Nomograms for preoperative prediction of axillary nodal status in 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

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.

Nomograms for prognostic factors of spinal giant cell tumor combining traditional clinical characteristics with inflammatory biomarkers after gross total resection

Giant cell tumor (GCT) of bone is a common primary bone tumor, which exhibits local aggressiveness and recurrent potential, especially for the spinal lesion. Increasing evidence indicates that inflammation plays a vital role in tumorigenesis and progression. The prognostic value of inflammatory biomarkers in GCT has not been established. A retrospective analysis was conducted in patients with spinal GCT in Changzheng Hospital Orthopedic Oncological Center (CHOOC) between January 2005 and October 2015 and 129 patients were identified eligible. Traditional clinical parameters and inflammatory indexes such as Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and albumin/globulin ratio (AGR) were concluded and analyzed. Kaplan-Meier analysis was used to calculate the disease-free survival (DFS). Cox regression analysis was performed to assess the prognostic factors. Nomograms were established to predict DFS quantitatively for the first time, and Harrell’s concordance index (c-index) was adopted to evaluate prediction accuracy. As results, the DFS was 78.3% in the cohort. Patients were stratified into 2 groups by NLR (≤ 2.70 and > 2.70), PLR (≤ 215.80 and > 215.80), LMR (≤ 2.80 and >2.80) and AGR (< 1.50 and ≥ 1.50). Patients with NLR > 2.70, PLR > 215.80, LMR ≤ 2.80 and AGR < 1.50 were significantly associated with decreased DFS (p < 0.05). Multivariate analysis indicated that treatment history, tumor length, bisphosphonate treatment, NLR and PLR were independent factors of DFS (p < 0.05, respectively). In addition, nomogram on DFS was established according to all significant factors, and c-index was 0.728 (95% CI: 0.710-0.743). Nomograms based on DFS can be recommended as practical models to evaluate prognosis for spinal GCT patients.