scholarly journals Planning CT Scans for the Treatment of HDR Vaginal Vault Brachytherapy: An Evaluation of Its Role to Determine the Dose to the Organs at Risk

Brachytherapy ◽  
2015 ◽  
Vol 14 ◽  
pp. S84-S85 ◽  
Author(s):  
Tracey Rose ◽  
Deidre Batchelar ◽  
Bart Robertson ◽  
Juanita Crook ◽  
David Petrik ◽  
...  
Keyword(s):  
At Risk ◽  
Organs At Risk ◽  
Vaginal Vault ◽  
Ct Scans ◽  
Planning Ct

scholarly journals Dosimetric Validation of a GAN-Based Pseudo-CT Generation for MRI-Only Stereotactic Brain Radiotherapy

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1082
Author(s):  
Vincent Bourbonne ◽  
Vincent Jaouen ◽  
Clément Hognon ◽  
Nicolas Boussion ◽  
François Lucia ◽  
...  
Keyword(s):  
At Risk ◽  
Brain Metastases ◽  
Stereotactic Radiotherapy ◽  
Organs At Risk ◽  
Treatment Plan ◽  
Ct Scans ◽  
Generative Adversarial Network ◽  
Electronic Density ◽  
Planning Ct ◽  
Synthetic Ct

Purpose: Stereotactic radiotherapy (SRT) has become widely accepted as a treatment of choice for patients with a small number of brain metastases that are of an acceptable size, allowing for better target dose conformity, resulting in high local control rates and better sparing of organs at risk. An MRI-only workflow could reduce the risk of misalignment between magnetic resonance imaging (MRI) brain studies and computed tomography (CT) scanning for SRT planning, while shortening delays in planning. Given the absence of a calibrated electronic density in MRI, we aimed to assess the equivalence of synthetic CTs generated by a generative adversarial network (GAN) for planning in the brain SRT setting. Methods: All patients with available MRIs and treated with intra-cranial SRT for brain metastases from 2014 to 2018 in our institution were included. After co-registration between the diagnostic MRI and the planning CT, a synthetic CT was generated using a 2D-GAN (2D U-Net). Using the initial treatment plan (Pinnacle v9.10, Philips Healthcare), dosimetric comparison was performed using main dose-volume histogram (DVH) endpoints in respect to ICRU 91 guidelines (Dmax, Dmean, D2%, D50%, D98%) as well as local and global gamma analysis with 1%/1 mm, 2%/1 mm and 2%/2 mm criteria and a 10% threshold to the maximum dose. t-test analysis was used for comparison between the two cohorts (initial and synthetic dose maps). Results: 184 patients were included, with 290 treated brain metastases. The mean number of treated lesions per patient was 1 (range 1–6) and the median planning target volume (PTV) was 6.44 cc (range 0.12–45.41). Local and global gamma passing rates (2%/2 mm) were 99.1 CI95% (98.1–99.4) and 99.7 CI95% (99.6–99.7) respectively (CI: confidence interval). DVHs were comparable, with no significant statistical differences regarding ICRU 91′s endpoints. Conclusions: Our study is the first to compare GAN-generated CT scans from diagnostic brain MRIs with initial CT scans for the planning of brain stereotactic radiotherapy. We found high similarity between the planning CT and the synthetic CT for both the organs at risk and the target volumes. Prospective validation is under investigation at our institution.

scholarly journals Clinical Validation and Treatment Plan Evaluation Based on Auto-Delineation of the Clinical Target Volume for Prostate Cancer Radiotherapy

2022 ◽  
Author(s):  
Jing Shen ◽  
Yinjie TAO ◽  
Hui GUAN ◽  
Hongnan ZHEN ◽  
Lei HE ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
At Risk ◽  
Organs At Risk ◽  
Treatment Plan ◽  
Statistical Significance ◽  
Locally Advanced ◽  
Target Volume ◽  
Ct Scans ◽  
Dice Similarity Coefficient ◽  
Treatment Plans

Abstract Purpose Clinical target volumes (CTV) and organs at risk (OAR) could be auto-contoured to save workload. The goal of this study was to assess a convolutional neural network (CNN) for totally automatic and accurate CTV and OAR in prostate cancer, while also comparing anticipated treatment plans based on auto-contouring CTV to clinical plans. Methods From January 2013 to January 2019, 217 computed tomography (CT) scans of patients with locally advanced prostate cancer treated at our hospital were collected and analyzed. CTV and OAR were delineated with a deep learning based method, which named CUNet. The performance of this strategy was evaluated using the mean Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD), and subjective evaluation. Treatment plans were graded using predetermined evaluation criteria, and % errors for clinical doses to the planned target volume (PTV) and organs at risk(OARs) were calculated. Results The defined CTVs had mean DSC and 95HD values of 0.84 and 5.04 mm, respectively. For one patient's CT scans, the average delineation time was less than 15 seconds. When CTV outlines from CUNetwere blindly chosen and compared to GT, the overall positive rate in clinicians A and B was 53.15% vs 46.85%, and 54.05% vs 45.95%, respectively (P>0.05), demonstrating that our deep machine learning model performed as good as or better than human demarcation Furthermore, 8 testing patients were chosen at random to design the predicted plan based on the auto-courtoring CTV and OAR, demonstrating acceptable agreement with the clinical plan: average absolute dose differences of D2, D50, D98, Dmean for PTV are within 0.74%, and average absolute volume differences of V45, V50 for OARs are within 3.4%. Without statistical significance (p>0.05), the projected findings are comparable to clinical truth. Conclusion The experimental results show that the CTV and OARs defined by CUNet for prostate cancer were quite close to the ground reality.CUNet has the potential to cut radiation oncologists' contouring time in half. When compared to clinical plans, the differences between estimated doses to CTV and OAR based on auto-courtoring were small, with no statistical significance, indicating that treatment planning for prostate cancer based on auto-courtoring has potential.

