaging was achieved in 51 (41.5%) patients, 1 (0.8%) had breast pCR, and 14 (11.4%) had breast upstaging. Axillary downstaging was achieved in 10 (23.8%), 6 patients (14.3%) had nodal pCR, and 14 (33.3%) had axillary upstaging. At 10-year follow-up, local recurrence was 1% and distant recurrence was 14%, while disease-free survival was 82%. After adjusting for demographic and clinical factors, age was the only characteristic associated with mortality (hazard ratio 1.07, 95% confidence interval 1.01-1.13). In HR+ BC treated with NET, long-term disease-free survival is good, although nodal pCR is uncommon for cN+ patients. Future studies are needed to elucidate optimal neoadjuvant systemic therapy and to delineate oncologically safe strategies to deescalate axillary management for residual microscopic disease.

Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer

To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enrolled and categorized into low (< 2 lymph nodes involved (LNs+)) and high (≥ 2 LNs+) burden groups based on surgical pathology. PE was evaluated on T2WI, and intra- and peri-tumoral radiomic features were extracted from MRI-visible tumors in DCE-MRI. Deep learning models were developed for LN burden prediction in the training cohort and validated in an independent cohort. The incremental value of PE was evaluated through receiver operating characteristic (ROC) analysis, confirming the improvement in the area under the curve (AUC) using the Delong test. This was complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. The deep learning combined model, incorporating PE with selected radiomic features, demonstrated significantly higher AUC values compared to the MRI model and the DLR model in the training cohort (n = 177) (AUC: 0.953 vs. 0.849 and 0.867, p < 0.05) and the validation cohort (n = 111) (AUC: 0.963 vs. 0.883 and 0.882, p < 0.05). The complementary analysis demonstrated that PE significantly enhances the prediction performance of the DLR model (Categorical NRI: 0.551, p < 0.001; IDI = 0.343, p < 0.001). These findings were confirmed in the validation cohort (Categorical NRI: 0.539, p < 0.001; IDI = 0.387, p < 0.001). PE improved preoperative ALNM burden prediction of DLR model, facilitating personalized axillary management in breast cancer patients.