Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

 
ORIGINAL ARTICLE
Ahead of print publication  

Radiobiological modeling of acute esophagitis after radiation therapy of head, neck, and thorax tumors: The influence of chemo-radiation


1 Molecular Medicine Research Center, Institute of Biomedicine; Medical Radiation Sciences Research Team; Medical Physics Department, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
2 Radiology and Radiotherapy Department, Medical School, Tabriz University of Medical Science, Tabriz, Iran
3 Road Traffic Injury Research Center; Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
4 Department of Radiology and Radiation Oncology, University of Mississippi Medical Center (UMMC), Jackson, Mississippi, USA

Date of Submission03-Mar-2020
Date of Decision18-Jun-2020
Date of Acceptance04-Jul-2020
Date of Web Publication03-Aug-2021

Correspondence Address:
Asghar Mesbahi,
Department of Medical Physics, Faculty of Medicine, Attare-Neishabouri Street, Tabriz University of Medical Sciences, Tabriz
Iran
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.JCRT_271_20

 > Abstract 


Aim: The aim of this study was to evaluate the performance of various radiobiological models in predicting the occurrence of acute esophagitis (AE) during radiation therapy (RT) of head, neck, and thoracic tumors with concurrent and sequential chemotherapy. According to recent studies, the probability of AE following RT by normal tissue complication probability models is predictable.
Materials and Methods: A total of 100 patients with nasopharynx, larynx, Hodgkin's lymphoma, spinal metastases, and oral cavity and lung tumors were included in the study. Half of these patients were treated by concurrent chemo-radiotherapy (Con. CRT) and the other half were treated by radiotherapy alone or sequential chemo-radiotherapy (RT + seq. CRT). Radiobiological models of several types were used as follows,: Lyman-generalized equivalent uniform dose (gEUD), Lyman-MED, log-logistic, logit, and logistic. Parameters were estimated using maximum likelihood estimation, and models were compared using Akaike information criteria.
Results: Based on follow-up data, the behavior of dose–response curves differed markedly between the Con. CRT and RT + seq. CRT groups. The best fit with clinical results was offered by the Lyman-MED model for the Con. CRT group and the Lyman-gEUD model for the RT + seq. CRT group. Depending on the model used, the parameter of D50 was considerably lower (up to three times) in the Con. CRT group compared to the RT + seq. CRT group.
Conclusions: The incidence of AE significantly differed between the two treatment groups in all the models. New parameter estimates could be used for predicting the probability of acute esophagitis after chemo-RT.

Keywords: Acute esophagitis, concurrent chemo-radiotherapy, normal tissue complication probability, radiobiological modeling



How to cite this URL:
Alizade-Harakiyan M, Jangjoo AG, Jafari-Koshki T, Fatemi A, Mesbahi A. Radiobiological modeling of acute esophagitis after radiation therapy of head, neck, and thorax tumors: The influence of chemo-radiation. J Can Res Ther [Epub ahead of print] [cited 2021 Dec 5]. Available from: https://www.cancerjournal.net/preprintarticle.asp?id=322903




 > Introduction Top


In recent years, many studies have focused on radiobiological modeling of the response of normal tissue to radiation therapy (RT). Several predictive models and their parameters have been suggested.[1] These different radiobiological models have been expanded to anticipate normal tissue complication probability (NTCP) and tumor control probability following RT.[2],[3],[4] Such models recently have been integrated within treatment planning systems (TPSs) as new indicators for RT plan assessment and optimization.[5]

NTCP models are dose–response models that quantify the probability of normal tissue complications by using mathematical functions.[6] Numerous NTCP models have been proposed, from simple models for homogeneous partial irradiation to complex models incorporating biological mechanisms.[7],[8],[9] Studies by Söhn et al.,[10] Bakhshandeh et al.,[11] Cheraghi et al.,[12] De Marzi et al.,[13] Moiseenko et al.,[14] and Mavroidis et al.[15] have compared the radiobiological models of NTCP using different radiobiological models for various organs.

