Individualized prediction of non-sentinel lymph node metastasis in Chinese breast cancer patients with ≥ 3 positive sentinel lymph nodes based on machine-learning algorithms.

BACKGROUND: Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sentinel lymph nodes (SLNs). However, ALND can lead to significant postoperative complications without always providing additional clinical benefits. This study aims to develop machine-learning (ML) models to predict non-sentinel lymph node (non-SLN) metastasis in Chinese BC patients with three or more positive SLNs, potentially allowing the omission of ALND. METHODS: Data from 2217 BC patients who underwent SLN biopsy at Shantou University Medical College were analyzed, with 634 having positive SLNs. Patients were categorized into those with ≤ 2 positive SLNs and those with ≥ 3 positive SLNs. We applied nine ML algorithms to predict non-SLN metastasis. Model performance was evaluated using ROC curves, precision-recall curves, and calibration curves. Decision Curve Analysis (DCA) assessed the clinical utility of the models. RESULTS: The RF model showed superior predictive performance, achieving an AUC of 0.987 in the training set and 0.828 in the validation set. Key predictive features included size of positive SLNs, tumor size, number of SLNs, and ER status. In external validation, the RF model achieved an AUC of 0.870, demonstrating robust predictive capabilities. CONCLUSION: The developed RF model accurately predicts non-SLN metastasis in BC patients with ≥ 3 positive SLNs, suggesting that ALND might be avoided in selected patients by applying additional axillary radiotherapy. This approach could reduce the incidence of postoperative complications and improve patient quality of life. Further validation in prospective clinical trials is warranted.

A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer.

BACKGROUND: It has been reported that patients with early breast cancer with 1-2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection. PURPOSE: To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer. MATERIAL AND METHODS: This retrospective study included 144 patients with 1-2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning-based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model. RESULTS: Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age. CONCLUSION: The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.

Association between postoperative pathological results and non-sentinel nodal metastasis in breast cancer patients with sentinel lymph node-positive breast cancer.

OBJECTIVE: For patients with 1-2 positive sentinel lymph nodes (SLN) identified by biopsy, the necessity of axillary lymph node dissection (ALND) remains a matter of debate. The primary aim of this study was to investigate the association between postoperative pathological factors and non-sentinel lymph node (NSLN) metastases in Chinese patients diagnosed with sentinel node-positive breast cancer. METHODS: This research involved a total of 280 individuals with SLN-positive breast cancer. The relationship between postoperative pathological variables and non-sentinel lymph node metastases was scrutinized using univariate, multivariate, and stratified analysis. RESULTS: Among the 280 patients with a complete count of SLN positives, 126 (45.0%) exhibited NSLN metastasis. Within this group, 45 cases (35.71%) had 1 SLN positive, while 81 cases (64.29%) demonstrated more than 1 SLN positive. Multivariate logistic regression analysis revealed that HER2 expression status (OR 2.25, 95% CI 1.10-4.60, P = 0.0269), LVI (OR 6.08, 95% CI 3.31-11.14, P < 0.0001), and the number of positive SLNs (OR 4.17, 95% CI 2.35-7.42, P < 0.0001) were positively correlated with NSLNM. CONCLUSION: In our investigation, the risk variables for NSLN metastasis included LVI, HER2 expression, and the quantity of positive sentinel lymph nodes. However, further validation is imperative, including this institution, distinct institutions, and diverse patient populations.

Correlation between sentinel lymph node biopsy and non-sentinel lymph node metastasis in patients with cN0 breast carcinoma: comparison of invasive ductal carcinoma and invasive lobular carcinoma.

