Different definitions of esophagus influence esophageal toxicity prediction for esophageal cancer patients administered simultaneous integrated boost versus standard-dose radiation therapy
Scientific RepoRts |
Different definitions of esophagus influence esophageal toxicity prediction for esophageal cancer patients administered simultaneous integrated boost versus standard-dose radiation therapy
Bao-tian Huang 0
Rui-hong Huang 0
Wu-zhe Zhang 0
Wen Lin 1
Long-jia Guo 0
Liang-yu Xu 0
Pei-xian Lin 2
Jian-zhou Chen 0 3
De-rui Li 0
Chuang-zhen Chen 0
0 Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College , Shantou , China
1 Department of Respiratory Medical Oncology, Cancer Hospital of Shantou University Medical College , Shantou , China
2 Department of Nosocomial Infection Management, The Second Affiliated Hospital of Shantou University Medical College , Shantou , China
3 CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford , Oxford
OPEN Published: xx xx xxxx We aim to evaluate whether different definitions of esophagus (DEs) impact on the esophageal toxicity prediction for esophageal cancer (EC) patients administered intensity-modulated radiation therapy with simultaneous integrated boost (SIB-IMRT) vs. standard-dose IMRT (SD-IMRT). The esophagus for 21 patients diagnosed with primary EC were defined in the following four ways: the whole esophagus, including the tumor (ESOwhole); ESOwhole within the treatment field (ESOinfield); ESOinfield, excluding the tumor (ESOinfield-tumor) and ESOwhole, excluding the tumor (ESOwhole-tumor). The difference in the dose variation, acute esophageal toxicity (AET) and late esophageal toxicity (LET) of four DEs were compared. We found that the mean esophageal dose for ESOwhole, ESOinfield, ESOinfield-tumor and ESOwhole-tumor were increased by 7.2Gy, 10.9Gy, 4.6Gy and 2.0Gy, respectively, in the SIB-IMRT plans. Radiobiological models indicated that a grade ? 2 AET was 2.9%, 3.1%, 2.2% and 1.6% higher on average with the Kwint model and 14.6%, 13.2%, 7.2% and 3.4% higher with the Wijsman model for the four DEs. A grade ? 3 AET increased by 4.3%, 7.2%, 4.2% and 1.2%, respectively. Additionally, the predicted LET increased by 0.15%, 0.39%, 1.2 ? 10?2% and 1.5 ? 10?3%. Our study demonstrates that different DEs influence the esophageal toxicity prediction for EC patients administered SIB-IMRT vs. SD-IMRT treatment.
clinical investigations have been conducted to explore the potential risk of esophageal toxicity using SIB-IMRT
vs. standard-dose IMRT (SD-IMRT). Furthermore, most of the studies on the risks of esophageal toxicity induced
by IMRT and concurrent chemotherapy strategies were primarily conducted on lung cancer patients4, 6, 7. In
contrast, EC patients are particularly different from lung cases because the tumor is within the esophagus, resulting
in the heterogeneity of the esophagus definition when implementing the radiation therapy treatment. Therefore,
whether different definitions of esophagus (DEs) influence the evaluation of the esophageal toxicity in response
to SIB-IMRT vs. SD-IMRT should be further investigated.
In this study, we aim to evaluate the impact of DEs on the dosimetric changes and esophageal toxicity
prediction between the SIB-IMRT and SD-IMRT dosing strategies in EC patients using predictive models derived from
Materials and Methods
Ethics statement. All experimental protocols were approved by the Clinical Research Ethics Review
Committee of Cancer Hospital of Shantou University Medical College. All methods in this study were conducted
in accordance with the relevant guidelines and regulations. Considering that this is not a treatment-based study,
our institutional review board waived the need for obtaining written informed consent from the participants.
Patient selection. CT simulating data sets of 21 upper thoracic esophageal cancer patients enrolled in a
phase II clinical trial (Clinicaltrial.gov number, NCT01670409, and Chinese Clinical Research Registry number,
ChiCTR-ONC-12002356) were used in this study.
