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Results for "sensitivity specificity": 45 found

A Method to Expedite Quality Assurance for Head and Neck Ctvs with Lymph Node Level Auto-Autocontouring and Identification

Authors: Beth M. Beadle, Adrian Celaya, Laurence Edward Court, David Fuentes, Anna Lee, Tze Yee Lim, Dragan Mirkovic, Amy Moreno, Raymond Mumme, Tucker J. Netherton, Callistus M. Nguyen, Jaganathan A Parameshwaran, Jack Phan, Carlos Sjogreen, Sara L. Thrower, Congjun Wang, He C. Wang, Xin Wang

Affiliation: Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Department of Radiation Oncology, Stanford University, The University of Texas MD Anderson Cancer Center, MD Anderson Cancer Center, MD Anderson, Rice University, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: Quality assurance of target volumes from radiotherapy clinical trials is a labor and resource intensive task. The purpose of this work is to quantify the accuracy of a tool that automatically...

A Tool to Quantitatively Assess Dose after Patient Motion

Authors: Asma Amjad, Renae Conlin, Beth A. Erickson, William Hall, Eric S. Paulson, Christina M. Sarosiek

Affiliation: Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: The adapt-to-shape (ATS) workflow on the MR-Linac involves manual contour edits followed by treatment plan reoptimization on daily pre-beam MRIs. A verification image is acquired after plan o...

Automated Case Prioritization in Breast Radiation Therapy Peer Review Rounds

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...

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...

BEST IN PHYSICS MULTI-DISCIPLINARY: Building a Cross-Modality Model to Integrate Bio-Clinical Features, Anatomical MRI, and White-Matter Pathlength Mapping for Personalized Glioblastoma RT Planning

Authors: Steve Braunstein, Angela Jakary, Hui Lin, Bo Liu, Janine Lupo, Tiffany Ngan, Ke Sheng, Nate Tran

Affiliation: Radiation Oncology, University of California San Francisco, Graduate Program in Bioengineering, University of California San Francisco-UC Berkeley, Department of Radiation Oncology, University of California San Francisco, Department of Radiology and Biomedical Imaging, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco

Abstract Preview: Purpose: Current RT clinical target volumes (CTVs) for Glioblastoma (GBM) employ 2cm isotropic expansions of gross tumor volumes. However, studies showed patients still experience progression beyond t...

BEST IN PHYSICS THERAPY: Overcoming Challenges in Developing Machine Learning-Driven Acute Kidney Injury Predictive Models Using Non-Standard Emrs in Resource-Limited Settings

Authors: Yuanhan Chen, Ziqiang Chen, Qi Cheng, Feng Ding, Rui Fang, Shengwen Guo, Li Hao, Qiang He, Haiquan Huang, Yu Kuang, Xinling Liang, Yuanjiang Liao, Guohui Liu, Chen Lu, Qingquan Luo, Jing Sun, Yanhua Wu, Zhen Xie, Qin Zhang, Lang Zhou

Affiliation: South China University of Technology, Dongguan people's hospital, Sichuan Provincial People's Hospital, People’s Hospital of Xinjiang Uygur Autonomous Region, Second Hospital of Anhui Medical University, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong Easy Life Information Technology Co., Ltd, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Medical Physics Program, University of Nevada, Second Hospital of Jilin University, Chongqing Ninth People's Hospital

Abstract Preview: Purpose: Acute kidney injury (AKI) is a global healthcare issue with a rapid onset and severe consequences. Repeated measurement of serum creatinine (SCr) levels, a clinical standard of care, is cruci...

Biologically Guided Deep Learning for MRI-Based Brain Metastasis Outcome Prediction after Stereotactic Radiosurgery

Authors: Evan Calabrese, Hangjie Ji, Kyle J. Lafata, Casey Y. Lee, Eugene Vaios, Chunhao Wang, Lana Wang, Zhenyu Yang, Jingtong Zhao

Affiliation: Duke University, Department of Radiation Oncology, Duke University, Duke Kunshan University, North Carolina State University

Abstract Preview: Purpose: To develop a biologically guided deep learning (DL) model for predicting brain metastasis(BM) local control outcomes following stereotactic radiosurgery (SRS). By integrating pre-SRS MR image...

