Authors: Sixue Dong, Weigang Hu, Jiazhou Wang, Zhen Zhang
Affiliation: Fudan University Shanghai Cancer Center
Abstract Preview: Purpose: This study aimed to evaluate the dosimetric benefits and treatment efficiency of different beam configurations in 4π non-coplanar VMAT for SBRT in recurrent head and neck tumors
Method...
Authors: Hao Peng, Yajun Yu
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel ablative radiation dosing scheme developed by our institution. This study aims to establish a regression...
Authors: Thomas Gee, Sofia Michopoulou, Amit Nautiyal
Affiliation: University Hospital Southampton
Abstract Preview: Purpose: Dosimetry software that is accessible to departments offers new opportunities to improve patient-specific dosimetry. Prior to clinical decision-making, it is essential to validate dosimetry s...
Authors: Ruiyan Du, Mingzhu Li, Ying Li, Wei Liu, Shihuan Qin, Yiming Ren, Biao Tu, Hui Xu, Lian Zhang, Xiao Zhang, Zengren Zhao
Affiliation: Department of General Surgery, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University, Medical AI Lab, The First Hospital of Hebei Medical University, Hebei Provincial Engineering Research Center for AI-Based Cancer Treatment Decision-Making, The First Hospital of Hebei Medical University, Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Department of Radiation Oncology, Mayo Clinic, Department of Oncology, The First Hospital of Hebei Medical University
Abstract Preview: Purpose: Fiducial tracking is widely used in CyberKnife to dynamically guide the gantry for moving target like liver cancer stereotactic body radiation therapy (SBRT). This study developed a robust fr...
Authors: Pradeep Bhetwal, Wookjin Choi, Adam Dicker, Rupesh Ghimire, Yingcui Jia, Lauren Nkwonta, Yevgeniy Vinogradskiy, Wentao Wang, Maria Werner-Wasik
Affiliation: Thomas Jefferson University
Abstract Preview: Purpose: Multi-disciplinary clinics are becoming standard of care for patients with lung cancer treated with SBRT. To improve clinical decision support in a multi-disciplinary clinic, it would be bene...
Authors: Sean P. Devan, Cory S. Knill, Charles K. Matrosic, Zheng Zhang
Affiliation: University of Michigan
Abstract Preview: Purpose: Physicists troubleshooting machine issues during patient treatments often face high-pressure situations, balancing error codes, resource constraints, and time-sensitive decisions. To streamli...
Authors: Zachery Colbert, Matthew Foote, Michael Huo, Mark Pinkham, Prabhakar Ramachandran, Mihir Shanker
Affiliation: Radiation Oncology, Princess Alexandra Hospital, Ipswich Road, Princess Alexandra Hospital
Abstract Preview: Purpose: The study aimed to develop and implement deep learning-based autosegmentation models for the autosegmentation of four key tumor types: brain metastasis, pituitary adenoma, vestibular schwanno...
Authors: James Ward, Anzi Zhao
Affiliation: Northwestern Medicine
Abstract Preview: Purpose: This study investigates the correlation between the backlight runtime in diagnostic displays and their corresponding performance, which may aid in making informed decisions regarding the timi...
Authors: Steve B. Jiang, Dan Nguyen, Chenyang Shen, Fan-Chi F. Su, Jiacheng Xie, Shunyu Yan, Daniel Yang, Ying Zhang, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas
Abstract Preview: Purpose: Accurate delineation of treatment targets and organs-at-risk is crucial for radiotherapy. Despite significant progress in artificial intelligence (AI)-based automatic segmentation tools, effi...
Authors: Eric Aliotta, Michalis Aristophanous, Laura I. Cervino, Yu-Chi Hu, Nancy Y Lee, Anyi Li, Jung Hun Oh, Teeradon Treechairusame, Pengpeng Zhang
Affiliation: Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: To automatically monitor weekly anatomical changes of parotid glands during adaptive radiotherapy of head and neck cancers, systematically assess the relationship between parotid gland shrink...
Authors: Daniel A. Alexander, Anish Butala, Anthony Austin Kassaee, Neil Malhotra, Ian Messing, Gabrielle W Peters, Ryan M. Scheuermann
Affiliation: Department of Radiation Oncology and Applied Sciences, University of Pennsylvania
Abstract Preview: Purpose: Stereotactic Body Radiation Therapy (SBRT) for spinal metastases uses CBCT-based localization and highly conformal treatment delivery to increase the biologically equivalent dose to the targe...