BACKGROUND: Some studies have suggested that axillary lymph node dissection (ALND) can be avoided in women with cN0 breast cancer with 1-2 positive sentinel nodes (SLNs). However, these studies included only a few patients with invasive lobular carcinoma (ILC), so the validity of omitting ALDN in these patients remains controversial. This study compared the frequency of non-sentinel lymph nodes (non-SLNs) metastases in ILC and invasive ductal carcinoma (IDC). MATERIALS METHODS: Data relating to a total of 2583 patients with infiltrating breast carcinoma operated at our institution between 2012 and 2023 were retrospectively analyzed: 2242 (86.8%) with IDC and 341 (13.2%) with ILC. We compared the incidence of metastasis to SLNs and non-SLNs between the ILC and IDC cohorts and examined factors that influenced non-SLNs metastasis. RESULTS: SLN biopsies were performed in 315 patients with ILC and 2018 patients with IDC. Metastases to the SLNs were found in 78/315 (24.8%) patients with ILC and in 460 (22.8%) patients with IDC (p = 0.31). The incidence of metastases to non-SLNs was significantly higher (p = 0.02) in ILC (52/78-66.7%) compared to IDC (207/460 - 45%). Multivariate analysis showed that ILC was the most influential predictive factor in predicting the presence of metastasis to non-SLNs. CONCLUSIONS: ILC cases have more non-SLNs metastases than IDC cases in SLN-positive patients. The ILC is essential for predicting non-SLN positivity in macro-metastases in the SLN. The option of omitting ALND in patients with ILC with 1-2 positive SLNs still requires further investigation.

Risk factors of non-sentinel lymph node metastasis in breast cancer with 1-2 sentinel lymph node macrometastases underwent total mastectomy: a case-control study.

BACKGROUND: The randomized trials which include ACOSOG Z0011 and IBCSG 23-01 had found that the survival rates were not different in patients with cT1/2N0 and 1-2 sentinel lymph node (SLN)-positive, macro/micrometastases who underwent breast-conserving therapy, and micrometastases who underwent total mastectomy (TM), when axillary lymph node dissection (ALND) was omitted. However, for patients with cT1/2N0 and 1-2 SLN macrometastases who underwent TM; there was still insufficient evidence from clinical studies to support whether ALND can be exempted. This study aimed to investigate the risk factors of non-sentinel lymph node (nSLN) metastasis in breast cancer patients with 1-2 SLN macrometastases undergoing TM. METHODS: The clinicopathological data of 1491 breast cancer patients who underwent TM and SLNB from January 2017 to February 2022 were retrospectively analyzed. Univariate and multivariate analyses were performed to analyze the risk factors for nSLN metastasis. RESULTS: A total of 273 patients with 1-2 SLN macrometastases who underwent TM were enrolled. Postoperative pathological data showed that 35.2% patients had nSLN metastasis. The results of multivariate analysis indicated that tumor size (TS) (P = 0.002; OR: 1.051; 95% CI: 1.019-1.084) and ratio of SLN macrometastases (P = 0.0001; OR: 12.597: 95% CI: 4.302-36.890) were the independent risk factors for nSLN metastasis in breast cancer patients with 1-2 SLN macrometastases that underwent TM. The ROC curve analysis suggested that when TS ≤22 mm and ratio of SLN macrometastases ≤0.33, the incidence of nSLN metastasis could be reduced to 17.1%. CONCLUSIONS: The breast cancer patients with cT1/2N0 stage, undergoing TM and 1-2 SLN macrometastases, when the TS ≤22 mm and macrometastatic SLN does not exceed 1/3 of the total number of detected SLN, the incidence of nSLN metastasis is significantly reduced, but whether ALND can be exempted needs further exploration.

Clinicopathologic models predicting non-sentinel lymph node metastasis in cutaneous melanoma patients: Are they useful for patients with a single positive sentinel node?

BACKGROUND AND OBJECTIVES: Of clinically node-negative (cN0) cutaneous melanoma patients with sentinel lymph node (SLN) metastasis, between 10% and 30% harbor additional metastases in non-sentinel lymph nodes (NSLNs). Approximately 80% of SLN-positive patients have a single positive SLN. METHODS: To assess whether state-of-the-art clinicopathologic models predicting NSLN metastasis had adequate performance, we studied a single-institution cohort of 143 patients with cN0 SLN-positive primary melanoma who underwent subsequent completion lymph node dissection. We used sensitivity (SE) and positive predictive value (PPV) to characterize the ability of the models to identify patients at high risk for NSLN disease. RESULTS: Across Stage III patients, all clinicopathologic models tested had comparable performances. The best performing model identified 52% of NSLN-positive patients (SE = 52%, PPV = 37%). However, for the single SLN-positive subgroup (78% of cohort), none of the models identified high-risk patients (SE > 20%, PPV > 20%) irrespective of the chosen probability threshold used to define the binary risk labels. Thus, we designed a new model to identify high-risk patients with a single positive SLN, which achieved a sensitivity of 49% (PPV = 26%). CONCLUSION: For the largest SLN-positive subgroup, those with a single positive SLN, current model performance is inadequate. New approaches are needed to better estimate nodal disease burden of these patients.

