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).

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

SU-E-T-707: Evaluation of the Quality of Organs-At-Risk Dose Sparing in Anorectal and Prostate IMRT Plans

2013 ◽  
Vol 40 (6Part22) ◽  
pp. 369-369
Author(s):  
S Lu ◽  
L Yuan ◽  
F Yin ◽  
Q Jackie Wu
Keyword(s):  
At Risk ◽  
Organs At Risk ◽  
Dose Sparing

scholarly journals Is aspiration as detected on pretreatment video fluorography, a harbinger of poor quality of life and early mortality in cancers of the upper aerodigestive tract treated with radiotherapy?

2014 ◽  
Vol 03 (04) ◽  
pp. 209-212 ◽  
Author(s):  
Punita Lal ◽  
Vipul Nautiyal ◽  
Tamojit Chaudhuri ◽  
Mranalini Verma ◽  
Koilpillai Joseph Maria Das ◽  
...  
Keyword(s):  
Quality Of Life ◽  
At Risk ◽  
Head And Neck ◽  
Karnofsky Performance Status ◽  
Organs At Risk ◽  
Aerodigestive Tract ◽  
Life Questionnaire ◽  
Upper Aerodigestive Tract ◽  
Group A

Abstract Background: Patients with cancers of the upper aerodigestive tract (head and neck cancer (HNC)) tend to aspirate, either due to disease or treatment. The association of aspiration (documented on video fluorography (VFG)) with quality of life (QOL) and unexpected mortality was studied prospectively in patients treated with simultaneous integrated boost technique of intensity-modulated radiotherapy (SIB-IMRT). Materials and Methods: Moderately advanced (stage III/IV) HNC were treated by SIB-IMRT delivering 66 Gy/30 fr, 60 Gy/30 fr, and 54 Gy/30 fr to high, intermediate, and low risk volumes, respectively. They underwent serial VFG and QOL assessments (Quality of Life Questionnaire-Core 30 ( QLQ-C30) and head and neck-35 (HN35) European Organisation for Research and Treatment of Cancer (EORTC) tools) at 0, 3, and 6 months. Pharyngeal musculature (PM) was additionally delineated on planning computed tomography (CT) scans as potential organs at risk (OARs). Results: Between November 2009 and May 2011, 20 HNC were treated as per protocol. All patients were fit (Karnofsky performance status (KPS) ≥ 80). Based on VFG findings, seven patients (4/9 oropharynx and 3/11 laryngopharynx) were grouped as aspirators (A) and remaining 13 as non-aspirators (NA). The QOL study showed that pretreatment coughing and swallowing difficulties were greater in group A versus NA and remained persistently higher. In group A, deaths attributable to aspiration were seen in 3/7 patients, while none occurred in the NA group (Fisher′s exact P = 0.03). The mean PM dose was 60 Gy in both the groups and mean V60 was similar at 69 and 67% in A and NA groups, respectively. Conclusions: VFG helps identify patients who aspirate and are at risk of premature death due to its complications, alerting caregivers to direct attention appropriately.

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 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 OC-0257: Delineation of organs-at-risk in the pelvic area: developing guidelines for RTTs

2013 ◽  
Vol 106 ◽  
pp. S100-S101
Author(s):  
B. Speleers ◽  
M. Rossi ◽  
M.J.H. van Os ◽  
H.P. van der Laan ◽  
J. Duppen ◽  
...  
Keyword(s):  
At Risk ◽  
Organs At Risk ◽  
Pelvic Area

scholarly journals Improvement of delineation quality of organs at risk in head and neck using the consensus guidelines

2016 ◽  
Vol 119 ◽  
pp. S137-S138
Author(s):  
R. Steenbakkers ◽  
C. Brouwer ◽  
J. Bourhis ◽  
W. Budach ◽  
C. Grau ◽  
...  
Keyword(s):  
At Risk ◽  
Head And Neck ◽  
Organs At Risk ◽  
Consensus Guidelines

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.

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