Can Neutrophil to Lymphocyte Ratio Be a Predictor Tool for The Non-Sentinel Lymph Node Metastasis in Breast Cancer?

Predictors of non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph node (Pilot study)

Background: Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) in early breast cancer patients as the first line surgical approach to axillary nodes. Further dissection is performed only when SLN is involved by tumor cells. However, in a significant proportion of patients, non-sentinel nodes are still not involved and axillary dissection has no additional therapeutic benefits. Selective axillary clearance has been considered to prevent unnecessary dissection. The purpose of this study was to define predictors of non-sentinel lymph node involvement in patients with positive SLNB.Methods: Patients with early stage breast cancer and positive SLNB who underwent ALNDin a referral hospital in Tehran, Iran between2010 and 2012 were recruited into the study. Relations between different clinico-pathological characteristics and involvement of non-sentinel nodes were investigated.Results: From 139 patients who had positive SLNB and underwent ALND, only in 71 cases (51%) positive non-sentinel lymph nodes (NSLNs) were detected. In univariate analysis, there was no association between tumor size, lymphovascular invasion, ER, PR and HER-2 expression and NSLN metastasis. In contrast, presence of more than one SLN (P = 0.016) and a sentinel node ratio (SNR) more than 0.5 showed a significant association (P< 0.001). Only the latter remained as the significant predictor of NSLN involvement in mutltivariate analysis (P < 0.001, OR = 3.706).Conclusions: Based on our results, patients with a SNR more than 0.5 were more commonly diagnosed with NSLN metastasis. Thus, it is recommended that surgeons think twice before skipping ALND in this subgroup of patients.

A quantitative analysis of tumour characteristics in breast cancer patients with extranodal extension in non-sentinel nodes

Objective To investigate the related factors of axillary non-sentinel lymph node (NSLN) metastasis in breast cancer patients with positive sentinel lymph node (SLN). Methods Ninety-eihgt patients with breast cancer, who were SLN positive according to sentinel lymph node biopsy (SLNB) and underwent axillary lymph node dissection (ALND) successfully in Shanxi General Hospital from March 2017 to March 2018, were selected as research objects. Thirty-two patients were allocated into NSLN positive group, and 66 patients were allocated into NSLN negative group according to the axillary NSLN condition. The relationship between the clinical indexes and pathological indexes and the axillary NSLN metastasis of breast cancer was analyzed by univariate factor analysis and multivariate factor Logistic regression analysis. Results The number of SLN and number of NSLN positive in NSLN positive group were slightly higher than those in NSLN negative group, but the difference was not statistically significant (P>0.05); and number of SLN positive and number of NSLN in NSLN positive group were significantly higher than those in NSLN negative group (P 1, P<0.05). Conclusions High grade of histology, ER+ , PR+ , no amplificayion of HER-2, vascular embolism, 2 cases of SLN positive and Luminal B molecular type are independent risk factors of breast tumor NSLN metastasis. Key words: Breast cancer; Sentinel lymph node positive; Axillary non sentinel lymph node metastasis; Risk factors

BackgroundAt present, few studies have explored the significance of POU5F1 (also known as octamer-bingding factor, Oct-4 or Oct-3) expression in breast cancer tissues.MethodsA total of 121 patients were retrospectively selected between May 2010 and March 2013 to investigate the relationship between POU5F1/Oct-4 expression in breast cancer tissues and non-sentinel lymph node (non-SLN) metastases and to validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram. All patients had early-stage breast cancer, which was histologically confirmed by the Department of Surgical Oncology, The First Affiliated Hospital of China Medical University. Histological type and grade of tumors were determined from tissue samples by hematoxylin and eosin staining, while the presence of POU5F1/Oct-4 protein was determined by immunohistochemistry. POU5F1/Oct-4 expression levels in tissues obtained from patients with sentinel lymph node (SLN) and non-SLN metastasis and in tissues obtained from patients without lymph node metastases were compared.ResultsPOU5F1/Oct-4 expression levels in breast cancer tissues were significantly higher in both the SLN metastasis and non-SLN metastasis groups (P = 0.003 and P = 0.030, respectively). Furthermore, POU5F1/Oct-4 expression was found to be associated to both histological (P = 0.01) and molecular type (P = 0.03). Thus, our data once again confirms the validity of the MSKCC nomogram. The area under curve (AUC) was 0.919 (95 % CI: 0.869–0.969, P < 0.001). The probability of non-SLN metastasis generated from the MSKCC nomgram was significantly higher in the POU5F1/Oct-4 positive group than in the POU5F1/Oct-4 negative group. Both univariate and multivariate analysis revealed that Oct-4 expression levels were significantly associated with non-SLN metastases (P = 0.030 and P = 0.034, respectively).ConclusionsPOU5F1/Oct-4 expression levels are significantly associated with non-SLN metastases. Patients with higher probabilities of metastasis generated from the MSKCC nomogram may also have higher POU5F1/Oct-4 expression levels.

