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Results for "towards decision": 8 found

Automated Framework for Predicting Tumour Growth in Vestibular Schwannomas Using Contrast-Enhanced T1-Weighted MRI

Authors: Mehdi Amini, Minerva Becker, Simina Chiriac, Alexandre Cusin, Dimitrios Daskalou, Ghasem Hajianfar, Sophie Neveu, Marcella Pucci, Yazdan Salimi, Pascal Senn, Habib Zaidi

Affiliation: Geneva University Hospital, Division of Radiology, Diagnostic Department, Geneva University Hospitals, Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences, Geneva University Hospitals

Abstract Preview: Purpose: Personalized prediction of vestibular schwannoma (VS) tumour growth is crucial for guiding patient management decisions toward observation versus intervention. This study proposes an automate...

From AI Towards Decision Support Frameworks in Radiotherapy: Moving Models in into Clinical Support Tools

Authors: Sanne van Dijk

Affiliation: UMC-Groningen

Abstract Preview: N/A...

Knowledge-Informed Deep Learning for Accurate and Interpretable Extracapsular Extension Detection in Head and Neck Squamous Cell Carcinoma

Authors: William N. Duggar, Amirhossein Eskorouchi, Haifeng Wang

Affiliation: Mississippi State University, University of Mississippi Medical Center

Abstract Preview: Purpose:
Extracapsular extension (ECE) in lymph nodes represents a critical prognostic factor in head and neck squamous cell carcinoma (HNSCC), bearing important implications for staging, treatment...

Revealing Unseen Risks: Hands-on Technologist Education to Prevent MRI Incidents Using Ferromagnetic Detecting Wands

Authors: Chris Beasley, Ngara Linda Bird, Flora Ivanova, Erin B. Macdonald, Lauren M. Neldner, Beth Reed, Scott H. Robertson

Affiliation: School of Medicine, Duke University, Duke University, Duke University Health System

Abstract Preview: Purpose: To develop a hands-on, data-driven educational program to improve MRI technologists' understanding and effective use of ferromagnetic detecting (FMD) wands.
Methods: A 30-minute interactiv...

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...

Toward Harmonized AI-Based Quantitative CT: A Voxel-Printed, Patient Specific Phantom for Cross-Platform Harmonization

Authors: Aditya P. Apte, Joseph O. Deasy, Yusuf Emre Erdi, Anqi Fu, Johannes Hertrich, Andrew Jackson, Usman Mahmood, Jason Ocana, Trahan Sean, Amita Shukla-Dave

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Automated AI-based quantitative CT tools hold immense promise for advancing clinical decision-making, yet their reproducibility and generalizability remain vulnerable to variability in imagin...

Towards AI Decision-Support for Online Adaptive Radiotherapy (oART): A Preliminary Study on CBCT-Guided Post-Prostatectomy Oart

Authors: Michael Cummings, Olga M. Dona Lemus, Hana Mekdash, Tyler Moran, Alexander R Podgorsak, Sean M. Tanny, Matthew J. Webster, Lexiang Yang, Dandan Zheng, Yuwei Zhou, Xiaofeng Zhu

Affiliation: Department of Radiation Oncology, University of Rochester, University of Miami, Inova Schar Cancer Institute, University of Rochester

Abstract Preview: Purpose: oART is revolutionizing radiotherapy by allowing treatment plans to be adjusted based on daily imaging, improving targeting precision and potentially enhancing patient outcomes. However, its ...

Towards AI-Driven Adaptive Radiotherapy: Developing a Framework for Utilizing Large-Vision Models in Head-and-Neck Cancer Treatment.

Authors: Anthony J. Doemer, Bing Luo, Benjamin Movsas, Humza Nusrat, Farzan Siddiqui, Chadd Smith, Kundan S Thind, Kyle Verdecchia

Affiliation: Department of Physics, Toronto Metropolitan University, Henry Ford Health

Abstract Preview: Purpose: Large-vision models (LVMs) are rapidly emerging, yet their application in radiation oncology remains largely unexplored. This study investigates the potential of LVMs for offline adaptive rad...