Immobilization and simulation. The patients were immobilized in supine position with the head and
shoulders wrapped in a thermoplastic mask (Guangzhou Klarity Medical & Equipment Co., Ltd, Guangzhou,
China). Contrast enhanced computer tomography (CT) scans of 0.5-mm slice thickness from the neck to the
upper abdomen were obtained using a 16-slice CT scanner (Philips Brilliance Big Bore Oncology Configuration,
Cleveland, OH) under free breathing. The CT images were subsequently delivered to the Eclipse Treatment
Planning System (Version 10.0, Varian Medical Systems, Palo Alto, CA) by DICOM (Digital Imaging and
Communications in Medicine) 3.0 interface for target volume contouring, organs at risk (OARs) contouring and
Target volume and OARs delineation. We previously introduced the methods of target and OARs
delineation for EC patients8. Briefly, the gross tumor volume (GTV) included the primary tumor (GTVP) and positive
regional lymph nodes (GTVLN). The delineation of GTV was determined using CT images, endoscopic reports
or barium swallow fluoroscopy. GTVLN included mediastinal or supraclavicular lymph nodes with the shortest
axis ?1 cm. Clinical target volume (CTV) was delineated with a 2-cm margin in the longitudinal direction and a
0.5?1.0 cm margin in the radial direction with respect to the GTVP and a 0.5-cm uniform margin from GTVLN.
Paraesophageal or tracheoesophageal groove lymph nodes that did not meet the criteria of positive lymph nodes,
but with their shortest axis ?0.5 cm were also encompassed in CTV. To generate two planning target volume
(PTV), PTV64.8 and PTV50.4, an isotropic 0.5-cm margin was expanded from GTV and CTV, respectively.
OARs, including spinal cord and lung were generated according to the reference9. Briefly, lung contouring was
limited to the air-inflated lung parenchyma without inclusion of the fluid and atelectasis visible on CT images. The
proximal bronchial tree should also be excluded. Contouring of the spinal cord should start at the same cranial
level as the esophagus to the bottom of L2, or the level at which the cord ended. The planning OAR volume (PRV)
for the spinal cord was generated from the spinal cord expanding a 0.5-cm margin. Four types of esophagus
delineation were generated to identify their dose-response differences. ESOwhole presented the whole esophagus from
the level of cricoid cartilage on every CT image to the gastroesophageal junction, including the tumor6, 7, 10, 11;
ESOinfield was the portion of ESOwhole within the treatment field, where the treatment field was defined as the
upper and lower edges of the largest fields10; ESOinfield-tumor was the portion of ESOinfield, excluding the tumor; and
ESOwhole-tumor was the portion of ESOwhole, also excluding the tumor.
Planning objectives. The following dose constraints for OARs were used: spinal cord, Dmax (maximum
dose) <45 Gy; PRV for spinal cord, V50 ? 1 cc; lung, V5 < 60%, V10 < 50%, V20 < 30% and mean lung dose
(MLD) < 15 Gy, where Vx is percentage of the target volume receiving ? x Gy dose. The dose was normalized to
ensure that 95% of the PTV received 100% of the prescription.
Treatment planning. The prescription for the SIB-IMRT plan was set at 64.8Gy in 28 fractions for PTV64.8
(delivered in 2.31 Gy/fraction) and 50.4 Gy in 28 fractions for PTV50.4 (delivered in 1.8 Gy/fraction)12. The
prescription for the SD-IMRT plans was set at 50.4 Gy in 28 fractions (delivered in 1.8 Gy/fraction)?for PTV50.4.
Treatment plans were generated using five sliding window-based coplanar fields, with beam arrangements of
210?, 300?, 0?, 60? and 150?. All plans were designed using 6 MV photon beam from a TrueBeam linear accelerator
(Varian Medical Systems, Palo Alto, CA). Plan optimization was performed using the Dose Volume Optimizer
(DVO, version 10.0.28) algorithm, selecting a maximum dose rate of 600 monitor units per minute (MU/min).
