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Optimal unified combination rule in application of Dempster-Shafer theory to lung cancer radiotherapy dose response outcome analysis.

ABSTRACT: Our previous study demonstrated the application of the Dempster-Shafer theory of evidence to dose/volume/outcome data analysis. Specifically, it provided Yager's rule to fuse data from different institutions pertaining to radiotherapy pneumonitis versus mean lung dose. The present work is a follow-on study that employs the optimal unified combination rule, which optimizes data similarity among independent sources. Specifically, we construct belief and plausibility functions on the lung cancer radiotherapy dose outcome datasets, and then apply the optimal unified combination rule to obtain combined belief and plausibility, which bound the probabilities of pneumonitis incidence. To estimate the incidence of pneumonitis at any value of mean lung dose, we use the Lyman-Kutcher-Burman (LKB) model to fit the combined belief and plausibility curves. The results show that the optimal unified combination rule yields a narrower uncertainty range (as represented by the belief-plausibility range) than Yager's rule, which is also theoretically proven.

SUBMITTER: He Y

PROVIDER: S-EPMC5690231 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

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Optimal unified combination rule in application of Dempster-Shafer theory to lung cancer radiotherapy dose response outcome analysis.

He Yanyan Y Hussaini M Yousuff MY Gong Yutao U T YU Xiao Ying Y

Journal of applied clinical medical physics 20160108 1

Our previous study demonstrated the application of the Dempster-Shafer theory of evidence to dose/volume/outcome data analysis. Specifically, it provided Yager's rule to fuse data from different institutions pertaining to radiotherapy pneumonitis versus mean lung dose. The present work is a follow-on study that employs the optimal unified combination rule, which optimizes data similarity among independent sources. Specifically, we construct belief and plausibility functions on the lung cancer ra ...[more]

PMID: 26894343

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Project description:BackgroundIn quantitative chemical risk assessment, a reference value is an estimate of an exposure to a chemical that is "likely to be without appreciable risk." Because current "deterministic" approaches do not quantitatively characterize the likelihood or severity of harm, the National Academies has recommended using reference values derived from a risk-specific dose that are treated as random variables, with probability distributions characterizing uncertainty and variability.ObjectivesIn order to build familiarity and address issues needed for routine and standardized derivation of probabilistic risk-specific dose distributions, a case example applying the unified probabilistic framework presented in Chiu and Slob (2015) is developed for acrolein. This case study is based on an updated systematic evidence map of literature (Keshava et al., 2020) identifying nasal lesions reported in Dorman et al. (2008) as the most appropriate endpoint and study for reference value derivation.MethodsThe probability distribution was calculated for the risk-specific dose, which in this implementation of the approach was calculated for the dose at which 1% of the human population is estimated to experience minimal lesions, and a probabilistic reference value was computed as the 5th percentile of this distribution. A deterministic reference value was also derived for comparison, and a sensitivity analysis of the probabilistic reference value was conducted investigating alternative assumptions for the point of departure type and exposure duration.ResultsThe probabilistic reference value of 6 × 10-4 mg/m3 was slightly lower than the deterministic reference value of 8 × 10-4 mg/m3, and the risk-specific dose distribution had an uncertainty spanning a factor of 137 (95th-5th percentile ratio). Sensitivity analysis yielded slightly higher probabilistic reference values ranging between 9 × 10-4 mg/m3 and 2 × 10-3 mg/m3.ConclusionsUsing a probabilistic approach for deriving a reference value allows quantitative characterization of the severity, incidence, and uncertainty of effects at a given dose. The results can be used to inform risk management decisions and improve risk communication.

Dataset Information

Optimal unified combination rule in application of Dempster-Shafer theory to lung cancer radiotherapy dose response outcome analysis.

Publications

Optimal unified combination rule in application of Dempster-Shafer theory to lung cancer radiotherapy dose response outcome analysis.

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