Persistent Homology of Tumor CT Scans is Associated with Survival In Lung Cancer

ABSTRACT Purpose Radiomics, the objective study of non-visual features in clinical imaging, has been useful in informing decisions in clinical oncology. However, radiomics currently lacks the ability to characterize the overall topological structure of the data. This niche can be filled by persistent homology, a form of topological data analysis that analyzes high-level structure. We hypothesized that persistent homology could be applied to lung tumor scans and analyze clinical outcomes. Methods We obtained segmented computed tomography lung scans (n = 565) from the NSCLC-Radiomics and NSCLC-Radiogenomics datasets in The Cancer Imaging Archive. For each scan, a cubical complex filtration based on Hounsfield units was generated. We calculated a feature curve that plotted the number of 0 dimensional topological features against each Hounsfield unit. This curve’s first moment of distribution was utilized as a summary statistic to predict survival in a Cox proportional hazards model. Results After controlling for tumor image size, age, and stage, the first moment of the 0D topological feature curve was associated with poorer survival (HR = 1.118; 95% CI = 1.026-1.218; p = 0.01). The patients in our study with the lowest first moment scores had significantly better survival (1238 days; 95% CI = 936-1599) compared to the patients with the highest first moment scores (429 days; 95% CI = 326-601; p = .0015). Conclusions We have shown that persistent homology can generate useful clinical correlates from tumor CT scans. Our 0-dimensional topological feature curve statistic predicts survival in lung cancer patients. This novel statistic may be used in tandem with standard radiomics variables to better inform clinical oncology decisions.