Search Submissions 🔎

Results for "response prediction": 22 found

18F-FDG PET/CT-Based Deep Radiomic Models for Enhancing Chemotherapy Response Prediction in Breast Cancer

Authors: Ke Colin Huang, Zirui Jiang, Joshua Low, Christopher F. Njeh, Oluwaseyi Oderinde, Yong Yue

Affiliation: Purdue University, Indiana University School of Medicine, Department of Radiation Oncology

Abstract Preview: Purpose: Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer (BCa). In this study, we developed deep-radiomi...

A Clinically Aligned Embedding Model for Glioma Prognostication Via Radiology-Pathology Report Matching

Authors: Steve Braunstein, Yannet Interian, Hui Lin, Bo Liu, Janine Lupo, Olivier Morin, Benedict Neo

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 Data Science, University of San Francisco, University of San Francisco

Abstract Preview: Purpose: Large Language Models (LLMs) demonstrate strong general text comprehension but remain limited in oncology due to insufficient contextual alignment. We pilot embedding alignment through radiol...

A Two-Layer, Two-Task Prediction Model Based on 3D Imaging and Residual Networks for Mid-Chemoradiation Tumor Response Prediction on FDG PET for La-NSCLC

Authors: Stephen R. Bowen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Jing Sun, Jiajie Wang, Shouyi Wang, Faisal Yaseen, Xiaojing Zhu

Affiliation: Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Shanghai University of Electric Power, Fred Hutchinson Cancer Center, University of Texas at Arlington

Abstract Preview: Purpose: Accurate prediction of patient response to radiotherapy plays a crucial role in monitoring disease progression and assessing treatment efficacy, enabling clinicians to develop personalized th...

Addressing Radionuclide Decay Complexities in Targeted Alpha Therapy: A Computational Approach.

Authors: Denis Bergeron, Ryan P Fitzgerald, Ravneet Kaur, Rao Khan

Affiliation: John Hopkins University, NIST Radiation Physics Division, Howard University, National Institute of Standards and Technology

Abstract Preview: Purpose: There is growing interest in short-lived alpha-emitting radionuclides for cancer therapy due to their ability to selectively target and destroy cancer cells while sparing healthy tissue. Howe...

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...

Data-Driven Gating in Ga-68 PET/CT: Impacts on Patient Selection and Dosimetry Predictions in Radiopharmaceutical Therapy

Authors: Zhuo Chen, Tinsu Pan, Allan Thomas

Affiliation: Mallinckrodt Institute of Radiology, Washington University School of Medicine, WashU Medicine, UT MD Anderson Cancer Center

Abstract Preview: Purpose: Misregistration between data-driven gated (DDG)-PET and CT can limit the benefits of motion correction and improved localization and quantitation. DDG-CT offers a solution to these issues. He...

Deep Learning-Based Eye Monitoring and Tracking System for Ocular Proton Therapy in a Regular Gantry Room

Authors: David H. Abramson, Christopher Barker, Jasmine H. Francis, Meng Wei Ho, Yen-Po Lee, Haibo Lin, Hang Qi, Andy Shim, Charles B. Simone, Weihong Sun, Xiaoxuan Xu, Siyu Yang, Francis Yu, Anna Zhai

Affiliation: College of Machine Intelligence, Nankai University, New York Proton Center, Department of Biomedical Engineering, Johns Hopkins University, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Proton therapy is an effective modality for treating ocular tumors such as uveal melanoma. We developed a novel camera‐based eye‐tracking system to provide real-time noninvasive eye positioni...

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 ...

Exploring the Universal Correlation between DNA Damage Distribution and Cell Surviving Fraction in Photon, Proton, and Carbon Ion Irradiations

Authors: Lawrence F. Bronk, Fada Guan, Xun Jia, Youfang Lai, Miao Qi

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Johns Hopkins University, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: DNA double-strand breaks (DSBs) are widely regarded as critical indicators of cellular response to ionizing radiation. This study aims to establish a direct, unbiased and universal correlatio...

