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: 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: Caroline Chung, Michael Knopp, Stephen F. Kry, Hunter S. Mehrens, John Rong
Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center, University of Cincinnati
Abstract Preview: Purpose: To evaluate the variability of CT dose index (CTDIvol) and radiomics features across a large cohort of radiotherapy simulation CT scans from multiple institutions.
Methods: Three IROC phan...
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: 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...
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...
Authors: Ozan Cem Guler, William Silva Mendes, Sangbo Oh, Cem Onal, Lei Ren, Apurva Singh, Phuoc Tran
Affiliation: University of Maryland School of Medicine, Baskent University Faculty of Medicine, Department of Radiation Oncology, Department of Radiation Oncology, University of Maryland School of Medicine
Abstract Preview: Purpose: To predict metastasis-free survival (MFS) for patients with prostate adenocarcinoma treated with androgen deprivation therapy and external radiotherapy using clinical factors and radiomics ex...
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...