Dose distribution prediction of Gamma index using Random Forests Regression. A retrospective comparison

2021 ◽  
Author(s):  
Stanislav Bozhikov ◽  
Philipa Vassileva ◽  
Karina Mitarova ◽  
Desislava Aleksandrova ◽  
Tsvetomira Hristova ◽  
...  
Keyword(s):  
Random Forests ◽  
Dose Distribution ◽  
Retrospective Comparison ◽  
Gamma Index

Analysis of the Physical and Radiobiological Equivalence of the Calculated and Measured Dose Distributions for Prostate Stereotactic Radiotherapy

2021 ◽  
Vol 66 (3) ◽  
pp. 68-75
Author(s):  
E. Sukhikh ◽  
L. Sukhikh ◽  
A. Vertinsky ◽  
P. Izhevsky ◽  
I. Sheino ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Dose Distribution ◽  
Three Dimensional ◽  
Anterior Wall ◽  
Dose Distributions ◽  
Stereotactic Radiation ◽  
Dose Volume Histograms ◽  
Dose Volume ◽  
Gamma Index ◽  
Measured Dose

Purpose: Carrying out the analysis of the physical and radiobiological equivalence of dose distributions obtained during the planning of hypofractionated stereotactic radiation therapy of the prostate cancer and verification using a three-dimensional cylindrical dosimeter. Material and Methods: Based on the anatomical data of twelve patients diagnosed with prostate carcinoma, stage T2N0M0 with low risk, plans were developed for stereotactic radiation therapy with volumetric modulates arc therapy (VMAT). The dose per fraction was 7,25 Gy for 5 fractions (total dose 36,25 Gy) with a normal photon energy of 10 MV. The developed plans were verified using a three-dimensional cylindrical ArcCHECK phantom. During the verification process, the three-dimensional dose distribution in the phantom was measured, based on which the values of the three-dimensional gamma index and the dose–volume histogram within each contoured anatomical structures were calculated with 3DVH software. The gamma index value γ (3 %, 2 mm, GN) at a threshold equal to 20 % of the dose maximum of the plan and the percentage of coincidence of points at least 95 % was chosen as a criterion of physical convergence of the calculated and measured dose distribution according to the recommendations of AAPM TG-218. To analyze the radiobiological equivalence of the calculated and measured dose distribution, the local control probability (TCP) and normal tissue complication probability (NTCP) criteria were used based on the calculated and measured dose–volume histograms. Contours of the target (PTV) and the anterior wall of the rectum were used for the analysis. The approach based on the concept of equivalent uniform dose (EUD) by A. Niemierko was used to calculate the values of TCP/NTCP criteria. Results: The results of physical convergence of plans for all patients on the contour of the whole body were higher than 95 % for the criteria γ (3 %, 2 mm, GN). The convergence along the PTV contour is in the range (75.5–95.2)%. The TCP and NTCP values obtained from the measured dose-volume histograms were higher than the planned values for all patients. It was found that the accelerator delivered a slightly higher dose to the PTV and the anterior wall of the rectum than originally planned. Conclusion: The capabilities of modern dosimetric equipment allow us move to the verification of treatment plans based on the analysis of TCP / NTCP radiobiological equivalence, taking into account the individual characteristics of the patient and the capabilities of radiation therapy equipment.

scholarly journals Interpretation of Gamma Index for Quality Assurance of Simultaneously Integrated Boost (SIB) IMRT Plans for Head and Neck Carcinoma

2017 ◽  
Vol 23 (4) ◽  
pp. 93-97 ◽  
Author(s):  
Maria Atiq ◽  
Atia Atiq ◽  
Khalid Iqbal ◽  
Quratul ain Shamsi ◽  
Farah Andleeb ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Head And Neck ◽  
Dose Distribution ◽  
Confidence Limit ◽  
Passing Rate ◽  
Gamma Analysis ◽  
Gamma Index ◽  
Integrated Boost ◽  
Calculated Dose ◽  
Dose Difference

Abstract Objective: The Gamma Index is prerequisite to estimate point-by-point difference between measured and calculated dose distribution in terms of both Distance to Agreement (DTA) and Dose Difference (DD). This study aims to inquire what percentage of pixels passing a certain criteria assure a good quality plan and suggest gamma index as efficient mechanism for dose verification of Simultaneous Integrated Boost Intensity Modulated Radiotherapy plans. Method: In this study, dose was calculated for 14 head and neck patients and IMRT Quality Assurance was performed with portal dosimetry using the Eclipse treatment planning system. Eclipse software has a Gamma analysis function to compare measured and calculated dose distribution. Plans of this study were deemed acceptable when passing rate was 95% using tolerance for Distance to agreement (DTA) as 3mm and Dose Difference (DD) as 5%. Result and Conclusion: Thirteen cases pass tolerance criteria of 95% set by our institution. Confidence Limit for DD is 9.3% and for gamma criteria our local CL came out to be 2.0% (i.e., 98.0% passing). Lack of correlation was found between DD and γ passing rate with R2 of 0.0509. Our findings underline the importance of gamma analysis method to predict the quality of dose calculation. Passing rate of 95% is achieved in 93% of cases which is adequate level of accuracy for analyzed plans thus assuring the robustness of SIB IMRT treatment technique. This study can be extended to investigate gamma criteria of 5%/3mm for different tumor localities and to explore confidence limit on target volumes of small extent and simple geometry.

SU-E-T-212: To Evaluate Significance of Gamma Index Between the Measurement of 2D Detector Arrays and the Calculated Patient 3D Dose Distribution

2013 ◽  
Vol 40 (6Part13) ◽  
pp. 253-253
Author(s):  
S Kim ◽  
Y Qiu ◽  
P Mavroidis ◽  
N Papanikolaou ◽  
S Stathakis
Keyword(s):  
Dose Distribution ◽  
Detector Arrays ◽  
Gamma Index

Novel Radiobiological Gamma Index for Evaluation of 3-Dimensional Predicted Dose Distribution

2015 ◽  
Vol 92 (4) ◽  
pp. 779-786 ◽  
Author(s):  
Iori Sumida ◽  
Hajime Yamaguchi ◽  
Hisao Kizaki ◽  
Keiko Aboshi ◽  
Mari Tsujii ◽  
...  
Keyword(s):  
Dose Distribution ◽  
3 Dimensional ◽  
Gamma Index

WE-A-BRB-08: Comparison of Brachytherapy Dose Distribution Using the Gamma-Index Method

2011 ◽  
Vol 38 (6Part31) ◽  
pp. 3793-3793
Author(s):  
Y Yang ◽  
M J Rivard
Keyword(s):  
Dose Distribution ◽  
Index Method ◽  
Gamma Index

The Childhood Trauma Questionnaire: A Prospective and Retrospective Comparison

2014 ◽  
Author(s):  
Kathryn Buchanan-Howland ◽  
Ruth Rose-Jacobs ◽  
Mark A. Richardson ◽  
Timothy Heeren ◽  
Clara A. Chen ◽  
...  
Keyword(s):  
Childhood Trauma ◽  
Childhood Trauma Questionnaire ◽  
Retrospective Comparison

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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