The dose calculation was performed using the Anisotropic Analytical Algorithm (AAA, version 10.0.28),
considering the heterogeneity correction. Several dose-limiting structures were generated to make the dose
conformal to the target. We employed the base dose function (BDF) method as reported in our previous study to
acquire a more homogeneous dose distribution13. Briefly, the fractions of the original plan were modified to half
(from 28 to 14 in the study), and subsequently, the half-prescribed plan was copied and reoptimized using the
half-prescribed plan as the base dose. After the dose was calculated, the fractions of the plan were doubled to
generate the target plan.
Prediction of esophageal toxicity. We used the Kwint model to predict grade ? 2 and grade ? 3 AET7.
The Kwint model derived from 139 patients after CCRT treatment for patients with non-small cell lung cancer
(NSCLC) shows a sigmoid-shaped relationship between grade ? 2 AET and V50. Moreover, the Wijsman model,
which is a Lyman-Kutcher-Burman (LKB)-based predicting model generated from 149 advanced stage NSCLC
patients undergoing CCRT, was also established for estimating grade ? 2 AET6. The following parameters were
used in the Wijsman model: n = 1.04, m = 0.65 and D50 = 32.84 Gy. The Chen model is an a LKB-based predicting
model derived from 171 patients NSCLC patients treated with CCRT, and we use it to predict the incidence of
LET4 using the following parameters: n = 0.03, m = 0.03 and TD50 = 76.1 Gy. All physical doses were converted to
a biologically equivalent dose in 2 Gy fraction (EQD2) dose to calculate the potential risk. For esophageal toxicity
prediction, ?/? of 10 and 3 Gy were employed to predict AET and LET, respectively. A detailed procedure of this
calculation was published in our previous work14.
Statistical analysis. All data in this study were shown as the mean plus standard deviation (mean ? SD).
Data analysis was performed using SPSS version 19.0 software (SPSS, Inc., Chicago, IL, USA). The Friedman
Test was used to determine the difference in dosimetry and predicted toxicity among four DEs. Comparison of
the sub-group data was compared using the Wilcoxon signed-rank test. The results were considered statistically
significant at a p-value < 0.05.
Patient characteristics. From September 2012 to December 2013, 21 upper thoracic esophageal cancer
patients were used in this study. The age of the patients ranged from 49 to 73 years old and the other
characteristics were listed in Table?1.
DEs influence dose changes for EC patients administered SIB-IMRT vs. SD-IMRT. The dose
differences of the four DEs were listed in Table?2. Compared with the SD-IMRT plan, different DEs resulted in
similar dose increase in the SIB-IMRT plan. The increase of V30, V40 and V50 for the four DEs was comparable
(p > 0.05). However, the increase of V60 and Dmean was significantly different (p< 0.05). Specifically, V60 values for
the ESOwhole, ESOinfield, ESOinfield-tumor and ESOwhole-tumor were increased by 29.6 cc, 29.5 cc, 3.4 cc and 3.7 cc,
respectively. Dmean for ESOwhole, ESOinfield, ESOinfield-tumor and ESOwhole-tumor were increased by 7.2 Gy, 10.9 Gy, 4.6 Gy and
2.0 Gy, respectively. However, Dmax values for the ESOwhole, ESOinfield, ESOinfield-tumor and ESOwhole-tumor were equally
increased by 14.2 Gy. Compared with ESOwhole-tumor and ESOinfield-tumor, the increase of V60 and Dmean was higher
for the ESOwhole and ESOinfield definitions. The dose volume histogram (DVH) for the four DEs was presented
in Fig.?1. The dose variation for the four DEs in the sagittal view from one representative case was illustrated in
DEs impact on the esophageal toxicity prediction for EC patients administered SIB-IMRT vs.
SD-IMRT. The esophageal toxicity prediction for the four DEs was listed in Table?3. Compared with the
SD-IMRT plan, the predicted grade ? 2 AET values using the Kwint model in the SIB-IMRT group were 2.9%,
3.1%, 2.2% and 1.6% higher on average for ESOwhole, ESOinfield, ESOinfield-tumor and ESOwhole-tumor, respectively.
