Dataset Information

Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool.

ABSTRACT:

Background

This study aimed to develop an easy to use prediction model to predict the risk of having a total of 1 to 2, ?3, or ?4 positive axillary lymph nodes (LNs), for patients with sentinel lymph node (SLN) positive breast cancer.

Methods

Data of 911 SLN positive breast cancer patients were used for model development. The model was validated externally in an independent population of 180 patients with SLN positive breast cancer.

Results

Final pathology after ALND showed additional positive LN for 259 (28%) of the patients. A total of 726 (81%) out of 911 patients had a total of 1 to 2 positive nodes, whereas 175 (19%) had ?3 positive LNs. The model included three predictors: the tumor size (in mm), the presence of a negative SLN, and the size of the SLN metastases (in mm). At external validation, the model showed a good discriminative ability (area under the curve?=?0.82; 95% confidence interval?=?0.74-0.90) and good calibration over the full range of predicted probabilities.

Conclusion

This new and validated model predicts the extent of nodal involvement in node-positive breast cancer and will be useful for counseling patients regarding their personalized axillary treatment.

SUBMITTER: van den Hoven I

PROVIDER: S-EPMC6771524 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool.

van den Hoven Ingrid I van Klaveren David D Verheuvel Nicole C NC van la Parra Raquel F D RFD Voogd Adri C AC de Roos Wilfred K WK Bosscha Koop K Heuts Esther M EM Tjan-Heijnen Vivianne C G VCG Roumen Rudi M H RMH Steyerberg Ewout W EW

Journal of surgical oncology 20190723 4

<h4>Background</h4>This study aimed to develop an easy to use prediction model to predict the risk of having a total of 1 to 2, ≥3, or ≥4 positive axillary lymph nodes (LNs), for patients with sentinel lymph node (SLN) positive breast cancer.<h4>Methods</h4>Data of 911 SLN positive breast cancer patients were used for model development. The model was validated externally in an independent population of 180 patients with SLN positive breast cancer.<h4>Results</h4>Final pathology after ALND showed ...[more]

PMID: 31338839

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Project description:BackgroundDual-targeted therapy is currently the standard adjuvant treatment for human epidermal growth factor receptor 2-positive (HER2+) and lymph node-positive (LN+) breast cancer. However, the optimal therapeutic strategy for patients with HER2+ and lymph node-negative (LN-) breast cancer remains unclear. This population-based study aimed to explore the factors associated with survival in patients with HER2+ and LN- breast cancer, and develop a survival-predicting nomogram in the era of trastuzumab-based single-targeted therapy.MethodsWe collected the clinicopathological information of HER2+ and LN- breast cancer patients who underwent chemotherapy and surgery from The Surveillance, Epidemiology, and End Results (SEER) database (2010-2016, the Trastuzumab-based single-targeted therapy era). We subsequently explored the risk factors for breast cancer-specific survival (BCSS) and overall survival (OS) using a Cox proportional hazards regression model, aiming to identify subgroups with worse prognosis, which would indicate potential demand for dual-targeted therapy. Three- and 5-year survival probability-predictive nomograms were established and subjected to bootstrap internal validation. The concordance index (C-index) and calibration curve were applied to evaluate the performance of the model.ResultsAfter data cleansing, a total of 13,755 patients were included in the current analysis. Using univariate and multivariate Cox proportional hazards regression, higher clinical T stage, hormone receptors-negative (HR-), and partial mastectomy without radiotherapy were identified as independent risk factors for BCSS and OS in patients with HER2+ and LN- breast cancer. Nomograms for 3- and 5-year BCSS and OS incorporating the selected prognostic factors were established. Calibration curves verified good consistency between the actual and nomogram-predicted survival probability. The C-index values of the BCSS and OS predictions and 95% confidence interval (CI) were 0.773 (0.740-0.806) and 0.764 (0.737-0.791), respectively.ConclusionsHigher clinical T stage, HR-, and partial mastectomy without radiotherapy predicted worse prognosis in patients with HER2+ and LN- breast cancer. In clinical practice, patients can be recommended for single-targeted or dual-targeted therapy according to the individualized factors.

Project description: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.

