Authors: Christos Ilioudis, Marios Myronakis, Sotirios Raptis, Kyriaki Theodorou
Affiliation: Medical Physics Department, Medical School, University of Thessaly, Department of Information and Electronic Engineering, International Hellenic University (IHU)
Abstract Preview: Purpose: This study presents a radiomics-driven, machine learning framework developed to predict the possibility of Radiation Pneumonitis (RP), as a side effect of radiation therapy in lung cancer pat...
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...
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: 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...
Authors: Zachary Buchwald, Chih-Wei Chang, Richard L.J. Qiu, Mojtaba Safari, Hui-Kuo Shu, Lisa Sudmeier, Xiaofeng Yang, David Yu, Xiaohan Yuan
Affiliation: Emory University and Winship Cancer Institute, Emory University, Georgia Institute of Technology, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: This study proposes a novel vision-language model (VLM) to predict survival outcomes in glioblastoma (GBM) patients. By integrating multimodal MRI data and clinical information, the proposed ...
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: 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: Yuanhan Chen, Ziqiang Chen, Qi Cheng, Feng Ding, Rui Fang, Shengwen Guo, Li Hao, Qiang He, Haiquan Huang, Yu Kuang, Xinling Liang, Yuanjiang Liao, Guohui Liu, Chen Lu, Qingquan Luo, Jing Sun, Yanhua Wu, Zhen Xie, Qin Zhang, Lang Zhou
Affiliation: South China University of Technology, Dongguan people's hospital, Sichuan Provincial People's Hospital, People’s Hospital of Xinjiang Uygur Autonomous Region, Second Hospital of Anhui Medical University, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong Easy Life Information Technology Co., Ltd, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Medical Physics Program, University of Nevada, Second Hospital of Jilin University, Chongqing Ninth People's Hospital
Abstract Preview: Purpose: Acute kidney injury (AKI) is a global healthcare issue with a rapid onset and severe consequences. Repeated measurement of serum creatinine (SCr) levels, a clinical standard of care, is cruci...
Authors: Jee Suk Chang, Hojin Kim, Jin Sung Kim, Jaehyun Seok
Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Integrative Medicine
Abstract Preview: Purpose: This study aims to leverage 3D dose distribution data to develop a machine learning model capable of accurately predicting lymphedema occurrence in patients undergoing 3D conformal radiation ...
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...
Authors: Jean Bourbeau, Jim Hogg, Miranda Kirby, Meghan Koo, Kalysta Makimoto, Wan Tan
Affiliation: Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Toronto Metropolitan University, Centre for Heart Lung Innovation, University of British Columbia
Abstract Preview: Purpose: Chronic obstructive pulmonary disease (COPD) exacerbations are burdensome to patients and healthcare systems. CT imaging-derived measures of emphysema and airway remodeling have been shown to...
Authors: David J. Carlson, Ming Chao, Tian Liu, Yong Hum Na, Kenneth E Rosenzweig, Robert Samstein, Lewis Tomalin
Affiliation: Icahn School of Medicine at Mount Sinai, Yale University School of Medicine, Department of Therapeutic Radiology, Yale University School of Medicine
Abstract Preview: Purpose: To investigate the potential of regional radiomic features extracted from five lung sub-lobes on pre-treatment CT as biomarkers for predicting radiation pneumonitis (RP) using machine learnin...
Authors: Fangfen Dong, Jiaming Li, Xiaobo Li, Weipei Wang, Zhixin Wang, Bing Wu, Benhua Xu, Yong Yang, Yifa Zhao
Affiliation: Department of Radiation Oncology, Fujian Medical University Union Hospital/Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors/Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Hematologi, Zhangpu County Hospital, School of Medical Imaging, Fujian Medical University
Abstract Preview: Purpose: To explore the construction and clinical application value of a deep learning-based positioning error prediction model, providing a reference for optimizing iSCOUT system-guided precision rad...
