Authors: Hao-Wen Cheng, Jonathan G. Li, Chihray Liu, Wen-Chih Tseng, Guanghua Yan
Affiliation: University of Florida
Abstract Preview: Purpose: This study develops and evaluates deep learning (DL) models for predicting 3D dose distributions in simultaneous integrated boost (SIB) prostate cancer treatment using the Elekta Unity MR-Lin...
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...
Authors: Amar Kishan, Jun Lian, Yunkui Pang, Jonathan Pham, X. Sharon Qi, Michael Steinberg, Luca F Valle, Pew-Thian Yap
Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of North Carolina at Chapel Hill
Abstract Preview: Purpose: Stereotactic body radiotherapy (SBRT) is a highly effective treatment for prostate cancer, yet predicting genitourinary (GU) toxicity has primarily relied on planned dosimetry. This study inv...
Authors: Cem Altunbas, Farhang Bayat, Roy Bliley, Brian Kavanagh, Uttam Pyakurel, Tyler Robin, Ryan Sabounchi
Affiliation: Department of Radiation Oncology, University of Colorado School of Medicine, Taussig Cancer Center, Cleveland Clinic
Abstract Preview: Purpose: The use of image features extracted from serial CBCT images to assess radiotherapy response and toxicity is an active research area. However, poor image quality often compromises reliability ...
Authors: Ara Alexandrian, Sadiki Daniel
Affiliation: Louisiana State University, Mary Bird Perkins Cancer Center
Abstract Preview: Purpose: To develop a learning-to-optimize machine learning model that accelerates optimization in VMAT treatment planning by training on prostate patient data.
Methods: A treatment plan dataset of...
Authors: Yufeng Cao, Luigi Marchionni, William Silva Mendes, Cem Onal, Lei Ren, Amit Sawant, Nicole L Simone, Philip Sutera, Phuoc Tran
Affiliation: University of Maryland School of Medicine, 9Department of Radiation Oncology, Thomas Jefferson University, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, University of Maryland, Baltimore, Baskent University Faculty of Medicine, Department of Radiation Oncology, Department of Radiation Oncology, University of Maryland School of Medicine, Maryland University Baltimore, 8Department of Pathology and Laboratory Medicine, Weill Cornell Medicine
Abstract Preview: Purpose: This study aims to predict 2-yr Metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) patients treated by metastasis-directed therapy (MDT) by devel...
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: Mojtaba Behzadipour, Suman Gautam, Tianjun Ma, Ikchit Singh Sangha, Bongyong Song, William Song, Kumari Sunidhi
Affiliation: UC San Diego, Virginia Commonwealth University
Abstract Preview: Purpose: This study aims to develop a knowledge-based voxel-wise dose prediction system using a convolutional neural network (CNN) for high-dose-rate (HDR) prostate brachytherapy and to evaluate its p...
Authors: Ricardo Garcia Santiago, Narges Miri, Daryl P. Nazareth, Ankit Pant, Mukund Seshadri
Affiliation: Roswell Park Comprehensive Cancer Center
Abstract Preview: Purpose: To develop a transformer-based deep learning network framework for predicting VMAT dose distributions. This can provide fast and efficient calculations with accuracies potentially comparable ...
Authors: Brian M. Anderson, Shiva K. Das, Meagan Foster, Anirudh Karunaker, Lawrence B. Marks, Lukasz Mazur, Michael Repka
Affiliation: UNC Chapel HIll, University of North Carolina at Chapel Hill, UNC School of Medicine, University of North Carolina
Abstract Preview: Purpose: Development of a peer review segmentation check system to identify deviations in physician contours of standard risk pelvic lymph nodes in patients receiving radiation therapy for prostate an...
Authors: Jeremy T. Booth, Martin Andrew Ebert, Robert Finnegan, Annette Haworth, George Hruby, Burhan Javed, Kazi Ridita Mahtaba, Leyla Moghaddasi, Yutong Zhao
Affiliation: Northern Sydney Cancer Centre, Royal North Shore Hospital, The University of Sydney, The University of Western Australia, Genesis Care, Rockhampton Hospital
Abstract Preview: Purpose:
To evaluate the efficacy of an atlas-based tumor control probability (TCP) model in predicting prostate cancer (PCa) recurrence by retrospectively integrating patient-specific primary radi...
Authors: Lian Duan, Stephen F. Kry, Hunter S. Mehrens, Paige A. Taylor
Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center
Abstract Preview: Purpose: To develop a machine learning model for predicting dose delivery accuracy and identifying its key factors in IROC’s proton phantom program.
Methods: IROC’s proton QA program has six proton...
Authors: David Alcorta, Joseph Farina, Timothy Haystead, Philip Hughes, Keshav Jha, Mark Oldham, Victoria J. P. Radosova
Affiliation: Duke University Department of Pharmacology and Cancer Biology, Medical Physics Graduate Program, Duke University, Duke University Department of Medicine, Duke University, Duke University Pharmacology and Cancer Biology, Department of Radiation Oncology, Duke University Medical Center
Abstract Preview: Purpose: This study attempts to simulate the activation of targeted photodynamic molecules using visible laser light and characterize the direct Reactive Oxygen Species (ROS) generated by Cherenkov an...
Authors: Abdullah Hidayat, Wazir Muhammad
Affiliation: Florida Atlantic University
Abstract Preview: Purpose: This study aims to predict Head and Neck cancer using an artificial neural network (ANN) through readily available basic health data. The goal is to uncover hidden patterns and predictors in ...