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Results for "learning early": 23 found

23na Magnetic Resonance Imaging k-Space Denoising

Authors: Lorenzo Arsini, Andrea Ciardiello, Fabio Massimo D'Amore, Stefano Giagu, Federico Giove, Carlo Mancini-Terracciano, Cecilia Voena

Affiliation: Istituto Superiore di Sanità, Sapienza University of Rome, Università Sapienza Roma, Magnetic Resonance for Brain Investigation Laboratory, Museo Storico della Fisica e Centro di Studi e Ricerche Enrico Fermi

Abstract Preview: Purpose: To leverage newly developed heteronuclear magnetic resonance imaging (MRI) techniques, particularly sodium (23Na) imaging, for identifying potential biomarkers of Alzheimer's disease—such as ...

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 Novel Non-Measured and DVH-Based IMRT QA Framework with Machine Learning for Instant Classification of Susceptible Lung SBRT VMAT Plans

Authors: Chuan He, Anh H. Le, Iris Z. Wang

Affiliation: Roswell Park Comprehensive Cancer Center, Cedars-Sinai

Abstract Preview: Purpose: To develop a non-measured and DVH-based (NMDB) IMRT QA framework integrating machine learning (ML) to classify lung SBRT VMAT plans prone to delivery errors
Methods: 560 Eclipse AcurosXB l...

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...

Automating Radiographic Sharp Score Prediction in Rheumatoid Arthritis Using Multistage Deep Learning Methods

Authors: Hajar Moradmand, Lei Ren

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

Abstract Preview: Purpose:
The Sharp-van der Heijde (SvH) score is essential for assessing joint damage in rheumatoid arthritis (RA) from radiographic images. However, manual scoring is time-intensive and prone to v...

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...

Comparison of AI-Based and Ants for Longitudinal Deformable Image Registration in Head and Neck Cancer

Authors: Aditya P. Apte, Joseph O. Deasy, Jue Jiang, Nancy Lee, Sudharsan Madhavan, Nishant Nadkarni, Lopamudra Nayak, Harini Veeraraghavan, Wei Zhao

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: To track early response to radiotherapy using digital twins, it is crucial to quantify tumor volume and mass changes. Traditional tumor detection methods, particularly in image registration, ...

Continuous Professional Development for Medical Physicists on AI Principles from the User's Perspective.

Authors: Mauro Carrara, Olivera Ciraj Bjelac, John E. Damilakis, Andre L. Dekker, Serafina Di Gioia, Renato Padovani, Egor Titovich, Qingrong Jackie Wu

Affiliation: University of Crete, Duke University Medical Center, Maastro Clinic, Dosimetry and Medical Radiation Physics Section, Division of Human Health, International Atomic Energy Agency, International Centre for Theoretical Physics

Abstract Preview: Purpose: The purpose of this work is to present the International Atomic Energy Agency (IAEA) activity in providing medical physicists (MPs) with knowledge, skills, and competencies to support the saf...

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...

Gene Interaction-Encoded Deep Learning Uncovers Microenvironment for Radiation-Induced Pulmonary Fibrosis

Authors: Md Tauhidul Islam, Junyan Liu, Lei Xing

Affiliation: Department of Radiation Oncology, Stanford University

Abstract Preview: Purpose: Radiation-induced lung injury (RILI) is a common complication in patients receiving radiotherapy for lung cancer, with approximately 16%–28% developing pulmonary fibrosis. The exact mechanism...

Generation of Virtual Lung PET Images from CT Data Via Deep Learning for Accelerated Tumor Detection and Preliminary Diagnosis

Authors: Pouya Azarbar, Nima Kasraie, Peyman Sheikhzadeh

Affiliation: UT Southwestern Medical Center, Shahid Beheshti University of Medical science, Imam Khomeini Hospital Complex,Tehran University of Medical Sciences

Abstract Preview: Purpose: Positron Emission Tomography (PET) is crucial for diagnosing and monitoring diseases due to its functional imaging capabilities. However, its high cost, significant radiation exposure, and li...

