Personalized Rx

Personalized Rx is designed to provide physicians and other health-care and research professionals with accurate prognostication, and to highlight the benefits of potential courses of treatment of non-small cell lung cancer (NSCLC). Currently, the model utilizes information about an individual patient to provide an estimate of disease-specific survival. Multiple factors are combined to give an integrated risk-assessment based on the clinical data of thousands of patients diagnosed with lung adenocarcinoma and squamous cell lung cancer. For each patient, estimates on overall survival, treatment-specific survival, and an estimation of risk relative to other patients are provided in graphical and text format.

What does Personalized Rx offer?

Personalized Rx gives an estimate of 2 or 5-year survival for a patient with NSCLC who received surgery, radiation, or surgery plus radiation. These treatments were administered alone, or in combination with chemotherapy.

What patient cohorts were used in the model development?

A total of 234,412 patients diagnosed with adenocarcinomas or squamous cell carcinomas of the lung or bronchus between 1988 and 2006 were retrieved from the SEER database to construct a prognostic model. Two additional patient cohorts (n = 1,991) were used as an external validation.

What are major advantages of this model over the tumor staging system?

  • The comprehensive model consistently outperformed the model using stage alone in prognostic stratification and on Harrell's C, Nagelkerke's R2, and Brier Scores in the whole patient population as well as in specific treatment modalities.
  • The comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P < 0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses.

What information is needed to use this tool?

  • Age
  • Sex
  • Tumor grade
  • Tumor stage
  • AJCC Staging Edition
  • Race
  • Histology


Putila J, Remick SC, Guo NL (2011) Combining Clinical, Pathological, and Demographic Factors Refines Prognosis of Lung Cancer: A Population-Based Study. PLoS ONE 6(2): e17493. doi:10.1371/journal.pone.0017493 Manuscript.

Putila J, Guo NL (2014) Combining COPD with Clinical, Pathological and Demographic Information Refines Prognosis and Treatment Response Prediction of Non-Small Cell Lung Cancer. PLoS One 9(6):e100994. doi: 10.1371/journal.pone.0100994. Manuscript.