Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features

Mazen Soufi, Hidetaka Arimura, Noriyuki Nagami

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Purpose: To identify the optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based (WDB) radiomic features in CT images. Materials and methods: The CT images of patients with histologically confirmed nonsmall cell lung carcinomas (NSCLCs) in training (Dataset T; n = 162) and validation (Dataset V; n = 143) datasets were analyzed for this study. The optimal mother wavelets were identified based on the impacts of the WDB radiomic features on the patient survival times. Four hundred and thirty-two three-dimensional WDB radiomic features were calculated from regions of interest (ROI) of 162 tumor contours. A Coxnet algorithm was used to select a subset of radiomic features (signature) based on the prediction of survival times with a fivefold cross validation. The impacts of the radiomic features on the patients’ survival times were assessed by using a multivariate Cox proportional hazard regression (MCPHR) model. The major contribution of this study was to identify optimal mother wavelets based on a maximization of a novel ranking index (RI) incorporating the Coxnet cross-validated partial log-likelihood and the summation of the P-values of the radiomic features in the MCPHR model on Dataset T. The prognostic performance of the optimal mother wavelets was validated based on the concordance index (CI) of the MCPHR models when applied to Dataset V. The proposed approach was tested by using 31 mother wavelets from 6 wavelet families that were available in a commercially available software (Matlab® 2016b). Results: The optimal mother wavelets were Symlet 5 and Biorthogonal 2.6 at 128 requantization levels, which yielded RIs of 4.27 ± 0.29 (3 features) and 6.50 ± 0.50 (5 features), respectively. The CIs of the MCPHR models of Symlet 5 were 0.66 ± 0.03 (Dataset T) and 0.64 ± 0.00 (Dataset V), whereas those of Biorthogonal 2.6 were 0.68 ± 0.03 (Dataset T) and 0.62 ± 0.02 (Dataset V). The radiomic signatures included the GLRLM-based LHL gray level nonuniformity feature that demonstrated statistically significant differences in stratifying patients with better and worse prognoses in Datasets T and V. Conclusion: This study has revealed the potential of Symlet and Biorthogonal mother wavelets in the survival prediction of lung cancer patients by using WDB radiomic features in CT images.

Original languageEnglish
Pages (from-to)5116-5128
Number of pages13
JournalMedical physics
Volume45
Issue number11
DOIs
Publication statusPublished - Nov 1 2018

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Lung Neoplasms
Mothers
Survival
Proportional Hazards Models
Datasets
Software
Carcinoma
Lung

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features. / Soufi, Mazen; Arimura, Hidetaka; Nagami, Noriyuki.

In: Medical physics, Vol. 45, No. 11, 01.11.2018, p. 5116-5128.

Research output: Contribution to journalArticle

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abstract = "Purpose: To identify the optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based (WDB) radiomic features in CT images. Materials and methods: The CT images of patients with histologically confirmed nonsmall cell lung carcinomas (NSCLCs) in training (Dataset T; n = 162) and validation (Dataset V; n = 143) datasets were analyzed for this study. The optimal mother wavelets were identified based on the impacts of the WDB radiomic features on the patient survival times. Four hundred and thirty-two three-dimensional WDB radiomic features were calculated from regions of interest (ROI) of 162 tumor contours. A Coxnet algorithm was used to select a subset of radiomic features (signature) based on the prediction of survival times with a fivefold cross validation. The impacts of the radiomic features on the patients’ survival times were assessed by using a multivariate Cox proportional hazard regression (MCPHR) model. The major contribution of this study was to identify optimal mother wavelets based on a maximization of a novel ranking index (RI) incorporating the Coxnet cross-validated partial log-likelihood and the summation of the P-values of the radiomic features in the MCPHR model on Dataset T. The prognostic performance of the optimal mother wavelets was validated based on the concordance index (CI) of the MCPHR models when applied to Dataset V. The proposed approach was tested by using 31 mother wavelets from 6 wavelet families that were available in a commercially available software (Matlab{\circledR} 2016b). Results: The optimal mother wavelets were Symlet 5 and Biorthogonal 2.6 at 128 requantization levels, which yielded RIs of 4.27 ± 0.29 (3 features) and 6.50 ± 0.50 (5 features), respectively. The CIs of the MCPHR models of Symlet 5 were 0.66 ± 0.03 (Dataset T) and 0.64 ± 0.00 (Dataset V), whereas those of Biorthogonal 2.6 were 0.68 ± 0.03 (Dataset T) and 0.62 ± 0.02 (Dataset V). The radiomic signatures included the GLRLM-based LHL gray level nonuniformity feature that demonstrated statistically significant differences in stratifying patients with better and worse prognoses in Datasets T and V. Conclusion: This study has revealed the potential of Symlet and Biorthogonal mother wavelets in the survival prediction of lung cancer patients by using WDB radiomic features in CT images.",
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