Authors: Leigh A. Conroy, Thomas G Purdie, Christy Wong
Affiliation: Department of Medical Biophysics, University of Toronto, Princess Margaret Cancer Centre
Abstract Preview: Purpose: To develop a novel machine learning (ML) algorithm to evaluate and rank breast radiation therapy (RT) treatment plans based on treatment complexity for prioritization in multidisciplinary pee...
Authors: Ergun E. Ahunbay, Abdul Parchur, Eric S. Paulson, Ilaria Rinaldi, Angelo Schiavi, Li Zhao
Affiliation: Sapienza University of Rome, Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: Online adaptive replanning is often necessary in Intensity Modulated Proton Therapy (IMPT) due to the sensitivity of proton dose distributions to daily anatomical changes. A rapid, automated ...
Authors: Majd Antaki, Rohini Bhatia, Gayoung Kim, Yosef Landman, Junghoon Lee, Akila N. Viswanathan
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Physics and Advanced Development Elekta
Abstract Preview: Purpose: Brachytherapy is a standard radiation therapy approach for cervical cancer, which directly delivers radiation source to the tumor using catheters. Treatment planning requires identification o...
Authors: Steve B. Jiang, Mu-Han Lin, Dan Nguyen, Beiqian Qi, Daniel Yang, Ying Zhang
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
Online adaptive radiotherapy (oART) is a resource-intensive workflow requiring significant time and effort required from clinicians, particularly for the online evaluation of plan quality....
Authors: Michael Dohopolski, Xuejun Gu, Hao Jiang, Steve B. Jiang, Christopher Kabat, Jingying Lin, Weiguo Lu, Michael Tang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Neuralrad LLC, 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
Abstract Preview: Purpose: To streamline access to clinical data stored in Oncology Information Systems such as MOSAIQ or ARIA, we developed an AI-powered chatbot capable of querying, summarizing, and interactively ans...
Authors: Gisell Ruiz Boiset, Paulo ROBERTO Costa, Elsa Bifano Pimenta
Affiliation: University of São Paulo (USP), Institute of Physics
Abstract Preview: Purpose: This study evaluates the similarity between the linear attenuation coefficients of soft tissues and 3D printing materials in diagnostic imaging range using the Euclidean distance approach.
Authors: Shengwen Deng, Sven L. Gallo, Robert S. Jones, David W. Jordan, Arashdeep Kaur, Aishwarya M. Kulkarni, Quibai Li, William R.M. Pedersen
Affiliation: Department of Radiology, University Hospitals Cleveland Medical Center; School of Medicine, Case Western Reserve University, Department of Radiology, Radiation Safety, University Hospitals Cleveland Medical Center, Department of Radiology, Radiation Safety, University Hospitals Cleveland Medical Center; School of Medicine, Case Western Reserve University; Department of Radiology, Louis Stokes Cleveland VA Medical Center
Abstract Preview: Purpose:
Y-90 (Yttrium) SIRT radioembolization takes advantage of delivering localized radiation to the liver. Pre-treatment dosimetry is highly dependent on accurate MAA mapping, which may have an...
Authors: Hao Peng, Yajun Yu
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a new treatment paradigm pioneered by our institution. But the early decision-making process in PULSAR is challe...
Authors: Ahssan Balawi, Peter Jermain, Timothy Kearney, Sonali Rudra, Michael H. Shang, Markus Wells, Mohammad Zarenia
Affiliation: Department of Radiation Medicine, MedStar Georgetown University Hospital
Abstract Preview: Purpose: To investigate the applicability and accuracy of a deep learning (DL) model in predicting radiation dose distribution for breast cancer patients treated with pencil-beam-scanning proton radio...
Authors: Ming Chao, Hao Guo, Tenzin Kunkyab, Yang Lei, Tian Liu, Kenneth Rosenzweig, Robert Samstein, James Tam, Junyi Xia, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai
Abstract Preview: Purpose:
The aim of the study is to develop a trade-off prediction model to efficiently guide the treatment planning process for patients with stage III non-small cell lung cancer (NSCLC).
Metho...