Breast Fascial Ligament Characterization Using Cryo-Fluorescence Tomography Imaging

Authors: Taylor A Beal, Kari J. Brewer Savannah, Kristy K. Brock, Alejandro Contreras, Natalie W Fowlkes, Megan Kalambo, Gregory P Reece, Erin P Snoddy, Tien T Tang

Affiliation: The University of Texas MD Anderson Cancer Center, Baylor College of Medicine

Abstract Preview: Purpose: Current anatomical and surgical research does not adequately detail the breast fascial system’s ligaments and connective tissues. Most available information stems from cadaver dissections, wh...

Breathing New Life into Pulmonary Imaging: Development and Clinical Validation of an X-Ray Interferometry System

Authors: Rachael Blair, Les Butler, Lillian Dickson, Kyungmin Ham, Charles Hartman, Kenneth (Kip) Matthews, Corinne Vanya

Affiliation: Louisiana State University, University of Minnesota, Refined Imaging LLC, Center for Advanced Microstructures and Devices

Abstract Preview: Purpose:
To develop and evaluate an x-ray interferometry system (XIS) for low-dose, high-sensitivity diagnostic imaging of lung diseases such as chronic obstructive pulmonary disease (COPD), asthma...

Cerebellar Mutism Syndrome Prediction with 3D Residual Convolutional Neural Network

Authors: Sahaja Acharya, Matthew Ladra, Junghoon Lee, Lina Mekki, Bohua Wan

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Department of Biomedical Engineering, Johns Hopkins University, Department of Computer Science, Johns Hopkins University

Abstract Preview: Purpose: Cerebellar mutism syndrome (CMS) is the most frequently observed complication in children undergoing surgical resection of posterior fossa tumors. Previous work explored lesion to symptom map...

Characterization of HCC Tumor Response in 90Y-Radioembolization Clinical Trial RAPY90D Using Prospective Voxel Dosimetry

Authors: E Courtney Henry, Srinivas Cheenu Kappadath, Armeen Mahvash

Affiliation: UT MD Anderson Cancer Center

Abstract Preview: Purpose:
Characterization of hepatocellular carcinoma (HCC) tumor responses for single-arm single-center prospective 90Y-radioembolization clinical trial (n=40) that used patient-specific voxel-dos...

Clinical Implementation of Automated Contour Quality Assurance in Head and Neck Radiotherapy

Authors: Sam Armstrong, Jamison Louis Brooks, Nicole Johnson, Douglas John Moseley, Cassie Sonnicksen, Erik J. Tryggestad

Affiliation: Mayo Clinic

Abstract Preview: Purpose: To evaluate the feasibility of a shallow learning-based quality assurance (QA) tool designed to assist human reviewers in assessing organ-at-risk (OAR) contours for head and neck radiotherapy...

Comparison of Multispectral Singlet Oxygen Luminescent Dosimetry (MSOLD) and Singlet Oxygen Explicit Dosimetry (SOED) in BPD-Mediated PDT for Mice

Authors: Robert H Hadfield, Madelyn Johnson, Baozhu Lu, Hongjing Sun, Vikas Vikas, Brian C. Wilson, Weibing Yang, Timothy C. Zhu

Affiliation: University of Glasgow, University of Toronto, University of Pennsylvania

Abstract Preview: Purpose:
Multispectral Singlet Oxygen Luminescent Dosimetry (MSOLD) has been developed as a real-time in vivo dosimetry technique for Photodynamic Therapy (PDT). This study investigates the feasibi...

Contrast-Free Enhancement of Coronary Artery Stenosis: Synthetic Ccta from Non-Contrast CT Using Diffusion Model

Authors: Abdusalam Abdukerim

Affiliation: Institute for Medical Imaging Technology, Ruijin Hospital

Abstract Preview: Purpose:
Coronary computed tomography angiography (CCTA) is the gold-standard non-invasive test for coronary artery disease (CAD), but iodine contrast agents (ICA) pose limitations in specific popu...

Decision Support for Adaptive Vs Non-Adaptive SBRT for Left-Sided Adrenal Tumors

Authors: Robbie Beckert, Austen N. Curcuru, Farnoush Forghani, Yi Huang, Geoffrey D. Hugo, Hyun Kim, Eric Laugeman, Luke Christian Marut, Thomas R. Mazur, Allen Mo, Emily Sigmund

Affiliation: Washington University in St. Louis School of Medicine, WashU Medicine, Washington University School of Medicine in St. Louis, Wash U Medicine, Washington University in St. Louis, Department of Radiation Oncology, Washington University School of Medicine in St. Louis

Abstract Preview: Purpose: Adaptive SBRT is resource intensive, requiring additional personnel for online planning, and should be reserved for cases where it is most beneficial. The purpose of this research is to creat...

