Mean residual life regression with functional principal component analysis on longitudinal data for dynamic prediction.

Predicting patient life expectancy is of great importance for clinicians in making treatment decisions. This prediction needs to be conducted in a dynamic manner, based on longitudinal biomarkers repeatedly measured during the patient's post-treatment follow-up period. The prediction is updated any time a new biomarker measurement is obtained. The heterogeneity across patients of biomarker trajectories over time requires flexible and powerful approaches to model noisy and irregularly measured longitudinal data. In this article, we use functional principal component analysis (FPCA) to extract the dominant features of the biomarker trajectory of each individual, and use these features as time-dependent predictors (covariates) in a transformed mean residual life (MRL) regression model to conduct dynamic prediction. Simulation studies demonstrate the improved performance of the transformed MRL model that includes longitudinal biomarker information in the prediction. We apply the proposed method to predict the remaining time expectancy until disease progression for patients with chronic myeloid leukemia, using the transcript levels of an oncogene, BCR-ABL.

Using multi-centre data to train and validate a knowledge-based model for planning radiotherapy of the head and neck.

Background and purpose: Knowledge-based radiotherapy planning models have been shown to reduce healthy tissue dose and optimisation times, with larger training databases delivering greater robustness. We propose a method of combining knowledge-based models from multiple centres to create a 'super-model' using their collective patient libraries, thereby increasing the breadth of training knowledge. Materials and methods: A head and neck super-model containing 207 patient datasets was created by merging the data libraries of three centres. Validation was performed on 30 independent datasets during which optimiser parameters were tuned to deliver the optimal set of model template objectives. The super-model was tested on a further 40 unseen patients from four radiotherapy centres, including one centre external to the training process. The generated plans were assessed using established plan evaluation criteria. Results: The super-model generated plans that surpassed the dose objectives for all patients with single optimisations in an average time of 10 min. Healthy tissue sparing was significantly improved over manual planning, with dose reductions to parotid of 4.7 ± 2.1 Gy, spinal cord of 3.3 ± 0.9 Gy and brainstem of 2.9 ± 1.7 Gy. Target coverage met the established constraints but was marginally reduced compared with clinical plans. Conclusions: Three centres successfully merged patient libraries to create a super-model capable of generating plans that met plan evaluation criteria for head and neck patients with improvements in healthy tissue sparing. The findings indicate that the super-model could improve head and neck planning quality, efficiency and consistency across radiotherapy centres.

Development and application of a dynamic prediction model for esophageal cancer.

BACKGROUND: Current prediction models of esophageal cancer (EC) are limited to predicting at a specific time point, and ignore changes in hazard ratios of predictive variables, known as time-varying effects. Our study aimed to investigate variables with time-varying effects in EC and to develop a prediction model that can update the 5-year predicted dynamic overall survival (DOS) probability during the follow-up period. METHODS: Firstly, the clinicopathological information and survival data of 4,541 patients with EC was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2007 and 2011 for modeling. Secondly, the time-varying effect of variables was assessed and the dynamic prediction model was developed based on the proportional baselines landmark supermodel. RESULTS: Here, we found that age at diagnosis, sex, location of primary tumor, histological type, chemotherapy, surgery, and T stage showed significant time-varying effects on overall survival. Thirdly, the prediction model was validated by an internal SEER validation cohort and a Chinese patient cohort, respectively, and achieved promising results as follows: area under the curve (AUC) =0.733 (internal validation) and 0.864 (external validation). The heuristic shrinkage factor was 0.995. Finally, several clear cases were selected as examples for model application to map the patient's 5-year DOS curves and to respectively demonstrate the impact of different variables' time-varying effect on survival. CONCLUSIONS: Overall, our results suggest that the existence of time-varying effect highlights the importance of updating the predicted survival probability during the follow-up period. Moreover, this prediction model can be used to assist doctors in making more-individualized treatment decisions based on a dynamic assessment of patient prognosis.