Feasibility Study of Deep Learning-Based MRI-to-PET Generation for Rectal Cancer: Overall Survival Prediction and Pathological Complete Response Assessment

Authors: Weigang Hu, Zhenhao Li, Jiazhou Wang, Xiaojie Yin, Zhen Zhang

Affiliation: Fudan University Shanghai Cancer Center

Abstract Preview: Purpose:
This study aims to develop and validate a novel deep learning method to generate synthetic PET images for rectal cancer from MRI data. By incorporating metabolic information from the synth...

Identifying Candidates for Endobronchial Valve (EBV) Treatment for Patients with Moderate to Severe Emphysema: Exploring the Use of Estimated Lung Elasticity Using a Biomechanically Based Model Derived from CT Image Data

Authors: Ryan Andosca, Peter Boyle, Grace Hyun Kim, Minji Victoria Kim, Michael Vincent Lauria, Michael F. McNitt-Gray, Gabriel Melendez-Corres, Jack Neylon, Brad Stiehl, Pang Yu Teng

Affiliation: David Geffen School of Medicine at UCLA, University of California, Los Angeles, UCLA Department of Radiology, Department of Radiation Oncology, University of California, Los Angeles, UCLA, Cedars-Sinai Medical Center

Abstract Preview: Purpose: Endobronchial Valves (EBV) are one of the few treatment options for patients with moderate to severe emphysema. Eligibility is typically assessed from CT image data analysis including Emphyse...

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...

Muilt-Instance Learning Model with 2D and 3D Features Representation and Transformer-Based Prediction for FDG PET Tumor Chemoradiation Response of La-NSCLC

Authors: Stephen R. Bowen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Jiajie Wang, Shouyi Wang, Faisal Yaseen, Han Zhou

Affiliation: Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Fred Hutchinson Cancer Center, University of Texas at Arlington

Abstract Preview: Purpose: Predicting the effects of the spatial-temporal tumor response to chemoradiation can assist in adjusting radiation dose and support clinical decision-making in radiotherapy. A multi-instance l...

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 Data Integration with Machine Learning for Predicting PARP Inhibitor Efficacy and Prognosis in Ovarian Cancer

Authors: Qianxi Ni, Xian Xiong

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:
Poly(ADP)-ribose polymerase inhibitors (PARPi) have brought a significant breakthrough in the maintenance treatment of ovarian cancer. However, beyond BRCA mutation/HRD, the direct impact ...

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-...

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 ...

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...

Testing the Performance of Rbe Models Using a Comprehensive Panel of Pancreatic Cancer Cell Lines

Authors: Joana Antunes, Scott James Bright, David B. Flint, Mojtaba Hoseini-Ghahfarokhi, Mandira Manandhar, Poliana Marinello, Gabriel O. Sawakuchi, Tingshi Su

Affiliation: MD Anderson Cancer Center, The University of Texas MD Anderson Cancer Center, Laboratory of Instrumentation and Experimental Particle Physics; Faculty of Science of University of Lisbon, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, MD Anderson Cancer Center

Abstract Preview: Purpose: To study the accuracy of relative biological effectiveness (RBE) models to predict the RBE of a comprehensive panel of pancreatic cancer cell lines.

Methods: Clonogenic cell survival w...

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...

Two-Stage Clustering and Auto Machine Learning to Predict Chemoradiation Response in Tumor Subregions on FDG PET for La-NSCLC

Authors: Stephen R. Bowen, Shijun Chen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Qianqian Tong, Jiajie Wang, Shouyi Wang, Faisal Yaseen

Affiliation: The University of Texas at Austin, Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Fred Hutchinson Cancer Center, University of Texas at Arlington

Abstract Preview: Purpose: Tumor subregion clustering and prediction of region-specific response can augment assessments and adaptive treatment decisions. A modeling framework was constructed to predict chemoradiation ...