scholarly journals A Unet-based Research on the Multi-Output Convolution Neural Network's Ability of Decreasing Mis-identification: Automatic Segmentation of Organs at Risk in Thorax

2020 ◽  
Author(s):  
Jie Zhang ◽  
Yiwei Yang ◽  
Kainan Shao ◽  
Xue Bai ◽  
Min Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Spinal Cord ◽  
At Risk ◽  
Organs At Risk ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Ct Scans ◽  
Average Standard Deviation ◽  
Widespread Application ◽  
Single Output

Abstract Background To study a multi-output convolution neural network (CNN)’s capability of reducing mis-identification. Material and Methods To guarantee that the CNN’s output number was the only experiment variable, we used Unet as research object. By modifying it into a multi-output (MO) one, we got a MO-Unet and the conventional single-output Unet (SO-Uent) as a comparing object. All images involved in this study were computed tomography (CT) scans coming from 105 patients with thoracic tumor. 3 organs at risk (OARs), i.e. lung, heart and spinal cord, were delineated by experienced radiation oncologists and were used as ground truth. The two models were both trained with 1240 CTs (856 images for learning and 384 images for monitor) and under the same learning settings. They were both tested on other 886 images. Dice and mis-identification pixels’ number(n) were 2 metrics for evaluation. Results MO-Unet and SO-Unet achieved Dice of 0.9400 ± 0.0612 (average ± standard deviation) and 0.9451 ± 0.0618 for lung, 0.9143 ± 0.1119 and 0.9160 ± 0.1071 for heart, 0.8988 ± 0.0657 and 0.9020 ± 0.0624 for spinal cord respectively. The two models’ all average Dices were ≤ 0.005. For the normalized number of cases with n = 0, MO-Unet and SO-Unet had 97.29% and 96.84% for spinal cord, 88.49% and 90.86% for heart, 81.26% and 77.09% for lung respectively. Compared to SO-Unet, the mis-identification cases of MO-Unet mainly felled in the range of small n. Conclusions The Dice results showed that the two models had comparable overlap. The n results suggested that the MO-Unet was better in decreasing mis-identification. Besides, a MO network is light-weighted to implement more delineation under the same computing source. Therefore, a MO network is promising in segmenting OARs and has the potential for a widespread application in China.

Ứng dụng trí tuệ nhân tạo để khoanh vùng cơ quan trong xạ trị

2020 ◽  
Author(s):  
Hung Tran Sy
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Input Data ◽  
Organs At Risk ◽  
Ct Scans ◽  
Pelvic Area ◽  
Self Study ◽  
Head Area

Med-Aid AI Contour is a software applying artificial intelligence (AI) to contour organs at risk (OAR) base on CT scans. This is a tool to assist oncologists on contouring OAR to reduce time and improve the quality with more accurate quality. Purpose: Evaluate the quality of AI Contour and the software’s self-study and self-improve ability when the amount of input data is increasing. Materials and Methods: Cases of cancer in different locations include: Head, Chest and Abdominal are used as input data for AI Contour to self-study, and then evaluate contour results based on 60 cases with contour samples reviewed by doctors. Implement statistics of results when input data for Abdominal increasing from 125 up to 716 patients. Results: The latest version of AI Contour showed results over 80% contours acceptable. Specifically: the Head area 83.02%, the Chest area 82.69%, the Abdominal/Pelvic area 82.41%. Discussion: AI Contour gives gradual better results when input data increases. For example Abdominal area, the acceptable rate increased from 52.02% (with the input was 125 patients) to 81.22% (with the input was 716 patients).

Pediatric Trauma Patients with Temporal Bone Fractures Are at Risk for Multiple Cranial CT Scans

2020 ◽  
Author(s):  
Joshua Ewy ◽  
Martin Piazza ◽  
Brian Thorp ◽  
Michael Phillips ◽  
Carolyn Quinsey
Keyword(s):  
At Risk ◽  
Temporal Bone ◽  
Bone Fractures ◽  
Pediatric Trauma ◽  
Ct Scans ◽  
Trauma Patients ◽  
Cranial Ct

PO-1718: Deep learning based auto-contouring of planning CT scans for rectal cancer

2020 ◽  
Vol 152 ◽  
pp. S949
Author(s):  
L. Bokhorst ◽  
M.H.F. Savenije ◽  
M.P.W. Intven ◽  
C.A.T. Van den Berg
Keyword(s):  
Rectal Cancer ◽  
Deep Learning ◽  
Ct Scans ◽  
Planning Ct
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