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Results for "early prediction": 25 found

A Combination of Radiomics and Dosiomics for Gross Tumor Volume Regression in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

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...

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, Peopleโ€™s Hospital of Xinjiang Uygur Autonomous Region, Second Hospital of Anhui Medical University, Guangdong Provincial Peopleโ€™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...

Compressed Sensing Enhanced Radiomic Feature Selection for Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

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...

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...

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 ...

Early GU Toxicity Prediction in Prostate SBRT Using Delivered Dosimetry Via Long Short-Term Memory Model

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...

Early Imaging Identification of Osteoradionecrosis and Classification Using the Novel Clinrad System

Authors: Serageldin Attia, Zayne Belal, Cem Dede, Clifton David Fuller, Andrew Hope, Laia Humbert Vidan, Kate Hutcheson, Zaphanlene Kaffey, Stephen Y. Lai, Abdallah Mohamed, Amy Moreno, Jillian Rigert, Erin Watson

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 Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Princess Margaret Cancer Centre, University Health Network, 610 University Ave., The University of Texas MD Anderson Cancer Center, UT MD Anderson, Princess Margaret Cancer Centre, UT MD Anderson Cancer Center, Hospital of the University of Pennsylvania, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Department of Head and Neck Surgery, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology

Abstract Preview: Purpose: Osteoradionecrosis (ORN) of the jaw is a debilitating radiation-induced toxicity lacking standardized classification criteria or treatment guidelines. Early identification of tissue injury co...

Exploring NSCLC Microenvironments: Multi-Score Survival Models Integrationg Radiomics-Based Regional Imaging Features and Genomics

Authors: Nobuki Imano, Daisuke Kawahara, Misato Kishi, Yuji Murakami

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

Abstract Preview: Purpose: This study aims to develop a comprehensive Multi-score by integrating Radiomics-score (Rad-score), Gene-score derived from gene expression levels, and tumor environment Rad-score (TE-Rad-scor...

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%โ€“28% developing pulmonary fibrosis. The exact mechanism...

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...

In-Silico Clinical Trials Enabled By Digital Twin Approach Can Accurately and Prospectively Predict Outcomes of Clinical Trials Combining Radiation and Systemic Therapy

Authors: Clemens Grassberger, David (Bo) McClatchy, Harald Paganetti

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Massachusetts General Hospital

Abstract Preview: Purpose: While randomized controlled trials (RCTs) are the gold standard for demonstrating efficacy, nearly 50% of late-stage clinical trials fail to meet their endpoint. Tools to study the design of ...

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...

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-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...

Multimodal Attention Fusion Model Leveraging Structured and Unstructured EHR Data for Hospital Readmission Prediction in Head and Neck Cancer

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...

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 patientsโ€™ responses and personalize treatment plans accordingly. Studies from the I-...

Predicting and Monitoring Response to Head and Neck Cancer Radiotherapy Using Multi-Modality Imaging and Radiobiological Digital Twin Simulations

Authors: Eric Aliotta, Michalis Aristophanous, Joseph O. Deasy, Bill Diplas, Milan Grkovski, James Han, Vaios Hatzoglou, Jeho Jeong, Nancy Y Lee, Ramesh Paudyal, Nadeem Riaz, Heiko Schoder, Amita Shukla-Dave

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

Abstract Preview: Purpose: To forecast radiotherapy treatment response for head and neck cancer (HNC) using multimodality imaging and personalized radiobiological modeling.
Methods: Multi-modality imaging data from ...

Prediction of Head and Neck Cancer Using Artificial Neural Network through Basic Health Data

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 ...

Preplanning Physics Consultation with Voxelized EQD2: A Proactive Approach for Streamlined Dose Management in High-Risk Re-Irradiation Patients

Authors: Katja M. Langen, Mark McDonald, Bill Stokes, Yinan Wang, Suk Whan (Paul) Yoon

Affiliation: Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: Effective dose management in high-risk re-irradiation (ReRT) patients entails early, detailed special physics consultation (SPC) to mitigate risks from cumulative radiation dose. SPCs have tr...

Radiomics Changes in MR Images of Prostate and Dominant Intra-Prostate Lesions during SBRT on MR-Linac

Authors: David L. Barbee, David Byun, Ting Chen, Paulina E. Galavis, Siming Lu, Sarah Rosemary Morris, Hesheng Wang, Michael J Zelefsky

Affiliation: NYU Langone Health

Abstract Preview: Purpose: MR-Linac enables dose escalation in prostate SBRT on accurately defined dominant intra-prostate lesion (DIL) on daily MR images. This study aims to evaluate inter-fraction changes in the radi...

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...