Deep Learning-Based Categorization of Brain Tumours Using Brain MRI : Advancing Precision Medicine in Neuroimaging

Authors: William F.B Igoniye, Belema Manuel, Christopher F. Njeh, O Ray-offor

Affiliation: Indiana University School of Medicine, Department of Radiation Oncology, Department of Radiology, University of Port Harcourt Teaching Hospital

Abstract Preview: Purpose: The accurate and efficient categorization of brain tumors is essential for effective treatment planning and improved patient outcomes. Current MRI-based diagnostic methods are time-intensive ...

Diffusion-Weighted MRI: An Early Biomarker for Treatment Response in MR-Guided Treatment of Rectal Cancer

Authors: Huiming Dong, Jonathan Pham, X. Sharon Qi, Ann Raldow

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

Abstract Preview: Purpose: The study aimed to investigate longitudinal apparent diffusion coefficient (ADC) as an early biomarker of treatment response in patients with locally advanced rectal cancer (LARC) undergoing ...

Discriminative Uncertainty Learning for Cancer Classification

Authors: Wei Wei, Yading Yuan

Affiliation: Columbia University Irving Medical Center, Department of Radiation Oncology

Abstract Preview: Purpose: To investigate an uncertainty modeling method to improve the performance of cancer classification with the ability to produce uncertainty score.
Methods: Deep learning has achieved state-o...

Enhanced Predictive Model for Toxicity and 3-Year Survival in HCC Patients Using Learning Health System Infrastructure and AI-Driven Statistical Profiling

Authors: Ameer Elaimy, Theodore Lawrence, Charles S. Mayo, Seyyedeh Azar Oliaei Motlagh, Benjamin S. Rosen

Affiliation: University of Michigan

Abstract Preview: Purpose: To analyze the impact of clinical features on short-term survival, toxicity, and poor outcomes in HCC patients treated with SBRT,using automated data aggregation and enhanced algorithms with ...

Explainable AI with Attention Gates for Transparent and Interpretable Lung Radiotherapy Plan Evaluation

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...

Explainable Hybrid CNN-LLM Model to Guide Treatment Planning of Cervical Cancer High Dose Rate Brachytherapy

Authors: Adnan Jafar, Xun Jia, Michael B. Roumeliotis

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Johns Hopkins University

Abstract Preview: Purpose: HDR brachytherapy (HDRBT) treatment planning is challenging due to the need for high-quality plans under time pressure, considering anatomy and applicator geometry. This study proposes an exp...

Feasibility of Extracting Diagnosis and Staging at Scale from Clinical Notes Via a Real-World Data Warehouse

Authors: Melissa Bronson, Elizabeth L. Covington, Robert T. Dess, Joseph R. Evans, William C. Jackson, Charles S. Mayo, Michelle L. Mierzwa, Benjamin S. Rosen, VG Vinod Vydiswaran, Grant Weyburne, Zheng Zhang, Henry Zocher

Affiliation: PCORnet®, The National Patient-Centered Clinical Research Network, University of Michigan

Abstract Preview: Purpose: Diagnosis and staging are an integral part of cancer care, but this information is often scattered across various electronic medical records. The fragmentation increases overall documentation...

Free Radical Irradiation Emergent (FIRE) MRI: Real-Time MR-Based Quantification of Radiation-Produced Free Radical Generation on a Clinical MR-Linac

Authors: Claire Keun Sun Park, Atchar Sudhyadhom, Veena Venkatachalam, Noah Warner

Affiliation: Harvard–MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Harvard Medical School,, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Harvard Medical School

Abstract Preview: Purpose: Modern radiotherapy achieves sub-millimeter spatial accuracy but fails to account for patient- and spatially-specific variations in biological response. Free radical generation (FRG), particu...

Iguard: A Fully Unsupervised Image-Guidance Anomaly Recognition and Detection Framework in CBCT-Guided Radiotherapy.

Authors: James M. Lamb, Dishane Chand Luximon, Jack Neylon, Rachel Petragallo, Moritz Ritter, Timothy Ritter

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, ETH Zurich, VCU Health System, Department of Radiation Oncology, University of Colorado

Abstract Preview: Purpose: Anomalies in cone beam computed tomography (CBCT) radiotherapy image guidance can signal treatment deviations. Repetitive review of setup image registrations by humans is inefficient, prone t...