Molecular subtype of breast cancer is associated with sentinel lymph node status. We sought to establish a mathematical prediction model that included breast cancer molecular subtype for risk of positive non-sentinel lymph nodes in breast cancer patients with sentinel lymph node metastasis and further validate the model in a separate validation cohort. We reviewed the clinicopathologic data of breast cancer patients with sentinel lymph node metastasis who underwent axillary lymph node dissection between June 16, 2014 and November 16, 2017at our hospital. Sentinel lymph node biopsy was performed and patients with pathologically proven sentinel lymph node metastasis underwent axillary lymph node dissection. Independent risks for non-sentinel lymph node metastasis were assessed in a training cohort by multivariate analysis and incorporated into a mathematical prediction model. The model was further validated in a separate validation cohort, and a nomogram was developed and evaluated for diagnostic performance in predicting the risk of non-sentinel lymph node metastasis. Moreover, we assessed the performance of five different models in predicting non-sentinel lymph node metastasis in training cohort. Totally, 495 cases were eligible for the study, including 291 patients in the training cohort and 204 in the validation cohort. Non-sentinel lymph node metastasis was observed in 33.3% (97/291) patients in the training cohort. The AUC of MSKCC, Tenon, MDA, Ljubljana, and Louisville models in training cohort were 0.7613, 0.7142, 0.7076, 0.7483, and 0.671, respectively. Multivariate regression analysis indicated that tumor size (OR = 1.439; 95% CI 1.025-2.021; P = 0.036), sentinel lymph node macro-metastasis versus micro-metastasis (OR = 5.063; 95% CI 1.111-23.074; P = 0.036), the number of positive sentinel lymph nodes (OR = 2.583, 95% CI 1.714-3.892; P < 0.001), and the number of negative sentinel lymph nodes (OR = 0.686, 95% CI 0.575-0.817; P < 0.001) were independent statistically significant predictors of non-sentinel lymph node metastasis. Furthermore, luminal B (OR = 3.311, 95% CI 1.593-6.884; P = 0.001) and HER2 overexpression (OR = 4.308, 95% CI 1.097-16.912; P = 0.036) were independent and statistically significant predictor of non-sentinel lymph node metastasis versus luminal A. A regression model based on the results of multivariate analysis was established to predict the risk of non-sentinel lymph node metastasis, which had an AUC of 0.8188. The model was validated in the validation cohort and showed excellent diagnostic performance. The mathematical prediction model that incorporates five variables including breast cancer molecular subtype demonstrates excellent diagnostic performance in assessing the risk of non-sentinel lymph node metastasis in sentinel lymph node-positive patients. The prediction model could be of help surgeons in evaluating the risk of non-sentinel lymph node involvement for breast cancer patients; however, the model requires further validation in prospective studies.

Almost half of the breast cancer patients with positive sentinel lymph nodes have no additional disease in the remaining axillary lymph nodes. This group of patients do not benefit from complete axillary lymph node dissection. This study was designed to assess the clinicopathologic factors that predict non-sentinel lymph node metastasis in Iranian breast cancer patients with positive sentinel lymph nodes. The records of patients who underwent sentinel lymph node biopsy, between 2003 and 2012, were reviewed. Patients with at least one positive sentinel lymph node who underwent completion axillary lymph node dissection were enrolled in the present study. Demographic and clinicopathologic characteristics including age, primary tumor size, histological and nuclear grade, lymphovascular invasion, perineural invasion, extracapsular invasion, and number of harvested lymph nodes, were evaluated. The data of 167 patients were analyzed. A total of 92 (55.1%) had non-sentinel lymph node metastasis. Univariate analysis of data revealed that age, primary tumor size, histological grade, lymphovascular invasion, perineural invasion, extracapsular invasion, and the number of positive sentinel lymph nodes to the total number of harvested sentinel lymph nodes ratio, were associated with non-sentinel lymph node metastasis. After logistic regression analysis, age (OR=0.13; 95% CI, 0.02-0.8), primary tumor size (OR=7.7; 95% CI, 1.4-42.2), lymphovascular invasion (OR=19.4; 95% CI, 1.4- 268.6), extracapsular invasion (OR=13.3; 95% CI, 2.3-76), and the number of positive sentinel lymph nodes to the total number of harvested sentinel lymph nodes ratio (OR=20.2; 95% CI, 3.4-121.9), were significantly associated with non-sentinel lymph node metastasis. According to this study, age, primary tumor size, lymphovascular invasion, extracapsular invasion, and the ratio of positive sentinel lymph nodes to the total number of harvested sentinel lymph nodes, were found to be independent predictors of non-sentinel lymph node metastasis.