Authors: Amar Kishan, Jun Lian, Yunkui Pang, Jonathan Pham, X. Sharon Qi, Michael Steinberg, Luca F Valle, Pew-Thian Yap
Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of North Carolina at Chapel Hill
Abstract Preview: Purpose: Stereotactic body radiotherapy (SBRT) is a highly effective treatment for prostate cancer, yet predicting genitourinary (GU) toxicity has primarily relied on planned dosimetry. This study inv...
Authors: Lian Duan, Stephen F. Kry, Hunter S. Mehrens, Christine Peterson, Paige A. Taylor
Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center
Abstract Preview: Purpose: To develop predictive models for IROC SRS head phantom audits and to identify important factors influencing institutional performance.
Methods: The IROC SRS head phantom includes two TLDs ...
Authors: Ameer Elaimy, Theodore Lawrence, Charles S. Mayo, Seyyedeh Azar Oliaei Motlagh, Benjamin S. Rosen
Affiliation: University of Michigan
Abstract Preview: Purpose: To analyze the impact of clinical features on short-term survival, toxicity, and poor outcomes in HCC patients treated with SBRT,using automated data aggregation and enhanced algorithms with ...
Authors: Li Chen, Shouliang Ding, Xiaoyan Huang, Lecheng Jia, Hua Li, Hongdong Liu, Yanfei Liu, Zun Piao, Guangyu Wang
Affiliation: State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Shenzhen United Imaging Research Institute of Innovative Medical Equipment
Abstract Preview: Purpose: Optimal radiotherapy planning is challenging, influenced by anatomical factors such as surrounding organs and tumor characteristics, which complicate dose distribution and target coverage. Wh...
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...
Authors: Cem Altunbas, Farhang Bayat, Roy Bliley, Brian Kavanagh, Uttam Pyakurel, Tyler Robin, Ryan Sabounchi
Affiliation: Department of Radiation Oncology, University of Colorado School of Medicine, Taussig Cancer Center, Cleveland Clinic
Abstract Preview: Purpose: The use of image features extracted from serial CBCT images to assess radiotherapy response and toxicity is an active research area. However, poor image quality often compromises reliability ...
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...
Authors: Ling Chen, Alexei V. Trofimov, Yi Wang, Dufan Wu
Affiliation: Massachusetts General Hospital, MGH
Abstract Preview: Purpose:
Selecting gaze angles of the eye is an important part of set-up of proton therapy for ocular tumors, ensuring that the treatment beam could irradiate the tumor while maximally sparing impo...
Authors: Mark Ashamalla, Renee Farrell, Jinkoo Kim, Kartik Mani, Xin Qian, Samuel Ryu, Yizhou Zhao
Affiliation: Stony Brook Medicine, Stony Brook University Hospital
Abstract Preview: Purpose: Adaptive planning is increasingly used in head and neck radiation therapy due to factors like tumor response or changes in patient anatomy. However, methods such as resimulation or offline re...
Authors: Ara Alexandrian, Sadiki Daniel
Affiliation: Louisiana State University, Mary Bird Perkins Cancer Center
Abstract Preview: Purpose: To develop a learning-to-optimize machine learning model that accelerates optimization in VMAT treatment planning by training on prostate patient data.
Methods: A treatment plan dataset of...
Authors: Akihiro Haga, Ren Iwasaki, Kenya Kusunose, Makoto Miyake, Kenji Moriuchi, Yasuharu Takeda, Hidekazu Tanaka, Hirotsugu Yamada
Affiliation: Department of Cardiovascular Medicine, Nephrology, and Neurology Graduate School of Medicine, University of the Ryukyus, Graduate School of Biomedical Sciences, Tokushima University, Tokushima university, Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Department of Cardiology, Tenri Hospital, Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Division of Heart Failure, Department of Heart Failure and Transplant, National Cerebral and Cardiovascular Center
Abstract Preview: Purpose: Device dependency is a significant challenge in medical AI, potentially limiting generalization performance. This study aimed to develop a robust deep learning model for predicting left ventr...
