Identification of Texture Features from FDG PET images that are associated with Survival Outcomes in Non-small Cell Lung Cancer
Session type: Poster / e-Poster / Silent Theatre session
Theme: Diagnosis and therapy
Tumour features derived from image texture represent relationships between grey-scale voxel intensity at local or global level, reflecting underlying tumour heterogeneity. The benefits of using imaging biomarkers over biopsy include its non-invasive nature, whole lesion analysis and ease of temporal follow-up (1). The ability to quantify heterogeneity allows user independency and may guide personalised treatment. The purpose of this study was to identify pre-treatment 2-deoxy-2-(18F)-fluoro-D-glucose PET/CT (FDG-PET/CT) texture features in primary non-small cell lung cancer (NSCLC) as predictors of survival.
This retrospective multi-centre study from 6 centres evaluated 340 subjects with NSCLC (Stage I-III) who underwent FDG-PET/CT prior to radical radiotherapy treatment. Basic PET-derived parameters were documented selecting a minimum tumour volume of 5ml. Primary tumour segmentation was performed using a semi-automated adaptive threshold method (2). 660 texture features, including local, regional, global, fractal and wavelet techniques were extracted for 7 different grey levels using TexLAB v2. The dataset was randomly split into three sets - for independent training, validation and test. Lasso regression analysis (3) was used for feature selection, radiomic signature building and overall survival prediction.
Of 340 patients, 261 died within the follow-up period (median 22 (range 0-85) months). The most robust composite radiomics feature-set comprised of 5 texture attributes derived from 3 different wavelet transformations at 32 grey-level, and was significantly associated with survival in the test set (p=0.008; HR 1.4 with stage). This feature-set was independent of established prognostic markers, such as tumour stage (p=0.012, HR 0.644) and was stable across data from different PET/CT scanners within the collaborating centres.
A radiomics feature-set in NSCLC that predicts survival has been defined in a large multi-centre patient cohort. The use of PET scans from different centres demonstrates the robustness of the technique.