Purpose Axillary lymph node (ALN) involvement is an important prognostic factor of early invasive breast cancer. The objective of this study was to establish simple nomograms for predicting ALN involvement based on ultrasound (US) characteristics and evaluate the predictive value of US in the detection of ALN involvement. Patients and methods A total of 1328 patients with cT1-2N0 breast cancer by physical exam were retrospectively analyzed. Univariate analysis was used for the comparison of variables, and multivariate analysis was performed by binary logistic regression analysis. The R software was used to establish simple nomograms based on the US characteristics alone. The receiver operating characteristic (ROC) curves of the prediction model and the verification group were drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of the prediction model. A calibration curve was plotted to assess the nomogram predictions vs the actual observations of the ALN metastasis rate and axillary tumor burden rate. Results The ALN metastasis rates of the training group and the validation group were 35.1% and 34.1%, respectively. Multivariate analysis showed that molecular subtype, lymphovascular invasion, mass descriptors (size, margin, microcalcification and blood flow signal) and LN descriptors (shape, cortical thickness and long-to-short ratio) were independent impact factors in early breast cancer. The AUC of ALN metastasis rate of prediction model based on US features was 0.802, the AUC of high tumor burden rate was 0.873, and the AUC of external validation group was 0.731 and 0.802, respectively. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual metastasis rate and the high tumor burden rate. The results showed that preoperative US had a sensitivity of 59.4% and a specificity of 88.9% for predicting the ALN metastasis rate. Conclusion The successfully established nomograms based on US characteristics to predict ALN metastasis rate and high axillary tumor burden rate in early breast cancer can achieve individual prediction. Compared with other nomogram predictions, it is more intuitive, and can help clinical decision-making; thus, it should be promoted. However, at this time US features alone are insufficient to replace sentinel lymph node biopsy.

/pdf/establishment-of-simple-nomograms-for-predicting-axillary-2rx5ual7hz.pdf

Establishment of Simple Nomograms for Predicting Axillary Lymph Node Involvement in Early Breast Cancer

To evaluate the diagnostic value of diffusion-weighted imaging (DWI) in combination with conventional MRI in predicting metastatic axillary lymph nodes (ALN) in breast cancer (BC). We reviewed pathological findings and clinical breast MRI examinations of 169 patients with invasive BC who were evaluated at the Hotel-Dieu de France Hospital in 2009-2015. Morphological parameters and apparent diffusion coefficient (ADC) value were compared with pathological nodal status. Independent t-test/chi-square test and a Pearson correlation analysis were used. With pathological diagnosis as reference, MRI-based interpretations were 87.5% specific and 70.3% sensitive. On conventional MRI, the round shape of lymph nodes (LNs), loss of fatty hilum, irregular margins and hypo-intensity/heterogeneous intensity on T2-weighted sequence were statistically significantly different between metastatic and nonmetastatic groups (P < .001, each). Mean size of metastatic ALN was larger compared with negative ALN (13.9 mm vs. 10.9 mm, P < .001). LNs ≥ 12 mm were associated with higher risk of metastasis (P < .001). ADC value was not significantly different between both groups (P = .862). Conventional MRI using the ALN shape, signal intensity in T2-weighted sequences, loss of fatty hilum, regularity of the margins and size of the LNs can evaluate the axilla with high specificity. ADC value could not be used as a reliable parameter.

MRI-based predictive factors of axillary lymph node status in breast cancer.

