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Results for "achieved highest": 45 found

A Dosimetric Analysis of Lu-177-Based Radiopharmaceutical Therapy for Optimizing Therapeutic Ratio

Authors: Travis James McCaw, Ravi Patel, Abdelhamid Saoudi, Joseph Shields

Affiliation: UPMC Hillman Cancer Center, UPMC Hillman Cancer Center and University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center, UPMC

Abstract Preview: Purpose: The current treatment approach for Lu-177-based radiopharmaceutical therapy involves multiple administrations, each delivering a constant activity of 200 mCi. This study evaluates whether the...

A Hybrid 4π-Proton Arc Robust Optimization

Authors: Wenhua Cao, Xianjin Dai, PhD, Hadis Moazami Goudarzi, Gino Lim, Miaolan Xie, Lei Xing, Lewei Zhao

Affiliation: University of Chicago Booth School of Business, Department of Radiation Oncology, Stanford University, Department of Industrial Engineering, University of Houston, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: Proton Arc Therapy (PAT) delivers a continuous dose of radiation during gantry rotation. 4π is a non-coplanar technique used for advanced proton therapy delivery. This work proposes a hybrid ...

A Hybrid Radiomics-Integrated Machine Learning Framework for Early Identification of Potential Radiation Pneumonitis in Lung Cancer Patients

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...

A Multi-Regional and Multi-Omics Approach to Predict Penumonitis in Patients with Locally Advanced Non-Small Cell Lung Cancer in Nrg Oncology Trial RTOG 0617

Authors: Katelyn M. Atkins, Indrin J. Chetty, Elizabeth M. McKenzie, Taman Upadhaya, Samuel C. Zhang

Affiliation: Department of Radiation Oncology,Cedars-Sinai Medical Center, Cedars-Sinai Medical Center

Abstract Preview: Purpose:
We explored a multi-regional and multi-omics approach to extract CT-based radiomics and 3D dosiomics features to predict radiation pneumonitis (RP) in patients with locally advanced Non-Sm...

An FMEA-Based Approach to Improve the Process and Quality Control on MR Imaging from Outside Diagnostic Imaging Centers to be Used for Radiation Treatment Planning

Authors: Olivier Blasi, Eric Cameron, Brad K. Lofton

Affiliation: CAMP, Colorado Assn in Medical Phys (CAMP)

Abstract Preview: Purpose:
Magnetic Resonance (MR) imaging obtained from external centers for radiation therapy (RT) planning can suffer from suboptimal protocols and geometric distortions. These issues can require ...

Analysis of Inter-Organ Noise Variability for Clinical CT Images across 3133 Image Series

Authors: Lavsen Dahal, Francesco Ria, Ehsan Samei, Justin B. Solomon, Liesbeth Vancoillie, Yakun Zhang

Affiliation: Duke University, Carilion Clinic, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System

Abstract Preview: Purpose: Clinical diagnostic task-based optimization of CT procedures require precise and organ-specific assessments. This study investigates inter-organ noise variability to highlight the limitations...

Assessment of Image Quality and Dose in Dual-Energy X-Ray Imaging for Lung Tumor Tracking

Authors: Nawal Alqethami, Felix Ginzinger, Prasannakumar Palaniappan, Marco Riboldi, Philipp Steininger, Wentao Xie

Affiliation: Research & Development, medPhoton GmbH, Department of Medical Physics, Ludwig-Maximilians-Universität (LMU) München, Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich)

Abstract Preview: Purpose:
This study evaluates the image quality of Dual-Energy (DE) X-ray imaging for lung tumor tracking across various sizes and protocols, comparing its imaging dose to Single-Energy (SE) imagin...

Automated Diagnosis of Pancreatic Cancer Using Both Radiomics and 3D-Convolutional Neural Network

Authors: Beth Bradshaw Ghavidel, Benyamin Khajetash, Yang Lei, Meysam Tavakoli

Affiliation: Icahn School of Medicine at Mount Sinai, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Emory University, Department of Radiation Oncology, Emory University

Abstract Preview: Purpose: Pancreatic cancer is among the most aggressive types of cancer, with a five-year survival rate of approximately 10%. Recent studies show that radiomics and deep learning (DL)-based methods ar...

