Nomogram to Predict Internal Mammary Lymph Nodes Metastasis in Patients With Breast Cancer.

ABSTRACT: Background: Numerous studies have showed that internal mammary lymph node (IMLN) metastasis is an important adverse prognostic factor in patients with breast cancer (BC), however, there are no available prediction model for the preoperative diagnosis of IMLN metastasis. Methods: Data from 102 breast cancer patients treated with IMLN operation were used to establish and calibrate a nomogram for IMLN status based on multivariate logistic regression. Prediction performance of this model was further validated with a second set of 50 patients with BC. Discrimination of the predict model was assessed by the C-index, and calibration assessed by calibration plots. Moreover, we conducted the decision curve analysis (DCA) to evaluate the clinical value of the nomogram. Finally, the survival status of patients in different risk groups based on nomogram were also compared. Results: The final multivariate regression model included tumor location, lymph vascular invasion (LVI), and pathological axillary lymph node stage (pALN stage). A nomogram was developed as a graphical representation of the model and had good calibration and discrimination in both sets (with C-index of 0.86 and 0.83 for the training and validation set, respectively). Moreover, the DCA showed the clinical usefulness of our constructed nomogram. False negative (FN) in low risk group classified by nomogram (FN-LR-nomogram) did not significantly impact adjuvant treatment decision making, and more importantly, patients with FN-LR-nomogram had recurrence-free survival equivalent to patients with pathologically ture negative in low risk group classified by nomogram (TN-LR-nomogram). Conclusions: As a non-invasive prediction tool, our nomogram shows favorable predictive accuracy for IMLN metastasis in patients with BC and can serve as a basis to integrate future molecular markers for its clinical application.

SUBMITTER: Xie X 

PROVIDER: S-EPMC6857087 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature