Dosimetric comparison of TomoDirect, helical tomotherapy, VMAT, and ff-IMRT for upper thoracic esophageal carcinoma

Zhang, Yaowen; Wang, Huitao; Huang, Xiao Yan; Zhang, Qiang; Ren, Runchuan; Sun, Ronggang; Zheng, Zhiyong; Dong, Siming; Zheng, Aijuan

doi:10.1016/j.meddos.2018.05.001

Cited by 11 publications

(10 citation statements)

References 26 publications

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“…Sun et al [27] reported the dosimetric comparisons of 12 newly histologically diagnosed intracranial medulloblastoma patients using HT, VMAT, and IMRT and found that HT showed superior dose conformity as compared with IMRT and VMAT in PTVs. As discussed by Zhang et al [28], the CI for PTV 59.4 was similar among the 4 techniques (TomoDirect, HT, VMAT, and fixed-field intensity-modulated radiotherapy), and the CI for PTV 50.4 showed a statistical significance of HT.…”

Section: Biomed Research Internationalmentioning

confidence: 52%

Comparison of Dosimetric Gains Provided by Intensity-Modulated Radiotherapy, Volume-Modulated Arc Therapy, and Helical Tomotherapy for High-Grade Glioma

Liu

Wang

et al. 2020

BioMed Research International

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Purpose. Because of the poor prognosis for high-grade glioma (HGG) patients, it is important to increase the dose of the tumor to improve the efficacy while minimizing the dose of organs at risk (OARs). Thus, we evaluated the potential dosimetric gains of helical tomotherapy (HT) versus intensity-modulated radiotherapy (IMRT) or volume-modulated arc therapy (VMAT) for high-grade glioma (HGG). Methods. A total of 42 HGG patients were retrospectively selected who had undergone helical tomotherapy; then, IMRT and VMAT plans were generated and optimized for comparison after contouring crucial neuronal structures for neurogenesis and neurocognitive function. IMRT and VMAT were optimized with the Eclipse treatment planning system (TPS) (Version 11.0.31) and HT using TomoTherapy Hi-Art Software (Version 2.0.7) (Accuray, Madison, WI, USA). All three techniques were optimized for simultaneously delivering 60 Gy to planning target volume (PTV) 1 and 50-54 Gy to PTV2. We also analyzed the homogeneity index (HI) and conformity index (CI) of PTVs and organ at risk (OAR) sparing. Results. There was no significant difference in the PTV coverage among IMRT, VMAT, or HT. As for the HI, HT plans (PTV1 HI: 0.09±0.03, PTV2 HI: 0.17±0.05) had the best homogeneity when compared to IMRT plans (PTV1 HI: 0.10±0.04, PTV2 HI: 0.18±0.04) and VMAT plans (PTV1 HI: 0.11±0.03, PTV2 HI: 0.20±0.03). The CI value of HT (PTV1 CI: 0.98±0.03, PTV2: 0.98±0.05) was closest to the optimal value. Except for the IMRT and VMAT groups, there were statistically significant differences between the other two groups of the CI values in both PTV1 and PTV2. The other comparison values were statistically significant except for the optic nerve, and VMAT had the best sparing of the optic chiasm. The mean and max doses of OARs declined significantly in HT. Conclusions. For high-grade glioma patients, HT had superior outcomes in terms of PTV coverage and OAR sparing as compared with IMRT/VMAT.

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Section: Biomed Research Internationalmentioning

confidence: 52%

Comparison of Dosimetric Gains Provided by Intensity-Modulated Radiotherapy, Volume-Modulated Arc Therapy, and Helical Tomotherapy for High-Grade Glioma

Liu

Wang

et al. 2020

BioMed Research International

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“…More importantly, the accurate prediction of RP is essential to facilitate individualized radiation dosing that leads to maximized therapeutic gain. At present, the risk assessment of RP is mainly predicted by using lung dosimetric parameters ( 5 , 6 ), such as the relative volume of total lung irradiated above a specified threshold dose (V X ) or mean lung dose (MLD): Although several metrics have appeared promising, the results vary across institutions, so these metrics do not seem to be perfect at predicting RP ( 7 , 8 ). In addition to dosimetric parameters, some clinical features (tumor stage, smoking history, preexisting lung diseases, concurrent chemotherapy, and radiation dose) are also considered to be related to RP occurrence.…”

Section: Introductionmentioning

confidence: 99%

A Novel Nomogram Model Based on Cone-Beam CT Radiomics Analysis Technology for Predicting Radiation Pneumonitis in Esophageal Cancer Patients Undergoing Radiotherapy

Tang

Cui

et al. 2020

Front. Oncol.