BEST IN PHYSICS IMAGING: Cross-Contrast Diffusion: A Synergistic Approach for Simultaneous Multi-Contrast MRI Super-Resolution

Authors: Yifei Hao, Wenxuan Li, Xiang Li, Tao Peng, Yulu Wu, Fang-Fang Yin, Yue Yuan, Lei Zhang, Yaogong Zhang

Affiliation: Duke University, School of Future Science and Engineering, Soochow University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Diffusion-based deep-learning frameworks have been recently used in MRI resolution enhancement, or super-resolution. Multi-contrast MRI share common anatomical structures while holding comple...

BEST IN PHYSICS IMAGING: Revolutionizing Neurocognitive Dynamic Pattern Discovery with Self-Supervised AI in Functional Brain Imaging

Authors: Lei Xing, Zixia Zhou

Affiliation: Department of Radiation Oncology, Stanford University, Department of Radiation Oncology, Stanford University, Stanford

Abstract Preview: Purpose: Functional brain imaging techniques, such as functional magnetic resonance imaging (fMRI), generate high-dimensional, dynamic data reflecting complex neural processes. However, extracting rob...

Bridging the Gap of Radiotherapy Planning Quality between a High-Income Countrie to a Middle-Income Country By the Dosimetric Validation of a KBP Model Vs Junior, Senior Dosimetrists and MCO Planning

Authors: Eduardo Florian, Hiram Gay, Geoffrey D. Hugo, Otto Hurtarte, Milton Ixquiac, Erick Orlando Montenegro, Franky Eduardo Reyes, Francisco Javier Reynoso, Edgar Aparicio Ruiz, Baozhou Sun, Jacaranda Van Rheenen, Kevin Vega, Angel Velarde, Vicky de Falla

Affiliation: WashU Medicine, Liga Nacional Contra el Cancer, Liga Nacional Contra el Cancer/INCAN, Liga Nacional Contra el Cancer / INCAN, Liga Nacional Contra el Cancer and Universidad de San Carlos de Guatemala, Washington Univ. in St. Louis, Liga Nacional Contra el Cáncer / INCAN, Washington University in St. Louis, Varian, Baylor College of Medicine

Abstract Preview: Purpose:
IMRT has become the standard of care in high-income countries (HICs) due to reduced toxicity and improved treatment outcomes.
The purpose of this work is validating the KBP model shared...

Combining Proton Flash and Spatially Fractionated Radiotherapy – Experimental and Simulation Based Dosimetric Characterization

Authors: Gulakhshan M Hamad, Sina Mossahebi, Yannick P. Poirier, Amit Sawant

Affiliation: University of Maryland School of Medicine, Maryland University Baltimore

Abstract Preview: Purpose:
The combination of ultra-high dose rate (UHDR) proton therapy, known for normal tissue sparing, with spatially-fractionated radiotherapy (SFRT), promising enhanced tumor control and tissue...

Comparative Analysis of Nine Deep Learning Architectures for Variable Density Grappa 1H Magnetic Resonance Spectroscopy Imaging (MRSI) Reconstruction

Authors: Kimberly Chan, Anke Henning, Mahrshi Jani, Andrew Wright, Xinyu Zhang

Affiliation: Advanced Imaging Research Center (AIRC), UT Southwestern Medical Center

Abstract Preview: Purpose: To evaluate the performance of multiple deep learning architectures for MRSI reconstruction and determine their effectiveness in maintaining high-resolution metabolite mapping while reducing ...