Introduction: Globally cervical cancer is the 4th most common cancer in females after breast, colorectal, and lung cancer. In patients with early-stage cervical cancer (stage IB-IIA) who have undergone radical hysterectomy, postoperative histopathologic features along with clinical staging predict prognosis and are used for planning further treatment. Neutrophil-lymphocyte ratio (NLR) is a nonspecific marker to predict prognosis. This study aimed to evaluate the predictive values of preoperative values of NLR for high-risk surgical-pathological features identification in patients with early-stage cervical cancer undergoing primary radical hysterectomy with pelvic lymphadenectomy. Methods: This cross-sectional study was carried out in the Department of Gynaecological Oncology at Bangabandhu Sheikh Mujib Medical University (BSMMU) between July 2019 and June 2020 to evaluate the relationship of preoperative NLR with surgical-pathological features of cervical cancer stage IB-IIA who underwent primary radical hysterectomy and pelvic lymph node dissection. A total of fifty cervical cancer patients were enrolled in this study. Their clinical and histopathological findings and complete blood count reports were collected and analyzed. Result: The ROC curve revealed NLR was 2.670 (sensitivity 63%; specificity 91.3%; Areas under the curves 0.801). Surgical pathological analysis showed that NLR was linked to lymphovascular space invasion. Sixty-three percent of patients having NLR &gt;2.670 were 17.85 times more likely to have LVSI. Conclusion: The study findings conclude that high pretreatment NLR was linked to lymphovascular space invasion. Thus NLR can be used preoperatively to predict the prognosis of cancer cervix and triage the patients into monovalent treatment- either surgery or chemoradiation.

The aim of this retrospective study was to identify biological features of primary breast cancer from which to predict the presence of further axillary involvement in patients bearing micrometastases in the sentinel lymph node (SLN). From a starting group of 690 patients, we isolated patients with micrometastases in the SLN. Those patients were classified according to the presence/absence of further metastases in nonsentinel lymph nodes (NSLNs). We examined primary tumor features to identify any relevant difference. Analysis of primary tumors evaluated histology, tumor size, lymphovascular invasion, mitotic index (Mib-1), estrogen and progesterone receptor status (ER/PR status), C-erb B-2 (HER-2/neu) expression and amplification, and p53 expression. Chi square analysis for statistical significance was applied. Of the original 690 patients, 296 showed some kind of metastases in the SLN; 238 patients had gross metastases in the SLN. After axillary lymph node dissection (ALND), 102 patients (43%) had NSLNs with metastases, and 136 (57%) had negative axillary non-sentinel nodes. Another 58 patients harbored solitary micrometastases in the SLN. After ALND, 8 (14%) patients had further NSLN involvement, and 50 (86%) had negative axillary nodes. Analysis of the primary breast lesion in patients with micrometastatic SLN and metastatic NSLNs revealed the presence of lymphovascular invasion, Mib-1 index > 10%, and tumor size > 2 cm. Patients without lymphovascular invasion, Mib-1 < 10% and T size < 2 cm could avoid further ALND.

Objective To discuss the correlation between molecular subtypes of early stage breast cancer patients with positive sentinel lymph nodes and the metastasis of non-sentinel lymph nodes, and find out the factors predicting the metastasis of non-sentinel lymph nodes. Methods The clinical data of 124 female breast cancer patients with sentinel lymph node positive were retrospectively analyzed, and the patients were treated with axillary lymph node dissection. And the correlations were analyzed by single factor analysis and multiple factor Logistic regression analysis. Results Among the 124 patients, non-sentinel lymph node metastasis was in 45 cases (36.3%), and only sentinel lymph node positive was in 79 cases (63.7%). The single factor analysis result showed that the age ≤ 35 years, number of sentinel lymph node positive ≥ 2, macrometastasis of sentinel lymph node had correlation with the metastasis of non-sentinel lymph node (P 0.05). The multiple factor Logistic regression analysis result showed that the number of sentinel lymph node positive and circumstance of sentinel lymph node positive lesions were the independent risk factors of the non-sentinel lymph node metastasis in patients with sentinel lymph node positive (OR= 4.589 and 2.948; P<0.01 or<0.05). Conclusions The circumstance of sentinel lymph node positive lesions and number of sentinel lymph node positive are the independent risk factors of the non-sentinel lymph node metastasis, but the molecular type is not correlated with the metastasis of non-sentinel lymph node. Predicting non-sentinel lymph node metastasis should be combined with clinical and pathological factors. Key words: Breast neoplasms; Lymphatic metastasis; Early diagnosis; Molecular typing; Retrospective studies

