Abstract
BACKGROUND AND PURPOSE: Radiotherapy targets DNA in cancer cell nuclei. Radiation dose, however, is prescribed to a macroscopic target volume assuming uniform distribution, failing to consider microscopic variations in dose absorbed by individual nuclei. This study investigated a potential link between pre-treatment tumour nuclear size distributions and post-radiotherapy outcomes in gynecological squamous cell carcinoma (SCC). MATERIALS AND METHODS: Our multi-institutional cohort consisted of 191 non-metastatic gynecological SCC patients who had received radiotherapy with diagnostic whole slide images (WSIs) available. Tumour nuclear size distribution mean and standard deviation were extracted from WSIs using deep learning, and used to predict progression-free interval (PFI) and overall survival (OS) in multivariate Cox proportional hazards (CoxPH) analysis adjusted for age and clinical stage. RESULTS: Multivariate CoxPH analysis revealed that a larger nuclear size distribution mean results in more favorable outcomes for PFI (HR = 0.45, 95% CI: 0.19 - 1.09, p = 0.084) and OS (HR = 0.55, 95% CI: 0.24 - 1.25, p = 0.16), and that a larger nuclear size standard deviation results in less favorable outcomes for PFI (HR = 7.52, 95% CI: 1.43 - 39.52, p = 0.023) and OS (HR = 4.67, 95% CI: 0.96 - 22.57, p = 0.063). The bootstrap-validated C-statistic was 0.56 for PFI and 0.57 for OS. CONCLUSION: Despite low accuracy, tumour nuclear size heterogeneity aided prognostication over standard clinical variables and was associated with outcomes following radiotherapy in gynecological SCC. This highlights the potential importance of personalized multiscale dosimetry and warrants further large-scale pan-cancer studies.