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...
Authors: Todd A Aguilera, Gaurav Khatri, Jiaqi Liu, Hao Peng, Nina N. Sanford, Robert Timmerman, Haozhao Zhang
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, UT southwestern medical center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
This study first integrates 3D topological data analysis with radiomics from local advanced rectal cancer T2-weighted MRI to evaluate therapeutic responses and quantify treatment-induced c...
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...
Authors: Nobuki Imano, Yuzuha Kadooka, Daisuke Kawahara, Misato Kishi, Yuji Murakami, Shumpei Onishi
Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Graduate School of Biomedical and Health Sciences, Hiroshima University, Department of Neurosurgery, Hiroshima University Hospital
Abstract Preview: Purpose: Radiomics has proven useful in predicting overall survival in glioblastoma (GBM) patients, but consistent molecular correlations remain unidentified, leaving its biological basis unclear. Thi...
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 ...
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...
Authors: Pradeep Bhetwal, Yingxuan Chen, Wookjin Choi, Michael Dichmann, Adam Dicker, Rupesh Ghimire, Yevgeniy Vinogradskiy, Maria Werner-Wasik
Affiliation: Thomas Jefferson University
Abstract Preview: Purpose: Radiomics has emerged as a powerful tool in medical research. However, the lack of standardized and reproducible pipelines limits its clinical adoption. This study developed a robust and scal...
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...
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...
Authors: Wilfred R Furtado, Gary Y. Ge, James Lee, Jie Zhang
Affiliation: University of Kentucky
Abstract Preview: Purpose: Despite advancements in Artificial Intelligence (AI) and its growing role in clinical practices like radiology, formal AI education remains limited in medical training. This gap contributes t...
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...
Authors: Todd A Aguilera, Bassel Dawod, Sebastian Diegeler, Eslam Elghonaimy, Purva Gopal, Jiaqi Liu, Hao Peng, Arely Perez Rodriguez, Nina N. Sanford, Robert Timmerman, Megan B Wachsmann, 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, UT Southwestern Medical Center
Abstract Preview: Purpose: This study pioneers the integration of CODEX (co-detection by indexing)-based spatial profiling and advanced computational techniques to investigate the tumor immune microenvironment (TIME) i...
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...
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...
Authors: Martin Frank, Oliver Jäkel, Niklas Wahl
Affiliation: Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Karlsruhe Institute of Technology (KIT)
Abstract Preview: Purpose: Machine learning (ML) models on normal tissue complication and tumor control probability ((N)TCP) exploiting e.g. dosiomic and radiomic features are playing an increasingly important role in ...
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 ...
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...
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...