Acute radiation esophagitis (AE) is a common toxicity related to thoracic RT when the treatment area includes the mediastinum and lung.[16],[17] Several studies have evaluated the esophagitis following head-and-neck radiotherapy.[18],[19] It has been shown that the incidence and severity of the resulting esophagitis is associated with the dose–volume of esophagus exposed to the radiation.[20] Moreover, it has been observed that AE is intensified with concurrent chemo-radiotherapy.[21],[22],[23],[24] And that AE ordinarily occurs 90 days after the beginning of treatment.[25]

Clinical features of AE include odynophagia, substernal pain, and dysphagia.[20] Some classification systems for clinical AE have been advanced and reported in the literature, including the National Cancer Institute Common Toxicity Criteria and RT Oncology Group (RTOG) scale reports.[20]

Several influencing parameters and radiobiological models have been proposed as predictors of AE in the literature. In this regard, according to the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) study of 2010, for acute esophagitis, dose–response is dependent on the volume exposed to irradiation.[26] However, some studies have investigated other effective dosimetric parameters for prediction of AE. For instance, Zehentmayr et al. performed radiobiological modeling of AE following differentiated accelerated radiotherapy (DART-bid) in lung tumors using Lyman- equivalent uniform dose (EUD), Lyman-MED, and cutoff dose models.[27] In another study, Zhu et al. obtained the radiobiological parameters of a group receiving concurrent radiotherapy and one receiving radiotherapy and sequential chemotherapy using a Lyman–Kutcher–Burman (LKB) model to clinical outcomes.[23] Belderbos et al. investigated acute esophagitis induced by the treatment of non-small cell lung cancer (NSCLC) after high-dose radiotherapy and proposed radiobiological model parameters for the LKB model.[24] In a similar study by Wijsman et al., acute esophageal toxicity due to intensity-modulated RT of advanced-stage NSCLC was predicted using multivariable logistic regression.[22] Finally, in two studies by Huang et al., who investigated acute radiation-induced esophagitis due to treatment of lung tumors using multivariate logistic regression models, the mean esophageal doses and concomitant radiation chemotherapy were found to be the most important predictors of AE.[28],[29]

To the best of our knowledge, no prior studies have compared different NTCP models of acute esophagitis with regard to the effect of chemotherapy. Thus, this study aimed to estimate the radiobiological parameters of acute esophagitis based on several models such as logit, logistic, Lyman-MED, Lyman-gEUD, and log-logistic using the maximum likelihood estimation (MLE) method. These parameters were estimated separately for two groups: those receiving concurrent chemo-radiotherapy (Con. CRT) and radiotherapy alone or with sequential chemo-radiotherapy (RT + seq. CRT). The models were ranked separately according to Akaike information criteria (AIC) index in each group.


 > Materials and Methods Top


Patient groups

This prospective study was conducted between January 2017 and December 2017 in Vali-Asr and Imam-Reza Hospitals in Tabriz-Iran. In total, 100 patients with head–neck or thorax tumors and spinal metastasis were treated with three-dimensional conformal radiotherapy (CRT). The participants included patients with lung (n = 33), oral cavity (n = 10), larynx (n = 16), and nasopharynx tumors (n = 16); Hodgkin's lymphoma (n = 15); and spinal metastases (n = 10). Half (50) of the participants were treated with concurrent chemo-radiotherapy (Con. CRT group) and other half were treated with radiotherapy alone or sequential chemo-radiotherapy (RT + seq. CRT group). Our participation criteria for selection were patients with normal esophageal function, no previous chemotherapy and radiotherapy, no gastroesophageal reflux, and no sign of oral cavity candidiasis. Lung tumors had histology of NSCLC and small-cell lung cancer (SCLC). Also, the tumors of oral cavity, larynx and nasopharynx had histology of squamous cell carcinoma (SCC). The median age was 60 years (range, 20–70 years). The Karnofsky Performance Score (KPS) were ≥90 for 85 patients and <90 for 15 patients. Eighty patients were male and twenty were female. This study was approved by the Ethics Committee of the Tabriz University of Medical Sciences under the Code of Ethics 59507, in accordance with national laws and the Helsinki declaration of 1975 (current amendment).