Axillary lymph node dissection (ALND) can be avoided in breast cancer patients with low-volume disease in the sentinel lymph nodes (SLNs) according to Z0011 trial. We believe that nomograms developed for predicting non-sentinel lymph node (NSLN) metastases can guide the axillary treatment in patients who do not fully match the criteria of Z0011 study. We identified risk factors and evaluated the performance of three nomograms to predict NSLN status in patients with positive SLNs. Data from 526 breast cancer patients with positive SLNs who underwent ALND at two Australian hospitals from 2002 to 2015 were studied. Univariate and multivariate associations for NSLN metastasis were analysed. Predictive models evaluated were MD Anderson Cancer Centre (MDA), Helsinki University Hospital and Memorial Sloan Kettering Cancer Centre. Thirty-nine per cent of patients demonstrated NSLN metastasis. The multivariate analysis identified extranodal extension (OR 3.2, 95% CI 2.07-4.80), tumour size >2 cm (OR 2.5, 95% CI 1.66-3.89), macrometastasis (OR 1.9, 95% CI 1.09-3.47), positive SLN ratio >0.5 (OR 1.7, 95% CI 1.16-2.60) and lymphovascular invasion (OR 1.6, 95% CI 1.09-2.44) as independent predictors for NSLN metastasis. MDA nomogram showed the best discrimination (area under the curve of 0.74) and a 9% false negative rate for predicted probability of NSLN metastasis ≤10%. Our results suggest that presence of extranodal extension and tumour size >2 cm may influence the need of further axillary treatment. Conversely, ALND can be safety spared in low risk patients identified by MDA nomogram.

e13060 Background: The accuracy of the nomogram about non-sentinel lymph node metastasis (NSLNM ) in breast cancer patients is still controversial to avoid axillary dissection particularly sentinel lymph node biopsy was positive. The aim of this study was to evaluate the necessity of adding breast cancer subtypes to the NSLNM nomgram variables. Methods: Between 2009 and 2011, consecutive breast cancer patients without clinically axillary lymph node metastasis (n=140) who received sentinel lymph node biopsy at Higashiosaka General Hospital were studied retrospectively. Twenty-two patients were turned out that breast cancer already spread to the sentinel nodes and all of 22 patients received complete axillary lymph node dissection. Results: Twelve patients had only sentinel lymph node metastases(Group S), 10 patients had non-SLN metastases (Group A). Patient characteristics and average probability of spread to additional lymph node developed by Memorial Sloan-Kettering Cancer Center (MSKCC) Nomogram were almost the same results in both groups. However, subtypes of Group S consisted of 8 HER2 positive , 2 triple negative, and 2 luminal A cases, subtypes of Group A consisted of 4 luminal A and 6 luminal B cases. Conclusions: Our data suggested that luminal type breast cancer tends to spread to non-sentinel lymph node metastasis and adding HER2, Ki-67, and intrinsic biological subtypes may improve predictivity of MSKCC nomogram.

Objective: To investigate the relationship between systemic inflammatory markers such as neutrophil/lymphocyte ratio (NLR) and C-reactive protein/albumin ratio (CAR), and lymph node metastasis in patients with cN0 gastric cancer. To establish a nomogram model to predict the risk of lymph node metastasis in patients with cN0 gastric cancer. Methods: The preoperative systemic inflammatory markers and clinical data of 134 patients with cN0 gastric cancer were retrospectively analyzed, and these markers of patients with negative (pN0) or positive (pN+ ) lymph node metastasis in postoperative pathological diagnosis were compared. The receiver operating characteristic (ROC) curve was used to evaluate the predictive effect of preoperative systemic inflammatory markers on lymph node metastasis. The influencing factors for lymph node metastasis were assessed by univariate analysis and multivariate logistic regression analysis. A nomogram subsequently established by R software was validated by Bootstrap resampling as internal validation. Results: Compared with pN0 group, NE (P=0.022), CRP (P<0.001), NLR (P<0.001), PLR (P=0.003) and CAR (P<0.001) were higher, LY (P=0.003) and Alb (P=0.042) were lower in pN+ group. ROC curve analysis showed that the area under the curve (AUC) of postoperative pathological lymph node metastasis in patients with cN0 gastric cancer diagnosed by NLR, PLR and CAR were 0.687, 0.651 and 0.694, respectively, and the best cutoff values were 2.12, 113.59 and 0.02, respectively. The corresponding sensitivity and specificity were 62.9% and 72.2%, 77.4% and 48.6%, 74.2% and 58.3%, respectively. Univariate analysis showed that tumor size, depth of invasion, NLR, PLR and CAR were associated with lymph node metastasis in cN0 gastric cancer patients (all P<0.05). Multivariate analysis showed that depth of invasion, NLR and CAR were independent influencing factors of lymph node metastasis in patients with cN0 gastric cancer. OR were 8.084, 3.540 and 3.092, respectively (all P<0.05). The C-index of the nomogram model was 0.847 (95% CI: 0.782-0.915). The predicting calibration curve was properly fit with the ideal curve in calibration chart. Conclusion: Combination of NLR and CAR to establish a nomogram model has a good consistency and can accurately predict the risk of lymph node metastasis in patients with cN0 gastric cancer.