Grade ? 2 AET predicted using the Wijsman predicting model were 14.6%, 13.2%, 7.2% and 3.4% higher on
average. Additonally, the SIB-IMRT plan was also 4.3%, 7.2%, 4.2% and 1.2% higher on average for grade ? 3 AET.
The predicted LET were increased by 0.15%, 0.39%, 1.2? 10?2% and 1.5 ? 10?3% for the four DEs. Compared
with ESOwhole-tumor and ESOinfield-tumor, the increase was higher with the ESOwhole and ESOinfield definition.
Whether different DEs influence the evaluation of the esophageal toxicity prediction for EC patients
administered SIB-IMRT vs. SD-IMRT remains unknown. To address this issue, we employed four DEs to distinguish
the?changes on esophageal toxicity prediction between the SIB-IMRT and SD-IMRT dosing regimens using
radiobiological models. We found that different DEs influence the esophageal toxicity prediction by up to 11.2%
(grade ? 2 AET predicted with the Wijsman model). To the best of our knowledge, this study is the first to
investigate the influence of different DEs on the esophageal toxicity prediction for EC patients received SIB-IMRT vs.
AET and LET characterized by dysphagia, odynophagia, stenosis and perforation are common
radiation-induced adverse events4, 15 that significantly affect the quality-of-life and negatively impact the
long-term survival of patients when received thoracic irradiation16. Compared to AET, LET is relatively rare16. In
the definitive treatment of EC patients, the esophagus is more prone to develop these symptoms because part of
the esophagus is inside the treatment field, leading to high dose irradiation during treatment. Three independent
studies showed that 60% of the patients developed grade ? 2 AET, 40% of the patients developed grade ? 3 AET
and 22% of the patients suffered from LET when experiencing SIB-IMRT treatment2, 4, 5. Accordingly, evaluation
of the incidence of esophageal toxicity is important for clinical treatment.
Although two dosimetric studies demonstrated an improved benefit of SIB-IMRT compared with the SD-IMRT
strategy12, 17, no further information on esophageal toxicity was provided in the two studies. Until recently, the DEs
are not consistent for EC?patients. Whether the portion of esophagus outside the treatment field should be included
in the toxicity prediction is unclear. Caglar et al. suggested that the in-field esophagus was a new predictor for
esophagitis in NSCLC patients10. However, other studies used the entire esophagus as the predictor6, 7, 18. Interestingly,
we found that two DEs (ESOwhole and ESOinfield) resulted in a similar?trend of increase for esophageal toxicity
prediction in the SIB-IMRT plans (Table?3), indicating that both of them are comparable for esophageal toxicity evaluation.
However, we also found that the increase in esophageal toxicity using ESOwhole and ESOinfield was higher than that
ESOwhole vs. ESOwhole-tumor; p2 ESOinfield vs. ESOinfield-tumor. aIndicates Kwint model for predicting grade ? 2 acute
esophageal toxicity. bIndicates Kwint model for predicting grade ? 3 acute esophageal toxicity. cIndicates
Wijsman model for predicting grade ? 2 acute esophageal toxicity. dIndicates Chen model for predicting late
of the ESOwhole-tumor and ESOinfield-tumor, particularly when the Wijsman model was used (Table?3). Therefore, more
attention should be paid on a consensus on the DEs during the radiation therapy treatment for EC patients.
The results of our analysis are partly dependent on the choice of radiobiological models and parameters used.
To strengthen the reliability of our data, we employed two esophageal toxicity predicting models from the
literature to predict the likelihood toxicity of grade ? 2 AET. Interestingly, we observed that two independent
models exhibited a similar trend of?increase in the esophageal toxicity using the SIB-IMRT technique, although the
absolute values were different between the two models. Considering that both of these models were generated
from more than one hundred patients experiencing clinical treatment, we propose that these data on the
prediction of esophageal toxicity are reliable. However, we observed that the increase using the Wijsman model was
more remarkable than that from the Kwint model. This finding might partly reflect the different chemotherapy
regimens used in the two independent investigations. Only low-dose cisplatin was used in the Kwint model7,
whereas gemcitabine combined with cisplatinum or etoposide combined with cisplatinum were selectively
delivered according to condition of the patients in the Wijsman model6. Because AET is enhanced with CCRT19, the
chemotherapy regimen might partly influence our prediction of esophageal toxicity.
