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 in | Cancer science Vol. 114; no. 7; pp. 2931 - 2938 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
England
John Wiley & Sons, Inc
01.07.2023
John Wiley and Sons Inc |
Subjects | |
Online Access | Get full text |
ISSN | 1347-9032 1349-7006 1349-7006 |
DOI | 10.1111/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. |
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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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CitedBy_id | crossref_primary_10_1016_j_compbiomed_2025_109861 |
Cites_doi | 10.1186/1476-069X-14-13 10.1088/0031-9155/51/11/009 10.1002/mp.15850 10.1084/jem.103.5.653 10.1016/j.semradonc.2008.04.004 10.1667/RR15505.1 10.1007/978-1-4614-8363-2_5 10.5114/aoms.2014.43751 10.3390/cancers14082009 10.1158/1078-0432.CCR-20-1725 10.1001/jamapsychiatry.2019.3671 10.1088/1361-6560/aaf26a 10.1186/s13014-022-02010-9 10.1016/j.ijrobp.2006.10.017 10.1016/j.ctro.2018.07.005 10.1038/nprot.2006.339 10.1371/journal.pone.0113591 10.7785/tcrt.2012.500306 10.1038/267425a0 10.23937/2378-3419/1410078 10.1007/BF02966706 10.3389/fonc.2022.847090 10.1186/s13014-015-0464-y 10.1080/095530099139232 10.1158/1535-7163.MCT-20-0271 10.1016/S0360-3016(00)00568-X 10.1093/jrr/rrab034 10.1186/s13014-018-1040-z 10.3109/09553002.2010.481322 10.1016/j.radonc.2022.06.023 10.1016/j.ijrobp.2020.01.030 10.3389/fphy.2020.00272 10.1080/09553009214551421 10.1016/0006-3002(58)90112-4 10.1016/j.radonc.2021.11.027 10.1111/cas.15446 10.1093/jrr/rrs114 10.1016/j.ctro.2017.03.001 10.1038/bjc.2012.265 10.1186/s12645-019-0049-9 10.1667/RR2964.1 10.1016/j.ijrobp.2022.05.010 |
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Keywords | cell survival predictive model radiobiology linear quadratic model radiation |
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References | 2021; 27 2015; 14 2017; 1 2022; 174 2017; 4 2006; 51 2000; 48 2019; 10 2008; 18 2015; 10 2006; 6 1958; 30 2005 2006; 1 2020; 77 2003 1977; 267 2018; 64 2022; 49 2012; 107 2020; 108 2022; 113 2020; 19 2022; 114 1982; 23 2022; 166 2020; 8 2010; 86 2012; 178 2013; 54 2013; 12 2020; 193 2022; 12 2022; 14 1999; 75 2015 2014; 9 1998; 5 2021; 62 2007; 67 1956; 103 2022; 17 1992; 61 2014; 10 2018; 13 Bloomer WD (e_1_2_10_7_1) 1982; 23 e_1_2_10_23_1 e_1_2_10_46_1 e_1_2_10_24_1 e_1_2_10_45_1 e_1_2_10_21_1 e_1_2_10_44_1 e_1_2_10_22_1 e_1_2_10_43_1 e_1_2_10_42_1 e_1_2_10_20_1 e_1_2_10_41_1 e_1_2_10_40_1 Hawkins R (e_1_2_10_12_1) 2017; 1 Munshi A (e_1_2_10_4_1) 2005 Hall EJ (e_1_2_10_5_1) 2006 e_1_2_10_2_1 Anselin L (e_1_2_10_26_1) 2003 e_1_2_10_18_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_6_1 e_1_2_10_16_1 e_1_2_10_39_1 e_1_2_10_17_1 e_1_2_10_38_1 e_1_2_10_8_1 e_1_2_10_14_1 e_1_2_10_37_1 e_1_2_10_15_1 e_1_2_10_36_1 e_1_2_10_35_1 e_1_2_10_9_1 e_1_2_10_13_1 e_1_2_10_34_1 e_1_2_10_10_1 e_1_2_10_33_1 e_1_2_10_11_1 e_1_2_10_32_1 e_1_2_10_31_1 e_1_2_10_30_1 e_1_2_10_29_1 e_1_2_10_27_1 e_1_2_10_28_1 e_1_2_10_49_1 e_1_2_10_25_1 e_1_2_10_48_1 e_1_2_10_47_1 |
References_xml | – volume: 86 start-page: 742 issue: 9 year: 2010 end-page: 751 article-title: Response of a radioresistant human melanoma cell line along the proton spread‐out Bragg peak publication-title: Int J Radiat Biol – volume: 48 start-page: 241 issue: 1 year: 2000 end-page: 250 article-title: Relative biological effectiveness for cell‐killing effect on various human cell lines irradiated with heavy‐ion medical accelerator in Chiba (HIMAC) carbon‐ion beams publication-title: Int J Radiat Oncol Biol Phys – volume: 62 start-page: 645 issue: 4 year: 2021 end-page: 655 article-title: Update of the particle irradiation data ensemble (PIDE) for cell survival publication-title: J Radiat Res – volume: 10 start-page: 578 issue: 3 year: 2014 