Search Submissions ๐Ÿ”Ž

Results for "oars baseline": 11 found

A Multi-Criteria Optimization Method Based on Reinforcement Learning and Adaptive Boosting in Radiation Therapy

Authors: Liqin HU, Tao He, Jing JIA, Pengcheng LONG, Wei Meng, Yang Yuan

Affiliation: SuperAccuracy Science & Technology Co. Ltd.

Abstract Preview: Purpose: A multi-criteria optimization method based on reinforcement learning and adaptive boosting(RLAB MCO) has been developed to enhance radiotherapy plan quality by offering reasonable and effecti...

A Novel Optimization Algorithm That Improves DVH Based Planning for Direction Modulated Brachytherapy Tandem Applicator.

Authors: Christopher L. Deufel, Suman Gautam, William Y. Song

Affiliation: Virginia Commonwealth University, Mayo Clinic

Abstract Preview: Purpose: Direction modulated brachytherapy creates anisotropic dose distribution from an isotropic source. This study aims to develop a truncated conditional value at risk optimization algorithm for D...

A Tool to Quantitatively Assess Dose after Patient Motion

Authors: Asma Amjad, Renae Conlin, Beth A. Erickson, William Hall, Eric S. Paulson, Christina M. Sarosiek

Affiliation: Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: The adapt-to-shape (ATS) workflow on the MR-Linac involves manual contour edits followed by treatment plan reoptimization on daily pre-beam MRIs. A verification image is acquired after plan o...

Deep Learning-Based Auto-Segmentation in Cervical High-Dose-Rate Brachytherapy with Clinical Considerations

Authors: Benjamin Haibe-Kains, Ruiyan Ni, Alexandra Rink

Affiliation: Department of Medical Biophysics, University of Toronto, University Health Network

Abstract Preview: Purpose: Accurate auto-segmentation for targets and organs-at-risk (OARs) using deep learning reduces the delineating time in radiotherapy. In high-dose-rate brachytherapy, specific clinical criteria ...

Deeptuning: A Deep Learning Dose Prediction Framework for Interactive Plan Tuning

Authors: Mingli Chen, Huan Amanda Liu, Weiguo Lu, Lin Ma

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Mayo Clinic

Abstract Preview: Purpose: To reduce the back-and-forth in planning process between physicians and dosimetrists resulting from trade-off exploration, we proposed a novel deep learning framework called DeepTuning.
Me...

Empowering Knowledge Transfer in Global Radiotherapy Planning: An Educational Case Study of Knowledge-Based Models in Nepal

Authors: Rita Buono, Elisabetta Cagni, Roberta Castriconi, Surendra Bahadur Chand, Marco Esposito, Claudio Fiorino, Valeria Landoni, Aldo Mazzilli, Eugenia Moretti, Lorenzo Placidi, Giulia Rambaldi Guidasci, Alessia Tudda

Affiliation: IRCCS San Raffaele Scientific Institute, Department of Advanced Technology, IRCCS Regina Elena National Cancer Institute, ASU FC Medical Physics, University Hospital of Parma AOUP, ICTP, B.P. Koirala Memorial Cancer Hospital, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Fatebenefratelli Isola Tiberina โ€“ Gemelli Isola

Abstract Preview: Purpose: To explore the feasibility and educational impact of transferring knowledge-based planning (KBP) modelsโ€”developed using Italian breast radiotherapy dataโ€”to a Nepalese hospital, thereby demons...

Investigation of the Impact of Dlg Changes on Plan Quality and Patient Specific QA

Authors: Nicole C. Detorie, Steven M. Kirsner, Remy Y. Manigold

Affiliation: Scripps Cancer Center

Abstract Preview: Purpose:
Dosimetric Leaf Gap (DLG) is an important factor in obtaining the proper beam model in the Eclipse treatment planning system. The purpose of this study was to investigate the dosimetric im...

Mid-Range Planning for Efficient and Robust Proton Arc Therapy

Authors: Mingli Chen, Xuejun Gu, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Zi Yang, Kangning Zhang, You Zhang

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine

Abstract Preview: Purpose: Delivery efficiency and robustness are critical in spot-scanning proton arc therapy (SPAT), yet the conventional use of redundant energy layers (ELs) prolongs switching times and reduces effi...

Multi-Scale, Multi-Task Framework with Jacobian Descent for Multi-Plan Dose Prediction in Sequential Boost Radiotherapy

Authors: Steve B. Jiang, Mu-Han Lin, Yu-Chen Lin, Austen Matthew Maniscalco, Dan Nguyen, David Sher, Xinran Zhong

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, UT Dallas

Abstract Preview: Purpose:
Sequential boost radiotherapy (RT) poses a challenge in allocating dose across multiple plans while protecting organs at risk (OARs). Clinicians must decide whether OAR sparing should occu...

Multimodal Framework for Predicting Radiation-Induced Severe Acute Esophagitis in Esophageal Cancer

Authors: Yeona Cho, Chloe Min Seo Choi, Joseph O. Deasy, Jue Jiang, Jihun Kim, Jin Sung Kim, Nikhil Mankuzhy, Aneesh Rangnekar, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu

Affiliation: University of Freibrug, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Memorial Sloan Kettering Cancer Center, Yonsei University

Abstract Preview: Purpose: We hypothesized that combining clinical, imaging, and radiotherapy dose-distribution features could increase predictive model accuracy in radiation-induced severe acute esophagitis (SAE) in e...

Scoring Functions for Reinforcement Learning in Accelerated Partial Breast Irradiation Treatment Planning

Authors: Rafe A. McBeth, Kuancheng Wang, Ledi Wang

Affiliation: Department of Radiation Oncology, University of Pennsylvania, Georgia Institute of Technology, University of Pennsylvania

Abstract Preview: Purpose:
The integration of AI in clinical workflows presents unprecedented opportunities to enhance treatment quality in radiation oncology, yet it also demands innovative approaches to address th...