Error detection and classification in patient‐specific IMRT QA with dual neural networks

2020 ◽  
Vol 47 (10) ◽  
pp. 4711-4720 ◽  
Author(s):  
Nicholas J. Potter ◽  
Karl Mund ◽  
Jacqueline M. Andreozzi ◽  
Jonathan G. Li ◽  
Chihray Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Error Detection ◽  
Patient Specific ◽  
Imrt Qa

Patient-specific IMRT QA verification using machine learning and gamma radiomics

2021 ◽  
Vol 82 ◽  
pp. 100-108
Author(s):  
Jéssica Caroline Lizar ◽  
Carolina Cariolatto Yaly ◽  
Alexandre Colello Bruno ◽  
Gustavo Arruda Viani ◽  
Juliana Fernandes Pavoni
Keyword(s):  
Machine Learning ◽  
Patient Specific ◽  
Imrt Qa

scholarly journals Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Michelle Przedborski ◽  
Munisha Smalley ◽  
Saravanan Thiyagarajan ◽  
Aaron Goldman ◽  
Mohammad Kohandel
Keyword(s):  
Neural Networks ◽  
Systems Biology ◽  
Clinical Data ◽  
Treatment Protocol ◽  
Response Prediction ◽  
Clinical Findings ◽  
Patient Specific ◽  
Patient Response ◽  
Triple Combination Therapy ◽  
Parameter Values

AbstractAnti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient’s immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate. Using this approach, we determine biomarkers of patient response and identify potential mechanisms of drug resistance. We develop systems biology informed neural networks (SBINN) to calculate patient-specific kinetic parameter values and to predict clinical outcome. We show how transfer learning can be leveraged with simulated clinical data to significantly improve the response prediction accuracy of the SBINN. Further, we identify novel drug combinations and optimize the treatment protocol for triple combination therapy consisting of IL-6 inhibition, recombinant IL-12, and anti-PD-1 immunotherapy in order to maximize patient response. We also find unexpected differences in protein expression levels between response phenotypes which complement recent clinical findings. Our approach has the potential to aid in the development of targeted experiments for patient drug screening as well as identify novel therapeutic targets.

scholarly journals EP-2163: Deep Neural Networks vs Medical Physicists: An IMRT QA case study

2018 ◽  
Vol 127 ◽  
pp. S1194
Author(s):  
Y. Interian ◽  
G. Valdes ◽  
R. Vincent ◽  
C. Joey ◽  
K. Vasant ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Imrt Qa ◽  
Medical Physicists

A patient specific forecasting model for human albumin based on deep neural networks

2020 ◽  
Vol 196 ◽  
pp. 105555
Author(s):  
Cheng Lei ◽  
Yu Wang ◽  
Jia Zhao ◽  
Kexun Li ◽  
Hua Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Human Albumin ◽  
Patient Specific ◽  
Forecasting Model

scholarly journals PO-0772: Patient-specific realtime error detection for VMAT based on transmission detector measurements

2017 ◽  
Vol 123 ◽  
pp. S409
Author(s):  
M. Pasler ◽  
K. Michel ◽  
L. Marrazzo ◽  
M. Obenland ◽  
S. Pallotta ◽  
...  
Keyword(s):  
Error Detection ◽  
Patient Specific ◽  
Transmission Detector
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