AimTo identify important clinicopathological parameters that are most helpful in predicting additional non-sentinel lymph node (SLN) metastasis among patients with a positive SLN biopsy in the Singapore breast cancer population.MethodsA...

Introduction: Up to 60% of patients with one or more positive sentinel lymph nodes (SLNs) have no additional nodal involvement and do not benefit from completion axillary lymph node dissection (ALND). Several predictive nomograms have been developed to predict non-SLN status in patients with breast cancer and SLN metastases. The purpose of our retrospective investigation was to compare some of these nomograms and we chose 3, all of which incorporate the size of the metastases in the SLNs among their variables. Methods: Three different predictive nomograms (MD Anderson, Helsinki and Cambridge) were tested on a cohort of 80 SLN-positive patients who subsequently underwent completion ALND. The nomograms utilise 3 (Cambridge), 7 (MD Anderson) and 9 (Helsinki) variables. The predictive accuracy of the nomograms for our cohort was evaluated by assessing receiver operating characteristic (ROC) curves and areas under curves (AUC) for each nomogram. Descriptive characteristics of the patient populationTotal number of patients80 Tumor size (T)T1: 36 (45%)T2: 40 (50%)T3: 4 (5%)Tumor typeDuctal: 58 (72.5%)Lobular:17 (21.25%)Mixed:2 (2.5%) Other:3 (3.75%)Lympovascular invasionPresent: 49 (61.25%)Absent:31 (38.75%) Histological GradeG1: 9 (11.25%)G2: 54 (67.5%)G3: 17 (21.25%)Multifocality:Present:48 (60%)Absent:32 (40%) Average SLNs removed (range)3.7 (1-11) Average SLNs positive (range)1.8 (1-7) Average non-SLNs positive (range)3.5 (1-16) Max. size of metastasesAverage:5.2mmMicrometastases: 13 (16.25%)Macrometastases: 67 (83.75%)Her-2 statusPositive:10 (12.5%)Negative:70 (87.5%) Prevalence of non-sentinel node metastasis in patient series31 patients (39%) Extranodal extensionPresent:26 (32.5%)Absent:54 (67.5%) Results: In our cohort, the average number of SLNs removed was 3.7 (1-11) and the average number of positive SLNs was 1.8 (1-7). 13 of the 80 patients had micrometastases within the SLNs (16.2%). 31 of the 80 patients had at least one positive non-SLN (39%) and the average number of additional positive nodes found in the ALND was 3.5 (1-16). For our cohort, all of the nomograms had a similar predictive accuracy (MD Anderson AUC = 0.69, Helsinki AUC = 0.69, Cambridge AUC = 0.63). All nomograms fell short of an acceptable AUC value (at least 0.8). Variables included in the different predictive models testedVariablesMD AndersonHelsinkiCambridgeTumor size++ Tumor type+ Nuclear grade +Lymphovascular invasion++ Number of positive SLNs++ Number of negative SLNs + Number of SLNs+ Proportion of positive SLNs +Extranodal extension++ Max. size of SLN metastases+++Her-2 status + Prevalence of non-sentinel node metastasis in patient series + Multifocality + Conclusions: Our study found that none of the chosen nomograms could reliably predict the presence of positive non-SLNs in our cohort. One reason for this may be the fact they utilize different methods of pathological assessment of nodes and different surgical techniques to those used in our centre. In view of this it may be desirable to produce a more accurate nomogram for our population based on specific variables. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-01-16.