Authors: Petr Bruza, Megan Clark, David J. Gladstone, Lesley A Jarvis, Allison Matous, Roman Vasyltsiv, Rongxiao Zhang
Affiliation: Dartmouth Health, Dartmouth College, Thayer School of Engineering, Dartmouth College, Dartmouth Cancer Center, University of Missouri
Abstract Preview: Purpose: On-patient dosimeters assist in clinical decision-making by measuring surface dose, but single-point measurements cannot monitor area-wide distributions and are misleading in high-gradient re...
Authors: Adi Robinson, Michael J. Tallhamer
Affiliation: Centura Health, AdventHealth Orlando
Abstract Preview: Purpose: This study aims to evaluate the integration of Cherenkov imaging into radiation therapy practices, focusing on its utility in enhancing treatment precision, patient safety, and clinical decis...
Authors: Rashmi Bhaskara, Oluwaseyi Oderinde
Affiliation: Purdue University
Abstract Preview: Purpose: This study proposes a novel approach to overcoming CBCT image quality limitations by developing an improved synthetic CT (sCT) generation method based on a CycleGAN architecture using Vision ...
Authors: Yijian Cao, Jenghwa Chang, Lyu Huang
Affiliation: Northwell, Hofstra University Medical Physics Program
Abstract Preview: Purpose: This study evaluates the treatment delivery efficiency and workflow of two advanced linear accelerator systems—Varian’s TrueBeam (C-arm) and Halcyon (O-ring)—for prostate Stereotactic Body Ra...
Authors: Jeffrey D. Bradley, Steven J. Feigenberg, Cole Friedes, Yin Gao, Xun Jia, Kevin Teo, Lingshu Yin, Jennifer Wei Zou
Affiliation: Department of Radiation Oncology, University of Pennsylvania, Johns Hopkins University
Abstract Preview: Purpose: Understanding how physicians evaluate plans is critical for automatic planning and ensuring consistent, high-quality care. While deep-learning models excel in complex decision-making, the lac...
Authors: Njood Alsaihati, Ehsan Samei, Justin B. Solomon
Affiliation: Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System
Abstract Preview: Purpose: Inconsistent imaging procedure naming across and within institutions challenges clinical decision-making, quality assessment, and data analysis in radiology. For example, The American College...
Authors: Rashmi Bhaskara, Shravan Bhavsar, Ananth Grama, Oluwaseyi Oderinde, Shourya Verma
Affiliation: Purdue University
Abstract Preview: Generating Synthetic Positron Emission Tomography from Computed Tomography using Lightweight Diffusion Model for Head and Neck Cancer
Purpose: To generate synthetic PET tumor avidity segments direc...
Authors: Michael Dohopolski, Jiaqi Liu, Hao Peng, Robert Timmerman, Zabi Wardak, Haozhao Zhang
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
This study introduces a gradient-based radiomics framework to enhance outcome prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR) for brain metastases...
Authors: Wilfred R Furtado, Gary Y. Ge, James Lee, Jie Zhang
Affiliation: University of Kentucky
Abstract Preview: Purpose: Despite advancements in Artificial Intelligence (AI) and its growing role in clinical practices like radiology, formal AI education remains limited in medical training. This gap contributes t...
Authors: Minbin Chen, Xiaoyi Dai, Xiaoyu Duan, Chunhao Wang, Fan Xia, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Rihui Zhang
Affiliation: Duke University, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University, The First People's Hospital of Kunshan
Abstract Preview: Purpose: Deep learning (DL)-based mammography diagnosis presents unique challenges, as accurate interpretation requires both global breast condition analysis and local lesion structural information. E...
Authors: Evan Calabrese, Scott R. Floyd, Kyle J. Lafata, Zachary J. Reitman, Eugene Vaios, Chunhao Wang, Lana Wang, Deshan Yang, Zhenyu Yang, Jingtong Zhao
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Duke Kunshan University
Abstract Preview: Purpose:
This study proposes a novel neural ordinary differential equation (NODE) framework to distinguish post-SRS radionecrosis from recurrence in brain metastases (BMs). By integrating imaging f...
Authors: Clint Bahler, Ruchika Reddy Chimmula, Harrison Louis Love, Oluwaseyi Oderinde, Courtney Yong
Affiliation: Purdue University, Department of Urology, Indiana University School of Medicine, Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, School of Health Sciences, Purdue University
Abstract Preview: Purpose: Prostate cancer (PCa) is a common malignancy in men, and predicting biochemical recurrence (BCR) is crucial for guiding treatment decisions. Integrating multimodal data, including clinical, i...
Authors: Ryan Alden, Tithi Biswas, Kaushik Halder, Felix Maria-Joseph, Michael Mix, Rihan Podder, Tarun Kanti Podder
Affiliation: SUNY Upstate Medical University, IIT-Roorkee, University of Florida
Abstract Preview: Purpose: Radiomics feature-based model for predicting distant recurrence can potentially provide critical insight for clinical decision-making and assistance in treatment strategies. This study focuse...
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...
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: Yang Sheng, Qingrong Jackie Wu, Qiuwen Wu, Xin Wu, Dongrong Yang
Affiliation: Duke University Medical Center
Abstract Preview: Purpose:
This study aims to leverage large language model (LLMs) to develop a human-in-the-loop agentic framework, enhancing the efficiency of treatment planning in radiotherapy.
Methods:
A L...
Authors: Waleed Mutlaq Almutairi, Ke Colin Huang, Vishwas Mukundan, Christopher F. Njeh, Oluwaseyi Oderinde, Yong Yue
Affiliation: Purdue University, Indiana University School of Medicine, Department of Radiation Oncology, Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, Purdue University, West Lafayette, Indiana, USA
Abstract Preview: Purpose:
This study aimed to develop a machine learning (ML) model for early prediction of chemoradiotherapy (CRT) response in order to enhance personalized treatment selection for oral or orophary...
Authors: Stephen R. Bowen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Jiajie Wang, Shouyi Wang, Faisal Yaseen, Han Zhou
Affiliation: Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Fred Hutchinson Cancer Center, University of Texas at Arlington
Abstract Preview: Purpose: Predicting the effects of the spatial-temporal tumor response to chemoradiation can assist in adjusting radiation dose and support clinical decision-making in radiotherapy. A multi-instance l...
Authors: Shreyas Anil, Jason Chan, Arushi Gulati, Yannet Interian, Hui Lin, Benedict Neo, Andrea Park, Bhumika Srinivas
Affiliation: Department of Otolaryngology Head and Neck Surgery, University of California San Francisco, Department of Data Science, University of San Francisco, Department of Radiation Oncology, University of California San Francisco
Abstract Preview: Purpose: Hospital readmission prediction models often rely on structured Electronic Health Record (EHR) data, overlooking critical insights from unstructured clinical notes. This study presents a mult...
Authors: James Chun Lam Chow, Kay Li
Affiliation: University of Toronto, Princess Margaret Cancer Centre
Abstract Preview: Purpose: This study explores the caution needed when using Generative AI for assessing radiotherapy ethics, highlighting Geoffrey Hinton’s warnings about the risks of relying solely on AI for ethical ...
Authors: Md Mainul Abrar, Yujie Chi
Affiliation: University of Texas at Arlington, Department of Physics, University of Texas at Arlington
Abstract Preview: Purpose: Healthcare 5.0, proposed in 2021, includes interpretable healthcare analysis as a core component. Achieving this requires the application of explainable artificial intelligence (XAI) to overc...
Authors: Soren Bentzen, Arezoo Modiri, Zaker Rana, Amit Sawant, Lena Specht, Ivan Vogelius
Affiliation: University of Maryland, University of Maryland in Baltimore, Dept. Of Oncology Copenhagen University Hospital – Rigshospitalet, Maryland University Baltimore, Department of Radiation Oncology, University of Maryland School of Medicine
Abstract Preview: Purpose: There is wide inter-physician radiotherapy planning variability for lymphomas and no systematic way to individualize a plan with respect to patient-specific outcome risks. In response to this...
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: John Ginn, Zhuoyun Huang, Yongbok Kim, Ke Lu, Chunhao Wang, Yibo Xie, Zhenyu Yang, Jingtong Zhao
Affiliation: Duke University, Duke Kunshan University
Abstract Preview: Purpose: This study aims to develop and validate a machine learning model for predicting V60%, a critical dosimetric metric in LINAC-based Single-Isocenter-Multiple-Targets (SIMT) stereotactic radiosu...
Authors: Qianxi Ni, Qionghui Zhou
Affiliation: The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University
Abstract Preview: Purpose:
This study aims to develop and evaluate interpretable machine learning models that use radiomic and dosimetric features to predict HT in advanced cervical cancer patients.
Methods:
R...