Background: Most patients diagnosed with breast cancer present with node-negative disease. Sentinel lymph node biopsy (SLNB) is routinely used to stage the axilla, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal (N) status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup. Lymphovascular invasion (LVI) is a top-ranked predictor of N metastasis; however, it is challenging to assess preoperatively. Objective: To externally validate a multilayer perceptron (MLP) model for noninvasive lymph node staging (NILS) in a large population-based register cohort (n=18 633) while developing a new N MLP in the same cohort. Data were extracted from the Swedish National Quality Register for Breast Cancer (NKBC), 2014-2017, comprising only routinely and preoperatively available documented clinicopathological variables. Furthermore, we aimed to develop and validate an LVI MLP to predict missing values of LVI status to increase the preoperative feasibility of the original NILS model. Methods: Three non-overlapping cohorts were used for model development and validation. Four N MLPs and one LVI MLP were developed using 11-12 routinely available predictors. Three N models were used to account for the different availabilities of LVI status in the cohorts and external validation in NKBC. The fourth N model was developed for 80% of NKBC cases (n=14 906) and validated in the remaining 20% (n=3727). Three alternatives for imputing missing values of the LVI status were compared using the LVI model. The discriminatory capacity was evaluated by validation area under the receiver operating characteristics curve (AUC) in three of the N models. The clinical feasibility of the models was evaluated using calibration and decision curve analyses. Results: External validation of the original NILS model was performed in NKBC (AUC 0.699, 95% CI: 0.690-0.708) with good calibration and the potential of sparing 16% of patients with node-negative disease from SLNB. The LVI model was externally validated (AUC 0.747, 95% CI: 0.694-0.799) with good calibration but did not improve the discriminatory performance of the N models. The new N model was developed in NKBC without information on LVI (AUC 0.709, 95% CI: 0.688-0.729) with excellent calibration in the hold-out internal validation cohort, resulting in the potential omission of 24% of patients from unnecessary SLNB. Conclusions: The NILS model was externally validated in NKBC, where the imputation of LVI status did not improve the model's discriminatory performance. A new N model demonstrated the feasibility of using register data comprising only the variables available in the preoperative setting for NILS using machine learning. Future steps include ongoing preoperative validation of the NILS model and extending the model with, for example, mammography images.

/pdf/noninvasive-staging-of-lymph-node-status-nils-in-breast-138pmax2.pdf

Noninvasive Staging of Lymph Node Status (NILS) in Breast Cancer using Machine Learning: External Validation and Further Model Development

Abstract Background: This study aims to estimate the amount of axillary lymph node (ALN) involvement in early-stage breast cancer utilizing a field of view (FOV) and constrained undistorted single-shot (FOCUS) diffusion-weighted imaging (DWI) approach, as well as a whole-lesion histogram analysis. Methods: This retrospective analysis involved 65 individuals with invasive breast cancer. The patients were divided into three groups: N 0 (negative ALN metastasis), N 1–2 (low metastatic burden with 1-2 ALNs), and N ≥3 (heavy metastatic burden with≥3 ALNs) based on their sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Histogram parameters of apparent diffusion coefﬁcient (ADC) depending basically on FOCUS DWI were performed using 3D-Slicer software for whole lesions. The typical histogram characteristics for N 0 , N 1–2 , and N ≥3 were compared to identify the significantly different parameters. To determine the diagnostic efficacy of significantly different factors, the area under their receiver operating characteristic (ROC) curves was examined. Results: There were significant differences in the energy, maximum, 90 percentile, range, and lesion size among N 0 , N 1–2 , and N ≥3 groups (P&lt;0.05). The energy differed significantly between N 0 and N 1–2 groups (P &lt; 0.05), and some certain ADC histogram parameters and lesion sizes differed significantly between N 0 and N ≥3 , or N 1–2 and N ≥3 groups. For ROC analysis, the energy yielded the best diagnostic performance in distinguishing N 0 and N 1-2 groups from N ≥3 group with an AUC value of 0.856. All parameters revealed excellent inter-observer agreement with inter-reader consistencies data ranging from 0.931 to 0.987. Conclusion: By employing FOCUS DWI method, the analysis of whole-lesion ADC histogram quantitatively provides a non-invasive way to evaluate the degree of ALN metastatic spread in early-stage breast cancer. 