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 Foundation Model with Self-Supervised Learning for Brain Lesion Segmentation with Multimodal and Diverse MRI Datasets

Authors: Zong Fan, Fan Lam, Hua Li, Rita Huan-Ting Peng, Yuan Yang

Affiliation: University of Illinois at Urbana Champaign, University of Illinois at Urbana-Champaign, Washington University School of Medicine, University of Illinois Urbana-Champaign

Abstract Preview: Purpose: Accurate lesion segmentation in MRI is critical for early diagnosis, treatment planning, and monitoring disease progression in various neurological disorders. Cross-site MRI data can alleviat...

Investigating the Multimodal Fusion Techniques to Improve Prediction Accuracy of Biochemical Recurrence of Prostate Cancer

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...

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...

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...

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-Path Deep Learning Model for Predicting Post-Radiotherapy Functional Liver Imaging in Patients with Hepatocellular Carcinoma

Authors: Smith Apisarnthanarax, Stephen R. Bowen, Sunan Cui, Jie Fu, Clemens Grassberger, Yulun He, Yejin Kim, Matthew J. Nyflot, Sharon Pai

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Department of Radiation Oncology, University of Washington, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Physics, University of Washington, University of Washington and Fred Hutchinson Cancer Center

Abstract Preview: Purpose: 99mTc-sulfur colloid SPECT imaging enables quantitative assessment of voxel-wise liver function in patients with hepatocellular carcinoma (HCC). Accurately predicting post-radiotherapy (RT) l...

Optimizing Fractionation Schedules for De-Escalation Radiotherapy in Head and Neck Cancers Using Deep Reinforcement Learning

Authors: Zhongjie Lu

Affiliation: Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine

Abstract Preview: Purpose: Patients with locally-advanced head and neck squamous cell carcinomas(HNSCCs), particularly those related to human papillomavirus(HPV), often achieve good locoregional control(LRC), yet they ...

Predicting Pathological Complete Response to Neoadjuvant Chemotherapy for Breast Cancer at Early Time Points Using a Two-Stage Dual-Task Deep Learning Strategy

Authors: Bowen Jing, Jing Wang

Affiliation: 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

Abstract Preview: Purpose: Medical images acquired at multiple time points during neoadjuvant chemotherapy allow physicians to assess patients’ responses and personalize treatment plans accordingly. Studies from the I-...

Ratoguide: Evaluation of AI-Driven Fully Automated Treatment Planning Support System for Lung SBRT

Authors: Keiichi Jingu, Noriyuki Kadoya, Takafumi Komiyama, Takeru Nakajima, Hikaru Nemoto, Hiroshi Onishi, Masahide Saito, Ryota Tozuka

Affiliation: Department of Radiology, University of Yamanashi, Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Department of Advanced Biomedical Imaging, University of Yamanashi

Abstract Preview: Purpose: We evaluated the accuracy of a new AI-based fully automated planning software in stereotactic body radiotherapy (SBRT) for early-stage lung cancer.
Methods: We collected data from 125 pati...

Scan Efficiency and Imaging Dose Analysis of Next-Generation Nonstop Gated CBCT for Respiratory Gating Lung Radiotherapy

Authors: Sean L. Berry, Weixing Cai, Laura I. Cervino, Yusuf Emre Erdi, Yabo Fu, Yiming Gao, Daphna Gelblum, Wendy B. Harris, Xiuxiu He, Tianfang Li, Xiang Li, Seng Boh Gary Lim, Jean M. Moran, Mitchell Yu, Hao Zhang

Affiliation: Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Gated CBCT (gCBCT) is commonly employed for respiratory gating lung cancer patients to ensure precise patient setup. However, the scan is time-consuming on C-arm linear accelerators (LINAC) d...