Dose Volume Toxicity Analysis for Organs at Risk in Patients Receiving Reirradiation for Recurrent High Grade Glioma

2015 ◽  
Vol 93 (3) ◽  
pp. E86 ◽  
Author(s):  
A.V. Krauze ◽  
H. Ning ◽  
J.Y. Cheng ◽  
Y. Zhuge ◽  
T. Cooley-Zgela ◽  
...  
Keyword(s):  
At Risk ◽  
Organs At Risk ◽  
High Grade Glioma ◽  
High Grade ◽  
Dose Volume ◽  
Toxicity Analysis

scholarly journals EP-1128: Outcome of high grade glioma patients: To prioritise dose to primary tumour or organs at risk?

2016 ◽  
Vol 119 ◽  
pp. S541
Author(s):  
F.C.J. YIM ◽  
L. Howell ◽  
S.Y.Y. Pan ◽  
V.S. Kumar ◽  
S.R. Kennedy
Keyword(s):  
At Risk ◽  
Organs At Risk ◽  
Primary Tumour ◽  
High Grade Glioma ◽  
High Grade

scholarly journals PP66. OUTCOMES OF HIGH GRADE GLIOMA PATIENTS TREATED WITH TWO DIFFERENT RADIOTHERAPY REGIMES: THE EFFECT OF PRIORITISING TUMOUR DOSE VERSUS LIMITING DOSE TO ORGANS AT RISK

2017 ◽  
Vol 19 (suppl_1) ◽  
pp. i18-i18
Author(s):  
Mr Justin Yim ◽  
Ms Laura Howell ◽  
Dr Varadarajan Kumar ◽  
Dr Stephen R Kennedy
Keyword(s):  
At Risk ◽  
Organs At Risk ◽  
High Grade Glioma ◽  
High Grade ◽  
Tumour Dose

scholarly journals A machine learning‐based survival prediction model of high grade glioma by integration of clinical and dose‐volume histogram parameters

2021 ◽  
Vol 10 (8) ◽  
pp. 2774-2786
Author(s):  
Haiyan Chen ◽  
Chao Li ◽  
Lin Zheng ◽  
Wei Lu ◽  
Yanlin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
High Grade Glioma ◽  
Survival Prediction ◽  
High Grade ◽  
Dose Volume Histogram ◽  
Dose Volume

RADT-20. SINGLE INSTITUTION REVIEW OF LARGE VOLUME HIGH GRADE GLIOMA PATIENTS TREATED WITH RADIOTHERAPY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi45-vi45
Author(s):  
Vishal Manik ◽  
Angela Swampillai ◽  
Omar Al-Salihi ◽  
Kazumi Chia ◽  
Lucy Brazil
Keyword(s):  
Large Volume ◽  
Brain Volume ◽  
Collaborative Study ◽  
High Grade Glioma ◽  
Study Data ◽  
Molecular Profile ◽  
Normal Brain ◽  
High Grade ◽  
Total Brain Volume ◽  
Dose Volume

Abstract AIM Not uncommonly, we come across significantly large high grade glioma cases (HGGs). With standard delineation protocols, we end up irradiating a large volume of normal brain. Emami & QUANTEC data define normal brain tolerance doses, however they are often of limited use in clinic practice. Thus, we reviewed our patients with significant tumor volumes to derive a safe dose/ volume level for brain. METHODOLOGY Patients with HGGs over the last 3 years were extracted from Mosaiq™ information system. The output was sorted with respect to clinical target volumes from lowest to highest. The top 25 percentile i.e. patients with a CTV of > 412cc (n=53) were identified for this study. Data was collected with respect to clinical, tumor characteristics and radiotherapy parameters. RESULTS Median age of population was 53 and majority (n=38) were males. Nine patients had multi-focal tumors while six had bilateral extension. Majority of the study group had Glioblastoma Multiforme (n=44), whereas 6 had Grade 3 tumors. Most of the patients could only have a biopsy (n=27). Molecular profile showed 42 were Isocitrate-Dehydrogenase negative and 26 were unmethylated tumors. Stupp’s & Perry’s regimen were the commonly used protocols, however patients (n=7) with significant volumes near critical structures were treated with doses in the range of 50.4 – 55Gy in 30 fractions. The CTV volumes in the population ranged from 412 – 1223 cc while total brain volume range was 1112 – 1667 cc. Median of 43.5% of brain volume was covered in the PTV, while median of 5% of brain volume outside the PTV was treated to BED2 of 100Gy. Median survival was 12.4 months. CONCLUSION Our study shows reasonable tolerance of radiotherapy doses of > 50 Gy to larger volumes of brain. We propose a multi-center collaborative study to derive a new standardized dose volume tolerance.

