Invalidity of, and alternative to, the linear quadratic model as a predictive model for postirradiation cell survival

The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a second‐order polynomial approximation, it suffers from two well‐known pitfalls: nonmonotonic behavior and poor extrapolation. This study examined t...

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Published inCancer science Vol. 114; no. 7; pp. 2931 - 2938
Main Author Li, Heng
Format Journal Article
LanguageEnglish
Published England John Wiley & Sons, Inc 01.07.2023
John Wiley and Sons Inc
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ISSN1347-9032
1349-7006
1349-7006
DOI10.1111/cas.15796

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Abstract The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a second‐order polynomial approximation, it suffers from two well‐known pitfalls: nonmonotonic behavior and poor extrapolation. This study examined the raw data of 253 sets of photons and 943 sets of the ion beam from the Particle Irradiation Data Ensemble (PIDE) project to understand how often the LQ model could result in a negative β, which would give unrealistic predictions. Additionally, the predictive performance of the LQ model, the power model, and the linear model's predictive performance was studied using leave‐one‐out cross‐validation (LOOCV) and twofold cross‐validation. It was found that, when fitted to the LQ model, 7.5% of the photon and 29.8% of the ion beam dose–response data would result in negative β, compared to 0.77% and 2.0%, respectively, reported in published works. The LQ model performed poorly in LOOCV compared to the alternative power model, and performed the worst among the three models in twofold cross‐validation. The LQ model leads to unrealistic parameters, which are vastly under‐reported in published studies, and performs poorly in standard cross‐validation tests. Therefore, the LQ model is not a valid predictive dose–response model for cell survival. Alternative models need to be investigated. This study establishes that LQ model is not a valid predicative dose‐response model for cell survival and alternative models need to be investigated. It was found 7.5% of photon and 29.8% ion beam raw cell survival data would result in negative β values when fitted to the LQ model, and as such, LQ and linear models could not provide adequate predictive results for higher dose.
AbstractList The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a second-order polynomial approximation, it suffers from two well-known pitfalls: nonmonotonic behavior and poor extrapolation. This study examined the raw data of 253 sets of photons and 943 sets of the ion beam from the Particle Irradiation Data Ensemble (PIDE) project to understand how often the LQ model could result in a negative β, which would give unrealistic predictions. Additionally, the predictive performance of the LQ model, the power model, and the linear model's predictive performance was studied using leave-one-out cross-validation (LOOCV) and twofold cross-validation. It was found that, when fitted to the LQ model, 7.5% of the photon and 29.8% of the ion beam dose–response data would result in negative β, compared to 0.77% and 2.0%, respectively, reported in published works. The LQ model performed poorly in LOOCV compared to the alternative power model, and performed the worst among the three models in twofold cross-validation. The LQ model leads to unrealistic parameters, which are vastly under-reported in published studies, and performs poorly in standard cross-validation tests. Therefore, the LQ model is not a valid predictive dose–response model for cell survival. Alternative models need to be investigated.
The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a second‐order polynomial approximation, it suffers from two well‐known pitfalls: nonmonotonic behavior and poor extrapolation. This study examined the raw data of 253 sets of photons and 943 sets of the ion beam from the Particle Irradiation Data Ensemble (PIDE) project to understand how often the LQ model could result in a negative β , which would give unrealistic predictions. Additionally, the predictive performance of the LQ model, the power model, and the linear model's predictive performance was studied using leave‐one‐out cross‐validation (LOOCV) and twofold cross‐validation. It was found that, when fitted to the LQ model, 7.5% of the photon and 29.8% of the ion beam dose–response data would result in negative β , compared to 0.77% and 2.0%, respectively, reported in published works. The LQ model performed poorly in LOOCV compared to the alternative power model, and performed the worst among the three models in twofold cross‐validation. The LQ model leads to unrealistic parameters, which are vastly under‐reported in published studies, and performs poorly in standard cross‐validation tests. Therefore, the LQ model is not a valid predictive dose–response model for cell survival. Alternative models need to be investigated.
The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a second-order polynomial approximation, it suffers from two well-known pitfalls: nonmonotonic behavior and poor extrapolation. This study examined the raw data of 253 sets of photons and 943 sets of the ion beam from the Particle Irradiation Data Ensemble (PIDE) project to understand how often the LQ model could result in a negative β, which would give unrealistic predictions. Additionally, the predictive performance of the LQ model, the power model, and the linear model's predictive performance was studied using leave-one-out cross-validation (LOOCV) and twofold cross-validation. It was found that, when fitted to the LQ model, 7.5% of the photon and 29.8% of the ion beam dose-response data would result in negative β, compared to 0.77% and 2.0%, respectively, reported in published works. The LQ model performed poorly in LOOCV compared to the alternative power model, and performed the worst among the three models in twofold cross-validation. The LQ model leads to unrealistic parameters, which are vastly under-reported in published studies, and performs poorly in standard cross-validation tests. Therefore, the LQ model is not a valid predictive dose-response model for cell survival. Alternative models need to be investigated.The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a second-order polynomial approximation, it suffers from two well-known pitfalls: nonmonotonic behavior and poor extrapolation. This study examined the raw data of 253 sets of photons and 943 sets of the ion beam from the Particle Irradiation Data Ensemble (PIDE) project to understand how often the LQ model could result in a negative β, which would give unrealistic predictions. Additionally, the predictive performance of the LQ model, the power model, and the linear model's predictive performance was studied using leave-one-out cross-validation (LOOCV) and twofold cross-validation. It was found that, when fitted to the LQ model, 7.5% of the photon and 29.8% of the ion beam dose-response data would result in negative β, compared to 0.77% and 2.0%, respectively, reported in published works. The LQ model performed poorly in LOOCV compared to the alternative power model, and performed the worst among the three models in twofold cross-validation. The LQ model leads to unrealistic parameters, which are vastly under-reported in published studies, and performs poorly in standard cross-validation tests. Therefore, the LQ model is not a valid predictive dose-response model for cell survival. Alternative models need to be investigated.
The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a second‐order polynomial approximation, it suffers from two well‐known pitfalls: nonmonotonic behavior and poor extrapolation. This study examined the raw data of 253 sets of photons and 943 sets of the ion beam from the Particle Irradiation Data Ensemble (PIDE) project to understand how often the LQ model could result in a negative β , which would give unrealistic predictions. Additionally, the predictive performance of the LQ model, the power model, and the linear model's predictive performance was studied using leave‐one‐out cross‐validation (LOOCV) and twofold cross‐validation. It was found that, when fitted to the LQ model, 7.5% of the photon and 29.8% of the ion beam dose–response data would result in negative β , compared to 0.77% and 2.0%, respectively, reported in published works. The LQ model performed poorly in LOOCV compared to the alternative power model, and performed the worst among the three models in twofold cross‐validation. The LQ model leads to unrealistic parameters, which are vastly under‐reported in published studies, and performs poorly in standard cross‐validation tests. Therefore, the LQ model is not a valid predictive dose–response model for cell survival. Alternative models need to be investigated. This study establishes that LQ model is not a valid predicative dose‐response model for cell survival and alternative models need to be investigated. It was found 7.5% of photon and 29.8% ion beam raw cell survival data would result in negative β values when fitted to the LQ model, and as such, LQ and linear models could not provide adequate predictive results for higher dose.
The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a second‐order polynomial approximation, it suffers from two well‐known pitfalls: nonmonotonic behavior and poor extrapolation. This study examined the raw data of 253 sets of photons and 943 sets of the ion beam from the Particle Irradiation Data Ensemble (PIDE) project to understand how often the LQ model could result in a negative β, which would give unrealistic predictions. Additionally, the predictive performance of the LQ model, the power model, and the linear model's predictive performance was studied using leave‐one‐out cross‐validation (LOOCV) and twofold cross‐validation. It was found that, when fitted to the LQ model, 7.5% of the photon and 29.8% of the ion beam dose–response data would result in negative β, compared to 0.77% and 2.0%, respectively, reported in published works. The LQ model performed poorly in LOOCV compared to the alternative power model, and performed the worst among the three models in twofold cross‐validation. The LQ model leads to unrealistic parameters, which are vastly under‐reported in published studies, and performs poorly in standard cross‐validation tests. Therefore, the LQ model is not a valid predictive dose–response model for cell survival. Alternative models need to be investigated. This study establishes that LQ model is not a valid predicative dose‐response model for cell survival and alternative models need to be investigated. It was found 7.5% of photon and 29.8% ion beam raw cell survival data would result in negative β values when fitted to the LQ model, and as such, LQ and linear models could not provide adequate predictive results for higher dose.
Author Li, Heng
AuthorAffiliation 1 Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University 401 N. Broadway Baltimore Maryland 21287 USA
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/36946242$$D View this record in MEDLINE/PubMed
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Keywords cell survival
predictive model
radiobiology
linear quadratic model
radiation
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Snippet The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a...
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SubjectTerms Accuracy
Cell culture
Cell survival
Datasets
Ion beams
linear quadratic model
Original
ORIGINAL ARTICLES
Photons
Prediction models
predictive model
Radiation
radiobiology
Statistical analysis
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Title Invalidity of, and alternative to, the linear quadratic model as a predictive model for postirradiation cell survival
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fcas.15796
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Volume 114
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