Abstract
BACKGROUND: The clinical outcomes of patients with resected T(1-3)N(0-2)M(0) non-small cell lung cancer (NSCLC) with the same tumor-node-metastasis (TNM) stage are diverse. Although other prognostic factors and prognostic prediction tools have been reported in many published studies, a convenient, accurate and specific prognostic prediction software for clinicians has not been developed. The purpose of our research was to develop this type of software that can analyze subdivided T and N staging and additional factors to predict prognostic risk and the corresponding mean and median survival time and 1-5-year survival rates of patients with resected T(1-3)N(0-2)M(0) NSCLC. RESULTS: Using a Cox proportional hazard regression model, we determined the independent prognostic factors and obtained a prognostic index (PI) eq. PI = ∑(βixi).=0.379X(1)-0.403X(2)-0.267X(51)-0.167X(61)-0.298X(62) + 0.460X(71) + 0.617X(72)-0.344X(81)-0.105X(91)-0.243X(92) + 0.305X(101) + 0.508X(102) + 0.754X(103) + 0.143X(111) + 0.170X(112) + 0.434X(113)-0.327X(122)-0.247X(123) + 0.517X(133) + 0.340X(134) + 0.457X(143) + 0.419X(144) + 0.407X(145). Using the PI equation, we determined the PI value of every patient. According to the quantile of the PI value, patients were divided into three risk groups: low-, intermediate-, and high-risk groups with significantly different survival rates. Meanwhile, we obtained the mean and median survival times and 1-5-year survival rates of the three groups. We developed the RNSCLC-PRSP software which is freely available on the web at http://www.rnsclcpps.com with all major browsers supported to determine the prognostic risk and associated survival of patients with resected T(1-3)N(0-2) M(0) non-small cell lung cancer. CONCLUSIONS: After prognostic factor analysis, prognostic risk grouping and corresponding survival assessment, we developed a novel software program. It is practical and convenient for clinicians to evaluate the prognostic risk and corresponding survival of patients with resected T(1-3)N(0-2)M(0) NSCLC. Additionally, it has guiding significance for clinicians to make decisions about complementary treatment for patients.