Lymph Node Status In Breast Cancer Research Articles

Investigating the role of tumour-to-skin proximity in predicting nodal metastasis in breast cancer

BackgroundUnderstanding the factors influencing nodal status in breast cancer is vital for axillary staging, therapy, and patient survival. The nodal stage remains a crucial factor in prognostication indices. This study investigates the relationship between tumour-to-skin distance (in T1–T3 tumours where the skin is not clinically involved) and the risk of nodal metastasis.MethodsWe retrospectively reviewed data from 100 patients who underwent neoadjuvant chemotherapy (NACT). Besides patient demographics and tumour variables, a radiologist retrospectively reviewed pre-operative MRI to measure tumour-to-skin distance. R core packages were used for univariate (χ2 and T-Wilcoxon tests) and bivariate logistic regression statistical analysis.ResultsOf 95 analysable datasets, patients’ median age was 51 years (IQR: 42–61), 97% were symptomatic (rest screen detected), and the median tumour size was 43 mm (IQR, 26–52). On multivariate analysis, increasing invasive tumour size (p = 0.02), ER positivity (p = 0.007) and shorter tumour-to-skin distance (p = 0.05) correlated with nodal metastasis. HER2 was not included in multivariate analysis as there was no association with nodal status on univariate analysis. In node-positive tumours, as tumour size increased, the tumour-to-skin distance decreased (r = − 0.34, p = 0.026). In node-negative tumours, there was no correlation (r = + 0.18, p = 0.23).ConclusionThis study shows that non-locally advanced cancers closer to the skin (and consequent proximity to subdermal lymphatics) are associated with a greater risk of nodal metastasis. Pre-operative identification of those more likely to be node positive may suggest the need for a second-look USS since a higher nodal stage may lead to a change in therapeutic strategies, such as upfront systemic therapy, node marking, and axillary clearance without the need to return to theatre following sentinel node biopsy.

A Retrospective Cohort Study Assessing Ability of Radiological Investigations to Predict Axillary Nodal Status in Breast Cancer

A Retrospective Cohort Study Assessing Ability of Radiological Investigations to Predict Axillary Nodal Status in Breast Cancer

Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning

The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190 patients from four medical centers in Hebei Province. Integrating clinicopathological data and image features build multi-modal and multi-instance (MMMI) deep learning model to obtain the final prediction. For predicting with or without lymph node metastasis, the AUC was 0.770, 0.709, 0.809 based on the clinicopathological features, WSI and MMMI, respectively. For predicting four classification of lymph node status (no metastasis, isolated tumor cells (ITCs), micrometastasis, and macrometastasis), the prediction based on clinicopathological features, WSI and MMMI were compared. The AUC for no metastasis was 0.770, 0.709, 0.809, respectively; ITCs were 0.619, 0.531, 0.634, respectively; micrometastasis were 0.636, 0.617, 0.691, respectively; and macrometastasis were 0.748, 0.691, 0.758, respectively. The MMMI model achieved the highest prediction accuracy. For prediction of different molecular types of breast cancer, MMMI demonstrated a better prediction accuracy for any type of lymph node status, especially in the molecular type of triple negative breast cancer (TNBC). In the external validation sets, MMMI also showed better prediction accuracy in the four classification, with AUC of 0.725, 0.757, 0.525, and 0.708, respectively. Finally, we developed a breast cancer lymph node metastasis prediction model based on a MMMI model. Through all cases tests, the results showed that the overall prediction ability was high.

Abstract P1-01-09: Prediction of node negative breast cancer and high disease burden through image analysis software on mammographic images and clinicopathological data