Comparative Evaluation of Nn-Unet Models for Radiotherapy Dose Prediction Using the Head and Neck Cancer Patients

Authors: Theodore Higgins Arsenault, Beatriz Guevara, Rojano Kashani, Raymond F. Muzic, Gisele Castro Pereira, Alex T. Price

Affiliation: University Hospitals Seidman Cancer Center, Case Western Reserve University Department of Biomedical Engineering

Abstract Preview: Purpose: Accurate dose prediction in radiotherapy is essential for treatment planning. This study evaluates four nnUnet-based models using the OpenKBP head and neck dataset: a baseline model (Model 1)...

Comparison of Image Quality and Radiation Dose Among Four Vendors of Mobile Digital Radiography Systems

Authors: Nathalie Correa, Janet Ching-Mei Feng, Chun-Han Huang, Jimmy Huynh

Affiliation: UTHealth McGovern Medical School

Abstract Preview: Purpose: To compare the image quality and the dose-area product (DAP) of four mobile digital radiography (DR) systems—Canon (Soltus 500), Shimadzu (MobileDaRt Evolution MX8), Solution for Tomorrow (M1...

Construction and Application Study of a Deep Learning-Based Iscout-Guided Precision Radiotherapy Positioning Error Prediction Model for Breast Cancer

Authors: Fangfen Dong, Jiaming Li, Xiaobo Li, Weipei Wang, Zhixin Wang, Bing Wu, Benhua Xu, Yong Yang, Yifa Zhao

Affiliation: Department of Radiation Oncology, Fujian Medical University Union Hospital/Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors/Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Hematologi, Zhangpu County Hospital, School of Medical Imaging, Fujian Medical University

Abstract Preview: Purpose: To explore the construction and clinical application value of a deep learning-based positioning error prediction model, providing a reference for optimizing iSCOUT system-guided precision rad...

Contrast-Dependent Loss of Edge Sharpness in Low-Contrast Targets with Increasing Iterative Reconstruction Strength

Authors: Emi Ai Eastman, Christina Lee, Xinhua Li, Alexander W. Scott, Yifang (Jimmy) Zhou

Affiliation: Cedars-Sinai Medical Center

Abstract Preview: Purpose: Iterative reconstruction (IR) methods are valuable for reducing dose in modern CT; however, IR methods have the effect of reducing spatial resolution and hence the lesion edge sharpness. Furt...

Deep Learning-Based Multileaf Collimator Sequence Prediction for Automated VMAT Treatment Planning in Pancreatic Cancer

Authors: Zixu Guan, Takahiro Iwai, Takashi Mizowaki, Mitsuhiro Nakamura, Michio Yoshimura

Affiliation: Kyoto University, Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University

Abstract Preview: Purpose:
The goal of this study is to develop a fully automated treatment planning approach for VMAT in pancreatic cancer that can convert patient anatomy into LINAC machine parameters. In this wor...

Deep-Learning Convolutional Neural Network-Based Breast Cancer Localization for Mammographic Images: A Study on Simulated and Clinical Images

Authors: Xiaoyu Duan, Xiang Li, Wenbo Wan, Lei Zhang, Yiwen Zhang

Affiliation: Duke University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Breast screening has been proved to reduce breast cancer mortality by early detection and treatment for patients. Mammography is the most common and widely used technique for breast cancer sc...

Developing an AI-Driven Predictor for Forecasting Treatment Outcomes in Patients with Early-Stage Breast Cancer

Authors: Lucy Jiang, Chengyu Shi

Affiliation: Department of Radiation Oncology, City of Hope Orange County, Amity Regional High School (10th Grade)

Abstract Preview: Purpose: Early-stage breast cancer is common among females, with typically high local tumor control rates (LCR). Brachytherapy is a common way to achieve LCR following surgery. However, the patients m...

Development of Patient-Specific Lead Ball Compensator for Total Body Irradiation

Authors: Chang Heon Choi, Minjae Choi, Yoonsuk Huh, Jin Jegal, Jung-in Kim, So-Yeon Park

Affiliation: Department of Radiation Oncology, Veterans Health Service Medical Center, Department of Radiation Oncology, Seoul National University Hospital

Abstract Preview: Purpose: Total Body Irradiation (TBI) aims to achieve a uniform dose distribution across the entire body. However, in conventional TBI setups that use extended source-to-surface distance (SSD) and lea...

