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Results for "radiomics adc": 34 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 Multi-Omics Approach for Predicting Acute Hematologic Toxicity in Patients with Cervical Cancer Undergoing External-Beam Radiotherapy

Authors: Sijuan Huang, Yongbao Li

Affiliation: Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Sun-Yat sen University Cancer Center

Abstract Preview: Purpose: Hematologic toxicity (HT) is one of the most prevalent treatment-related toxicities experienced by locally advanced cervical cancer (LACC) patients receiving radiotherapy (RT). This study aim...

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...

A Novel Feature Selection Method for Survival Prediction of Head-and-Neck Following Radiation Therapy

Authors: Xiaoying Pan, X. Sharon Qi

Affiliation: Department of Radiation Oncology, University of California, Los Angeles, School of Computer Science and technology,Xi'an University of Posts and Telecommunications

Abstract Preview: Purpose:
Survival prediction for cancer presents a substantial hurdle in personalized oncology, due to intricate, high-dimensional medical data. Our study introduces an innovative feature selection...

A Radiomic Quantification Framework for Hyperparameter Optimization in Texture Characterization

Authors: Yuli Lu, Chendong Ni, Cheng Qian, Kun Qian, Weiwei Sang, Chunhao Wang, Fan Xia, Zhenyu Yang, Fang-Fang Yin, Rihui Zhang, Haiming Zhu

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

Abstract Preview: Purpose: To develop a radiomic quantification framework to evaluate the effects of radiomic image preprocessing hyperparameters (i.e., image resampling and discretization) on texture characterization ...

A Radiomics and Dosomics-Based Approach for Predicting Hematologic Toxicity in Patients with Cervical or Endometrial Cancer

Authors: Yongrui Bai, Xuming Chen, Yong Liu, Xiumei Ma

Affiliation: Department of Radiation Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, Department of Radiation Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Abstract Preview: Purpose: Hematologic toxicity (HT) is a common complication in patients with cervical or endometrial cancer. This study aims to develop a precise predictive model for acute HT in patients with cervica...

An Image Representation of Radiomics Data for Enhanced Deep Radiomics Analysis with Consideration of Feature Interactions

Authors: Xiaolong Fu, Runping Hou, Md Tauhidul Islam, Lei Xing

Affiliation: Department of Radiation Oncology, Stanford University, Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine

Abstract Preview: Purpose: To introduce a novel schematic image representation of radiomics data, called OmicsMap, for high-performance deep radiomics analysis. OmicsMap transforms tabular radiomics data into an image ...

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 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...

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...

CBCT-Based Radiomics of Head and Neck Cancer for Predicting Patient Toxicity to Radiotherapy

Authors: Rodrigo Delgadillo, Nesrin Dogan, Benjamin J. Rich, Stuart E Samuels, Levent Sensoy

Affiliation: University of Miami Sylvester Comprehensive Cancer Center

Abstract Preview: Purpose: Daily Cone beam CT (CBCT) images may be useful in detecting early morphological changes during head and neck cancer radiotherapy. The aim of this study was to evaluate the performance of CBCT...

Comparison of Radiomic Feature Normalizations, Feature Selection, and Modeling with Different Datasets

Authors: Eric N Carver, Julia Marks

Affiliation: Brown University

Abstract Preview: Purpose: The clinical applicability of radiomic features is hindered by challenges in stability and reproducibility. To address this, researchers are establishing image and feature standardizations an...

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...

Evaluating Supervised Learning Models for Binary Classification of Radiomic Data in Predicting Head and Neck Cancer Treatment Outcomes

Authors: Theodore Higgins Arsenault, Kyle O'Carroll, Christian Erik Petersen, Alex T. Price, Meiying Xing

Affiliation: University Hospitals Seidman Cancer Center

Abstract Preview: Purpose: To assess the performance of various supervised learning models’ ability to predict binary classification of radiomic data for head and neck (H&N) cancer treatment outcomes.
Methods: Using...