Abstract Background/Aim: Sentinel lymph node biopsy is the standard staging procedure performed on all clinically node negative breast cancer patients, although more than 70% have no lymph node metastases and would not benefit from the procedure. Thus, a preoperative method of predicting lymph node status is warranted. Recently, artificial intelligence-based breast malignancy detection systems for mammograms have been developed, detecting both lesions and microcalcification. Previous models based on clinicopathological data, proposed by Dihge et al.1, 2, received an area under the curve (AUC) of 0.74 for prediction of negative lymph node status and an AUC of 0.75 for prediction of high-burden disease. In this study we aim to predict node negativity (N0) and high-burden disease (N2) in breast cancer patients through mammographic features captured by image analysis software on mammography, added to previous prediction models. Method: This is a retrospective cohort study including 770 women with unilateral breast cancer operated at Lund University Hospital 2009-2012. Mammographic images were identified for 755 women and analyzed by two image analysis software applications, Transpara and Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). Transpara findings were cross-checked with Picture Archiving and Communication System for tumor localization. Clinicopathological variables, soft tissue lesion scores, calc cluster scores, breast density, malignancy score and radiologic tumor size were collected. Prediction models were created using multivariable logistic regression. AUC assessed the performance of the models to predict N0 and N2. Results: Univariable logistic regression showed an association between axillary lymph node status and radiologic size, highest score of soft tissue lesion, highest score of calc cluster and malignancy score. Addition of highest score of soft tissue lesion and highest score of calc cluster to the previously published model1 for prediction of N0 resulted in an AUC of 0.75 (confidence interval (CI) 0.70-0.79). Addition of highest score of calc cluster to a modified version of the previously published model2 for prediction of high-burden disease (N2 versus N0 and N1) resulted in an AUC of 0.83 (CI 0.75-0.90). A comparison of pathologic and radiologic tumor size showed a strong correlation between the variables and associations with lymph node status. Mammographic density was not associated to nodal status. Conclusion: The prediction models proposed in this abstract, including radiomic features, did not significantly improve the previous clinicopathological models1,2. Nevertheless, point estimates of the AUCs were improved by 0.01 and 0.08, respectively, indicating that radiomic features could be of added value in the prediction of node-negative disease and high nodal disease burden and should be further investigated. The strong correlation between measurements of tumor size suggests that radiologic tumor size could replace pathologic size to enable preoperative prediction of nodal status. 1 Dihge L, Bendahl PO, Ryden L. Nomograms for preoperative prediction of axillary nodal status in breast cancer. Br J Surg 2017;104:1494-5. 2 Dihge L, Ohlsson M, Edén P, Bendahl, PO, Rydén L. Artificial neural network models to predict nodal status in clinically node-negative breast cancer. BMC Cancer 2019 Jun 21;19(1):610. doi: 10.1186/s12885-019-5827-6 Table: Multivariable logistic regression of prediction models.Predictions modelsPrediction of negative lymph node status. (n=560)Prediction of high axillary disease burden. (n=576)OR (95% CI)POR (95% CI)PTumor size mm (continuous)0.950 (0.926-0.974)<0.001Tumor size mm (continuous)1.062 (1.019-1.107)0.005Multifocality0.006Multifocality0.655Yes1.00 (ref)Yes1.00 (ref)No1.89 (1.20-2.98)No0.82 (0.34-1.98)Lymphovascular invasion<0.001Lymphovascular invasion<0.001Yes1.00 (ref)Yes1.00 (ref)No3.97 (2.25-7.02)No0.20 (0.08-0.48)Age years (continuous)1.022 (1.004-1.040)0.015PR status0.122Mode of detection0.019Positive1.00 (ref)Symptomatic1.00 (ref)Negative0.35 (0.09-1.33)Mammographic screening1.70 (1.09-2.66)Histological type0.857Molecular subtypes0.027aLobular1.00 (ref)Luminal A1.00 (ref)Other0.89 (0.25-3.21)Luminal B HER2 negative1.20 (0.76-1.91)0.452Tumor localization0.716aLuminal B HER2 positive1.00 (0.48-2.09)0.995Upper outer1.00 (ref)HER2 positive2.00 (0.47-8.59)0.352Central1.29 (1.13-13.0)0.830Triple negative5.68 (1.95-16.5)0.001Upper inner1.40 (0.32-6.12)0.656Highest score of soft tissue lesion0.283aLower inner1.92 (0.35-10.7)0.4561-791.00 (ref)Lower outer2.79 (0.77-10.2)0.12080-911.40 (0.65-3.03)0.393Overlapping1.96 (0.69-5.60)0.20992-951.13 (0.53-2.42)0.745Highest score of calc cluster0.456a≥960.83 (0.37-1.88)0.6611-921.00 (ref)Absence1.69 (0.74-3.87)0.216≥932.86 (0.55-14.9)0.212Highest score of calc cluster0.510aAbsence2.20 (0.46-10.4)0.3231-921.00 (ref)≥930.64 (0.31-1.36)0.246Absence0.78 (0.42-1.44)0.425Constant0.1700.026Constant0.0230.003AUC0.75 (0.70-0.79)AUC0.83 (0.75-0.90)P of ≤0.05 was considered significant. Abbreviations: OR =odds ratio, CI = confidence interval, HER2 = human epidermal growth factor receptor 2. a Test of overall effect Citation Format: Cornelia Rejmer, Looket Dihge, Pär-Ola Bendahl, Daniel Förnvik, Magnus Dustler, Lisa Rydén. Prediction of node negative breast cancer and high disease burden through image analysis software on mammographic images and clinicopathological data [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-01-09.