Enhanced Prediction of Iroc Stereotactic Radiosurgery Phantom Audit Results with Treatment Parameters, Complexity Metrics, DVH, and Dosiomics Using Machine Learning

Authors: Lian Duan, Stephen F. Kry, Hunter S. Mehrens, Christine Peterson, Paige A. Taylor

Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center

Abstract Preview: Purpose: To develop predictive models for IROC SRS head phantom audits and to identify important factors influencing institutional performance.
Methods: The IROC SRS head phantom includes two TLDs ...

Evaluating Deep Learning Models for Accurate Segmentation of GTV and Oars in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer

Authors: Christopher G. Ainsley, Pradeep Bhetwal, Yingxuan Chen, Wookjin Choi, Vimal K. Desai, Karen E. Mooney, Adam Mueller, Hamidreza Nourzadeh, Yevgeniy Vinogradskiy, Maria Werner-Wasik

Affiliation: Thomas Jefferson University

Abstract Preview: Purpose: MR-guided adaptive radiotherapy (MRgART) has demonstrated improved outcomes for patients with pancreatic cancer. However, the time-consuming re-segmentation of targets and organs-at-risk (OAR...

Evaluating Uncertainty Estimation Models for Clinical Integration of AI-Generated Radiotherapy Dose Distributions

Authors: Jacob S. Buatti, Kristen A. Duke, Malena Fassnacht, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia, Michelle de Oliveira

Affiliation: The University of Texas San Antonio, UT Southwestern Medical Center, UT Health San Antonio

Abstract Preview: Purpose:
Quantifying and visualizing uncertainty is critical for building clinical trust in AI-generated dose distributions. This study evaluates Monte Carlo Dropout (MCD), Snapshot Ensemble (SE), ...

Evaluation of MR Proton Density Fat Fraction (PDFF) for Bone Marrow Protection in RT

Authors: Li Tong, Chuyan Wang, Zhengkui Wang, Yingli Yang, Jie Zhang

Affiliation: Shanghai United imaging Healthcare Advanced Technology Research Institute, Shanghai United Imaging Healthcare Co., LTD, Department of Radiology, Ruijin Hospital, Institute for Medical Imaging Technology, Ruijin Hospital

Abstract Preview: Purpose:
Pelvic radiotherapy (RT)-induced bone marrow (BM) damage affects patient prognosis by causing hematologic toxicity. However, consensus on BM-sparing (BMS) RT is still lacking, owing to the...

Feasibility of Real-Time Monitoring in Tumor Treating Fields Therapy Using Electrical Impedance Tomography: Analysis of Current Injection and Measurement Patterns

Authors: Sung Hwan Ahn, Yongha Gi, Jinju Heo, Jinyoung Hong, Yunhui Jo, Yousun KO, Hyeongjin Lim, Sang Yoon PARK, Myonggeun Yoon

Affiliation: Institute of Global Health Technology (IGHT), Korea University, Republic of Korea, Korea University, Samsung Medical Center

Abstract Preview: Purpose:
This study aims to evaluate the feasibility of real-time monitoring of conductivity changes induced by thermal variations during tumor treating fields (TTFields) therapy using Electrical I...

Gaze Angle Selection in Proton Therapy for Ocular Tumors with Machine Learning

Authors: Ling Chen, Alexei V. Trofimov, Yi Wang, Dufan Wu

Affiliation: Massachusetts General Hospital, MGH

Abstract Preview: Purpose:
Selecting gaze angles of the eye is an important part of set-up of proton therapy for ocular tumors, ensuring that the treatment beam could irradiate the tumor while maximally sparing impo...

Gradient-Based Radiomics for Outcome Prediction and Decision-Making in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR): A Preliminary Study

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...