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PurposeWe quantitatively analyzed the characteristics of cone-beam computed tomography (CBCT) radiomics in different periods during radiotherapy (RT) and then built a novel nomogram model integrating clinical features and dosimetric parameters for predicting radiation pneumonitis (RP) in patients with esophageal squamous cell carcinoma (ESCC).MethodsAt our institute, a retrospective study was conducted on 96 ESCC patients for whom we had complete clinical feature and dosimetric parameter data. CBCT images of each patient in three different periods of RT were obtained, the images were segmented using both lungs as the region of interest (ROI), and 851 image features were extracted. The least absolute shrinkage selection operator (LASSO) was applied to identify candidate radiomics features, and logistic regression analyses were applied to construct the rad-score. The optimal period for the rad-score, clinical features, and dosimetric parameters were selected to construct the nomogram model and then the receiver operating characteristic (ROC) curve was used to evaluate the prediction capacity of the model. Calibration curves and decision curves were used to demonstrate the discriminatory and clinical benefit ratios, respectively.ResultsThe relative volume of total lung treated with ≥5 Gy (V5), mean lung dose (MLD), and tumor stage were independent predictors of RP and were finally incorporated into the nomogram. When the three time periods were modeled, the first period was better than the others. In the primary cohort, the area under the ROC curve (AUC) was 0.700 (95% confidence interval (CI) 0.568–0.832), and in the independent validation cohort, the AUC was 0.765 (95% CI 0.588–0.941). In the nomogram model that integrates clinical features and dosimetric parameters, the AUC in the primary cohort was 0.836 (95% CI 0.700–0.918), and the AUC in the validation cohort was 0.905 (95% CI 0.799–1.000). The nomogram model exhibits excellent performance. Calibration curves indicate a favorable consistency between the nomogram prediction and the actual outcomes. The decision curve exhibits satisfactory clinical utility.ConclusionThe radiomics model based on early lung CBCT is a potentially valuable tool for predicting RP. V5, MLD, and tumor stage have certain predictive effects for RP. The developed nomogram model has a better prediction ability than any of the other predictors and can be used as a quantitative model to predict RP.

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“…Some studies [17,18] suggest that TOMO is safe in the treatment of lung cancer. EC studies [19,20] compared lung dose parameters by selecting a few patients and designing several different physical plans; however, there is no real-world report on the occurrence of RP in EC after different irradiation techniques. We analysed the incidence of RP in 247 patients with EC undergoing RT.…”

Section: Discussionmentioning

confidence: 99%

Predicting radiation pneumonitis after radiotherapy or chemoradiotherapy for oesophageal cancer: a single-center retrospective analysis

Du¹,

Wang²,

Li³

et al. 2021

Preprint

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Background: Multimodality therapy for oesophageal cancer (EC) can cause a variety of treatment-related sequelae, especially pulmonary toxicities. The accurate prediction of radiation pneumonitis（RP）is essential to facilitate individualized radiation dosing that leads to maximized therapeutic gain. In this study, we performed a retrospective analysis to determine important factors that predict RP after radiotherapy (RT) for thoracic segment EC.Methods: Two hundred and forty-seven patients with locally advanced EC who received RT or chemoradiotherapy（CRT）were enrolled. The factors associated with RP in different grades were analyzed by univariate and multivariate analyses, such as basic pulmonary disease, smoking index(SI), three mainstream RT techniques, and dose-volume histogram(DVH). Results: The median RT dose was 60Gy, and the median follow-up time was 10 months. There were 118 cases of RP in 247 cases of EC patients who underwent RT or CRT. Among them, there were 54 cases of symptomatic pneumonitis (≥2 grade). The overall rate of symptomatic pneumonitis was 21.9%. In terms of RT techniques, there was no significant difference in the incidence of RP among three dimensional conformal radiotherapy (3D-CRT), intensity modulated radiotherapy (IMRT) , and tomography (TOMO) (P>0.05). V5-V40 and MLD were associated with all grades of RP（P<0.05). Target volume, lung volume and their ratio were correlated with the incidence of RP（P<0.05). Among clinical factors, the highest risk of RP（≥3 grade）was in patients > 400 of SI. Chronic obstructive pulmonary disease (COPD) is also related to the occurrence of RP (≥1 grade). In addition, V5 and V40 were an independent risk factor for RP grade≥1 (AUC 55.74%, 4.13%). MLD was an independent risk factor for RP grade≥2(AUC 11.91Gy). V5 was an independent risk factor for RP grade≥3 (AUC 57.60%). Conclusions: There was no significant difference in the incidence of RP regardless of radiation therapy technique (3D-CRT, IMRT , and TOMO) and treatment-related factors. SI and COPD are closely related to the occurrence of the corresponding grades of RP. Several lung dosimetric parameters (V5, V40, MLD) may be the most effective predictive factor of RP.

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Dosimetric comparison of TomoDirect, helical tomotherapy, VMAT, and ff-IMRT for upper thoracic esophageal carcinoma

Cited by 11 publications

References 26 publications

Comparison of Dosimetric Gains Provided by Intensity-Modulated Radiotherapy, Volume-Modulated Arc Therapy, and Helical Tomotherapy for High-Grade Glioma

Comparison of Dosimetric Gains Provided by Intensity-Modulated Radiotherapy, Volume-Modulated Arc Therapy, and Helical Tomotherapy for High-Grade Glioma

A Novel Nomogram Model Based on Cone-Beam CT Radiomics Analysis Technology for Predicting Radiation Pneumonitis in Esophageal Cancer Patients Undergoing Radiotherapy

Predicting radiation pneumonitis after radiotherapy or chemoradiotherapy for oesophageal cancer: a single-center retrospective analysis

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