Publication: Assessment of the confidence interval in the multivariable normal tissue complication probability model for predicting radiation-induced liver disease in primary liver cancer
Issued Date
2021-05-01
Resource Type
ISSN
13499157
04493060
04493060
Other identifier(s)
2-s2.0-85106554110
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Radiation Research. Vol.62, No.3 (2021), 483-493
Suggested Citation
Anussara Prayongrat, Natchalee Srimaneekarn, Sira Sriswasdi, Yoichi M. Ito, Norio Katoh, Masaya Tamura, Yasuhiro Dekura, Chie Toramatsu, Chonlakiet Khorprasert, Napapat Amornwichet, Petch Alisanant, Yuichi Hirata, Anthony Hayter, Hiroki Shirato, Shinichi Shimizu, Keiji Kobashi Assessment of the confidence interval in the multivariable normal tissue complication probability model for predicting radiation-induced liver disease in primary liver cancer. Journal of Radiation Research. Vol.62, No.3 (2021), 483-493. doi:10.1093/jrr/rrab011 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/77047
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Title
Assessment of the confidence interval in the multivariable normal tissue complication probability model for predicting radiation-induced liver disease in primary liver cancer
Abstract
We developed a confidence interval-(CI) assessing model in multivariable normal tissue complication probability (NTCP) modeling for predicting radiation-induced liver disease (RILD) in primary liver cancer patients using clinical and dosimetric data. Both the mean NTCP and difference in the mean NTCP (ΔNTCP) between two treatment plans of different radiotherapy modalities were further evaluated and their CIs were assessed. Clinical data were retrospectively reviewed in 322 patients with hepatocellular carcinoma (n = 215) and intrahepatic cholangiocarcinoma (n = 107) treated with photon therapy. Dose-volume histograms of normal liver were reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose. The most predictive variables were used to build the model based on multivariable logistic regression analysis with bootstrapping. Internal validation was performed using the cross-validation leave-one-out method. Both the mean NTCP and the mean ΔNTCP with 95% CIs were calculated from computationally generated multivariate random sets of NTCP model parameters using variance-covariance matrix information. RILD occurred in 108/322 patients (33.5%). The NTCP model with three clinical and one dosimetric parameter (tumor type, Child-Pugh class, hepatitis infection status and MLD) was most predictive, with an area under the receiver operative characteristics curve (AUC) of 0.79 (95% CI 0.74-0.84). In eight clinical subgroups based on the three clinical parameters, both the mean NTCP and the mean ΔNTCP with 95% CIs were able to be estimated computationally. The multivariable NTCP model with the assessment of 95% CIs has potential to improve the reliability of the NTCP model-based approach to select the appropriate radiotherapy modality for each patient.