43: Predictors of Toxicity in a Randomized Study of Hypofractionated Versus Conventional Radiotherapy for Glioblastoma: Dosimetric Analysis of Organs at Risk

2021 ◽  
Vol 163 ◽  
pp. S21
Author(s):  
Fan Yang ◽  
Deepak Dinakaran ◽  
Amr A. Heikal ◽  
Sunita Ghosh ◽  
Shima Yaghoobpour Tari ◽  
...  
Keyword(s):  
At Risk ◽  
Organs At Risk ◽  
Randomized Study ◽  
Conventional Radiotherapy ◽  
Dosimetric Analysis

scholarly journals The Potential Role of Intensity-Modulated Proton Therapy in Hepatic Carcinoma in Mitigating the Risk of Dose De-Escalation

2020 ◽  
Vol 19 ◽  
pp. 153303382098041
Author(s):  
Luca Cozzi ◽  
Tiziana Comito ◽  
Mauro Loi ◽  
Antonella Fogliata ◽  
Ciro Franzese ◽  
...  
Keyword(s):  
At Risk ◽  
Proton Therapy ◽  
Organs At Risk ◽  
Volumetric Modulated Arc Therapy ◽  
Intensity Modulated Proton Therapy ◽  
Intensity Modulated ◽  
Number Of Patients ◽  
Target Volumes ◽  
Dosimetric Analysis

Purpose: To investigate the role of intensity-modulated proton therapy (IMPT) for hepatocellular carcinoma (HCC) patients to be treated with stereotactic body radiation therapy (SBRT) in a risk-adapted dose prescription regimen. Methods: A cohort of 30 patients was retrospectively selected as “at-risk” of dose de-escalation due to the proximity of the target volumes to dose-limiting healthy structures. IMPT plans were compared to volumetric modulated arc therapy (VMAT) RapidArc (RA) plans. The maximum dose prescription foreseen was 75 Gy in 3 fractions. The dosimetric analysis was performed on several quantitative metrics on the target volumes and organs at risk to identify the relative improvement of IMPT over VMAT and to determine if IMPT could mitigate the need of dose reduction and quantify the consequent potential patient accrual rate for protons. Results: IMPT and VMAT plans resulted in equivalent target dose distributions: both could ensure the required coverage for CTV and PTV. Systematic and significant improvements were observed with IMPT for all organs at risk and metrics. An average gain of 9.0 ± 11.6, 8.5 ± 7.7, 5.9 ± 7.1, 4.2 ± 6.4, 8.9 ± 7.1, 6.7 ± 7.5 Gy was found in the near-to-maximum doses for the ribs, chest wall, heart, duodenum, stomach and bowel bag respectively. Twenty patients violated one or more binding constraints with RA, while only 2 with IMPT. For all these patients, some dose de-intensification would have been required to respect the constraints. For photons, the maximum allowed dose ranged from 15.0 to 20.63 Gy per fraction while for the 2 proton cases it would have been 18.75 or 20.63 Gy. Conclusion: The results of this in-silico planning study suggests that IMPT might result in advantages compared to photon-based VMAT for HCC patients to be treated with ablative SBRT. In particular, the dosimetric characteristics of protons may avoid the need for dose de-escalation in a risk-adapted prescription regimen for those patients with lesions located in proximity of dose-limiting healthy structures. Depending on the selection thresholds, the number of patients eligible for treatment at the full dose can be significantly increased with protons.

scholarly journals The Dosimetric Impact of Deep Learning-Based Auto-Segmentation of Organs at Risk on Nasopharyngeal and Rectal Cancer

2021 ◽  
Author(s):  
Hongbo Guo ◽  
Jiazhou Wang ◽  
Xiang Xia ◽  
Yang Zhong ◽  
Jiayuan Peng ◽  
...  
Keyword(s):  
At Risk ◽  
Rectal Cancer ◽  
Deep Learning ◽  
Organs At Risk ◽  
Dice Similarity Coefficient ◽  
Pass Rates ◽  
Temporal Lobes ◽  
Dose Volume ◽  
Treatment Plans ◽  
Dosimetric Analysis

Abstract PurposeTo investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer.Methods and MaterialsTwenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs set (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume indices and 3D gamma pass rates. Spearman’s correlation analysis was performed to investigate the correlation between dosimetric deviation and geometric metrics.ResultsFD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume indices. The only significant dosimetric difference was the Dmax of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01).ConclusionsDeep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume indices. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs.

scholarly journals Dosimetric Analysis of Three Different Radiotherapy Techniques in Patients with Breast Cancer and Their Impact on Organs at Risk (OAR)

2018 ◽  
Vol 07 (02) ◽  
pp. 131-140
Author(s):  
Vikas Jagtap ◽  
Dimpal Saikia ◽  
Shashi Bhushan Sharma ◽  
Shayori Bhattacharjee ◽  
Moirangthem Nara Singh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
At Risk ◽  
Organs At Risk ◽  
Radiotherapy Techniques ◽  
Dosimetric Analysis

scholarly journals The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Hongbo Guo ◽  
Jiazhou Wang ◽  
Xiang Xia ◽  
Yang Zhong ◽  
Jiayuan Peng ◽  
...  
Keyword(s):  
At Risk ◽  
Rectal Cancer ◽  
Deep Learning ◽  
Organs At Risk ◽  
Dice Similarity Coefficient ◽  
Temporal Lobes ◽  
Gamma Analysis ◽  
Dose Volume ◽  
Treatment Plans ◽  
Dosimetric Analysis

Abstract Purpose To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer. Methods and materials Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman’s correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics. Results FD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics. Conclusions Deep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs.

scholarly journals Evaluation of the organs at risk doses for lung tumors in gated and conventional radiotherapy

2020 ◽  
Vol 16 (4) ◽  
pp. 726
Author(s):  
Somayeh Gholami ◽  
Sara Shahzadeh ◽  
SeyedMahmood Reza Aghamiri ◽  
Hojjat Mahani ◽  
Mansoure Nabavi
Keyword(s):  
At Risk ◽  
Organs At Risk ◽  
Lung Tumors ◽  
Conventional Radiotherapy
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