An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [F]FDG PET/CT radiomics.

[PURPOSE] Accurate prognostication is crucial for guiding personalised treatment strategies in non-small cell lung cancer (NSCLC). While radiomics offers promise, few features are derived from cancer models with causal justification to support their biological validity. This study evaluated the prognostic utility of normalised hotspot-to-centroid distance (NHOC), a recently proposed [18F]FDG PET imaging metric derived from a cancer evolutionary model, and its integration with PET/CT radiomics and clinical features to form a composite signature, non-invasive lung cancer evolution vector (nLCEV).

[METHODS] A retrospective, multi-centre study was conducted using pre-treatment [18F]FDG PET/CT scans from 285 NSCLC patients (mean age: 67.7 ± 10.1 years; male:female = 171:114, International Association for the Study of Lung Cancer stage: T1/2/3/4/unknown = 61/118/53/52/1, N0/1/2/3/unknown = 133/46/71/34/1, M0/1/unknown = 222/62/1) from Imperial College Healthcare NHS Trust as the discovery cohort. External validation cohorts included patients from King's College (n = 53), Royal Marsden (n = 63), Mount Vernon (n = 61), and Nottingham University (n = 38) hospitals. NHOC was evaluated for 3-year overall survival prediction and combined with a multi-regional PET/CT radiomics predictive vector (RPV) and disease stage to develop nLCEV.

[RESULTS] NHOC and RPV demonstrated independent prognostic value (hazard ratio (HR) [95% confidence interval]: 2.52 [1.60-3.98] and 2.68 [2.13-3.38], respectively). nLCEV achieved an area under the receiver operating characteristic curve of 0.76 [0.60-0.92] and stratified patients into high- and low-risk groups across all validation cohorts with significant HR: KCL 3.27 [1.31, 8.16], Marsden 2.21 [1.02, 4.78], Mount Vernon 2.60 [1.42, 4.76], and Nottingham 4.14 [1.44, 11.90] (all p < 0.05).

[CONCLUSION] NHOC enhances NSCLC patient survival prediction, and when integrated with PET-CT radiomics and disease stage, offers a robust, non-invasive approach to disease prognostication.