RNSCLC-PRSP software to predict the prognostic risk and survival in patients with resected T1-3N0–2 M0 non-small cell lung cancer

• Published in: BioData mining Vol. 12; no. 1; pp. 17 - 14
• Main Authors: Zhang, Yunkui, Li, YaoChen, Zhang, Rongsheng, Zhang, Yujie, Ma, Haitao
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.08.2019; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Bioinformatics; Biomedical and Life Sciences; Cancer metastasis; Cancer patients; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer programs; Data Mining and Knowledge Discovery; Factor analysis; Health aspects; Life Sciences; Lung cancer; Medical prognosis; Medical research; Metastases; Non-small cell lung cancer; Non-small cell lung carcinoma; Novels; Patients; Prognosis; Prognostic index; Prognostic risk prediction; Regression analysis; Regression models; Resected non-small cell lung cancer; Risk analysis; Risk groups; Small cell lung cancer; Software; Software Article; Survival; Survival prediction; Tumors; Web sites (World Wide Web); Prognostic risk prediction; Survival prediction; Software; Prognostic index; Resected non-small cell lung cancer
• ISSN: 1756-0381; 1756-0381
• DOI: 10.1186/s13040-019-0205-0

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.