Pathological factors associated with non-sentinel lymph node metastasis in early stage cervical cancer.

OBJECTIVE: To analyze the predictive factors for non-sentinel lymph node (non-SLN) metastasis in early-stage cervical cancer. METHODS: We analyzed a series of 113 patients who underwent sentinel lymph node (SLN) mapping for cervical cancer. The SLNs were examined by immunohistochemistry (IHC) when the hematoxylin-eosin stain was negative. RESULTS: The overall bilateral detection rate was 81.5%, with a median of two SLNs resected. The study ultimately included 92 patients with SLNs that were mapped who had also undergone systematic pelvic lymph node dissection. Thirteen (14.1%) patients had positive SLNs, with a median of one positive SLN. Regarding the size of SLN metastasis, one (1.1%) had isolated tumor cells (ITC), seven (7.6%) had micrometastases, and five (5.4%) had macrometastases. Notably, 46.1% (6/13) had lymph node metastases detected only after IHC. Five (38.5%) cases had positive non-SLNs, with a median count of one positive lymph node. Parametrial invasion was the only risk factor for positive non-SLN (p = .045). Regarding the size of SLN metastasis, non-SLN involvement was present in the only case with ITC (1/1), 42.9% (3/7) of cases with micrometastases, and in 20% (1/5) with macrometastases. CONCLUSIONS: Our data suggest that parametrial invasion correlates with the risk of non-SLN metastasis in cervical cancer.

The potential risk of contralateral non-sentinel groin node metastasis in women with early primary vulvar cancer following unilateral sentinel node metastasis: a single center evaluation in University Hospital of Düsseldorf.

BACKGROUND: Since the introduction of sentinel node biopsy (SLNB) in unifocal vulvar cancer (diameter of < 4 cm) and unsuspicious groin lymph nodes, the morbidity rate of patients has significantly decreased globally. In contrast to SLNB, bilateral inguinofemoral lymphadenectomy (IFL) has been associated with increased risk of common morbidities. Current guidelines (NCCN, ESGO, RCOG, and German) recommend that in cases of unilaterally positive sentinel lymph node (SLN), bilateral IFL should be performed. However, two recent publications by Woelber et al. and Nica et al. contradict the current guideline, since a significant rate of positive non sentinel lymph nodes in IFL contralaterally was not observed [Woelber et al. 0% (p = 0/28) and Nica et al. 5.3% (p = 1/19)]. METHODS: A retrospective single-center analysis conducted in the University Hospital of Dusseldorf, evaluating vulvar cancer patients treated with SLNB from 2002 to 2018. RESULTS: 22.2% of women (n = 4/18) were found to have contralateral IFL groin metastasis after an initial diagnosis of unilateral SLN metastasis. The depth of tumor infiltrating cells correlated significantly and positively with the rate of incidence of groin metastasis (p = 0.0038). CONCLUSION: Current guideline for bilateral IFL should remain as the standard management. Therefore, this depth may be taken into account as an indication for bilateral IFL. The management of VC and SLNB should be performed in a high volume center with an experienced team in marking SLN and performing the adequate surgical procedure. Well conducted counseling of the patients outlining advantages but also potential oncological risks of this technique especially concerning rate of groin recurrence is critical.

Axillary lymph node and non-sentinel lymph node metastasis among the ACOSOG Z0011 eligible breast cancer patients with invasive ductal, invasive lobular, or other histological special types: a multi-institutional retrospective analysis.