Evaluating the Impact of Contour Variability on the Effectiveness of Deep Learning Features in Head and Neck Imaging

Authors: Hania A. Al-Hallaq, Xuxin Chen, Anees H. Dhabaan, Elahheh (Ella) Salari, Xiaofeng Yang

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

Abstract Preview: Purpose:
Radiomics image analysis could lead to the development of predictive signatures and personalized radiotherapy treatments. However, variations in delineation are known to affect hand-crafte...

Feasibility of Developing a Radiomic Fingerprint to Predict Pulmonary Embolism Clot Types to Aid in Determining Intervention for Intermediate-Risk Patients.

Authors: Lindsay Hammons, Lisa Baumann Kreuziger, Haidy G. Nasief, Matthew Scheidt, Farrell Sean, Antonio Sosa Lozano

Affiliation: Division of Hematology and Oncology, University of Washington, Vascular and Interventional Radiology, Medical college of wisconsin, Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: Venous thromboembolism, which includes pulmonary embolism (PE), is the third leading cause of acute cardiovascular syndrome behind myocardial infarction and stroke. Current research categoriz...

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-Based Feature Selection to Improve Stability and Reproducibility of CT-Based Radiomics in Head and Neck Squamous Cell Carcinoma: A Cross-Institutional Study

Authors: Daria Gaykalova, Ranee Mehra, Jason K Molitoris, Hajar Moradmand, Lei Ren, Amit Sawant, Phuoc Tran

Affiliation: University of Maryland School of Medicine, Maryland University Baltimore, University of Maryland, Department of Radiation Oncology, University of Maryland School of Medicine

Abstract Preview: Purpose: Radiomics extracts quantitative imaging biomarkers from medical images. However, maintaining the reproducibility and stability of selected features across institutions and parameter settings ...

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 Radiomics and ADC Ratio for Multicenter Prostate Cancer Diagnosis: A Harmonized Machine Learning Approach

Authors: George Agrotis, Marios Myronakis, Dimitrios Samaras, Kyriaki Theodorou, Ioannis Tsougos, Vassilios Tzortzis, Maria Vakalopoulou, Alexandros Vamvakas, Aikaterini Vassiou, Marianna Vlychou

Affiliation: Medical Physics Department, Medical School, University of Thessaly, Department of Radiology, University of Thessaly, Netherland Cancer Institute, Department of Urology, University of Thessaly, CentraleSupelec, University Paris-Saclay

Abstract Preview: Purpose: Prostate cancer (PCa) diagnosis remains challenging due to discrepancies in Gleason Scoring (GS) and risks of overdiagnosis and underdiagnosis. Multiparametric MRI (mpMRI), including Apparent...

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...

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...

Multi-Omics-Based Prognostic Prediction for Locally Advanced Hypopharyngeal Cancer Treated with Postoperative Chemoradiotherapy: A Dual-Center Study

Authors: Sixue Dong, Chaosu Hu, Weigang Hu, Xiaomin Ou, Jiazhou Wang, Zhen Zhang

Affiliation: Fudan University Shanghai Cancer Center

Abstract Preview: Purpose:
This study aimed to predict the PFS of the patients who were diagnosed with hypopharyngeal cancer and received postoperative chemoradiotherapy by using multi-omics which integrating clinic...

Multi-Region Multiomic Features Improve Random Forest Toxicity Modeling of Radiation Pneumonitis

Authors: Laurence Edward Court, Alexandra Olivia Leone, Zhongxing Liao, Saurabh Shashikumar Nair, Joshua S. Niedzielski, Ramon Maurilio Salazar, Ting Xu

Affiliation: The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: Radiation Pneumonitis (RP) predictive models often rely on clinical and DVH parameters, but multiomic features from CT imaging and 3D dose distributions from various regions could provide add...