The value of whole-lesion histogram analysis based on field‑of‑view (FOV) and FOCUS diffusion‑weighted imaging (DWI) for predicting axillary lymph node status in early-stage breast cancer

Abstract Background This study aims to estimate the amount of axillary lymph node (ALN) involvement in early-stage breast cancer utilizing a field of view (FOV) optimized and constrained undistorted single-shot (FOCUS) diffusion-weighted imaging (DWI) approach, as well as a whole-lesion histogram analysis. Methods This retrospective analysis involved 81 individuals with invasive breast cancer. The patients were divided into three groups: N 0 (negative ALN metastasis), N 1–2 (low metastatic burden with 1–2 ALNs), and N ≥3 (heavy metastatic burden with ≥ 3 ALNs) based on their sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Histogram parameters of apparent diffusion coefficient (ADC) depending basically on FOCUS DWI were performed using 3D-Slicer software for whole lesions. The typical histogram characteristics for N 0 , N 1–2 , and N ≥ 3 were compared to identify the significantly different parameters. To determine the diagnostic efficacy of significantly different factors, the area under their receiver operating characteristic (ROC) curves was examined. Results There were significant differences in the energy, maximum, 90 percentile, range, and lesion size among N 0 , N 1–2 , and N ≥ 3 groups ( P &lt; 0.05). The energy differed significantly between N 0 and N 1–2 groups ( P &lt; 0.05), and some certain ADC histogram parameters and lesion sizes differed significantly between N 0 and N ≥3 , or N 1–2 and N ≥3 groups. For ROC analysis, the energy yielded the best diagnostic performance in distinguishing N 0 and N 1–2 groups from N ≥3 group with an AUC value of0.853. All parameters revealed excellent inter-observer agreement with inter-reader consistencies data ranging from0.919 to 0.982. Conclusion By employing FOCUS DWI method, the analysis of whole-lesion ADC histogram quantitatively provides a non-invasive way to evaluate the degree of ALN metastatic spread in early-stage breast cancer. 

/pdf/the-value-of-whole-lesion-histogram-analysis-based-on-field-1vgikagy.pdf

The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer

Purpose: To develop and validate nomogram models using noninvasive imaging parameters with related clinical variables to predict the extent of axillary nodal involvement and stratify treatment options based on the essential cut-offs for axillary surgery according to the ACOSOG Z0011 criteria. Materials and Methods: From May 2007 to December 2017, 1799 patients who underwent preoperative breast and axillary magnetic resonance imaging (MRI) were retrospectively studied. Patients with data on axillary ultrasonography (AUS) were enrolled. The MRI images were interpreted according to Breast Imaging Reporting and Data system (BI-RADS). Using logistic regression analyses, nomograms were developed to visualize the associations between the predictors and each lymph node (LN) status endpoint. Predictive performance was assessed based on the area under the receiver operating characteristic curve (AUC). Bootstrap resampling was performed for internal validation. Goodness-of-fit of the models was evaluated using the Hosmer-Lemeshow test. Results: Of 397 early breast cancer patients, 200 (50.4%) had disease-free axilla, 119 (30.0%) had 1 or 2 positive LNs, and 78 (19.6%) had ≥3 positive LNs. Patient age, MRI features (mass margin, LN margin, presence/absence of LN hilum, and LN symmetry/asymmetry), and AUS descriptors (presence of cortical thickening or hilum) were identified as predictors of nodal disease. Nomograms with these predictors showed good calibration and discrimination; the AUC was 0.809 for negative axillary node (N0) vs. any LN metastasis, 0.749 for 1 or 2 involved nodes vs. N0, and 0.874 for ≥3 nodes vs. ≤2 metastatic nodes. The predictive ability of the 3 nomograms with additional pathological variables was significantly greater. Conclusion: The nomograms could predict the extent of ALN metastasis and facilitate decision-making preoperatively.

/pdf/development-and-validation-of-nomograms-predictive-of-2mvt9vdfku.pdf

Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Runcong Nie

Author Tools

Chat about Author

Papers

Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer.