Authors: Christos Ilioudis, Marios Myronakis, Sotirios Raptis, Kyriaki Theodorou
Affiliation: Medical Physics Department, Medical School, University of Thessaly, Department of Information and Electronic Engineering, International Hellenic University (IHU)
Abstract Preview: Purpose: This study presents a radiomics-driven, machine learning framework developed to predict the possibility of Radiation Pneumonitis (RP), as a side effect of radiation therapy in lung cancer pat...
Authors: Yabo Fu, Yang Lei, Yu Lei, Haibo Lin, Ruirui Liu, Tian Liu, Kenneth Rosenzweig, Charles B. Simone, Shouyi Wei, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai, University of Nebraska Medical Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York Proton Center
Abstract Preview: Purpose: Beam orientation optimization (BOO) in intensity-modulated radiation therapy (IMRT) is traditionally a complex, non-convex problem tackled with heuristic methods. This study benchmarks global...
Authors: Laila A Gharzai, Bharat B Mittal, Poonam Yadav
Affiliation: Northwestern Feinberg School of Medicine, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Northwestern University Feinberg School of Medicine
Abstract Preview: Purpose: Multiple studies have shown the increasing role of deep learning in segmenting regions of interest. This work presents the feasibility of auto-segmenting the critical structures for head and ...
Authors: Xianjin Dai, PhD, Michael Gensheimer, Praveenbalaji Rajendran, Lei Xing, Yong Yang
Affiliation: Department of Radiation Oncology, Stanford University, Massachusetts General Hospital, Harvard Medical School
Abstract Preview: Purpose: Recent advances in the automatic delineation of radiotherapy treatment targets, which incorporate linguistic clinical data extracted by large language models (LLMs) into traditional visual-on...
Authors: Kofi M. Deh, Tamas Jozsa, Tsang-Wei Tu
Affiliation: Cranfield University, Howard University Hospital, Howard University
Abstract Preview: Purpose: To enhance the quality of hyperpolarized (HP) 13C magnetic resonance images by integrating deep learning with perfusion modeling.
Methods: A convolutional neural network (CNN) and a superr...
Authors: Hassan Bagher-Ebadian, Anthony J. Doemer, Ryan Hall, Joshua P. Kim, Bing Luo, Benjamin Movsas, Humza Nusrat, Kundan S Thind
Affiliation: Department of Physics, Toronto Metropolitan University, Henry Ford Health
Abstract Preview: Purpose: This study investigates the development and feasibility of local LLM-based agents to automate radiotherapy treatment planning, aiming to improve planning efficiency and consistency, while pre...
Authors: Blessing Akinro, Soumyanil Banerjee, Ming Dong, Carri K. Glide-Hurst, Prashant Nagpal, Chase Ruff, Nicholas R. Summerfield, Timothy P. Szczykutowicz
Affiliation: Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Departments of Radiology and Medical Physics, University Wisconsin-Madison, Department of Radiology, University of Wisconsin-Madison, Department of Computer Science, Wayne State University, Department of Human Oncology
Abstract Preview: Purpose: Radiation dose to coronary arteries (CAs) during thoracic radiotherapy (RT) is linked to cardiotoxicity. However, precise CA delineation for avoidance is limited by image quality and CA compl...
Authors: Malvern Madondo, Mark McDonald, Zhen Tian, Christopher Valdes, Ralph Weichselbaum, Xiaofeng Yang, David Yu, Jun Zhou
Affiliation: Department of Radiation & Cellular Oncology, University of Chicago, University of Chicago, Emory University, Department of Radiology, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: Head-and-neck (HN) cancer patients often experience significant anatomical changes during treatment course. Proton therapy, particularly intensity-modulated proton therapy (IMPT), is sensitiv...
Authors: Vasiliki Chatzipavlidou, Ilias Gatos, George C. Kagadis, Theodoros Kalathas, Paraskevi Katsakiori, Anna Makridou, Dimitris N. Mihailidis, Nikos Papathanasiou, Ioanna Stamouli, Stavros Tsantis
Affiliation: Theageneio Hospital, University of Pennsylvania, University of Patras
Abstract Preview: Purpose: To compare the performance of multiple deep learning (DL) networks, including DenseNet201, InceptionV3, MobileNetV3, EfficientNetB2, NASNetMobile, VGG19, ResNet50, and Xception, in classifyin...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Xianjin Dai, PhD, Praveenbalaji Rajendran, Lei Xing, Yong Yang
Affiliation: Department of Radiation Oncology, Stanford University, Massachusetts General Hospital, Harvard Medical School
Abstract Preview: Purpose: Artificial intelligence (AI)-driven methods have transformed dose prediction, streamlining the automation of radiotherapy treatment planning. However, traditional approaches depend exclusivel...
Authors: Muhammad Ramish Ashraf, Kerriann Casey, Suparna Dutt, Jie Fu, Edward Elliot Graves, Xuejun Gu, Hao Jiang, Brianna Caroline Lau, Billy W Loo, Weiguo Lu, Rakesh Manjappa, Stavros Melemenidis, Erinn Bruno Rankin, Lawrie Skinner, Luis Armando Soto, Murat Surucu, Vignesh Viswanathan, Zi Yang, Amy Shu-Jung Yu
Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine, Department of Comparative Medicine, Stanford University School of Medicine, Department of Radiation Oncology, Stanford University Cancer Center
Abstract Preview: Purpose: The intestine is a classical preclinical model for studying radiation injury, and histological quantification of intestinal crypts is a key assay for assessing this response. However, substan...
Authors: Ara Alexandrian, Jessica Ashford, Jean-Guy Belliveau, Allison Dalton, Nathan Dobranski, Krystal M. Kirby, Garrett M. Pitcher, David E. Solis, Hamlet Spears, Angela M. Stam, Sotirios Stathakis, Jason Stevens, Rodney J. Sullivan, Sean Xavier Sullivan, Natalie N. Viscariello
Affiliation: Louisiana State University, Mary Bird Perkins Cancer Center, The University of Alabama at Birmingham, University of Alabama at Birmingham
Abstract Preview: Purpose: To improve retrospective risk analysis in radiation oncology by leveraging Large Language Models (LLMs) to extract richly annotated data from unstructured clinical incident reports.
Method...