Diffusion weighted imaging to predict nodal status in breast cancer: A systematic review and meta-analysis.

Diffusion weighted imaging is a functional imaging modality based upon Brownian water movement in tissues.1-3 This movement can be quantified by apparent diffusion coefficient (ADC). According to the literature, apparent diffusion coefficient (ADC) inversely correlates with histopathological features.1, 2 It was identified that benign breast lesions have significant higher ADC values compared to breast cancer (BC).5 Nodal status is one of the most important prognosis parameters in BC, which is assessed by axillary lymph node dissection.6 Our purpose was to systematically review the published literature regarding ADC values of BC in accordance with nodal status and the ADC values of axillary lymph nodes and perform a meta-analysis to establish, whether ADC values can reliably predict nodal status in BC. The literature was searched for suitable papers investigating the associations between ADC values and nodal status in BC. The meta-analysis was performed as previously described.5 The primary end point of the systematic review was association between nodal status of BC and ADC values of the primary tumor and axillary LN. In total, 23 studies were suitable for the analysis and were included into the present study. The acquired 23 studies comprised overall 1669 BC and 1423 LN. In total, 1025 nodal negative and 548 nodal positive BC were included into the analysis. The mean ADC of the primary tumor of the nodal positive BC was 0.89 × 10− 3 mm2/s [95% CI 0.84–0.94, I2 = 91%], and 0.96 × 10− 3 mm2/s [95% CI 0.91–1.01, I2 = 92%] of the nodal negative BC (Figure 1). In total, 876 benign and 547 malignant LN were included into the analysis. The mean ADC value of the malignant axillary LN was 0.90 × 10− 3 mm2/s [95% CI 0.80–1.01, I2 = 98%] and 1.17 × 10− 3 mm2/s [95% CI 1.02–1.32, I2 = 99%] of the benign LN (Figure 2). Three studies reported data regarding ADC values and LN size in large and short axis with overall 57 lymph nodes. Spearman's correlation coefficient for the large axis was r = −0.143, p = 0.29 and for the short axis was r = 0.024, p = 0.86. The present analysis showed that ADC values of the axillary LN are lower in malignant LN compared to benign LN and might aid in clinical decision-making. Similar findings were identified of the ADC values of primary tumors to predict nodal status in BC. Thus, the present analysis based upon a large sample size corroborates the results of previous single-center studies. Nodal status is one of the most important prognosis factors in BC, which leads to a worse outcome in patients with positive axillary lymph nodes compared to nodal negative patients.6 A precise definition of the status of axillary LN in BC patients is also important to select treatment modality or surgical method. Axillary LN dissection is the reference standard for evaluating nodal involvement. Several imaging modalities were used to predict LN status non-invasively. So, ultrasound was deemed to be unreliable.7 The cost expensive modality with radiation exposure, namely PET-CT, achieved only a pooled sensitivity of 56% in a recent meta-analysis, whereas morphological MRI achieved a sensitivity of 55% only.7 However, both modalities reached a high specificity of 91% and 86%, respectively. Yet, there is definite need for a better diagnostic modality to predict nodal status in BC. The present analysis tried to emphasize that DWI and ADC values might be able to fill this diagnostic gap with measurement of the primary tumor itself as well as with the measurement of the lymph nodes. Interestingly, ADC values are independent of the lymph node size, which is most often used in clinical routine as a sign for malignancy. The present analysis can state that ADC values are not correlated with the short and long axis size of the lymph nodes. Presumably, a primary tumor with nodal involvement has a more aggressive tumor biology with distinctive microstructure, which can be assessed by ADC values. Moreover, there is clear evidence that ADC values can aid in the discrimination between benign and malignant tumors, which was shown in a recent meta-analysis.5 So, the present analysis can harmonize the published results that ADC values of the primary tumor as well as of the axillary lymph nodes can aid to predict nodal status in BC. As a limitation to address, the meta-analysis is based upon published results in the literature. There might be a certain publication bias because there is a trend to report positive or significant results. In conclusion, ADC values of axillary LN and of primary tumors may aid in predict nodal status of BC in clinical routine. These results are based upon a large patient sample and can be considered representative.