Chemo-radiation therapy

For patients undergoing sequential chemo-radiotherapy, adriamycin, bleomycin, vinblastine, and dacarbazine were prescribed. For patients undergoing concurrent chemo-radiotherapy, cisplatin (CIS), CIS + navelbin, and CIS + etoposide were used. Demographic characteristics and prescribed doses are presented in [Table 1].
Table 1: Demographic and clinical characteristics of the study participants, described as n (%) and mean±standard deviation

Click here to view


Assessment of AE esophagitis

All patients were visited weekly during radiotherapy, with visits continuing for 3 months after the onset of radiotherapy. AE was recognized according to the RTOG criteria: mild dysphagia (Grade 1), moderate dysphagia (Grade 2), and severe dysphagia (Grade 3).

Treatment planning, esophagus delineation, and dose–volume histogram parameters

The radiation treatments were delivered using a medical linear accelerator (Siemens, Germany) equipped with 41 multi-leaf collimators and 6 and 18 MV photon beams. Treatments were performed as one session each day five times a week. We used the TiGRT (Linatech, Sunnyvale, CA, USA) TPS. For treatment planning, X-ray computed tomography images with 3- and 5-mm slice thickness were generated in spiral mode with a pitch number of 1.2. For all treatment plans, the prescribed dose was normalized to an isodose of 95% covering the planning target volume. In all plans, the esophagus was contoured on each slice from the cricoid cartilage to the gastro-esophageal junction. For all patients, dose–volume histograms (DVHs) were extracted in differential format as absolute dose and relative volume. The minimum, mean, and maximum esophageal doses were recorded. To ensure comparable results, dose per fraction and total dose were converted to the equivalent dose of 2 Gy per session for all patients. Dose limitations for the esophagus were adapted according to the QUANTEC criteria for acute esophagitis Grade 2 and above: V35 <50%, V50 <40%, and V70 <20%. As shown in [Figure 1], a treatment plan of a lung tumor is represented by three radiation fields in two different axial and frontal views. The DVH of the tissues exposed to this radiation field is also shown.
Figure 1: A sample treatment plan for a patient with lung cancer. (a) Isodose distribution from axial view and (b) coronal view. (c) Dose–volume histogram of lung tumor and irradiated organs at risk. The gross tumor volume and esophagus are marked in red and red–violet, respectively

Click here to view


Normal tissue complication probability models and statistical approaches

In the current study, AE ≥ Grade 2 was the clinical endpoint for radiobiological modeling. Based on the systematic review of Brodin et al.,[1] we used the following five models: Lyman-gEUD, Lyman-MED, log-logistic using generalized EUD (gEUD), logit, and logistic. All models are listed and described in [Table 2]. The gEUD was calculated for all models using the following formulas:
Table 2: Normal tissue complication probability models and definition of parameters

Click here to view






Where Vi and Di are relative volumes and absolute doses extracted from DVHs, respectively. The parameter α/β ratio is defined as the proportion of intrinsic radiosensitivity to repair capability, considered 10 for the esophagus according to Wijsman et al.[22] Parameters n and d represent the volume-effect parameter and the dose per session, respectively. For gEUD calculation by the Lyman-MED model, n = 1 was considered for both groups.

The Lyman-gEUD and log-logistic models did not converge for simultaneous estimation of all parameters. Hence, as proposed in the literature, we estimated parameter n based on the area under receiver operating characteristic curves (AUC) separately for two groups, and used this estimate in the model to estimate the other parameters.[13]

Based on clinical data, parameter estimation for all models was performed based on clinical endpoint using the maximum likelihood estimation (MLE) method using R software (version 3.5.2), (Lucent Technologies, USA). The MLE method was calculated by the following formula:



In this formula, x represents the model parameters. NTCPi was used when the ith patient experiences AE ≥ 2 and (1 − NTCPi) used when no AE ≥ 2 was observed.

The AIC was used for ranking the models using the following equation:

AIC = −2 ln (likelihood) + 2k (4)

Where likelihood is the probability of normal tissue complications and k is the number of coefficients that are estimated. In addition, the lower amount of AIC demonstrates a better fit.