PURPOSE: Given the histological special types (HST) of breast carcinoma accounted for minority of the Z0011 study population, this study aimed to assess the rates of axillary lymph node (ALN) involvement and non-sentinel lymph node (SLN) metastasis in patients with invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), or other HST. METHODS: Patients with cT1-2N0M0 breast cancer treated between 2009 and 2018 were retrospectively included from a multi-institutional database. Rates of nodal involvement were analyzed among different histological subgroups. The impact of ALN dissection (ALND) on adjuvant treatment decisions and prognosis were also analyzed among patients with 1-2 + SLNs. RESULTS: A total of 8294 patients were included: 6854 (82.6%), 257 (3.1%), and 1183 (14.3%) patients with IDC, ILC, and other HST, respectively. IDC patients had a significantly higher rate of ALN metastasis compared with ILC or other HST (31.9% vs. 22.6% vs. 16.4%, P < 0.001). However, in patients with 1-2 + SLNs, rates of non-SLN metastasis were similar among three groups (IDC: n = 182, 28.6% vs. ILC: n = 5, 31.2% vs. other HST: n = 29, 34.9%, P = 0.481). For patients with 1-2 + SLNs, rates of adjuvant chemotherapy and the estimated 3-year recurrence-free survival were similar between the SLN biopsy and ALND arms, regardless of the histological types. CONCLUSION: Among patients with 1-2 + SLNs, ILC or other HST had similar rates of non-SLN metastasis compared with IDC. Omission of ALND may not influence adjuvant chemotherapy usage or disease outcome regardless of histological types.

A nomogram to predict non-sentinel lymph node metastasis in patients with initial cN+ breast cancer that downstages to cN0 after neoadjuvant chemotherapy.

BACKGROUND AND OBJECTIVES: This study mainly explored the factors that influence non-sentinel lymph node (NSLN) metastasis in patients with breast cancer (BC) whose axillary lymph nodal status changed from clinically node positive (cN+) to clinically node negative (cN0) after neoadjuvant chemotherapy (NAC). METHODS: We retrospectively analyzed the clinicopathological factors affecting NSLN metastasis in a total of 179 patients with cN+ BC downstaged to cN0 (120 in the training set and 59 in the validation set) who underwent both sentinel lymph node (SLN) biopsy and axillary lymph node dissection following NAC. RESULTS: Among 179 patients enrolled, the overall NSLN metastatic rate was 24.0% (95% confidence interval [CI]: 17.7%-30.3%). In multivariate logistic regression analysis, the number of positive SLNs achieving a pathological complete remission of the breast and clinical node staging was independent predictors of NSLN metastasis. A nomogram was established based on these factors and displayed a good discriminatory capability, with an area under the curve of 0.919 (95% CI: 0.865-0.973) for the training set and 0.900 (95% CI: 0.812-0.988) for the validation set and its clinical utility was confirmed by the decision curve analysis. CONCLUSIONS: The nomogram established showed the ability to predict NSLN metastases in patients with initial cN+ BC that downstaged to cN0 after NAC.

[A nomogram to predict non-sentinel lymph node metastasis for breast cancer patients with positive axillary sentinel lymph node].

Objective: To identify the risk factors of non-sentinel lymph node (nSLN) metastasis in breast cancer patients with 1~2 positive axillary sentinel lymph node (SLN) and construct an accurate prediction model. Methods: Retrospective chart review was performed in 917 breast cancer patients who underwent surgery treatment between 2002 and 2017 and pathologically confirmed 1-2 positive SLNs. According to the date of surgery, patients were divided into training group (497 cases) and validation group (420 cases). A nomogram was built to predict nSLN metastasis and the accuracy of the model was validated. Results: Among the 917 patients, 251 (27.4%) had nSLN metastasis. Univariate analysis showed tumor grade, lymphovascular invasion (LVI), extra-capsular extension (ECE), the number of positive and negative SLN and macro-metastasis of SLN were associated with nSLN metastasis (all P<0.05). Multivariate Logistic regression analysis showed the numbers of positive SLN, negative SLN and macro-metastasis of SLN were independent predictors of nSLN metastasis (all P<0.05). A nomogram was constructed based on the 6 factors. The area under the receiver operating characteristic curve was 0.718 for the training group and 0.742 for the validation group. Conclusion: We have developed a nomogram that uses 6 risk factors commonly available to accurately estimate the likelihood of nSLN metastasis for individual patient, which might be helpful for radiation oncologists to make a decision on regional nodal irradiation.

One Step Nucleic Acid Amplification (OSNA) positive micrometastases and additional histopathological NSLN metastases: Results from a single institution over 53 months.

