Dataset Information

Lymph node positivity in different early breast carcinoma phenotypes: a predictive model.

ABSTRACT: BACKGROUND:A strong correlation between breast cancer (BC) molecular subtypes and axillary status has been shown. It would be useful to predict the probability of lymph node (LN) positivity. OBJECTIVE:To develop the performance of multivariable models to predict LN metastases, including nomograms derived from logistic regression with clinical, pathologic variables provided by tumor surgical results or only by biopsy. METHODS:A retrospective cohort was randomly divided into two separate patient sets: a training set and a validation set. In the training set, we used multivariable logistic regression techniques to build different predictive nomograms for the risk of developing LN metastases. The discrimination ability and calibration accuracy of the resulting nomograms were evaluated on the training and validation set. RESULTS:Consecutive sample of 12,572 early BC patients with sentinel node biopsies and no neoadjuvant therapy. In our predictive macro metastases LN model, the areas under curve (AUC) values were 0.780 and 0.717 respectively for pathologic and pre-operative model, with a good calibration, and results with validation data set were similar: AUC respectively of 0.796 and 0.725. Among the list of candidate's regression variables, on the training set we identified age, tumor size, LVI, and molecular subtype as statistically significant factors for predicting the risk of LN metastases. CONCLUSIONS:Several nomograms were reported to predict risk of SLN involvement and NSN involvement. We propose a new calculation model to assess this risk of positive LN with similar performance which could be useful to choose management strategies, to avoid axillary LN staging or to propose ALND for patients with high level probability of major axillary LN involvement but also to propose immediate breast reconstruction when post mastectomy radiotherapy is not required for patients without LN macro metastasis.

SUBMITTER: Houvenaeghel G

PROVIDER: S-EPMC6327612 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Lymph node positivity in different early breast carcinoma phenotypes: a predictive model.

Houvenaeghel Gilles G Lambaudie Eric E Classe Jean-Marc JM Mazouni Chafika C Giard Sylvia S Cohen Monique M Faure Christelle C Charitansky Hélène H Rouzier Roman R Daraï Emile E Hudry Delphine D Azuar Pierre P Villet Richard R Gimbergues Pierre P Tunon de Lara Christine C Martino Marc M Fraisse Jean J Dravet François F Chauvet Marie Pierre MP Boher Jean Marie JM

BMC cancer 20190110 1

<h4>Background</h4>A strong correlation between breast cancer (BC) molecular subtypes and axillary status has been shown. It would be useful to predict the probability of lymph node (LN) positivity.<h4>Objective</h4>To develop the performance of multivariable models to predict LN metastases, including nomograms derived from logistic regression with clinical, pathologic variables provided by tumor surgical results or only by biopsy.<h4>Methods</h4>A retrospective cohort was randomly divided into ...[more]

PMID: 30630443

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Project description:BackgroundAxillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes.MethodsWe present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis.ResultsThe comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model.ConclusionsThe experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process.

Project description:ImportanceMore than half of all new melanoma diagnoses present as clinically localized T1 melanoma, yet sentinel lymph node biopsy (SLNB) is controversial in this population given the overall low yield. Guidelines for SLNB have focused on pathologic factors, but patient factors, such as age, are not routinely considered.ObjectivesTo identify indicators of lymph node (LN) metastasis in thin melanoma in a large, generalizable data set and to evaluate the association between patient age and LN positivity.Design, setting, and participantsA retrospective cohort study using the National Cancer Database, an oncology database representing patients from more than 1500 hospitals throughout the United States, was performed (2010-2013). Data analysis was conducted from October 1, 2016, to January 15, 2017. A total of 8772 patients with clinical stage I 0.50 to 1.0 mm thin melanoma undergoing wide excision and surgical evaluation of regional LNs were included for study.Main outcome and measuresThe primary outcome of interest was presence of melanoma in a biopsied regional LN. Clinicopathologic factors associated with LN positivity were characterized, using logistic regression. Age was categorized as younger than 40 years, 40 to 64 years, and 65 years or older for multivariable analysis. Classification tree analysis was performed to identify high-risk groups for LN positivity.ResultsAmong the study cohort (n = 8772), 333 patients had nodal metastases, for an overall positivity rate of 3.8% (95% CI, 3.4%-4.2%). A total of 4087 (54.0%) patients were women. Median age was 56 years (interquartile range [IQR], 46-67) in patients with negative LNs and 52 years (IQR, 41-61) in those with positive LNs (P < .001). In multivariable analysis, younger age, female sex, thickness of 0.76 mm or larger, increasing Clark level, mitoses, ulceration, and lymphovascular invasion were independently associated with LN positivity. In decision tree analysis, patient age was identified as an important risk stratifier for LN metastases, after mitoses and thickness. Patients younger than 40 years with category T1b tumors 0.50 to 0.75 mm, who would generally not be recommended for SLNB, had an LN positivity rate of 5.6% (95% CI, 3.3%-8.6%); conversely, patients 65 years or older with T1b tumors 0.76 mm or larger, who would generally be recommended for SLNB, had an LN positivity rate of only 3.9% (95% CI, 2.7%-5.3%).Conclusions and relevancePatient age is an important factor in estimating lymph node positivity in thin melanoma independent of traditional pathologic factors. Age therefore should be taken into consideration when selecting patients for nodal biopsy.

Dataset Information

Lymph node positivity in different early breast carcinoma phenotypes: a predictive model.

Publications

Lymph node positivity in different early breast carcinoma phenotypes: a predictive model.

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