Factors Affecting Nodal Status in Breast Cancer

Background: Nodal status in breast cancer [BC] is the most prognostic factor for survival and prognosis, and affects treatment decisions. Nodal status had reciprocal relation with pre- and post-operative factors. All these reflect the crucial importance of nodal status in BC. Aim of the work: Evaluation of pre- and post-operative factors related to nodal status in breast cancer. Patients and Methods: The current work included 40 patients with confirmed BC, who had been scheduled for modified radical mastectomy or conservative breast surgery with axillary evacuation. All were assessed in a systematic manner preoperatively. In addition, an intraoperative and post-operative evaluation had been carried out. Postoperative histopathological examination of excised tissues had been done for all specimens. Both intra- and post-surgical complications and factors associated with positive nodal status were documented. Results: The most significant factors associated with high positive nodal status were age (patient ≤ 45 years was significantly associated with increased positive nodes when compared to > 45 years (37.68±35.23 vs 17.66±19.45 respectively), obesity (30.66±27.33 vs 18.49±25.07 nodes for obese and lean individuals respectively)., tumors greater than 2 cm, positivity of the human-epidermal-growth-factor receptor 2 [Her2]/neu, positive lymphovasular invasion, tumors of the upper quadrant, and the type of histopathology. Conclusion: Nodal status affected by pre- and post-surgery factors (e.g., age, obesity, tumor site, tumor size, Her2/neu, lymphovascular invasion and histopathological type of the tumor). Thus, these factors help in planning of the treatment such as the type of surgery, endocrine therapy, radiation therapy and the adjuvant chemotherapy.

Artificial neural network models to predict nodal status in clinically node-negative breast cancer

BackgroundSentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical strategies are applied depending on the extent of nodal involvement. Preoperative prediction of nodal status is thus important for individualizing axillary surgery avoiding unnecessary surgery. We aimed to predict nodal status in clinically node-negative breast cancer and identify candidates for SLNB omission by including patient-related and pathological characteristics into artificial neural network (ANN) models.MethodsPatients with primary breast cancer were consecutively included between January 1, 2009 and December 31, 2012 in a prospectively maintained pathology database. Clinical- and radiological data were extracted from patient’s files and only clinically node-negative patients constituted the final study cohort. ANN-based models for nodal prediction were constructed including 15 risk variables for nodal status. Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit test (HL) were used to assess performance and calibration of three predictive ANN-based models for no lymph node metastasis (N0), metastases in 1–3 lymph nodes (N1) and metastases in ≥ 4 lymph nodes (N2). Linear regression models for nodal prediction were calculated for comparison.ResultsEight hundred patients (N0, n = 514; N1, n = 232; N2, n = 54) were included. Internally validated AUCs for N0 versus N+ was 0.740 (95% CI = 0.723–0.758); median HL was 9.869 (P = 0.274), for N1 versus N0, 0.705 (95% CI = 0.686–0.724; median HL: 7.421; P = 0.492) and for N2 versus N0 and N1, 0.747 (95% CI = 0.728–0.765; median HL: 9.220; P = 0.324). Tumor size and vascular invasion were top-ranked predictors of all three end-points, followed by estrogen receptor status and lobular cancer for prediction of N2. For each end-point, ANN models showed better discriminatory performance than multivariable logistic regression models. Accepting a false negative rate (FNR) of 10% for predicting N0 by the ANN model, SLNB could have been abstained in 27.25% of patients with clinically node-negative axilla.ConclusionsIn this retrospective study, ANN showed promising result as decision-supporting tools for estimating nodal disease. If prospectively validated, patients least likely to have nodal metastasis could be spared SLNB using predictive models.Trial registrationRegistered in the ISRCTN registry with study ID ISRCTN14341750.Date of registration 23/11/2018. Retrospectively registered.

Serum ATX as a novel biomarker for breast cancer.