A significant diversity of predictors used for AET prediction have been reported in previous studies. Dose
volume parameters, such as V30, V40, V50, V60, Dmax and mean esophagus dose were reported to enable AET prediction15,
18, 20?22. Palma et al. performed a meta-analysis enrolling the largest population to date (1082 patients) to show that
V60 emerged as the best predictor of grade ? 2 and grade ? 3 radiation-induced esophagitis with good calibration
and discrimination21. The results indicated that the high dose region might principally contribute to the formation
of esophageal toxicity. From this perspective, we infer that it is more reasonble to use ESOwhole and ESOinfield, which
include the GTV for esophageal toxicity prediction. However, further clinical validation studies are warranted to
confirm our speculation. Recently, Wang et al. used a receiver operating characteristic (ROC) curve to analyze the
predictive values of three methods of lung definitions?for radiation pneumonitis (RP)23. The authors concluded
that the?definition of? lungs-GTV (lung subtracts GTV) might be the most accurate definition for predicting RP.
We have?completed a phase II study implementing the SIB-IMRT?strategy combined with chemotherapy for EC
patients3, and more accurate DE is expected after analyzing the clinical data using the ROC method.
In the past few years, many studies have developed many dosimetric parameters to predict the occurrence of
grade ? 2 or grade ? 3 AET20?22; however, only several studies have developed models to predict their incidence6, 7, 18,
24. Furthermore, two independent studies have?proposed models to predict the incidence of grade ? 2 or grade ? 3
esophagitis; unfortunately, both studies were conducted using 3-dimensional conformal radiation therapy (3DCRT)
technique11, 18, 24, 25. Because IMRT has been reported as superior in delivering a more conformal dose and improving
normal tissue sparing compared with 3DCRT26, 27, the models generated from 3DCRT might potentially limit the
evaluation of AET for patients undergoing IMRT treatment. To date, AET predicting models derived from patients
undergoing IMRT and chemotherapy treatment are scarce except for the Kwint and Wijsman models. Consistent
with this information, we used these models for esophageal toxicity prediction in the study.
Although our study has demonstrated that different DEs influence the esophageal toxicity prediction for EC
patients administered SIB-IMRT vs. SD-IMRT dosing strategies, there are some limitations. (
) The sample size
of our study was a bit small to fully distinguish the changes in dosimetry and esophageal toxicity between the
SIB-IMRT and SD-IMRT dosing strategies. Thus, a larger patient cohort is needed for further validation in the
) We employed the esophagitis-predicting models from lung cancer patients, but the applicability and
feasibility of these models should to be further validated. (
) Notably, only the Kwint model was used to predict
grade ? 3 AET which might partially weaken the reliability of our results. However, to the best of our knowledge,
the Kwint model is the only grade ? 3 AET-predicting model derived from IMRT and chemotherapy practices,
and this model should be used it for prediction.
We must state that AET and LET is dose relavent. With standard dose, SIB-IMRT strategy for EC patients was
reportly feasible without increasing AET or LET at neoadjuvant and adjuvant CCRT settings28, 29.
In summary, our study demonstrated that different DEs influence the esophageal toxicity prediction for EC
patients administered SIB-IMRT vs. SD-IMRT. Our results require further validation in clinical samples.
This work was sponsored by National Natural Science Foundation of China (81602667), Science and Technology
Planning Project of Guangdong Province (2016ZC0166), Medical Scientific Research Foundation of Guangdong
Province (A2015534), Shantou University Medical College Clinical Research Enhancement Initiative (201424),
The Creative and Facilitating Program of Shantou University, Collaborative and Creative Center, Molecular
Diagnosis and Personalized Medicine, Shantou University, Guangdong Province, China.
Competing Interests: The authors declare that they have no competing interests.
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