TY - JOUR
T1 - Homological radiomics analysis for prognostic prediction in lung cancer patients
AU - Ninomiya, Kenta
AU - Arimura, Hidetaka
N1 - Funding Information:
The authors are grateful to Dr. Kazuaki Nakane, Osaka University and all the members of the Arimura Laboratory (http://web.shs.kyushu-u.ac.jp/~arimura), whose comments and suggestions made enormous contributions to this study. We would also like to thank Editage (www.editage.jp) for their English language editing service.
Publisher Copyright:
© 2019 Associazione Italiana di Fisica Medica
PY - 2020/1
Y1 - 2020/1
N2 - Purpose: This study explored a novel homological analysis method for prognostic prediction in lung cancer patients. Materials and methods: The potential of homology-based radiomic features (HFs) was investigated by comparing HFs to conventional wavelet-based radiomic features (WFs) and combined radiomic features consisting of HFs and WFs (HWFs), using training (n = 135) and validation (n = 70) datasets, and Kaplan–Meier analysis. A total of 13,824 HFs were derived through homology-based texture analysis using Betti numbers, which represent the topologically invariant morphological characteristics of lung cancer. The prognostic potential of HFs was evaluated using statistically significant differences (p-values, log-rank test) to compare the survival curves of high- and low-risk patients. Those patients were stratified into high- and low-risk groups using the medians of the radiomic scores of signatures constructed with an elastic-net-regularized Cox proportional hazard model. Furthermore, deep learning (DL) based on AlexNet was utilized to compare HFs by stratifying patients into the two groups using a network that was pre-trained with over one million natural images from an ImageNet database. Results: For the training dataset, the p-values between the two survival curves were 6.7 × 10−6 (HF), 5.9 × 10−3 (WF), 7.4 × 10−6 (HWF), and 1.1 × 10−3 (DL). The p-values for the validation dataset were 3.4 × 10−5 (HF), 6.7 × 10−1 (WF), 1.7 × 10−7 (HWF), and 1.2 × 10−1 (DL). Conclusion: This study demonstrates the excellent potential of HFs for prognostic prediction in lung cancer patients.
AB - Purpose: This study explored a novel homological analysis method for prognostic prediction in lung cancer patients. Materials and methods: The potential of homology-based radiomic features (HFs) was investigated by comparing HFs to conventional wavelet-based radiomic features (WFs) and combined radiomic features consisting of HFs and WFs (HWFs), using training (n = 135) and validation (n = 70) datasets, and Kaplan–Meier analysis. A total of 13,824 HFs were derived through homology-based texture analysis using Betti numbers, which represent the topologically invariant morphological characteristics of lung cancer. The prognostic potential of HFs was evaluated using statistically significant differences (p-values, log-rank test) to compare the survival curves of high- and low-risk patients. Those patients were stratified into high- and low-risk groups using the medians of the radiomic scores of signatures constructed with an elastic-net-regularized Cox proportional hazard model. Furthermore, deep learning (DL) based on AlexNet was utilized to compare HFs by stratifying patients into the two groups using a network that was pre-trained with over one million natural images from an ImageNet database. Results: For the training dataset, the p-values between the two survival curves were 6.7 × 10−6 (HF), 5.9 × 10−3 (WF), 7.4 × 10−6 (HWF), and 1.1 × 10−3 (DL). The p-values for the validation dataset were 3.4 × 10−5 (HF), 6.7 × 10−1 (WF), 1.7 × 10−7 (HWF), and 1.2 × 10−1 (DL). Conclusion: This study demonstrates the excellent potential of HFs for prognostic prediction in lung cancer patients.
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U2 - 10.1016/j.ejmp.2019.11.026
DO - 10.1016/j.ejmp.2019.11.026
M3 - Article
C2 - 31855844
AN - SCOPUS:85076497962
SN - 1120-1797
VL - 69
SP - 90
EP - 100
JO - Physica Medica
JF - Physica Medica
ER -