Project description:BackgroundPathological complete response (pCR) of axillary lymph nodes (ALNs) is frequently achieved in patients with clinically node-positive breast cancer after neoadjuvant chemotherapy (NAC), and ALN status is an important prognostic factor for breast cancer patients. This study aims to develop a new predictive clinical model to assess the ALN pCR rate after NAC.MethodsThis was a retrospective series of 467 patients who had biopsy-proven positive ALNs at diagnosis and underwent ALN dissection from 2007 to 2014 at the National Cancer Center/Cancer Hospital of the Chinese Academy of Medical Sciences. We analyzed the clinicopathologic features of the patients and developed a nomogram to predict the probability of ALN pCR. A multivariable logistic regression stepwise model was used to construct a nomogram to predict ALN pCR in node-positive patients. The adjusted area under the receiver operating characteristic curve (AUC) was calculated to quantify the ability to rank patients by risk. Internal validation was performed using the 50/50 hold-out validation method. The nomogram was externally validated with prospective cohorts of 167 patients from 2016 to 2018 at the Cancer Hospital of the Chinese Academy of Medical Sciences and 114 patients from 2018 to 2020 at Beijing Tiantan Hospital.ResultsIn this retrospective study, 115 (24.6%) patients achieved ALN pCR after NAC. Multivariate analysis showed that clinical tumor stage (Odds ratio [OR]: 0.321, 95% confidence interval [CI]: 0.121-0.856; P = 0.023); primary tumor response (OR: 0.189; 95% CI: 0.123-0.292; P < 0.001), and estrogen receptor status (OR: 0.530, 95% CI: 0.304-0.925; P = 0.025) were independent predictors of ALN pCR. The nomogram was constructed based on the result of multivariate analysis. In the internal validation of performance of nomogram, the AUCs for the training and test sets were 0.719 and 0.753, respectively. The nomogram was validated in external cohorts with AUCs of 0.720, which demonstrated good discriminatory power in these data sets.ConclusionWe developed a nomogram to predict the likelihood of axillary pCR in node-positive breast cancer patients after NAC. The predictive model performed well in multicenter prospective external validation. This practical tool could provide information to surgeons regarding whether to perform additional ALN dissection after NAC.Trial registrationChiCTR.org.cn, ChiCTR1800014968.

Project description:Significant advances in lymph node involvement (LNI) risk modeling in prostate cancer (PCa) have been achieved with the addition of visual interpretation of magnetic resonance imaging (MRI) data, but it is likely that quantitative analysis could further improve prediction models. In this study, we aimed to develop and internally validate a novel LNI risk prediction model based on radiomic features extracted from preoperative multimodal MRI. All patients who underwent a preoperative MRI and radical prostatectomy with extensive lymph node dissection were retrospectively included in a single institution. Patients were randomly divided into the training (60%) and testing (40%) sets. Radiomic features were extracted from the index tumor volumes, delineated on the apparent diffusion coefficient corrected map and the T2 sequences. A ComBat harmonization method was applied to account for inter-site heterogeneity. A prediction model was trained using a neural network approach (Multilayer Perceptron Network, SPSS v24.0©) combining clinical, radiomic and all features. It was then evaluated on the testing set and compared to the current available models using the Receiver Operative Characteristics and the C-Index. Two hundred and eighty patients were included, with a median age of 65.2 y (45.3-79.6), a mean PSA level of 9.5 ng/mL (1.04-63.0) and 79.6% of ISUP ≥ 2 tumors. LNI occurred in 51 patients (18.2%), with a median number of extracted nodes of 15 (10-19). In the testing set, with their respective cutoffs applied, the Partin, Roach, Yale, MSKCC, Briganti 2012 and 2017 models resulted in a C-Index of 0.71, 0.66, 0.55, 0.67, 0.65 and 0.73, respectively, while our proposed combined model resulted in a C-Index of 0.89 in the testing set. Radiomic features extracted from the preoperative MRI scans and combined with clinical features through a neural network seem to provide added predictive performance compared to state of the art models regarding LNI risk prediction in PCa.

Dataset Information

Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool.

Background

Methods

Results

Conclusion

Publications

Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool.

Similar Datasets

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