Improve the Risk Prediction of Radiation-Induced Esophagitis in Lung IMRT By an Anisotropic Dose Convolution Neural Network

Authors: Ibtisam Almajnooni, Elisabeth Weiss, Lulin Yuan

Affiliation: Virginia Commonwealth University

Abstract Preview: Purpose: We developed a deep learning neural network (DLNN) to predict the risk of radiation-induced esophagitis (RE) during lung cancer radiation therapy based on the spatial dose distribution, for t...

Improving Post-SRS Brain Metastasis Radionecrosis Diagnosis Accuracy Via Deep Feature Space Analysis

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...

Improving the Robustness of AI-Based Detection and Segmentation for Brain Metastasis By Optimizing Loss Function and Multi-Dataset Training

Authors: Omar Awad, Alfredo Enrique Echeverria, Issam M. El Naqa, Daniel Allan Hamstra, Yiding Han, Ryan Lafratta, Abdallah Sherif Radwan Mohamed, Piyush Pathak, Zaid Ali Siddiqui, Baozhou Sun, Vincent Ugarte

Affiliation: H. Lee Moffitt Cancer Center, Harris Health, Baylor College of Medicine

Abstract Preview: Purpose:
Accurate detection and segmentation of brain metastases are critical for diagnosis, treatment planning, and follow-up imaging but are challenging due to labor-intensive manual assessments ...

Innovative Deep Learning Network for Overall Survival Prediction for NSCLC: Outperforming Pre-Trained Models VGG16 and ResNet50

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: Early-stage NSCLC patients undergoing SBRT often die due to intercurrent illnesses. However, prediction of overall survival (OS) remains crucial due to the risk of disease recurrence. This st...

Integrating Large Kernel Attention Mechanism into Deep Learning Model for Automatic and Auccrate Segmentation of Gross Tumor Volume in Lung Cancer Patients

Authors: Xuezhen Feng, Li-Sheng Geng, Haoze Li, Xi Liu, Tianyu Xiong, Ruijie Yang

Affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, School of Physics, Beihang University, School of Nuclear Science and Technology, University of South China, Department of Radiation Oncology, Peking University Third Hospital

Abstract Preview: Purpose: This study aimed to develop a deep learning-based algorithm for automatically delineate gross tumor volume (GTV) for lung cancer patients, alleviating the workload of radiologists and improvi...

Investigating Racial Differences in 68ga-PSMA-11 PET/CT Imaging for Patients with Biochemical Recurrence after Radical Prostatectomy: Preliminary Findings

Authors: Maya Ataya, Clint Bahler, Ramotallah Dami Jubril, Michael Koch, Oluwaseyi Oderinde, Mark Tann

Affiliation: Purdue University, epartment of Urology, Indiana University School of Medicine, Department of Urology, Indiana University School of Medicine, Department of Radiology and Imaging Sciences, Indiana University School of Medicine

Abstract Preview: Purpose:
To investigate racial differences in the detection of biochemical recurrence (BCR) using 68Ga-PSMA-11 PET/CT, known for its improved sensitivity and specificity in patients after radical p...

Key Tumor Volume Zones for Advancing the Radiomics-Based Distant Recurrence Prediction

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...

LLM-Enhanced Multi-Modal Framework for Predicting Pain Relief of Stereotactic Body Radiotherapy for Spine Metastases Using Clinical Factors and Imaging Reports

Authors: John Byun, Steven D Chang, Mingli Chen, Cynthia Chuang, Xuejun Gu, Melanie Hayden Gephart, Yusuke Hori, Hao Jiang, Mahdieh Kazemimoghadam, Fred Lam, Gordon Li, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang, Qingying Wang, Zi Yang, Xianghua Ye, Kangning Zhang

Affiliation: Department of Radiation Oncology, Stanford University, Department of Neurosurgery, Stanford University, 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: Accurate prediction of pain relief is crucial in determining the clinical effectiveness of Stereotactic body radiotherapy (SBRT) regimen for spine metastases. We propose a deep-learning frame...

Lymph Node Malignancy Prediction in Head and Neck Cancer Using a Graph Neural Network

Authors: Liyuan Chen, Meixu Chen, Bowen Jing, Sepeadeh Radpour, Erich Josef Schmitz, David Sher, Jing Wang

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) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose: Prospective clinical trials have shown that involved nodal radiation therapy (INRT) can substantially improve patients’ quality of life without increasing the risk of elective nodal failure. ...

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 ...

Machine Learning Model for Early Prediction of Chemoradiotherapy Response in Oropharyngeal Cancer Patients

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...

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...