Graph Neural Network with Long Short-Term Memory for CT-Based Macrotrabecular-Massive Hepatocellular Carcinoma Diagnosis

Authors: Enhui Chang, Yunfei Dong, Yifei Hao, Chengliang Jin, Shengsheng Lai, Yi Long, Mengni Wu, Yulu Wu, Ruimeng Yang, Zhenyu Yang, Yue Yuan, Lei Zhang, Wanli Zhang, Yaogong Zhang

Affiliation: Duke Kunshan University, Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Macrotrabecular-Massive Hepatocellular Carcinoma (MTM-HCC) is one type of liver cancer showed minimum image signature for accurate non-invasive diagnosis. This study aims to develop and evalu...

Improving Quantitative Accuracy of Maximum-a-Posteriori Expectation Maximization Reconstruction By Optimizing Gibbs Prior Parameters in SPECT

Authors: Krishnendu Saha

Affiliation: Cleveland Clinic Foundation

Abstract Preview: Purpose: The goal is to improve quantitative accuracy of a Maximum-a-posteriori expectation maximization (MAPEM) reconstruction of SPECT phantom images by optimizing Gibbs prior parameters.
Methods...

Innovative in-Silico Modeling for Timing Immunotherapy in Radiotherapy-Combined Treatments

Authors: Daisuke Kawahara, Yoichi Watanabe

Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Department of Radiation Oncology, University of Minnesota Medical School

Abstract Preview: Purpose: Combination therapies with immunotherapy (IT) and radiotherapy (RT) are widely used, but the optimal IT timing is unclear. This study leverages a computational cellular automaton (CA) model, ...

Large Language Model Agents for Automated Radiotherapy Planning: A Knowledge-Enhanced Reinforcement Learning Approach

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...

MRI Radiomics-Based Machine Learning Model for Predicting BNCT Treatment Response in Glioblastoma

Authors: Huang Chi-Shiuan, Wu Chih-Chun, Hui-Yu Cathy Tsai, Chen Yan-Han, Chen Yi-Wei, Pan Yi-Ying

Affiliation: Institute of Nuclear Engineering and Science, National Tsing Hua University, Taipei Veterans General Hospital, Tri-Service General Hospital

Abstract Preview: Purpose:
This study aims to develop and validate a machine learning (ML) model based on MRI-derived radiomic features to predict progressive disease (PD) in glioblastoma (GBM) patients four months ...

Multi-Institutional Analysis of CT Dose Index Variability and Radiomics Features

Authors: Caroline Chung, Michael Knopp, Stephen F. Kry, Hunter S. Mehrens, John Rong

Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center, University of Cincinnati

Abstract Preview: Purpose: To evaluate the variability of CT dose index (CTDIvol) and radiomics features across a large cohort of radiotherapy simulation CT scans from multiple institutions.
Methods: Three IROC phan...

Multi-Mechanism CNN and Long Short-Term Memory Fusion Model for Improved CT-Based Thyroid Cancer Diagnosis

Authors: Yunfei Dong, Dongyang Guo, Jiongli Pan, Tao Peng, Caiyin Tang, Zhenyu Yang, Fang-Fang Yin, Lei Zhang, Tianyi Zhang, Yaogong Zhang

Affiliation: Duke Kunshan University, Department of Radiology, Taizhou People’s Hospital Affiliated to Nanjing Medical University, School of Future Science and Engineering, Soochow University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: This study aims to improve the accuracy of CT-based diagnosis of thyroid cancer by developing a hybrid model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (...

Multi-Modality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma

Authors: Tyler J Bradshaw, Sharon M Castellino, Steve Y Cho, David Hodgson, Bradford S Hoppe, Kara M Kelly, Andrea Lo, Sarah Milgrom, Xin Tie

Affiliation: Department of Radiation Oncology, University of Toronto, Department of Radiology, University of Wisconsin, University of Colorado Anschutz, Department of Medical Physics, University of Wisconsin, Department of Radiation Oncology, Mayo Clinic, Department of Radiation Oncology, BC Cancer, Vancouver Center, Department of Radiology, University of Wisconsin - Madison, Roswell Park Comprehensive Cancer Center, Emory University School of Medicine

Abstract Preview: Purpose: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming due to the need to analyze multi-time-point PET/CT scans co-regi...

