Log odds of positive lymph nodes is superior to the number- and ratio-based lymph node classification systems for colorectal cancer patients undergoing curative (R0) resection.

The metastatic lymph node status (N classification) is an important prognostic factor for patients with colorectal cancer (CRC). The aim of the present study was to evaluate and compare the prognostic assessment of three different lymph node staging methods, namely standard lymph node (pN) staging, metastatic lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) in CRC patients who undergo curative resection (R0). Data were retrospectively collected from 192 patients who had undergone R0 resection. Kaplan-Meier survival curves, Cox proportional hazards model and accuracy of the three methods (pN, LNR and LODDS) were compared to evaluate the prognostic effect. Univariate analysis demonstrated that pN, LNR and LODDS were all significantly correlated with survival (P=0.001, P<0.001 and P<0.001, respectively). The final result of the 3-step multivariate analysis demonstrated that LODDS was superior to the other two N categories. Patients in the same pN or LNR classifications may be classified into different LODDS stages with different prognoses. Thus, LODDS may be a meaningful prognostic indicator and superior to the pN and LNR classifications in CRC patients who undergo curative (R0) resection.

LDA-SVM-based EGFR mutation model for NSCLC brain metastases: an observational study.

Epidermal growth factor receptor (EGFR) activating mutations are a predictor of tyrosine kinase inhibitor effectiveness in the treatment of non-small-cell lung cancer (NSCLC). The objective of this study is to build a model for predicting the EGFR mutation status of brain metastasis in patients with NSCLC. Observation and model set-up. This study was conducted between January 2003 and December 2011 in 6 medical centers in Southwest China. The study included 31 NSCLC patients with brain metastases. Eligibility requirements were histological proof of NSCLC, as well as sufficient quantity of paraffin-embedded lung and brain metastases specimens for EGFR mutation detection. The linear discriminant analysis (LDA) method was used for analyzing the dimensional reduction of clinical features, and a support vector machine (SVM) algorithm was employed to generate an EGFR mutation model for NSCLC brain metastases. Training-testing-validation (3 : 1 : 1) processes were applied to find the best fit in 12 patients (validation test set) with NSCLC and brain metastases treated with a tyrosine kinase inhibitor and whole-brain radiotherapy. Primary and secondary outcome measures: EGFR mutation analysis in patients with NSCLC and brain metastases and the development of a LDA-SVM-based EGFR mutation model for NSCLC brain metastases patients. EGFR mutation discordance between the primary lung tumor and brain metastases was found in 5 patients. Using LDA, 13 clinical features were transformed into 9 characteristics, and 3 were selected as primary vectors. The EGFR mutation model constructed with SVM algorithms had an accuracy, sensitivity, and specificity for determining the mutation status of brain metastases of 0.879, 0.886, and 0.875, respectively. Furthermore, the replicability of our model was confirmed by testing 100 random combinations of input values. The LDA-SVM-based model developed in this study could predict the EGFR status of brain metastases in this small cohort of patients with NSCLC. Further studies with larger cohorts should be carried out to validate our findings in the clinical setting.