Recent accumulating evidence indicates the biological actions of Autotaxin-Lysophosphatidic acid (ATX-LPA) signaling axis in malignant tumors. However, the role of Autotaxin-Lysophosphatidic acid signaling axis in breast cancer has not been reported. The present study aims to examine the alterations of serum autotaxin in breast cancer and discuss whether serum autotaxin could be useful as a novel parameter of breast cancer.Serum autotaxin antigen was measured in 112 patients with breast cancer and 50 healthy volunteers by ELISA. The association of serum autotaxin antigen levels with clinicopathological parameters and outcomes of breast cancer was analyzed.Serum autotaxin antigen was significantly higher in breast cancer patients than healthy volunteers (291.32 ± 38.02 ng/ml vs 254.04 ± 21.03 ng/ml, respectively; P < .0001). Serum autotaxin measurement successfully discriminated breast cancer patients from normal and healthy controls (AUC = 0.798, 95% CI: 0.732–0.864) with an optimal cut-off value of 267.34 ng/ml (sensitivity = 0.741, specificity = 0.800). Increased serum autotaxin was associated with breast cancer nodal status (P = .007), Tumor-Node- Metastasis (TNM) stage (P = .009) and Ki-67 index (P = .004). Univariate and multivariate Cox regression analysis revealed that elevated serum autotaxin showed an independent prognostic value for poor Disease-free survival.Our present study confirmed the elevation, potential diagnostic, and independent prognostic value of serum autotaxin for breast cancer. Serum autotaxin could serve as a reliable novel biomarker for breast cancer.

Nomograms for preoperative prediction of axillary nodal status in breast cancer.

BackgroundAxillary staging in patients with breast cancer and clinically node‐negative disease is performed by sentinel node biopsy (SLNB). The aim of this study was to integrate feasible preoperative variables into nomograms to guide clinicians in stratifying treatment options into no axillary staging for patients with non‐metastatic disease (N0), SLNB for those with one or two metastases, and axillary lymph node dissection (ALND) for patients with three or more metastases.MethodsPatients presenting to Skåne University Hospital, Lund, with breast cancer were included in a prospectively maintained registry between January 2009 and December 2012. Those with a preoperative diagnosis of nodal metastases were excluded. Patients with data on hormone receptor status, human epidermal growth factor receptor 2 and Ki‐67 expression were included to allow grouping into surrogate molecular subtypes. Based on logistic regression analyses, nomograms summarizing the strength of the associations between the predictors and each nodal status endpoint were developed. Predictive performance was assessed using the area under the receiver operating characteristic (ROC) curve. Bootstrap resampling was performed for internal validation.ResultsOf the 692 patients eligible for analysis, 248 were diagnosed with node‐positive disease. Molecular subtype, age, mode of detection, tumour size, multifocality and vascular invasion were identified as predictors of any nodal disease. Nomograms that included these predictors demonstrated good predictive abilities, and comparable performances in the internal validation; the area under the ROC curve was 0·74 for N0 versus any lymph node metastasis, 0·70 for one or two involved nodes versus N0, and 0·81 for at least three nodes versus two or fewer metastatic nodes.ConclusionThe nomograms presented facilitate preoperative decision‐making regarding the extent of axillary surgery.

Diagnostic strategy for the assessment of axillary lymph node status in breast cancer.

The nodal status in breast cancer is a major prognostic factor in terms of survival. It also plays a role in the therapeutic decision-making process. Therefore, the evaluation of lymph node involvement in breast cancer is imperative in establishing a personalized treatment scheme. The sentinel lymph node procedure has proved successful for small breast tumors (T1-T2), limiting axillary lymphadenectomy and its side effects without changing overall survival. Even so, a substantial number of women must undergo axillary lymphadenectomy during a second surgery when the analysis of the sentinel node discloses major nodal involvement. Imaging can improve patient selection, especially those who appear eligible for immediate axillary lymphadenectomy. Ultrasound is able to depict morphological abnormalities in the lymph nodes such as cortical thickening, peripheral vascularization, hilar infiltration and loss of the kidney-shaped appearance of a normal node. When ultrasound is negative, the risk of massive nodal involvement is limited, thus allowing the oncologist to take an approach with the sentinel lymph node procedure. Magnetic resonance imaging (MRI) can also be useful in detecting pathological lymph nodes, particularly with diffusion-weighted MRI sequence.

P0192 Sentinel node biopsy in breast cancer using only methylene blue dye: A prospective study in a rural tertiary care centre