end-page: 586 article-title: Radiosensitivity of human ovarian carcinoma and melanoma cells to γ‐rays and protons publication-title: Arch Med Sci – volume: 178 start-page: 385 issue: 5 year: 2012 end-page: 394 article-title: Modeling cell survival after photon irradiation based on double‐strand break clustering in megabase pair chromatin loops publication-title: Radiat Res – volume: 6 year: 2006 – volume: 77 start-page: 534 issue: 5 year: 2020 end-page: 540 article-title: Establishment of best practices for evidence for prediction: a review publication-title: JAMA Psychiat – volume: 103 start-page: 653 issue: 5 year: 1956 end-page: 666 article-title: Action of x‐rays on mammalian cells publication-title: J Exp Med – volume: 17 start-page: 1 issue: 1 year: 2022 end-page: 11 article-title: Variability of α/β ratios for prostate cancer with the fractionation schedule: caution against using the linear‐quadratic model for hypofractionated radiotherapy publication-title: Radiat Oncol – volume: 19 start-page: 2465 issue: 12 year: 2020 end-page: 2475 article-title: Glutaminase inhibitors induce thiol‐mediated oxidative stress and Radiosensitization in treatment‐resistant cervical CancersGlutaminase inhibitors sensitize resistant cervical cancers publication-title: Mol Cancer Ther – volume: 75 start-page: 1357 issue: 11 year: 1999 end-page: 1364 article-title: RBE for carbon track‐segment irradiation in cell lines of differing repair capacity publication-title: Int J Radiat Biol – volume: 108 start-page: 81 issue: 1 year: 2020 end-page: 92 article-title: Combination therapy with radiation and PARP inhibition enhances responsiveness to anti‐PD‐1 therapy in colorectal tumor models publication-title: Int J Radiat Oncol Biol Phys – volume: 5 start-page: 261 issue: 3 year: 1998 end-page: 268 article-title: Biological effects of heavy ion beam on human breast cancers publication-title: Breast Cancer – volume: 67 start-page: 587 issue: 2 year: 2007 end-page: 593 article-title: α/β ratio: a dose range dependence study publication-title: Int J Radiat Oncol Biol Phys – volume: 10 start-page: 1 issue: 1 year: 2015 end-page: 7 article-title: VE‐821, an ATR inhibitor, causes radiosensitization in human tumor cells irradiated with high LET radiation publication-title: Radiat Oncol – volume: 166 start-page: 162 year: 2022 end-page: 170 article-title: Radiosensitisation of SCCVII tumours and normal tissues in mice by the DNA‐dependent protein kinase inhibitor AZD7648 publication-title: Radiother Oncol – volume: 8 year: 2020 article-title: Characterizing radiation effectiveness in ion beam therapy part I: introduction and biophysical modeling of RBE using the LEMIV publication-title: Frontiers in Physics – volume: 54 start-page: 494 issue: 3 year: 2013 end-page: 514 article-title: Systematic analysis of RBE and related quantities using a database of cell survival experiments with ion beam irradiation publication-title: J Radiat Res – volume: 13 start-page: 7 year: 2018 end-page: 13 article-title: Effect of X‐ray minibeam radiation therapy on clonogenic survival of glioma cells publication-title: Clin Transl Radiat Oncol – volume: 30 start-page: 636 issue: 3 year: 1958 end-page: 637 article-title: A radiation‐sensitive mutant of Escherichia coli publication-title: Biochim Biophys Acta – volume: 64 issue: 1 year: 2018 article-title: The linear quadratic model: usage, interpretation and challenges publication-title: Phys Med Biol – volume: 12 start-page: 183 issue: 2 year: 2013 end-page: 192 article-title: A comparative analysis of radiobiological models for cell surviving fractions at high doses publication-title: Technol Cancer Res Treat – volume: 10 start-page: 1 issue: 1 year: 2019 end-page: 22 article-title: Phenomenon‐based evaluation of relative biological