INTRODUCTION: The role of sentinel lymph node micrometastases on histopathological analysis is controversial in axillary staging and management in clinically node negative breast cancer. Long-term studies addressing the clinical relevance of occult breast cancer in sentinel lymph nodes based on molecular analysis are lacking. One Step Nucleic Acid Amplification (OSNA), a highly sensitive assay of cytokeratin 19 mRNA, is used intra-operatively for the detection of lymph node macro- and micrometastases in breast cancer. AIM: The aim of this study is to review the rate of micrometastases and further histopathological NSLN metastases, in our unit following the introduction of OSNA in Guildford. METHODS: Data was collected prospectively from the period of introduction 01/12/2008 to 31/05/2013. All patients eligible for sentinel lymph node biopsy were offered OSNA and operations were performed by the consultant breast surgeons. Presence or absence of micro-metastases depends on the agreed cut-off point on the amplification curve. On detection of micrometastases (+) and positive but inhibited (i+) metastases, a level 1 axillary clearance (ANC) was performed and for a macrometastasis (++), a level 3 ANC was carried out. RESULTS: 66% of the patients had negative SLN (n = 672) and 34% (n = 336) had positive sentinel lymph nodes who had further axillary surgery. Of these, 45% (n = 152/336) had macrometastases, 40% (n = 136/336) had micrometastases and 15% (48/336) had positive but inhibited results. There was no difference in the patient demographics and tumour characteristics in the various positive SLN groups. In patients with micrometastases, 15% (20/136) had further positive NLSNs and a further 6% (8/136) had >4 overall positive nodes (SLN + NSLN) thus requiring adjuvant supraclavicular/chest wall radiotherapy (p < 0.05). 25% of node positive patients had further NLSN metastases (85/336) and in these patients, the ratio of positive SLN/harvested SLN (+SLN/SLN) is constant at 1:1. This shows the likelihood of further positive NSLNs if all the harvested lymph nodes are positive. This linear trend is present in both micro-and macrometastases, thus correlating with the size and number of NSLN metastases. CONCLUSION: Our study reflects the tumour burden of NSLNs based on the molecular analysis of the SLN. OSNA has the potential to accurately identify axillary micrometastases. Micro-metastases are important as some of the patients with micrometastases had overall four positive nodes [SLN + NSLN] (criteria for radiotherapy in the absence of other adverse clinicopathological features). Also, our study highlights certain factors that predict the NSLN metastases, pending validation by further prospective long-term data. This will allow accurate calculation of the axillary tumour burden, particularly in patients with micro-metastases.

Predictors of non-sentinel lymph node (non-SLN) metastasis in patients with sentinel lymph node (SLN) metastasis in endometrial cancer.

OBJECTIVES: The aim of this study was to determine the risk of metastasis in the remaining non-SLNs when the SLN is positive and to identify the factors that can predict this risk. METHODS: We reviewed all patients who underwent primary surgery for endometrial carcinoma with lymphadenectomy and SLN mapping (November 2010-November 2013) in our center. SLNs were ultra-staged on final pathology. RESULTS: A total of 268 patients were included. Overall, 43/268 patients (16%) were found to have SLN metastasis: macro-metastasis in 24 patients, micro-metastasis in 7 and ITC in 12. Non-SLN metastases were found in 15 of the 43 patients (34.8%) with positive SLN. Size of the SLN metastasis was the only factor associated with an increased likelihood of non-SLN metastasis (p=0.005). When the size of the SLN metastasis was ≤2mm, the risk of having another positive lymph node was only 5%, conversely, when the size of the SLN metastasis was >2mm, the risk of having another positive lymph node was 60.8% (p<0.0001). Histologic type, grade, depth of myometrial invasion, LVSI, cervical stromal invasion and CA-125 were not predictive. CONCLUSION: When the SLN is positive, the risk of metastasis in the remaining non-SLNs was 34.8%. Size of the metastasis within the SLN was the only factor that could predict the risk of non-SLN metastasis; 2mm seems to be the cutoff size below which the risk of non-SLN metastasis is low.

p53 expression in breast cancer predicts tumors with low probability of non-sentinel nodes infiltration.