Multi-Organ Segmentation of Pelvic Cone-Beam Computed Tomography (CBCT) with Transformer Models to Enhance Adaptive Radiotherapy for Prostate Cancer

Authors: Ming Chao, Thomas Chum, Tenzin Kunkyab, Yang Lei, Tian Liu, Richard G Stock, Hasan Wazir, Junyi Xia, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract Preview: Purpose:
This study aims to develop effective strategies for multi-organ segmentation of pelvic cone-beam computed tomography (CBCT) images based on transformer models to facilitate adaptive radiat...

Multi-Sid Optimization for 4 Pi Robotic Radiotherapy

Authors: Qihui Lyu, Dan Ruan, Ke Sheng, Jingjie Yu

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of California, San Francisco, Department of Radiation Oncology, University of California, San Francisco

Abstract Preview: Purpose: The robotic arm radiotherapy platform enables flexible delivery of non-coplanar and non-isocentric radiotherapy with variable Source-to-Isocenter Distances (SIDs). However, the high degrees o...

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...

Pancrea-Seg-Net: A Semi-Supervised Deep Learning Framework for Pancreatic Tumor and Vessel Segmentation

Authors: Manju Liu, Ning Wen, Fuhua Yan, Yanzhao Yang, Zhenyu Yang, Haoran Zhang, Lei Zhang, Yajiao Zhang

Affiliation: Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy where precise segmentation of tumors and adjacent vessels is crucial for effective treatment planning. This study dev...

Performance Analysis of Various Deep Learning Networks for Classification of True and False Positive 18F-PSMA Findings

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...

Quantitative Image Review Metric for Vertebral Body Misalignment

Authors: Luis E. Fong de los Santos, Douglas John Moseley, Satomi Shiraishi, Cenji Yu

Affiliation: Mayo Clinic

Abstract Preview: Purpose: To quantitatively detect errors in vertebral body localization using normalized cross correlation (NCC)

Methods: Volumetric images of a thoracic end-to-end SBRT phantom with a removabl...

Reasoning-Driven Prompts Improve EHR-Based Outcome Prediction and Clinical Interpretability in Large Language Models

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, University of San Francisco

Abstract Preview: Purpose: As Large Language Models (LLMs) continue to evolve, their ability to analyze Electronic Health Record (EHR) notes for clinical decision support expands. Chain of Thought (COT) reasoning, an e...

The Role of 3D Vane MRI in Accurate Phase Matching with 4D-CT for Motion Representation in Liver Cancer Radiotherapy

Authors: Jiayun Chen, Shengqi Chen, Junchao Li, Fei Liu, Jianan Wu

Affiliation: Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, Tongji Medical College, Huazhong University of Science & Technology, School of Electronic Engineering, Beijing University of Posts and Telecommunications, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Abstract Preview: Purpose: To assess if 3D Vane MRI can accurately depict the motion of the target volume and OARs.
Methods: This retrospective study included 54 liver cancer patients who underwent both 3D Vane MRI ...

To Investigate the Utility of Magnetic Resonance Imaging (MRI)-Based Radiomics for Predicting Tumor Response and Adverse Effects, Specifically Gastrointestinal (GI) Toxicity, in Cervical Cancer Patients Undergone Radiotherapy.

Authors: Issam M. El Naqa, Kurukulasuriya Ruwani Fernando, Himani Himani, Vivek Kumar, Arun Oinam, Manju Sharma

Affiliation: Panjab University, Moffitt Cancer Center, H. Lee Moffitt Cancer Center, Post Graduate Institute of Medical Sciences, University of California San Francisco

Abstract Preview: Purpose: To investigate the utility of Magnetic Resonance Imaging (MRI)-based radiomics for predicting tumor response and adverse effects, specifically gastrointestinal (GI) toxicity, in cervical canc...