TY - GEN
T1 - Homology-based approach for prognostic prediction of lung cancer using novel topologically invariant radiomic features
AU - Ninomiya, K.
AU - Arimura, H.
N1 - Publisher Copyright:
© 2020 SPIE. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85092593543&partnerID=8YFLogxK
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U2 - 10.1117/12.2548918
DO - 10.1117/12.2548918
M3 - Conference contribution
AN - SCOPUS:85092593543
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2020
A2 - Isgum, Ivana
A2 - Landman, Bennett A.
PB - SPIE
T2 - Medical Imaging 2020: Image Processing
Y2 - 17 February 2020 through 20 February 2020
ER -