 > Results Top


Clinical outcomes

All patients were followed up and finished their treatment course without interruption. Forty percent (40/100) of the patients had Grade 1 AE, 38% (38/100) of the patients had Grade 2 AE, while 22% (22/100) of the patients had Grade 3 AE. The average of minimum, mean, and maximum doses of esophagus for all patients was 2.37 ± 6.58 Gy, 22.49 ± 12.19 Gy, and 52.67 ± 13.07 Gy, respectively. The average doses administered to tumors of the lung (58.16 ± 13.45 Gy), nasopharynx (62.81 ± 9.42 Gy), larynx (63.13 ± 10.02 Gy), oral cavity (59.82 ± 8.21 Gy), Hodgkin's lymphoma (45.80 ± 9.51 Gy), and spinal metastasis (41.34 ± 4.86 Gy) were recorded.

[Figure 2] shows the distribution of the mean esophageal dose received for all patients. In addition, radiation and clinical data for total patients are provided in [Appendix 1].
Figure 2: Frequency distribution of esophagus mean dose in all the patients

Click here to view



Normal tissue complication probability models

It is worth reiterating here that the parameter n was derived separately for the two treatment groups for gEUD calculations according to the method suggested by De Marzi et al.[13] The calculated value was n = 0.12 (with AUC = 0.97) for the Con. CRT group and n = 0.012 (with AUC = 0.66) for the RT + seq. CRT group.

The Lyman-gEUD, log-logistic, logistic, logit, and Lyman-MED models were fitted to clinical data [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]. The curves for all models were plotted separately for the two treatment groups. Different dose–response behavior was observed for various models, but overall, with an increase in gEUD and mean dose, the incidence of AE ≥ 2 increases. The response to dose for Con. CRT group had a sigmoid shape for the Lyman-gEUD [Figure 3] and log-logistic [Figure 4] models, but both models approximated a straight line for the RT + seq. CRT group [Figure 3] and [Figure 4]. In addition, the slope of curve for Con. CRT group was higher than that obtained for the RT + seq. CRT group. It should be noticed that a steeper slope is indicative of sensitivity in response to RT for the Con. CRT group. In [Figure 5], [Figure 6], [Figure 7], curves for two groups show a sigmoid curve with little difference in slope, and the only major difference between the two treatment groups was the threshold dose for onset of complications.
Figure 3: Dose–response relationship for the Lyman-generalized equivalent uniform dose model with the two treatment groups

Click here to view
Figure 4: Dose–response relationship for the log-logistic model with the two treatment groups

Click here to view
Figure 5: Dose–response relationship for the logistic model with the two treatment groups

Click here to view
Figure 6: Dose–response relationship for the logit model with the two treatment groups

Click here to view
Figure 7: Dose–response relationship for the Lyman-MED model with the two treatment groups

Click here to view


To compare the effects of concurrent chemo-radiotherapy in the incidence of AE ≥ 2, the radiobiological parameters of D50 and dose–response steepness were calculated separately for each group. Estimates of D50 and dose–response steepness parameters for all radiobiological models of this study are provided in [Table 3]. As can be seen in the Con. CRT group for all models, D50 is lower than that of the RT + seq. CRT group. The dose–response steepness parameter was lower in the Con. CRT group than that in the RT + seq CRT group except for the Lyman-MED and log-logistic models. As can be seen, Con. CRT group models showed the highest agreement with clinical results compared to RT + seq. CRT group models according to AIC criteria [Table 3]. In the Con. CRT group, the highest D50 estimates were related to the log-logistic model and the least estimates were for the logit model. In the RT + seq. CRT group, the highest D50 parameter was obtained with the Lyman-MED model and the lowest for the logit model.
Table 3: The parameter estimates (standard errors) and Akaike Information Criteria values for various normal tissue complication probability models

Click here to view


There was a large difference (three times approximately) in the estimation of D50 for two groups between the logit and Lyman-MED models. The estimated D50 parameter for Lyman-gEUD was very close to that of the log-logistic model, while the D50 of the logistic model was similar to that of the logit model.


 > Discussion Top


AE is a common dose-limiting complication in radiotherapy of tumors adjacent to esophageal tissue.[26] Hence, the prediction of AE is an important issue to consider at the time of treatment planning in order to avoid interruption of therapy and improve local control of tumor growth. NTCP models have been utilized as predictive tools to estimate the likelihood of complications before treatment.[26]

Based on previous studies on the effect of Con. CRT on the occurrence of acute esophagitis, we decided to investigate the effect of Con. CRT on the occurrence of acute esophagitis using radiobiological models. Five radiobiological models were compared with clinical outcomes to provide new parameters for evaluating treatment plans.

