Analysis of dose–volume histogram parameters for radiation pneumonitis after definitive concurrent chemoradiotherapy for esophageal cancer

2010 ◽  
Vol 95 (2) ◽  
pp. 240-244 ◽  
Author(s):  
Hirofumi Asakura ◽  
Takayuki Hashimoto ◽  
Sadamoto Zenda ◽  
Hideyuki Harada ◽  
Koichi Hirakawa ◽  
...  
2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Chanon Puttanawarut ◽  
Nat Sirirutbunkajorn ◽  
Suphalak Khachonkham ◽  
Poompis Pattaranutaporn ◽  
Yodchanan Wongsawat

Abstract Objective The purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2). Materials and methods DVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade ≥ 1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs). Result The AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC = 0.70) and without correction of the dose to EQD2 (AUC = 0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2. Conclusion Dosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.


PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244143
Author(s):  
Yasuki Uchida ◽  
Takuya Tsugawa ◽  
Sachiko Tanaka-Mizuno ◽  
Kazuo Noma ◽  
Ken Aoki ◽  
...  

The constraint values of dose-volume histogram (DVH) parameters for radiation pneumonitis (RP) prediction have not been uniform in previous studies. We compared the differences between conventional DVH parameters and DVH parameters with high attenuation volume (HAV) in CT imaging in both esophageal cancer and lung cancer patients to determine the most suitable DVH parameters in predicting RP onset. Seventy-seven and 72 patients who underwent radiation therapy for lung cancer and esophageal cancer, respectively, were retrospectively assessed. RP was valued according to the Common Terminology Criteria for Adverse Events. We quantified HAV with quantitative computed tomography analysis. We compared conventional DVH parameters and DVH parameters with HAV in both groups of patients. Then, the thresholds of DVH parameters that predicted symptomatic RP and the differences in threshold of DVH parameters between lung cancer and esophageal cancer patient groups were compared. The predictive performance of DVH parameters for symptomatic RP was compared using the area under the receiver operating characteristic curve. Mean lung dose, HAV30% (the proportion of the lung with HAV receiving ≥30 Gy), and HAV20% were the top three parameters in lung cancer, while HAV10%, HAV5%, and V10 (the percentage of lung volume receiving 10 Gy or more) were the top three in esophageal cancer. By comparing the differences in the threshold for parameters predicting RP between the two cancers, we saw that HAV30% retained the same value in both cancers. DVH parameters with HAV showed narrow differences in the threshold between the two cancer patient groups compared to conventional DVH parameters. DVH parameters with HAV may have higher commonality than conventional DVH parameters in both patient groups tested.


Author(s):  
M.K. Martel ◽  
R.K. Ten Haken ◽  
M.B. Hazuka ◽  
A.T. Turrisi ◽  
B.A. Fraass ◽  
...  

2020 ◽  
Vol 9 (13) ◽  
pp. 4540-4549
Author(s):  
Kuniaki Katsui ◽  
Takeshi Ogata ◽  
Kenta Watanabe ◽  
Norihisa Katayama ◽  
Masahiro Kuroda ◽  
...  

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