Development of a prediction model for lymph node metastasis in luminal A subtype breast cancer: The possibility to omit sentinel lymph node biopsy

Nakauchi, Chiaki; Naoi, Yasuto; Shimazu, Kenzo; Tsunashima, Ryo; Nishio, Minako; Maruyama, Naomi; Shimomura, Atsushi; Kagara, Naofumi; Shimoda, Masashi; Kim, Seung Jin; Noguchi, Shinzaburo

doi:10.1016/j.canlet.2014.07.003

Cited by 14 publications

(11 citation statements)

References 37 publications

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“…In other malignancies, nodal prediction accuracies between 50% to 60% have been reported based on TCGA data (28). Our predictors containing gene expression data (GEX and MIXED models) revealed AUCs of 0.58 to 0.72 during validation and were consistent with a midrange performance as defined in previous publications (23)(24)(25)(26)(27)(28). The current results are, however, validated in an unselected contemporary large primary breast cancer cohort.…”

Section: Discussionsupporting

confidence: 88%

“…S3). Previous studies investigating prediction of nodal metastasis in breast cancer have reported diverse performances of GEX-based predictors, with AUCs between chance level to near perfect separation (23)(24)(25)(26)(27)(28), likely to be due to differences in patient characteristics, cohort sizes, definition of nodal disease, gene expression analysis platforms, and feature-selection strategies. In other malignancies, nodal prediction accuracies between 50% to 60% have been reported based on TCGA data (28).…”

Section: Discussionmentioning

confidence: 99%

“…However, risk assessment of metastatic axillary spread based on these clinicopathological variables is considered imperfect and consequently SLNB remains the standard axillary staging procedure (18,21,22). Previous studies of gene expression patterns from the primary breast tumor for prediction of nodal metastasis have shown inconsistent results, ranging from being nearlyperfect to almost inadequate in small, selected cohorts (23)(24)(25)(26)(27)(28). The classifiers developed to date are not sufficient to support relevant clinical conclusions (29).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Dihge

Vallon‐Christersson

Hegardt

et al. 2019

Clinical Cancer Research

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Purpose: More than 70% of patients with breast cancer present with node-negative disease, yet all undergo surgical axillary staging. We aimed to define predictors of nodal metastasis using clinicopathological characteristics (CLINICAL), gene expression data (GEX), and mixed features (MIXED) and to identify patients at low risk of metastasis who might be spared sentinel lymph node biopsy (SLNB).Experimental Design: Breast tumors (n ¼ 3,023) from the population-based Sweden Cancerome Analysis Network-Breast initiative were profiled by RNA sequencing and linked to clinicopathologic characteristics. Seven machine-learning models present the discriminative ability of N0/Nþ in development (n ¼ 2,278) and independent validation cohorts (n ¼ 745) stratified as ER þ HER2 À , HER2 þ , and TNBC. Possible SLNB reduction rates are proposed by applying CLINICAL and MIXED predictors.Results: In the validation cohort, the MIXED predictor showed the highest area under ROC curves to assess nodal metastasis; AUC ¼ 0.72. For the subgroups, the AUCs for MIXED, CLINICAL, and GEX predictors ranged from 0.66 to 0.72, 0.65 to 0.73, and 0.58 to 0.67, respectively. Enriched proliferation metagene and luminal B features were noticed in node-positive ER þ HER2 À and HER2 þ tumors, while upregulated basal-like features were observed in node-negative TNBC tumors. The SLNB reduction rates in patients with ER þ HER2 À tumors were 6% to 7% higher for the MIXED predictor compared with the CLINICAL predictor accepting false negative rates of 5% to 10%.Conclusions: Although CLINICAL and MIXED predictors of nodal metastasis had comparable accuracy, the MIXED predictor identified more node-negative patients. This translational approach holds promise for development of classifiers to reduce the rates of SLNB for patients at low risk of nodal involvement. a Values are median. b According to the TNM classification for breast cancer, seventh edition. c Percentages are calculated from the total number of patients who received adjuvant treatment. d Percentages are calculated from the total number of patients within the age range (40-74 years) for mammography within the National Breast Cancer Screening Programme.Dihge et al.Abbreviations: FN, false negative; FP, false positive; Max NPV, maximum negative predictive value; Nþ, axillary lymph node metastasis; No, no regional lymph node metastasis; SLNB, sentinel lymph node biopsy; TN, true negative; TP, true positive. Dihge et al.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Dihge

Vallon‐Christersson

Hegardt

et al. 2019

Clinical Cancer Research

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“…The spread of screening mammography has led to increasing occurrences of microcalcifications [14, 15]. Mammographically detected microcalcifications represent the earliest mammographic findings of non-palpable breast cancers, which are found in approximately 70% of minimal breast carcinomas [16, 17] To the best of our knowledge, no studies have determined whether a calcification features combined with clinicopathological parameters would enable superior prediction of LN metastasis in IDC of breast.…”