Background Breast carcinoma is the most common malignancy in women and is the leading cause of death in their middle age. Sentinel lymph node biopsy (SLNB) is a reliable and minimally invasive diagnostic method to determine the regional nodal status in breast cancer and provides accurate staging, such that axillary lymph node dissection can be avoided in negative sentinel node patients. The aim of this study was to assess SLNB, using methylene blue dye, and its accuracy. The complications of using methylene blue dye were also studied. Methods 138 patients with breast cancer were subjected to SLNB (using methylene blue dye) followed by complete axillary lymph node dissection. The lymph nodes with positive dye were identified. The dye was injected 30 min prior to surgery and the stained lymph nodes were identified during dissection. The haemodynamics of the patients was assessed during and after the procedure. Patients were followed up in the post-operative period, and for the final histopathology report, complications such as wound healing and urine discolouration were assessed. Findings Of 138 patients with dye injected, 124 (89.85%) patients showed stained lymph nodes. Of 124 patients with positive stain, 67 (54.03%) patients showed evidence of malignancy. Of 67 patients with malignancy-positive lymph nodes, 18 patients had positive sentinel node only. In 49 patients, both sentinel and one or more axillary nodes were positive, whereas in 53 patients both nodal statuses were negative. Four patients had negative sentinel node and positive axillary node. All 14 patients in whom sentinel node could not be identified were negative for cancer in axillary nodes. Seven patients had minor dye-related complications. Interpretation SLNB with methylene blue dye alone can be considered in breast carcinoma as a reliable, accurate, cost effective, and safe method to detect lymph node status. Breast carcinoma is the most common malignancy in women and is the leading cause of death in their middle age. Sentinel lymph node biopsy (SLNB) is a reliable and minimally invasive diagnostic method to determine the regional nodal status in breast cancer and provides accurate staging, such that axillary lymph node dissection can be avoided in negative sentinel node patients. The aim of this study was to assess SLNB, using methylene blue dye, and its accuracy. The complications of using methylene blue dye were also studied. 138 patients with breast cancer were subjected to SLNB (using methylene blue dye) followed by complete axillary lymph node dissection. The lymph nodes with positive dye were identified. The dye was injected 30 min prior to surgery and the stained lymph nodes were identified during dissection. The haemodynamics of the patients was assessed during and after the procedure. Patients were followed up in the post-operative period, and for the final histopathology report, complications such as wound healing and urine discolouration were assessed. Of 138 patients with dye injected, 124 (89.85%) patients showed stained lymph nodes. Of 124 patients with positive stain, 67 (54.03%) patients showed evidence of malignancy. Of 67 patients with malignancy-positive lymph nodes, 18 patients had positive sentinel node only. In 49 patients, both sentinel and one or more axillary nodes were positive, whereas in 53 patients both nodal statuses were negative. Four patients had negative sentinel node and positive axillary node. All 14 patients in whom sentinel node could not be identified were negative for cancer in axillary nodes. Seven patients had minor dye-related complications. SLNB with methylene blue dye alone can be considered in breast carcinoma as a reliable, accurate, cost effective, and safe method to detect lymph node status.

A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breastcancer

Axillary Lymph Node (ALN) status is an extremely important factor to assess metastatic breast cancer. Surgical operations which may be necessary and cause some adverse effects are performed in determination ALN status. The purpose of this study is to predict ALN status by means of selecting breast cancer patient's basic clinical and histological feature(s)that can be obtained in each hospital. 270 breast cancer patients' data are collected from Ankara Numune Educational and Research Hospital and Ankara Oncology Educational and Research Hospital. These are classified using back propagation MultiLayer Perceptron (MLP), Logistic Regression (LR) and Genetic Algorithm (GA) based MLP models. Receiver Operating Characteristics (ROC) such as sensitivity, specificity, accuracy and area under of ROC (AUC) and regression are used to evaluate performances of the developed models. It is concluded from LR and GA based MLP, that menopause status and lymphatic invasion are the most significant features for determining ALN status. GA provides to select best features as MLP inputs. It also optimizes the weights of backpropagation algorithm in MLP. The values of regression and accuracy of the GA based MLP with 9features (numerical age, categorical age, menopause status, tumor size, tumor type, tumor location, T staging, tumor grade and lymphatic invasion) are found as 0.96 and 98.0% with respectively. According to results, proposed GA based MLP classifier can be used to predict the ALN status of breast cancer without surgical operations.

Why did the breast cancer lymph node status distribution improve in Denmark in the pre-mammography screening period of 1978–1994?