Nomogram Based on Interpretable Multiregional Radiomics of Cone-Beam Breast CT and Clinicopathologic Features for Predicting FISH Status in HER2 2+ Breast Cancer to Differentiate HER2-Low from -Positive: A Multi-Center Study

Authors: Keyi Bian, Marco Caballo, Wenxiu Guo, Haijie Li, Jiao Li, Aidi Liu, Yue Ma, Ioannis Sechopoulos, Yafei Wang, Yaopan Wu, Zhaoxiang Ye, Yuwei Zhang, Yueqiang Zhu, Daan van den Oever

Affiliation: Radboud University Medical Center, Tianjin Medical University Cancer Institute & Hospital, Sun Yat-Sen University Cancer Center

Abstract Preview: Purpose: To develop and validate a nomogram integrating intra- and peritumoral radiomics of contrast-enhanced cone-beam breast CT (CE-CBBCT) and clinicopathologic features for predicting fluorescence ...

PET Imaging and Novel Cardiac Radiomics to Predict Pre-Radiotherapy Cardiac Conditions for Lung Cancer Patients Undergoing Radiotherapy.

Authors: Wookjin Choi, Michael Dichmann, Adam Dicker, Nilanjan Haldar, Yingcui Jia, Nicole L Simone, Eugene Storozynsky, Yevgeniy Vinogradskiy, Maria Werner-Wasik

Affiliation: Thomas Jefferson University, 9Department of Radiation Oncology, Thomas Jefferson University

Abstract Preview: Purpose: Cardiotoxicity remains a significant limitation for lung cancer patients treated with radiotherapy. Pre-radiotherapy cardiac conditions increase the probability of patients developing cardiot...

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...

Predicting Hormone Receptor Status in Breast Cancer Using Mammographic and Sonographic Data and Machine Learning Models

Authors: Zahra Bagherpour, Manijeh Beigi, Pedram Fadavi, Faraz Kalantari, Moghadaseh Khaleghibizaki, Hengameh Nazari, Mojtaba Safari, Sepideh Soltani

Affiliation: Department of Radiation Oncology, School of Medicine, Iran University of Medical Sciences, Department of Radiation Oncology, School of Medicine, Emory University and Winship Cancer Institute, Department of Radiation Oncology, Iran University of Medical Sciences, University of Arkansas for medical sciences, Department of Radiation physics, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences

Abstract Preview: Purpose: This study aims to evaluate whether readily available mammographic and sonographic data, combined with machine learning (ML) models, can predict critical molecular factors (ER, PR, HER2) in b...

Prediction of Vertebral Compression Fracture after Stereotactic Body Radiotherapy for Spinal Metastases Using Clinical, Radiomic and Dosiomic Features

Authors: Yukio Fujita, Syoma Ide, Kei Ito, Tomohiro Kajikawa, Satoshi Kito, Keiko Murofushi, Yujiro Nakajima, Yuhi Suda, Kentaro Taguchi, Naoki Tohyama, Fumiya Tsurumaki

Affiliation: Komazawa University Graduate School, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Department of Radiology, Kyoto Prefectural University of Medicine

Abstract Preview: Purpose: Stereotactic body radiotherapy (SBRT) for spine metastases is more effective for pain relief and local control than conventional radiotherapy. However, it is associated with vertebral compres...

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...

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 ...

Utilizing Multiple Modalities to Improve Models to Predict Changes in International Prostate Score for Prostate Cancer

Authors: Matthew C Abramowitz, Alan Dal Pra, Rodrigo Delgadillo, Nesrin Dogan, John C. Ford, Kyle R. Padgett, Levent Sensoy, Benjamin Spieler, Matthew T. Studenski, Jace Allen Walker

Affiliation: University of Miami, Department of Radiation Oncology, University of Miami, University of Miami Sylvester Comprehensive Cancer Center, University of Miami School of Medicine

Abstract Preview: Purpose:
Toxicities that affect a patient’s quality-of-life due to prostate cancer (pCa) radiation therapy (RT) are receiving more attention as RT has become increasingly successful in treating pCA...