Section: Introductionmentioning

confidence: 99%

BI-RADS 3-5 microcalcifications: prediction of lymph node metastasis of breast cancer

et al. 2017

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PurposeTo determine whether the clinicopathological parameters and Breast Imaging Reporting and Data System (BI-RADS) 3–5 microcalcifications differed between lymph node positive (LN (+)) and lymph node negative (LN (−)) invasive ductal carcinoma (IDC).ResultsFor microcalcification-associated breast cancers, seven selected features (age, tumor size, Ki-67 status, lymphovascular invasion, calcification range, calcification diameter and calcification density) were significantly associated with LN status (all P < 0.05). Multivariate logistic regression analysis found that three risk factors (age: older vs. younger OR: 0.973 P = 0.006, tumor size: larger vs. smaller OR: 1.671, P < 0.001 and calcification density: calcifications > 20/cm2 vs. calcifications ≤ 20/cm2 OR: 1.698, P < 0.001) were significant independent predictors. This model had an area under the receiver operating characteristic curve (AUC) of 0.701. The nodal staging (N0 and N1 χ2 = 5.701, P = 0.017; N0 and N2 χ2 = 6.614, P = 0.013) was significantly positively associated with calcification density. The luminal B subtype had the highest risk of LN metastasis. Multivariate analysis demonstrated that calcification > 2 cm in range (OR: 2.209) and larger tumor size (OR: 1.882) were independently predictive of LN metastasis in the luminal B subtype (AUC = 0.667).Materials and MethodsMammographic images of 419 female breast cancer patients were included. Associations between the risk factors and LN status were evaluated using a Chi-square test, ANOVA and binary logistic regression analysis.ConclusionsThis study found that age, tumor size and calcifications density can be conveniently used to facilitate the preoperative prediction of LN metastasis. The luminal B subtype has the highest risk of LN metastasis among the microcalcification-associated breast cancers.

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“…The combined analysis of a panel of biomarkers, rather than individual analyses, as a nomogram is the most promising approach that is powerful enough to change clinical management . Although classifier models based on clinical‐associated factors, such as axillary ultrasound (AUS) and tumor size, have been investigated and demonstrated to be useful for ALN metastasis prediction in patients with BC, an optimal approach that combines multiple miRNAs biomarkers as a predictive signature has yet to be developed . To the best of our knowledge, there is no literature that has determined whether a miRNAs signature would enable superior prediction of ALN status.…”

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confidence: 99%

Preoperative prediction nomogram based on primary tumor miRNAs signature and clinical‐related features for axillary lymph node metastasis in early‐stage invasive breast cancer

Xie

Tan

Chen

et al. 2018

Intl Journal of Cancer

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More than half patients who undergo axillary lymph node (ALN) surgery are ALN negative in early-stage invasive breast cancer (EIBC). Thus, to avoid excessive treatment, we aim to establish and validate a novel nomogram model for the preoperative diagnosis of ALN status in patients with EIBC. In total, 864 patients with EIBC from two independent centers were enrolled in our study. For the discovery set, miRNAs expression profiling with functional roles in ALN metastasis was discovered by microarray analysis and validated by quantitative polymerase chain reaction (PCR). For the training and validation cohorts, we used PCR to quantify miRNAs expression in a model development cohort and assessed miRNAs signature in an internal validation cohort and external independent validation cohort. Multivariable logistic regression analyses were used to establish a nomogram model for the likelihood of ALN metastasis from miRNAs signature and clinical variables. A signature of nine-miRNA was significantly associated with ALN status. The predictive ability of our nomogram that included miRNAs signature and clinical-related variables (age, tumor size, tumor location and axillary ultrasound-reported ALN status) was significantly greater than a model that only considered clinical-related factors (concordance index: 0.856, 0.796) and also performed well in the two validation cohorts (concordance index: 0.841, 0.747). Our nomogram is a reliable prediction method that can be conveniently used to preoperatively predict ALN status in patients with EIBC. Therefore, after further confirmation in prospective and multicenter clinical trial, omission of axillary surgery may be feasible for some patients with EIBC in the future.

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Development of a prediction model for lymph node metastasis in luminal A subtype breast cancer: The possibility to omit sentinel lymph node biopsy

Cited by 14 publications

References 37 publications

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

BI-RADS 3-5 microcalcifications: prediction of lymph node metastasis of breast cancer

Preoperative prediction nomogram based on primary tumor miRNAs signature and clinical‐related features for axillary lymph node metastasis in early‐stage invasive breast cancer

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