There was a greater response for Con. CRT (lower D50) compared to the RT + seq. CRT group. This means that the esophagus became more sensitive in patients undergoing Con. CRT treatment. For instance, in the Lyman-gEUD model, the D50 was 38.87 for the Con. CRT group, but in the RT + seq. CRT group, a D50 of 48.63 was obtained.

These results can be compared with those of two similar studies.[23],[27] In the study of Zehentmayr et al., radiobiological modeling of acute esophageal complications following DART-bid treatment of lung tumors was performed using Lyman-EUD, Lyman-MED, and cutoff dose models. In their study, a Lyman-MED model was selected as the optimal model according to the AIC criteria. The parameters for a Lyman-EUD model (D50 = 44.9 Gy, m = 0.34, and n = 0.34) and for a Lyman-MED model (D50 = 32.8 Gy and m = 0.48) were estimated. Chemotherapy was administered sequentially with radiotherapy.[27] As can be seen, their estimations are in close agreement with the results of this study, and both studies have chosen Lyman-MED model as the optimal model that offers the best fit with clinical results.

In another comparable study by Zhu et al., the values of D50 = 36 Gy, m = 0.42, and n = 0.09 for the Con. CRT group and D50 = 46 Gy, m = 0.15, and n = 0.29 for the RT + seq. CRT group were obtained by fitting LKB model results to the clinical outcome for incidence of acute esophagitis grade ≥2;[23] these findings are in close agreement with those of the present study.

Other studies, such as those by Belderbos et al.,[24] Chapet et al.,[30] Wijsman et al.,[22] and Burman et al.,[31] have investigated esophagus toxicity of lung tumors using the LKB model and presented different parameters with their clinical results. Burman et al.[31] estimated the parameters of the LKB model for predicting clinical stricture/perforation, which was different with our studied endpoint. Thus, our estimated parameters did not agree with those they derived. In the studies of Belderbos et al.,[24] Chapet et al.,[30] and Wijsman et al.,[22] predictive parameters were estimated for acute esophagitis, but the same parameters were used for both Con. CRT and RT + seq groups; thus, the parameters we derived cannot be directly compared to theirs.

Paximadis et al. also studied acute esophagitis during the treatment of lung tumors after definitive RT and estimated a parameter, a, using maximum likelihood analysis, finding a = 10.[32] Because the parameter a = 1/n, the respective estimated values for this parameter in our study were 8.3 and 83.3 for the Con. CRT and RT + seq. CRT groups. The difference between our values and their result can be attributed to two reasons: first, they obtained the parameter for all patients irrespective of the concurrent chemotherapy effect; second, they found that 18% of their participants had an AE of 0, while in the current study, no patients had an AE of 0.

Alevronta et al.[19] and Mavroidis et al.[18] studied radiobiological modeling and used a relative seriality (RS) model to estimate the stricture of proximal esophagus following head-and-neck radiotherapy. They estimated parameters of D50 = 61.5 Gy, &#s947; = 1.4, s = 0.1 and D50 = 68.4 Gy, γ = 6.55, s = 0.22, for the head and neck, respectively. The results of our study were not comparable with their findings because they used a different model and the same parameters were employed for all patients regardless of treatment type without considering the effect of concurrent chemotherapy.

As far as we know, previous predictions of acute esophagitis using multivariate logistic regression models have been reported by two groups, Huang et al.[29] and Wijsman et al.;[22] they investigated the effects of Con. CRT group as a predictor of acute esophagitis. Studies by Dankers et al.[33] and Pan et al.[21] also predicted acute esophagitis using logistic regression models and physical variables. Because of differences in the models used, our estimated parameters cannot be directly compared with the results of these studies. However, they investigated the effect of Con. CRT on the occurrence of acute esophagitis and reported a higher probability of acute esophagitis for a Con. CRT group, in close agreement with our findings.