Background. Danish breast cancer patients diagnosed in 1978–1994 experienced a trend over time towards a more favourable distribution of lymph node status at time of diagnosis, which was not due to mammography screening. We investigated how this trend could be explained by patient characteristics at diagnosis: age (biological processes), calendar period (e.g. environmental changes), birth cohort (living conditions over a life time), post-menopausal status (a predictor of less favourable nodal status), and tumour diameter (a marker of detection time). Material and methods. The data set consisted of 22 955 patients aged 30–69 years at time of diagnosis with known lymph node status, known tumour diameter, known menopausal status, and clinically detected tumours, available from the Danish Breast Cancer Cooperative Group (DBCG). Age, period, cohort, menopausal status, and tumour diameter were used as predictors in generalised linear models with either node-positive status (at least one of the excised lymph nodes being tumour-positive) or severely node-positive status (at least half of the excised lymph nodes being tumour-positive) as outcomes. Lymph node status was assessed both empirically and estimated using an EM algorithm in order to reduce misclassification. Results and discussion. We found that the improved lymph node status distribution was most likely a period effect due to a combination of earlier detection of clinical tumours, explaining most of the trend in node-positive breast cancer and half of the trend in severely node-positive breast cancer, and some unknown factor affecting lymph node status but not necessarily other tumour characteristics.

Polymorphism (ALA16VAL) correlates with regional lymph node status in breast cancer

We studied the possible association between Ala16Val manganese-dependent superoxide dismutase (MnSOD) gene genotypes and breast cancer lymph node status because previous investigations suggested an association between the AA genotype and breast cancer. We included 281 women (188 controls and 93 cases of invasive breast cancer with axillary lymph node metastasis (LN+) and without lymph node metastasis (LN–). DNA was extracted from paraffin-embedded tumor tissue or peripheral blood leukocytes, and MnSOD polymorphism was determined by polymerase chain reaction—restriction fragment length polymorphism techniques. In addition, the immunohistochemical profile (p53, Ki-67 and estrogen/progesterone receptors) was also compared between invasive breast cancer groups and different MnSOD genotypes. The frequency of the VV genotype was higher in the LN+ group than in the control and LN– groups (χ 2 = 5.081, P = 0.02). Subjects with LN+ breast cancer (LN+ group) showed a higher incidence of VV genotype carriers associated with positive Ki-67 marker. Subjects with LN+ breast cancer (LN+ group) showed a higher incidence of VV genotype carriers associated with negative p53 marker. Despite the fact that the AA genotype is well established as being associated with an increased risk of breast cancer, the VV genotype may be associated with a higher metastatic potential, suggesting that MnSOD imbalance is the condition associated with carcinogenesis.

Comparison of axillary nodal ratios and absolute number of involved nodes as prognostic factors for stage II/III breast cancer treated with neoadjuvant chemotherapy

622 Background: Pathologic axillary nodal status in breast cancer treated with neoadjuvant chemotherapy differs from those of early breast cancer treated upfront surgery. Neoadjuvant chemotherapy might have modified the yield of involved axillary lymph nodes. The purpose of this study was to identify the clinical significance of the involved nodal ratios (INR) in stage II/III breast cancer treated with neoadjuvant chemotherapy. Methods: From March 2002 to January 2007, a total of 205 stage II and III breast cancer patients (median age: 45) received neoadjuvant docetaxel/doxorubicin chemotherapy were enrolled in this prospective study. The patients received three cycles of neoadjuvant chemotherapy followed by curative surgery either breast conserving surgery or mastectomy with level II/III axillary lymph node dissection and received three more cycles of docetaxel/doxorubicin chemotherapy as adjuvant. Results: The median follow-up duration was 19.3 months. The overall response rate (RR) for neoadjuvant chemotherapy was 77.6%. The distributions of absolute number of involved nodes (ANIN) were follows: pN0= 61 (29.8%), pN1= 65 (31.7%), pN2= 49 (23.9%), pN3= 30 (14.6%). The mean INR was 29.3% (range 0–100.0%, INR≤30%: 126 (61.5%) vs. >30%: 79 (38.5%)). RR was associated with lower INR (p= 0.031). Relapse-free survival (RFS) of the patients who showed more than 30% of INR was significantly shorter (Hazard Ratio [HR]= 2.602, p= 0.002). The INR of more than 30% was also associated with shorter overall survival (OS) (HR= 3.301, p= 0.047). However, RFS and OS did not differ according to absolute number of involve nodes ANIN (p= 0.2420, p= 0.3186, respectively). In multivariate analysis, INR was independent prognostic factor for RFS and OS (HR= 3.757, p<0.001; HR= 6.487, p= 0.003). Conclusions: Axillary nodal ratios have better prognostic value than ANIN in stage II/III breast cancer treated with neoadjuvant chemotherapy. Nodal ratio may be used to identify the patients at high risks of recurrence in neoadjuvant setting. Our results suggest that the patients with high nodal ratio should be candidates for additional post operative treatment with chemotherapeutic or targeted agents which has different mechanism of action. No significant financial relationships to disclose.

