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

K. Ninomiya, H. Arimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
PublisherSPIE
ISBN (Electronic)9781510633933
DOIs
Publication statusPublished - 2020
EventMedical Imaging 2020: Image Processing - Houston, United States
Duration: Feb 17 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11313
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Image Processing
Country/TerritoryUnited States
CityHouston
Period2/17/202/20/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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