A deep learning model to predict dose–volume histograms of organs at risk in radiotherapy treatment plans

2020 ◽  
Vol 47 (11) ◽  
pp. 5467-5481
Author(s):  
Zhiqiang Liu ◽  
Xinyuan Chen ◽  
Kuo Men ◽  
Junlin Yi ◽  
Jianrong Dai
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Organs At Risk ◽  
Learning Model ◽  
Dose Volume Histograms ◽  
Dose Volume ◽  
Treatment Plans ◽  
Deep Learning Model ◽  
Radiotherapy Treatment

scholarly journals Study of the dose-volume parameters variation in tumor target volumes and organs at risk during nasopharyngeal carcinoma radiotherapy applying deformation registration

2019 ◽  
Vol 8 (8) ◽  
pp. 2886-2892
Author(s):  
Xinsen Yao ◽  
Guanzhong Gong ◽  
Guoping Zuo ◽  
Jianxin Ren ◽  
Ming Su ◽  
...  
Keyword(s):  
At Risk ◽  
Nasopharyngeal Carcinoma ◽  
Organs At Risk ◽  
Tumor Target ◽  
Dose Volume ◽  
Target Volumes

scholarly journals RTHP-14. PSEUDOPROGRESSION IN HIGH GRADE GLIOMA PATIENTS AFTER PROTON OR PHOTON THERAPY

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi213-vi213
Author(s):  
Vonetta Williams ◽  
Lia Halasz ◽  
Jason Rockhill ◽  
James Fink
Keyword(s):  
Mr Imaging ◽  
High Grade Glioma ◽  
Extent Of Resection ◽  
High Dose ◽  
High Grade ◽  
Proton Group ◽  
Kaplan Meier ◽  
Dose Volume ◽  
Biological Effectiveness ◽  
Grade Iii

Abstract Pseudoprogression is defined as the appearance of false progression on MR imaging following radiation therapy. Proton therapy is thought to have increased relative biological effectiveness-the ratio of the doses required by two types of radiation to cause the same level of effect-near the edges of the high dose volume. This could lead to different rates of pseudoprogression for protons compared to photons. In our IRB approved study, a board-certified neuroradiologist reviewed serial imaging of 74 patients (photons: n=37, protons: n=37) treated from 2013–2018 with either proton or photon radiotherapy to 59.4–60 Gy in 30–33 fractions and temozolomide for high grade glioma. MR imaging was performed 1 month after completion of treatment and then every 3 months. True progression was scored based on updated RANO criteria. Pseudoprogression was determined if imaging improved without change in therapy. Cumulative incidences of these outcomes and survival were calculated utilizing Kaplan-Meier analyses. Patient and treatment factors were analyzed for their association with incidence of pseudoprogression. Median follow-up for alive patients in the proton and photon groups were 15 and 29 months, respectively. Median age was 49 years in the proton group and 54 years in the photon group (p=0.17). Among proton patients, 14 had grade III glioma and 23 had grade IV glioblastoma. Among photon patients, 1 had grade III glioma. Median survival was 23 and 35 months for the proton and photon groups, respectively (p=0.57). The cumulative incidence of pseudoprogression was 14.4% and 10.4% at 12 months for the proton and photon groups, respectively (p=0.53). Grade, extent of resection, age, and IDH status, were not significantly associated with development of pseudoprogression. MGMT methylated tumors showed a trend toward association with pseudoprogression compared to unmethylated tumors (p=0.058). We concluded that the incidence of pseudoprogression is similar regardless of whether proton or photon therapy was utilized.

scholarly journals Application of dose-volume histogram prediction in biologically related models for nasopharyngeal carcinomas treatment planning

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Wufei Cao ◽  
Yongdong Zhuang ◽  
Lixin Chen ◽  
Xiaowei Liu
Keyword(s):  
Spinal Cord ◽  
At Risk ◽  
Treatment Planning ◽  
Organs At Risk ◽  
Planning System ◽  
Treatment Planning System ◽  
Dose Volume Histogram ◽  
Parotid Glands ◽  
Dose Volume ◽  
Dose Sparing

Abstract Purpose In this study, we employed a gated recurrent unit (GRU)-based recurrent neural network (RNN) using dosimetric information induced by individual beam to predict the dose-volume histogram (DVH) and investigated the feasibility and usefulness of this method in biologically related models for nasopharyngeal carcinomas (NPC) treatment planning. Methods and materials One hundred patients with NPC undergoing volumetric modulated arc therapy (VMAT) between 2018 and 2019 were randomly selected for this study. All the VMAT plans were created using the Monaco treatment planning system (Elekta, Sweden) and clinically approved: > 98% of PGTVnx received the prescribed doses of 70 Gy, > 98% of PGTVnd received the prescribed doses of 66 Gy and > 98% of PCTV received 60 Gy. Of these, the data from 80 patients were used to train the GRU-RNN, and the data from the other 20 patients were used for testing. For each NPC patient, the DVHs of different organs at risk were predicted by a trained GRU-based RNN using the information given by individual conformal beams. Based on the predicted DVHs, the equivalent uniform doses (EUD) were calculated and applied as dose constraints during treatment planning optimization. The regenerated VMAT experimental plans (EPs) were evaluated by comparing them with the clinical plans (CPs). Results For the 20 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved good consistency relative to the CPs. The EPs showed better consistency in PTV dose distribution and better dose sparing for many organs at risk, and significant differences were found in the maximum/mean doses to the brainstem, brainstem PRV, spinal cord, lenses, temporal lobes, parotid glands and larynx with P-values < 0.05. On average, compared with the CPs, the maximum/mean doses to these OARs were altered by − 3.44 Gy, − 1.94 Gy, − 1.88 Gy, 0.44 Gy, 1.98 Gy, − 1.82 Gy and 2.27 Gy, respectively. In addition, significant differences were also found in brainstem and spinal cord for the dose received by 1 cc volume with 4.11 and 1.67 Gy dose reduction in EPs on average. Conclusion The GRU-RNN-based DVH prediction method was capable of accurate DVH prediction. The regenerated plans guided by the predicted EUDs were not inferior to the manual plans, had better consistency in PTVs and better dose sparing in critical OARs, indicating the usefulness and effectiveness of biologically related model in knowledge-based planning.

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