effectiveness of ion beams by means of the multiscale approach publication-title: Cancer Nanotechnology – start-page: 21 year: 2005 end-page: 28 – volume: 107 start-page: 291 issue: 2 year: 2012 end-page: 299 article-title: Targeting radiation‐resistant hypoxic tumour cells through ATR inhibition publication-title: Br J Cancer – volume: 4 year: 2017 article-title: In vitro reaction of cells derived from human Normal lung tissues to carbon‐ion beam irradiation publication-title: Int J Cancer Clin Res – volume: 1 start-page: 2315 issue: 5 year: 2006 end-page: 2319 article-title: Clonogenic assay of cells in vitro publication-title: Nat Protoc – volume: 174 start-page: 69 year: 2022 end-page: 76 article-title: An ion‐independent phenomenological relative biological effectiveness (RBE) model for proton therapy publication-title: Radiother Oncol – volume: 4 start-page: 32 year: 2017 end-page: 38 article-title: Effect of heterogeneous radio sensitivity on the survival, alpha beta ratio and biologic effective dose calculation of irradiated mammalian cell populations publication-title: Clin Transl Radiat Oncol – volume: 49 start-page: 6221 year: 2022 end-page: 6236 article-title: An empirical model of proton RBE based on the linear correlation between x‐ray and proton radiosensitivity publication-title: Med Phys – volume: 18 start-page: 234 issue: 4 year: 2008 end-page: 239 article-title: The linear‐quadratic model is an appropriate methodology for determining isoeffective doses at large doses per fraction publication-title: Semin Radiat Oncol – volume: 267 start-page: 425 issue: 5610 year: 1977 end-page: 427 article-title: Mutation and inactivation of mammalian cells by various ionising radiations publication-title: Nature – start-page: 57 year: 2015 end-page: 71 – volume: 14 start-page: 1 issue: 1 year: 2015 end-page: 15 article-title: Non‐monotonic dose‐response relationships and endocrine disruptors: a qualitative method of assessment publication-title: Environ Health – volume: 113 start-page: 2807 issue: 8 year: 2022 end-page: 2813 article-title: Biological effectiveness and relative biological effectiveness of ion beams for in‐vitro cell irradiation publication-title: Cancer Sci – start-page: 25 year: 2003 – volume: 14 issue: 8 year: 2022 article-title: A consistent protocol reveals a large heterogeneity in the biological effectiveness of proton and carbon‐ion beams for various sarcoma and Normal‐tissue‐derived cell lines publication-title: Cancer – volume: 114 start-page: 153 year: 2022 end-page: 162 article-title: Microdosimetric modeling of relative biological effectiveness for skin reactions: possible linkage between in vitro and in vivo data publication-title: Int J Radiat Oncol Biol Phys – volume: 23 start-page: 259 issue: 3 year: 1982 end-page: 265 article-title: The mammalian radiation survival curve publication-title: J Nucl Med – volume: 12 year: 2022 article-title: Biological Effectiveness of Ion Beam for in‐vitro Cell Irradiations publication-title: Front Oncol – volume: 27 start-page: 3224 issue: 11 year: 2021 end-page: 3233 article-title: Targeting a Radiosensitizing antibody–drug conjugate to a radiation‐inducible antigen publication-title: Clin Cancer Res – volume: 13 start-page: 1 issue: 1 year: 2018 end-page: 11 article-title: The alfa and beta of tumours: a review of parameters of the linear‐quadratic model, derived from clinical radiotherapy studies publication-title: Radiat Oncol – volume: 51 start-page: 2813 issue: 11 year: 2006 end-page: 2823 article-title: Fitting the linear–quadratic model to detailed data sets for different dose ranges publication-title: Phys Med Biol – volume: 9 issue: 12 year: 2014 article-title: The relative biological effectiveness for carbon and oxygen ion beams using the raster‐scanning technique in