As far as we know, the estimation of parameters for log-logistic or gEUD-based models in predicting acute esophagitis has not yet been studied. We were unable to find any previous studies for direct comparison of our log-logistic results. However, in a related study by Alizade-Harakiyan et al., parameters of TD50 = 68 Gy, γ50 = 3, and a = 8 were used for a log-logistic model to evaluate their treatment plans for estimation of acute esophagitis following RT.[34]

According to our review of the literature, predictions of acute esophagitis grade ≥2 have not been performed using logit and logistic models. Thus, we were unable to compare the results of the estimated parameters of these two models with other studies. Similarly, while several previous studies have been conducted to predict radiotherapy-induced esophagitis and model parameters for the LKB, RS, and logistic regression models with respect to clinical outcomes, no previous study has compared the five different radiobiological models used in this work.


 > Conclusions Top


It can be concluded that there was a higher response to radiation dose in the Con. CRT group, as the D50 parameter was markedly lower for this group. The slope of the dose–response curves differed between the two treatment groups, indicative of a difference in response to RT. Furthermore, an estimation of the parameters of radiobiological models suggests that specific radiobiological parameters should be considered to evaluate treatment plans for Con. CRT patients. The Lyman-MED and Lyman-gEUD models, respectively, appear to be the best options among the radiobiological models studied in this work for evaluating treatment plans for patients receiving Con. CRT and RT + seq. CRT.

Acknowledgments

The authors wish to thank the Molecular Medicine Research Center of Tabriz University of Medical Sciences for financial support under MSc grant No. 59507. We would also like to thank the Radiation Therapy Department of Imam-Reza and Vali-Asr Hospitals of Tabriz for providing data required for completion of the current work. We also appreciate the sincere cooperation of Dr. Andrzej Niemierko, Dr. Alan Nahum, Dr. Julien Uzan, and Dr. Eleftheria Alevronta.

Financial support and sponsorship

This study was financially supported by the Molecular Medicine Research Center of Tabriz University of Medical Sciences for financial support under MSc grant No. 59507.

Conflicts of interest

There are no conflicts of interest.



 
 > References Top

1.
Brodin NP, Kabarriti R, Garg MK, Guha C, Tomé WA. Systematic review of normal tissue complication models relevant to standard fractionation radiation therapy of the head and neck region published after the QUANTEC reports. Int J Radiat Oncol 2018;100:391-407.  Back to cited text no. 1
    
2.
Mesbahi A, Rasouli N, Motlagh BN, Mohammadzadeh M. Radiobiological model-based comparison of three-dimensional conformal and intensity-modulated radiation therapy plans for nasopharyngeal carcinoma. Iran J Med Phys 2017;14:190-6.  Back to cited text no. 2
    
3.
Pour NH, Farajollahi A, Jamali M, Zeinali A, Jangjou AG. Comparison of three and four-field radiotherapy technique and the effect of laryngeal shield on vocal and spinal cord radiation dose in radiotherapy of non-laryngeal head and neck tumors. Polish J Med Phys Eng 2018;24:25-31.  Back to cited text no. 3
    
4.
Mosleh-Shirazi MA, Amraee A, Mohaghegh F. Dose-response relationship and normal-tissue complication probability of conductive hearing loss in patients undergoing head-and-neck or cranial radiotherapy: A prospective study including 70 ears. Phys Med 2019;61:64-9.  Back to cited text no. 4
    
5.
Miller JA, Fuller M, Vinod S, Suchowerska N, Holloway L. The significance of the choice of Radiobiological (NTCP) models in treatment plan objective functions. Australas Phys Eng Sci Med 2009;32:81-87.  Back to cited text no. 5
    
6.
Gay HA, Niemierko A. A free program for calculating EUD-based NTCP and TCP in external beam radiotherapy. Phys Medica 2007;23:115-25.  Back to cited text no. 6
    
7.
Klaus-Rüdiger T, Wolfgang D, Angelica F, Hopewell J, Langendijk J, Luijk PV, et al. Biological mechanisms of normal tissue damage: Importance for the design of NTCP models. Radiother Oncol 2012;105:79-85.  Back to cited text no. 7
    