Authors: Zahra Bagherpour, Manijeh Beigi, Pedram Fadavi, Faraz Kalantari, Moghadaseh Khaleghibizaki, Hengameh Nazari, Mojtaba Safari, Sepideh Soltani
Affiliation: Department of Radiation Oncology, School of Medicine, Iran University of Medical Sciences, Department of Radiation Oncology, School of Medicine, Emory University and Winship Cancer Institute, Department of Radiation Oncology, Iran University of Medical Sciences, University of Arkansas for medical sciences, Department of Radiation physics, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences
Abstract Preview: Purpose: This study aims to evaluate whether readily available mammographic and sonographic data, combined with machine learning (ML) models, can predict critical molecular factors (ER, PR, HER2) in b...
Authors: Chih-Chiang Chang, Chingyun Cheng, Ben Durkee, Minglei Kang, Elissa Khoudary, Yangguang Ma, Xuanqin Mou, YuFei Wang, Tengda Zhang, Wang Zhengda
Affiliation: Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, School of Software Engineering, Xi’an Jiaotong University, School of information and communications engineering, Faculty of electronic and information engineering, Xi’an Jiaotong University, Department of Medical Physics, Columbia University, University of Pennsylvania, Department of Human Oncology, University of Wisconsin-Madison
Abstract Preview: Purpose: This study presents a voxel-based analysis method based on deformable image registration to accurately quantify respiratory-induced motion and deformation of both targets and organs at risk (...
Authors: Ruiyan Du, He Huang, Mingzhu Li, Ying Li, Hongyu Lin, Wei Liu, Shihuan Qin, Yiming Ren, Hui Xu, Lian Zhang, Xiao Zhang, Zunhao Zhang
Affiliation: Department of Radiation Oncology, Mayo Clinic, Medical AI Lab, The First Hospital of Hebei Medical University, Hebei Provincial Engineering Research Center for AI-Based Cancer Treatment Decision-Making, The First Hospital of Hebei Medical University, Department of Oncology, The First Hospital of Hebei Medical University
Abstract Preview: Purpose: Monte Carlo (MC) dose calculation is the gold standard in clinical CyberKnife radiation therapy (RT), considering its steep dose gradients and high-freedom non-coplanar beam angles, but extre...
Authors: Daming LI, Jinsen Xie, Zhe Zhang
Affiliation: Peking University Shenzhen Hospital Radiotherapy Department, School of Nuclear Science and Technology, University of South China
Abstract Preview: Purpose: To analyze the actual doses received during radiotherapy for head and neck cancers (HNC) using Velocity, providing insights for adaptive radiotherapy decision-making.
Methods: Thirty-three...
Authors: Tirthraj Adhikari, Timothy J Allen, Abdullah A. Alshreef, Clara Ferreira
Affiliation: Medical School Department of Radiation Oncology University of Minnesota, University of Minnesota, Loma Linda University Medical Center
Abstract Preview: Purpose: GammaTile Brachytherapy with 131Cs is an effective treatment for glioblastomas, offering local control with minimal radiation necrosis. While the line source model is preferred for dose distr...
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...
Authors: Rajeev K. Badkul, Ronald C Chen, Ying Hou, Harold Li, Chaoqiong Ma, Jufri Setianegara
Affiliation: Department of Radiation Oncology, University of Kansas Medical Center
Abstract Preview: Purpose:
Postimplant urinary toxicity is common in prostate low-dose-rate (LDR) brachytherapy. We developed a machine learning (ML) model to explore the correlation between spatial dose distributio...
Authors: Kevin Albuquerque, Ti Bai, Yesenia Gonzalez, Brian A. Hrycushko, Zohaib Iqbal, Paul M. Medin, Shanshan Tang
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Cervical cancer remains one of the most common and significant gynecological (GYN) malignancies globally, often presenting at advanced stages where radiation therapy and high-dose-rate (HDR) ...
Authors: Sara Allievi, Stefano Bonvini, Gloria Miori, Laura Orsingher, Andrea Passerini, Igor Raunig, Daniele Ravanelli, Erich Robbi, Annalisa Trianni
Affiliation: Department of Information Engineering and Computer Science, University of Trento, Vascular Surgery Department, S.Chiara Hospital, APSS, Medical Physics Department, S.Chiara Hospital, APSS
Abstract Preview: Purpose:
This study evaluates the performance of an AI-driven tool in segmenting and analyzing tissue composition in abdominal aortic aneurysm (AAA) patients, specifically focusing on the sealing z...
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...
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 ...