hepatocellular carcinoma cell lines publication-title: PLoS One – volume: 61 start-page: 611 issue: 5 year: 1992 end-page: 624 article-title: Direct comparison between protons and alpha‐particles of the same LET: I. irradiation methods and inactivation of asynchronous V79, HeLa and C3H 10T½ cells publication-title: Int J Radiat Biol – volume: 1 year: 2017 article-title: Biophysical models, microdosimetry and the linear quadratic survival relation publication-title: Ann radiat ther Oncol – volume: 193 start-page: 359 issue: 4 year: 2020 end-page: 371 article-title: Generalized multi‐hit model of radiation‐induced cell survival with a closed‐form solution: an alternative method for determining isoeffect doses in practical radiotherapy publication-title: Radiat Res – ident: e_1_2_10_49_1 doi: 10.1186/1476-069X-14-13 – ident: e_1_2_10_19_1 doi: 10.1088/0031-9155/51/11/009 – ident: e_1_2_10_16_1 doi: 10.1002/mp.15850 – ident: e_1_2_10_2_1 doi: 10.1084/jem.103.5.653 – ident: e_1_2_10_23_1 doi: 10.1016/j.semradonc.2008.04.004 – ident: e_1_2_10_21_1 doi: 10.1667/RR15505.1 – ident: e_1_2_10_22_1 doi: 10.1007/978-1-4614-8363-2_5 – ident: e_1_2_10_45_1 doi: 10.5114/aoms.2014.43751 – ident: e_1_2_10_34_1 doi: 10.3390/cancers14082009 – ident: e_1_2_10_46_1 doi: 10.1158/1078-0432.CCR-20-1725 – ident: e_1_2_10_18_1 doi: 10.1001/jamapsychiatry.2019.3671 – ident: e_1_2_10_6_1 doi: 10.1088/1361-6560/aaf26a – ident: e_1_2_10_15_1 doi: 10.1186/s13014-022-02010-9 – ident: e_1_2_10_35_1 doi: 10.1016/j.ijrobp.2006.10.017 – ident: e_1_2_10_39_1 doi: 10.1016/j.ctro.2018.07.005 – ident: e_1_2_10_3_1 doi: 10.1038/nprot.2006.339 – ident: e_1_2_10_28_1 doi: 10.1371/journal.pone.0113591 – volume-title: Radiobiology for the Radiologist year: 2006 ident: e_1_2_10_5_1 – ident: e_1_2_10_20_1 doi: 10.7785/tcrt.2012.500306 – ident: e_1_2_10_31_1 doi: 10.1038/267425a0 – ident: e_1_2_10_37_1 doi: 10.23937/2378-3419/1410078 – ident: e_1_2_10_30_1 doi: 10.1007/BF02966706 – start-page: 21 volume-title: Clonogenic cell survival assay year: 2005 ident: e_1_2_10_4_1 – ident: e_1_2_10_24_1 doi: 10.3389/fonc.2022.847090 – ident: e_1_2_10_40_1 doi: 10.1186/s13014-015-0464-y – ident: e_1_2_10_27_1 doi: 10.1080/095530099139232 – ident: e_1_2_10_47_1 doi: 10.1158/1535-7163.MCT-20-0271 – ident: e_1_2_10_29_1 doi: 10.1016/S0360-3016(00)00568-X – ident: e_1_2_10_8_1 – ident: e_1_2_10_9_1 doi: 10.1093/jrr/rrab034 – ident: e_1_2_10_14_1 doi: 10.1186/s13014-018-1040-z – ident: e_1_2_10_33_1 doi: 10.3109/09553002.2010.481322 – ident: e_1_2_10_17_1 doi: 10.1016/j.radonc.2022.06.023 – ident: e_1_2_10_42_1 doi: 10.1016/j.ijrobp.2020.01.030 – ident: e_1_2_10_44_1 doi: 10.3389/fphy.2020.00272 – ident: e_1_2_10_32_1 doi: 10.1080/09553009214551421 – ident: e_1_2_10_36_1 doi: 10.1016/0006-3002(58)90112-4 – ident: e_1_2_10_43_1 doi: 10.1016/j.radonc.2021.11.027 – start-page: 25 volume-title: An Introduction to Spatial Regression Analysis in R year: 2003 ident: e_1_2_10_26_1 – volume: 23 start-page: 259 issue: 3 year: 1982 ident: e_1_2_10_7_1 article-title: The mammalian radiation survival curve publication-title: J Nucl Med – ident: e_1_2_10_25_1 doi: 10.1111/cas.15446 – ident: e_1_2_10_13_1 doi: 10.1093/jrr/rrs114 – ident: e_1_2_10_11_1 doi: 10.1016/j.ctro.2017.03.001 – volume: 1 year: 2017 ident: e_1_2_10_12_1 article-title: Biophysical models, microdosimetry and the linear quadratic survival relation publication-title: Ann radiat ther Oncol – ident: e_1_2_10_41_1 doi: 10.1038/bjc.2012.265 – ident: e_1_2_10_38_1 doi: 10.1186/s12645-019-0049-9 – ident: e_1_2_10_10_1 doi: 10.1667/RR2964.1 – ident: e_1_2_10_48_1 doi: 10.1016/j.ijrobp.2022.05.010 |
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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 |
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