Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with (chemo)radiotherapy are of limited quality. In this work, we develop a predictive model of survival at two years based on a large volume of historical patient data, as a proof of concept, using a distributed learning approach.
Patients and methods
Aim: Improve the prognostic prediction of clinical variables for non-small cell lung cancer (NSCLC), by selecting from blood-biomarkers, non-invasively describing hypoxia, inflammation and tumour load.
Purpose: Although homogeneous according to TNM staging system, stage III NSCLC patients form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but also complicate treatment decision making. Decision Support Systems (DSS), providing individualized prognostic information, can overcome this, but are currently lacking. A DSS for stage III NSCLC requires development and integration of multiple models.
Maximum, mean and peak SUV of primary tumor at baseline FDG-PET scans, have often been found predictive for overall survival in non-small cell lung cancer (NSCLC) patients. In this study we further investigated the prognostic power of advanced metabolic metrics derived from intensity volume histograms (IVH) extracted from PET imaging.
Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features.
For the EuroCAT project, a research protocol that describes a standardized data collection for non-small cell lung cancer was written and has been approved by the Medical Ethical Board of our hospital. A copy of the protocol and the appendices, including scoring of side effects, quality of life questionnaires and optional biobank procedure can be downloaded below.