AIM: Several predictive tools of non-sentinel lymph nodes neoplastic involvement when a positive sentinel lymph node is found have been described. However, molecular factors have been rarely evaluated to build these tools. The aim of this study was to establish which factors predicted non-sentinel lymph nodes infiltration in our setting, including some molecular factors. MATERIAL AND METHODS: We carried out a retrospective review of 161 patients with breast cancer and a positive sentinel lymph node who had undergone axillary lymph node dissection, none of whom had received neoadjuvant treatment. Features evaluated as predictive factors for non-sentinel node positivity were: menopausal status, tumor size, histological subtype, histological grade, lymphovascular invasion, extracapsular invasion, Ki67 index, hormonal receptors, CerbB2 and p53 expression, size of sentinel lymph node metastases and number of sentinel lymph nodes affected. RESULTS: Tumor size (P = 0.001), size of sentinel lymph node metastases (P = 0.001), lobular invasive carcinoma (P = 0.05) and lymphovascular invasion (P = 0.006) were significantly associated with non-sentinel lymph node positivity. Tumor p53 positive expression was strongly associated with non-sentinel lymph node negativity (P = 0.000). In multivariate analysis, all these factors but tumor size maintained their significance. The discrimination power of the model calculated by the area under the receiver-operator curve was 0.811 (95% confidence interval, 0.741-0.880). CONCLUSION: p53 expression in breast cancer was highly predictive of non-sentinel lymph node negativity in our study. New studies should evaluate if it would be useful to add p53 expression to other existing predictive tools.

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs). MATERIALS AND METHODS: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction. CONCLUSION: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.

Risk Factors for Non-sentinel Lymph Node Metastasis in HR+/HER2- Breast Cancer With cN0.

BACKGROUND/AIM: This study aimed to identify the risk factors associated with non-sentinel lymph node (non-SLN) metastasis in case of hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer with cN0 on preoperative exam, where the sentinel lymph node (SLN) is positive. PATIENTS AND METHODS: We conducted a retrospective review of medical records from the Chonnam National University Hwasun Hospital, spanning from January 2013 to January 2020, focusing on patients with HR+, HER2- breast cancer. Specifically, we collected the clinical and pathological data for those patients who underwent axillary lymph node dissection (ALND) due to positive SLN. RESULTS: Among the 166 patients who underwent ALND after positive SLNs, median patient age was 52 years. Univariate analyses demonstrated a significant association between non-SLN metastasis and the number of positive SLNs (p=0.039), SLN positive ratio (p<0.001), and primary tumor size (p=0.018). Multivariate analysis revealed that an SLN ratio >0.55 (p=0.004, HR=3.007, 95% CI=1.427-6.335) was independently associated with non-SLN metastasis. However, neither the number of positive SLN nor primary tumor size showed associations with non-SLN metastases. CONCLUSION: In patients with HR+, HER2- breast cancer who are cN0, completion of ALND should be considered when the positive SLN ratio is ≥0.55. This approach aims to provide the opportunity for survival benefit through additional adjuvant therapy or to contribute to de-escalation of unnecessary surgery.

Development and validation of an ultrasonography and clinicopathological features-based nomogram for non-sentinel lymph node metastasis.

Background: With the development of precise treatment for breast cancer, the current trend of clinical treatment aims to limit axillary surgery as much as possible. At present, there is an unmet need to predict the probability of patients with a low risk of non-sentinel lymph node (SLN) metastasis and determine whether the omission of axillary lymph node dissection (ALND) is appropriate. Methods: We retrospectively analyzed the data of patients with breast cancer who underwent sentinel lymph node biopsy (SLNB) and ALND. The patients were randomly assigned to training and validation sets. The associations between non-SLN metastasis (NSLNM) and ultrasonography and clinicopathological characteristics were assessed by multivariate logistic regression. Then, a nomogram model was constructed and validated using the calibration curve and the receiver operating characteristic curve. Results: Vascular infiltration, positive SLN number, negative SLN number, human epidermal growth factor receptor 2 (HER2) status, and lymph node shape were identified as independent predictive factors for positive NSLNM. The areas under the curve of the nomogram model to predict NSLNM were 0.793 and 0.780 in the training and validation sets, respectively, and P=0.161 and P=0.768 in the Hosmer-Lemeshow goodness of fit test, respectively. Conclusions: A nomogram model based on ultrasonography and clinicopathological features predicting NSLNM was established in our study, which is helpful for accurately assessing the risk of NSLNM in invasive breast cancer and providing evidence for individual surgical procedures involving axillary lymph nodes.

A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients.

