Background
Lung cancer is increasingly being detected at early stages. Although small tumor size associates with increased likelihood of curative resection, 5-year overall survival is only 63% and 44% for stage I and II respectively. For operable patients, lobectomy is the current standard of care, where stereotactic body radiation therapy (SBRT) is the standard of care for inoperable patients. Lung-sparing treatments (e.g. segmentectomy) are also available. An accurate staging of the disease, which includes determining the extent of lymph node involvement, is crucial to determine whether additional local or systemic adjuvant treatment is needed. Although these local and systemic adjuvant treatments are available, these are effective in only a subsets of patients and come with side-effects.
Aim
This project aimed to improve prognostication of early stage non-small cell lung cancer based on pre-operative imaging, enabling clinicians to personalize treatment to patients’ individual characteristics. We hypothesized that the incorporation of metabolic features derived from 18F-FDG PET/CT, advanced quantitative features derived from CT, and additional molecular and morphological features from pathology have superior prognostic impact to conventional staging to predict time and/or localisation of disease recurrence. This will increase efficacy and minimize exposure to potential side effects. Furthermore, it enables better patient counselling, which is critical for shared decision making.
Results
This project has resulted in an comprehensive dataset consisting of CT, 18F-FDG PET/CT, and pathology images from patients with early stage lung cancer. The dataset is used in numerous follow-up projects that investigate early-stage lung cancer.
Funding
This project has received funding from Stichting Bergh in 't Zadel and Radboud Oncologie Fonds.
