LLPi: Liverpool Lung Project Risk Prediction Model for Lung Cancer Incidence

Abstract

Identification of high-risk individuals will facilitate early diagnosis, reduce overall costs, and also improve the current poor survival from lung cancer. The Liverpool Lung Project prospective cohort of 8,760 participants ages 45 to 79 years, recruited between 1998 and 2008, was followed annually through the hospital episode statistics until January 31, 2013. Cox proportional hazards models were used to identify risk predictors of lung cancer incidence. C-statistic was used to assess the discriminatory accuracy of the models. Models were internally validated using the bootstrap method. During mean follow-up of 8.7 years, 237 participants developed lung cancer. Age [hazard ratio (HR), 1.04; 95% confidence interval (CI), 1.02-1.06], male gender (HR, 1.48; 95% CI, 1.10-1.98), smoking duration (HR, 1.04; 95% CI, 1.03-1.05), chronic obstructive pulmonary disease (HR, 2.43; 95% CI, 1.79-3.30), prior diagnosis of malignant tumor (HR, 2.84; 95% CI, 2.08-3.89), and early onset of family history of lung cancer (HR, 1.68; 95% CI, 1.04-2.72) were associated with the incidence of lung cancer. The LLPi risk model had a good calibration (goodness-of-fit c 2 7.58, P ¼ 0.371). The apparent C-statistic was 0.852 (95% CI, 0.831-0.873) and the optimism-corrected bootstrap resampling C-statistic was 0.849 (95% CI, 0.829-0.873). The LLPi risk model may assist in identifying individuals at high risk of developing lung cancer in population-based screening programs. Cancer Prev Res; 8(6); 570-5. Ó2015 AACR.

Validation of the SHOX2 / PTGER4 DNA Methylation Marker Panel for Plasma-Based Discrimination between Patients with Malignant and Nonmalignant Lung Disease

“…As a consequence, the NELSON trial used a substantially different definition of a positive screening result, which led to a 10-fold decrease in the positive rate (2.7%), reducing the proportion of false positives to 60% at the expense of some reduction in sensitivity for LC detection 5, 6. The need for better definition of the screening-eligible population led to risk assessment models developed from large trials 7, 8. Similarly, algorithms for management of so-called intermediate nodules have been published 9, 10, 11.…”

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

“…In the European Prospective Investigation into Cancer and Nutrition cohort, Hoggart et al [25] developed and internally validated separate models to predict one-year lung cancer risk for former, current, and ever smokers (AUCs, 0.82–0.84), considering age and smoking history. Based on longitudinal data on LLP participants, Marcus et al [26] constructed the LLPi model to include similar predictors to the original LLP model, which displayed good calibration and excellent discrimination (AUC, 0.85). Leveraging routinely-collected data from English general health practices on >6.5 million adults, Hippisley-Cox and Coupland [27] built highly discriminatory and well-calibrated sex-specific models that predict ten-year lung cancer risk based on age, race/ethnicity, BMI, Townsend deprivation score, smoking status and intensity, COPD, asthma, history of cancer, family history of lung cancer, asbestos exposure, and alcohol use (AUCs >0.90).…”

Design and application of tumor prediction model based on statistical method

“…Number of hidden layer units: In this paper we construct the models of units with 1, 2, 3, (1,1), (1,2) and (2,1) in the hidden layer and test them on the test data set. The results show that (1,1) with two hidden layers has the highest accuracy in the measurement of correctness.…”

“…The cox proportional hazards regression models have also been used to predict the risk of lung cancer [2]. It is a multivariate analysis method, has a more flexible application, and it cannot consider the survival time distribution.…”