Background: Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals about 40-75% of patients without non-sentinel lymph node (NSLN) metastasis after ALND. Unnecessary ALND increases the risk of complications and detracts from quality of life. In this study, we expect to develop a nomogram based on genotypic and clinicopathologic factors to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients. Methods: This retrospective study collected data from 1,879 women breast cancer patients enrolled from multiple centers. Genotypic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, therapy and prognosis. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the mutational stability. The normalized polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Recursive feature elimination (RFE) based on linear discriminant analysis (LDA) was adopted to select the most useful predictive features, and RFE based on support vector machine (SVM) was used to reduce the number of SNPs. Multivariable logistic regression models (i.e., nomogram) were built for predicting NSLN metastasis. The predictive abilities of three types of model (based on only clinicopathologic information, the integrated clinicopathologic and all SNPs information, and integrated clinicopathologic and significant SNPs information) were compared. Internal and external validations were performed and the area under ROC curves (AUCs) as well as a series of evaluation indicators were assessed. Results: 229 patients underwent SLNB followed by ALND and without any neo-adjuvant therapy, 79 among them (34%) had a positive axillary NSLN metastasis. The LDA-RFE identified the characteristics including lymphovascular invasion, number of positive SLNs, number of negative SLNs and two SNP clusters as significant predictors of NSLN metastasis. Furthermore, the SVM-RFE selected 29 significant SNPs in the prediction of NSLN metastasis. In internal validation, the median AUCs of the clinical and all SNPs combining model, the clinical and 29 significant SNPs combining model, and the clinical model were 0.837, 0.795 and 0.708 respectively. Meanwhile, in external validation, the AUCs of the three models were 0.817, 0.815 and 0.745 respectively. Conclusion: We present a new nomogram by combining genotypic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLN metastasis in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations.

Non-sentinel lymph node metastases risk factors in patients with breast cancer with one or two sentinel lymph node macro-metastases.

Approximately 59 % of patients with breast cancer with one or two sentinel lymph nodes (1-2 SLN) macrometastases do not benefit from axillary lymph node dissection (ALND), which may also incur morbidities. It is necessary to evaluate the association between various clinicopathological characteristics and non-sentinel lymph node metastases (non-SLNM) in patients with breast cancer with 1-2 SLN macrometastases, and determine whether they 1-2 should avoid ALND. Eight electronic literature databases (PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Chinese Scientific Journal, Wanfang, and Chinese Biomedical Literature) were searched from their inception to June 30, 2023, and two reviewers independently extracted the data and assessed the risk of bias. Association strength was summarized using odds ratios (OR) and 95 % confidence intervals (CI). Heterogeneity was accounted for using a subgroup analysis. Publication bias was evaluated using funnel plots and Egger's test. There were 25 studies with 8021 participants, and 27 potential risk factors were evaluated. The risk factors for non-SLNM in patients with 1-2 SLN macrometastatic breast cancer include the following: factors of primary tumor: multifocality (OR (95 % CI (2.63 (1.96, 3.54))), tumor size ≥ T2 (2.64 (2.22, 3.14)), tumor localization (upper outer quad) (2.06 (1.23, 3.43)), histopathological grade (G3) (2.45 (1.70, 3.52)), vascular invasion (VI) (2.60 (1.35, 4.98)), lymphovascular invasion (LVI) (2.87 (1.80, 4.56)), perineural invasion (PNI) (3.16 (1.18,8.43)). Factors of lymph nodes: method of SLNs detected (blue dye) (3.85 (1.54, 9.60)), SLN metastasis ratio ≥0.5 (2.79 (2.24, 3.48)), two positive SLNs (3.55, (2.08, 6.07)), zero negative SLN (3.72 (CI 2.50, 4.29)), extranodal extension (ENE) (4.69 (2.16, 10.18)). Molecular typing: Her-2 positive (2.08 (1.26, 3.43)), Her-2 over-expressing subtype (1.83 (1.22, 2.73)). Factors of examination/inspection: axillary lymph nodes (ALNs) positive on imaging (3.18 (1.68, 6.00)), cancer antigen 15-3 (CA15-3) (4.01 (2.33,6.89)), carcinoembryonic antigen (CEA) (2.13 (1.32-3.43)). This review identified the risk factors for non-SLNM in patients with 1-2 SLN macrometastatic breast cancer. However, additional studies are needed to confirm the above findings owing to the limited number and types of studies included.