8.
Lee KN, Jung WG. Dose response analysis program (DREAP): A user-friendly program for the analyses of radiation-induced biological responses utilizing established deterministic models at cell population and organ scales. Phys Med 2019;64:132-44.  Back to cited text no. 8
    
9.
Chang JH, Gehrke C, Prabhakar R, Gill S, Wada M, Joon DL, et al. RADBIOMOD: A simple program for utilising biological modelling in radiotherapy plan evaluation. Phys Med 2016;32:248-54.  Back to cited text no. 9
    
10.
Söhn M, Yan D, Liang J, Meldolesi E, Vargas C, Alber M. Incidence of late rectal bleeding in high-dose conformal radiotherapy of prostate cancer using equivalent uniform dose-based and dose-volume-based normal tissue complication probability models. Int J Radiat Oncol Biol Phys 2007;67:1066-73.  Back to cited text no. 10
    
11.
Bakhshandeh M, Hashemi B, Mahdavi SR, Nikoofar A, Vasheghani M, Kazemnejad A. Normal tissue complication probability modeling of radiation-induced hypothyroidism after head-and-neck radiation therapy. Int J Radiat Oncol Biol Phys 2013;85:514-21.  Back to cited text no. 11
    
12.
Cheraghi S, Nikoofar A, Bakhshandeh M, Khoei S, Farahani S, Abdollahi H, et al. Normal tissue complication probability modeling of radiation-induced sensorineural hearing loss after head-and-neck radiation therapy. Int J Radiat Biol 2017;93:1327-33.  Back to cited text no. 12
    
13.
De Marzi L, Feuvret L, Boulé T, Habrand JL, Martin F, Calugaru V, et al. Use of gEUD for predicting ear and pituitary gland damage following proton and photon radiation therapy. Br J Radiol 2015;88:20140413.  Back to cited text no. 13
    
14.
Moiseenko V, Song WY, Mell LK, Bhandare N. A comparison of dose-response characteristics of four NTCP models using outcomes of radiation-induced optic neuropathy and retinopathy. Radiat Oncol 2011;6:61.  Back to cited text no. 14
    
15.
Mavroidis P, Price A, Fried D, Kostich M, Amdur R, Mendenhall W, et al. Dose-volume toxicity modeling for de-intensified chemo-radiation therapy for HPV-positive oropharynx cancer. Radiother Oncol 2017;124: 240-7.  Back to cited text no. 15
    
16.
Anni YS, Maja VM, Nils Patrik B, Marianne CA, Ivan RV, Per M, et al. The effect on esophagus after different radiotherapy techniques for early stage Hodgkin's lymphoma. Acta Oncologica 2013;52:1559-65.  Back to cited text no. 16
    
17.
Lideståhl A, Mondlane G, Gubanski M, Lind PA, Siegbahn A. An in silico planning study comparing doses and estimated risk of toxicity in 3D-CRT, IMRT and proton beam therapy of patients with thymic tumours. Phys Med 2019;60:120-6.  Back to cited text no. 17
    
18.
Mavroidis P, Laurell G, Kraepelien T, Fernberg J, Lind B, Brahme A. Determination and clinical verification of dose-response parameters for esophageal stricture from head and neck radiotherapy. Acta Oncol (Madr) 2003;42:865-81.  Back to cited text no. 18
    
19.
Alevronta E, Ahlberg A, Mavroidis P, al-Abany M, Friesland S, Tilikidis A, et al. Dose-response relations for stricture in the proximal oesophagus from head and neck radiotherapy. Radiother Oncol 2010;97:54-9.  Back to cited text no. 19
    
20.
Rose J, Rodrigues G, Yaremko B, Lock M, D'Souza D. Systematic review of dose-volume parameters in the prediction of esophagitis in thoracic radiotherapy. Radiother Oncol 2009;91:282-7.  Back to cited text no. 20
    
21.
Pan Y, Brink C, Knap M, Khalil AA, Nyhus CH, McCulloch T, et al. Acute esophagitis for patients with local-regional advanced non small cell lung cancer treated with concurrent chemoradiotherapy. Radiother Oncol 2016;118:465-70.  Back to cited text no. 21
    