Performance Evaluation of CT-Based Lung Tumor Classification Deep Learning Algorithms Under Centralized and Federated Learning Frameworks

Authors: Yifei Hao, Chengliang Jin, Wenxuan Li, Bing Luo, Tao Peng, Yulu Wu, Fang-Fang Yin, Yue Yuan, Lei Zhang, Ruojun Zhou

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

Abstract Preview: Purpose: Federated learning is a patient privacy-protecting technique that has recently been applied in the medical field. This study aims to evaluate the performance of several deep learning networks...

Predicting Hematologic Toxicity in Advanced Cervical Cancer Patients Using Interpretable Machine Learning Models Based on Radiomics and Dosimetrics

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...

Quality Assurance for Prior Radiotherapy Using a Large Language Model

Authors: Douglas John Moseley, David M. Routman, Satomi Shiraishi, Donald C Smith, Mark R. Waddle

Affiliation: University of Denver, Mayo Clinic

Abstract Preview: Purpose: About 6% of patients treated for the first time in our department have received radiotherapy previously at an outside institution. We aim to provide an automatic quality assurance of identify...

Quantification of Global Dosimetry Audit Performance: Assessing the Sensitivity and Specificity of Dosimetry Audits to Establish Reciprocity Among Different International Clinical Trials Credentialing Organizations

Authors: Fre'Etta Brooks, Catharine H Clark, Patricia Diez, Peter B. Greer, Hideaki Hirashima, Rebecca M. Howell, Mohammad Hussein, Stephen F. Kry, Joerg Lehmann, Jessica Lye, Mitsuhiro Nakamura, Christopher L. Nelson, Rushil Patel, Christine Peterson, Julianne M. Pollard-Larkin

Affiliation: Kyoto University, The University of Texas MD Anderson Cancer Center, Radiotherapy Trials Quality Assurance Group, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Medical Physics & Biomedical Engineering, University College London, Metrology for Medical Physics, National Physical Laboratory, Austin Health, University of Washington Medical Center, University of Newcastle

Abstract Preview: Purpose: To assess the consistency of end-to-end dosimetry audits currently used by different international clinical trial QA organizations with the goal of harmonizing audits and reducing audit overl...

Skin Lesion Subtype Classification Using Lesion and Border Radiomic Features

Authors: Rituparna Basak, Maede Boroji, Renee F Cattell, Vahid Danesh, Imin Kao, Kartik Mani, Xin Qian, Samuel Ryu, Tiezhi Zhang

Affiliation: Stony Brook Medicine, Stony Brook University, Washington University in St. Louis, Stony Brook University Hospital

Abstract Preview: Purpose: Fundamental qualitative characteristics physicians use to differentiate skin lesion subtypes include asymmetry, border irregularity, and color. Radiomic features have potential to quantify th...

Spatial Dosimetric-Based Prediction of Long-Term Urinary Toxicity after Permanent Prostate Brachytherapy

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...

Spatially Aware Radiomics Integrating Anatomical Knowledge to Improve Lymph Node Malignancy Prediction in Head and Neck Cancer

Authors: Liyuan Chen, Sepeadeh Radpour, David Sher, Jing Wang

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center

Abstract Preview: Purpose: Accurate lymph node malignancy prediction is pivotal in optimizing radiation treatment strategies for head and neck (HN) cancer patients. While conventional radiomics models leverage intensit...

Spherical Slicing and Convolutions for Accurate Glioma Tumor Segmentation Using Multi-Parametric MRI

Authors: Ke Lu, Chunhao Wang, Ruoxu Xia, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Lei Zhang, Rihui Zhang, Jingtong Zhao, Haiming Zhu

Affiliation: Duke University, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University, The First People's Hospital of Kunshan

Abstract Preview: Purpose: The human brain’s spherical geometry offers unique opportunities for improving the segmentation of tiny and irregular anatomical structures. We hypothesize that representing the brain in sphe...

Study of PSMA-PET/MR Biomarkers for Prostate Stereotactic Radiotherapy Planning

Authors: Guillermo Daniel Alvarez, Daniel Fino, Rocio Luz Gilli, Dorian Alexander Romero

Affiliation: School Foundation of Nuclear Medicine, INTECNUS Foundation, Nuclear Medicine and Radiotherapy Center of Southern Patagonia

Abstract Preview: Purpose: To evaluate the anatomical, functional, and molecular information from PET/MR images with F18-PSMA to identify intraprostatic lesions and delineate a boost in stereotactic body radiotherapy (...