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: 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...
Authors: Amir Abdollahi, Oliver Jäkel, Maxmillian Knoll, Rakshana Murugan, Adithya Raman, Patrick Salome
Affiliation: UKHD & DKFZ, Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), German Cancer Research Centre(DKFZ), DKFZ, MGH
Abstract Preview: Purpose:
Missing MRI sequences, due to technical issues in data handling or clinical constraints like contrast agent intolerance, limit the use of medical imaging datasets in computational analysis...
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: 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...
Authors: William F.B Igoniye, Belema Manuel, Christopher F. Njeh, O Ray-offor
Affiliation: Indiana University School of Medicine, Department of Radiation Oncology, Department of Radiology, University of Port Harcourt Teaching Hospital
Abstract Preview: Purpose: The accurate and efficient categorization of brain tumors is essential for effective treatment planning and improved patient outcomes. Current MRI-based diagnostic methods are time-intensive ...
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...
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: John Ginn, Chenlu Qin, Deshan Yang
Affiliation: Duke University, Department of Radiation Oncology, Duke University
Abstract Preview: Purpose: Clinical implementation of auto-segmentation tools has been hindered by poor interpretability and generalizability of AI models, necessitating the development of automated contour quality ass...
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...
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...