Homology-based approach for prognostic prediction of lung cancer using novel topologically invariant radiomic features

K. Ninomiya; H. Arimura

doi:10.1117/12.2548918

10 March 2020 Homology-based approach for prognostic prediction of lung cancer using novel topologically invariant radiomic features

K. Ninomiya, H. Arimura

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113131N (2020) https://doi.org/10.1117/12.2548918
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

We aimed to develop a homology-based approach for prognostic prediction of lung cancer using novel topologically invariant radiomic features. The feasibility of homology-based radiomic features (HFs) was investigated by comparing them with conventional wavelet-based features (WFs) using a Kaplan–Meier analysis for a training dataset (n=135) and a validation dataset (n=70). A total of 13,825 HFs were obtained from histogram and texture features within gross tumor volumes on the computed tomography images using Betti numbers in homology. Similarly, 216 WFs were derived from four wavelet-decomposed images. The prognostic potentials of the HFs were evaluated using statistically significant differences (p-values < 0.05, log-rank test) to compare two survival curves of high- and low-risk patients, which were stratified with medians of radiomic scores of signatures constructed by using an elastic-net-regularized Cox proportional hazard model derived from a Cox-net algorithm. For the training dataset, p-values with hazard ratios (HRs) between the two survival curves were 6.7 × 10^-6 for the HF (HR: 0.41, 95% confidence interval (CI): 0.26-0.65) and 5.9 × 10^-3 for the WF (HR: 0.57, 95%CI: 0.37-0.88). For the validation dataset, p-values with HRs were 3.4 × 10^-5 for the HF (HR: 0.32, 95%CI: 0.16-0.62) and 6.7 × 10^-1 for the WF (HR: 0.88, 95%CI: 0.48-1.6). The HFs showed the more promising potential than the conventional features for prognostic prediction in lung cancer patients.

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K. Ninomiya and H. Arimura "Homology-based approach for prognostic prediction of lung cancer using novel topologically invariant radiomic features", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131N (10 March 2020); https://doi.org/10.1117/12.2548918

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