Comparison of three different staining techniques for intraoperative assessment of nodal metastasis in breast cancer

Imprint cytology has increasingly been used for intraoperative assessment of nodal status in breast cancer. We carried out this study to compare the efficacy of Jenner Giemsa (JG), hematoxylin-eosin (H&E), and Papanicolaou (Pap) stains for intraoperative lymph node imprint cytology (IIC) in breast cancer. One hundred and seven cases of stage I-III breast cancer were studied. Overall, IIC was accurate in 95.3% cases and had a sensitivity and specificity of 98.5% and 90.0%, respectively. The accuracy of JG (95.3%) was better than that of H&E (90.6%) and Pap (94.0%), although the differences were not statistically significant. Problems encountered included cell loss and drying artifacts with H&E and Pap and the inability to distinguish between tumor cells and histiocytes confidently in tight cellular clusters that were occasionally seen. Opinion was possible in all JG cases, but not in five and four cases by H&E and Pap, respectively. Although the choice of the stain would vary depending on the experience of the pathologist, our work suggests that JG, because of fewer technical problems and superior accuracy, may be preferable over H&E and Pap.

Novel surgical sampling using mri for assessment of nodal status in breast cancer with failure to identify a sentinel lymph node

Novel surgical sampling using mri for assessment of nodal status in breast cancer with failure to identify a sentinel lymph node

Axillary lymph node maping by using 3-dimensional magnetic resonance imaging and novel surgical sampling for assessment of nodal status in breast cancer

Axillary lymph node maping by using 3-dimensional magnetic resonance imaging and novel surgical sampling for assessment of nodal status in breast cancer

Value of axillary sentinel nodal status in breast cancer.

Axillary clearance in node-negative breast cancer patients is performed only for staging and prognostic purposes. The sentinel node concept may provide an alternative conservative approach for these patients. This paper reports on the learning experience with lymphatic mapping involving the use of patent blue dye for the identification of sentinel lymph nodes (SLNs), followed by axillary dissection. The histopathology of the SLNs included serial sectioning and immunostaining for cytokeratin and epithelial membrane antigen, the remaining nodes being processed as usual. Of the 70 mapping procedures, 58 were successful; the surgical performance revealed a well defined learning period. The mean diameter of the successfully mapped tumors was 2.4 cm (ranging from in situ carcinoma to 4.8-cm invasive cancer). The mean numbers of SLNs and non-SLNs were 1.3 (range 1-3) and 19 (range 7-42), respectively. There were 36 SLN-positive cases, 21 of which had metastases only to these nodes. There were 19 node-negative cases, and 3 SLNs were falsely negative. Possible causes of the errors during lymphatic mapping are analyzed in the light of experiences published to date. SLN biopsy seems a good approach to enhancing the selectivity of axillary lymphadenectomy, but the limitations of the procedure must be evaluated and carefully considered.

Computed Tomography for Preoperative Evaluation of Axillary Nodal Status in Breast Cancer.

BACKGROUND: We prospectively evaluated axillary lymph node metastasis by preoperative thin-slice computed tomography (CT) and compared those results with theresults of dye-guided sentinel lymph node biopsy (SNB). PATIENTS AND METHODS: Fifty-one breast cancer cases were examined. Preoperative axillary CT, dyeguided SNB and complete axillary lymph node dissection were performed. We prospectively diagnosed lymph node metastasis in the whole axillaryregion and qualitatively predicted the sentinel lymph node (SN) status with thepreoperative CT images. Based on the results of SNB and the pathological nodal status as revealed by complete axillary dissection, we evaluated the usefulness of preoperative axillary CT as a predictor of axillary node metastasis, and we also investigated the qualitative diagnostic value of the SN predicted by CT. RESULTS: For the whole axillary region, the results of CT diagnosis showed anaccuracy of 71% and a sensitivity of 60%. The SN predicted by CT correlated with the SN identified by the dye-guided method in 86% of all cases. The SN status predicted by CT had an accuracy of 81%, a sensitivity of 78%, and a negative predictive value of 79%. The dye-guided methods resulted in three false-negative SNcases, however, CT predicted one of those cases as positive. CONCLUSIONS: Diagnosis of axillary lymph node metastasis by CT alone is inadequate. However, CT achieves a comparatively high rate of identification of axillary SN. The combined use of dye-guided SNB and preoperative CT will facilitate identification of the SN and also lower the false-negative rate.