22.
Wijsman R, Dankers F, Troost EG, Hoffmann AL, van der Heijden EH, de Geus-Oei LF, et al. Multivariable normal-tissue complication modeling of acute esophageal toxicity in advanced stage non-small cell lung cancer patients treated with intensity-modulated (chemo-) radiotherapy. Radiother Oncol 2015;117:49-54.  Back to cited text no. 22
    
23.
Zhu J, Zhang ZC, Li BS, Liu M, Yin Y, Yu JM, et al. Analysis of acute radiation-induced esophagitis in non-small-cell lung cancer patients using the Lyman NTCP model. Radiother Oncol 2010;97:449-54.  Back to cited text no. 23
    
24.
Belderbos J, Heemsbergen W, Hoogeman M, Pengel K, Rossi M, Lebesque J. Acute esophageal toxicity in non-small cell lung cancer patients after high dose conformal radiotherapy. Radiother Oncol 2005;75:157-64.  Back to cited text no. 24
    
25.
Werner-Wasik M, Yorke E, Deasy J, Nam J, Marks LB. Radiation dose-volume effects in the esophagus. Int J Radiat Oncol Biol Phys 2010;76:S86-93.  Back to cited text no. 25
    
26.
Marks LB, Yorke ED, Jackson A, Ten Haken RK, Constine LS, Eisbruch A, et al. Use of normal tissue complication probability models in the clinic. Int J Radiat Oncol Biol Phys 2010;76:3.  Back to cited text no. 26
    
27.
Zehentmayr F, Söhn M, Exeli AK, Wurstbauer K, Tröller A, Deutschmann H, et al. Normal tissue complication models for clinically relevant acute esophagitis (≥ grade 2) in patients treated with dose differentiated accelerated radiotherapy (DART-bid). Radiat Oncol 2015;10.  Back to cited text no. 27
    
28.
Huang EX, Robinson CG, Molotievschi A, Bradley JD, Deasy JO, Oh JH. Independent test of a model to predict severe acute esophagitis. Adv Radiat Oncol 2017;2:37-43.  Back to cited text no. 28
    
29.
Huang EX, Bradley JD, El Naqa I, Hope AJ, Lindsay PE, Bosch WR, et al. Modeling the risk of radiation-induced acute esophagitis for combined Washington University and RTOG trial 93-11 lung cancer patients. Int J Radiat Oncol Biol Phys 2012;82:1674-9.  Back to cited text no. 29
    
30.
Chapet O, Kong FM, Lee JS, Hayman JA, Ten Haken RK. Normal tissue complication probability modeling for acute esophagitis in patients treated with conformal radiation therapy for non-small cell lung cancer. Radiother Oncol 2005;77:176-81.  Back to cited text no. 30
    
31.
Burman C, Kutcher GJ, Emami B, Goitein M. Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys 1991;21:123-35.  Back to cited text no. 31
    
32.
Paximadis P, Schipper M, Matuszak M, Feng M, Jolly S, Boike T, et al. Dosimetric predictors for acute esophagitis during radiation therapy for lung cancer: Results of a large statewide observational study. Pract Radiat Oncol 2018;8:167-73.  Back to cited text no. 32
    
33.
Dankers F, Wijsman R, Troost EG, Monshouwer R, Bussink J, Hoffmann AL. Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-) radiotherapy. Phys Med Biol 2017;62:3668-81.  Back to cited text no. 33
    
34.
Alizade-Harakiyan M, Ghasemi Jangjoo A, Nasiri B, Jafari-Koshki T, Okutan M, Mesbahi A. Radiobiological Modeling of Acute Esophagitis Following Radiotherapy of Thorax and Head-Neck Tumors: A Comparison of Lyman Kutcher Burman with Equivalent Uniform Dose-Based Models. Mashhad Univ Med Sci 2019;17:225-34.  Back to cited text no. 34
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

 
Top
 
 
  Search
 
     Search Pubmed for
 
    -  Alizade-Harakiyan M
    -  Jangjoo AG
    -  Jafari-Koshki T
    -  Fatemi A
    -  Mesbahi A
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  >Abstract>Introduction>Materials and Me...>Results>Discussion>Conclusions>Article Figures>Article Tables
  In this article
>References

 Article Access Statistics
    Viewed368    
    PDF Downloaded12    

Recommend this journal