Authors: Yufeng Cao, Luigi Marchionni, William Silva Mendes, Cem Onal, Lei Ren, Amit Sawant, Nicole L Simone, Philip Sutera, Phuoc Tran
Affiliation: University of Maryland School of Medicine, 9Department of Radiation Oncology, Thomas Jefferson University, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, University of Maryland, Baltimore, Baskent University Faculty of Medicine, Department of Radiation Oncology, Department of Radiation Oncology, University of Maryland School of Medicine, Maryland University Baltimore, 8Department of Pathology and Laboratory Medicine, Weill Cornell Medicine
Abstract Preview: Purpose: This study aims to predict 2-yr Metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) patients treated by metastasis-directed therapy (MDT) by devel...
Authors: Clint Bahler, Ruchika Reddy Chimmula, Harrison Louis Love, Oluwaseyi Oderinde, Courtney Yong
Affiliation: Purdue University, Department of Urology, Indiana University School of Medicine, Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, School of Health Sciences, Purdue University
Abstract Preview: Purpose: Prostate cancer (PCa) is a common malignancy in men, and predicting biochemical recurrence (BCR) is crucial for guiding treatment decisions. Integrating multimodal data, including clinical, i...
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...
Authors: Mojtaba Behzadipour, Suman Gautam, Tianjun Ma, Ikchit Singh Sangha, Bongyong Song, William Song, Kumari Sunidhi
Affiliation: UC San Diego, Virginia Commonwealth University
Abstract Preview: Purpose: This study aims to develop a knowledge-based voxel-wise dose prediction system using a convolutional neural network (CNN) for high-dose-rate (HDR) prostate brachytherapy and to evaluate its p...
Authors: John Byun, Steven D Chang, Mingli Chen, Cynthia Chuang, Xuejun Gu, Melanie Hayden Gephart, Yusuke Hori, Hao Jiang, Mahdieh Kazemimoghadam, Fred Lam, Gordon Li, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang, Qingying Wang, Zi Yang, Xianghua Ye, Kangning Zhang
Affiliation: Department of Radiation Oncology, Stanford University, Department of Neurosurgery, Stanford University, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: Accurate prediction of pain relief is crucial in determining the clinical effectiveness of Stereotactic body radiotherapy (SBRT) regimen for spine metastases. We propose a deep-learning frame...
Authors: Liyuan Chen, Meixu Chen, Bowen Jing, Sepeadeh Radpour, Erich Josef Schmitz, David Sher, Jing Wang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Prospective clinical trials have shown that involved nodal radiation therapy (INRT) can substantially improve patients’ quality of life without increasing the risk of elective nodal failure. ...
Authors: Rico Castillo, Katherine Gonzalez, Casey C. Heirman, Kyle J. Lafata, Casey Y. Lee, Xiang Li, Yvonne M Mowery, Yvonne M Mowery, Daniel Murphy, Allison Pittman, Ashlyn G. Rickard
Affiliation: Duke University, Department of Radiation Oncology, Duke University, University of Pittsburgh
Abstract Preview: Purpose: To evaluate the ability of a deep learning model to identify pathomic features in lymph nodes of preclinical head and neck squamous cell carcinoma (HNSCC) models as surrogates for predicting ...
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 ...
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...
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 ...
Authors: Yeona Cho, Chloe Min Seo Choi, Joseph O. Deasy, Jue Jiang, Jihun Kim, Jin Sung Kim, Nikhil Mankuzhy, Aneesh Rangnekar, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu
Affiliation: University of Freibrug, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Memorial Sloan Kettering Cancer Center, Yonsei University
Abstract Preview: Purpose: We hypothesized that combining clinical, imaging, and radiotherapy dose-distribution features could increase predictive model accuracy in radiation-induced severe acute esophagitis (SAE) in e...
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: Hilary P Bagshaw, Mark K Buyyounouski, Xianjin Dai, PhD, Praveenbalaji Rajendran, Lei Xing, Yong Yang
Affiliation: Department of Radiation Oncology, Stanford University, Massachusetts General Hospital, Harvard Medical School
Abstract Preview: Purpose: Artificial intelligence (AI)-driven methods have transformed dose prediction, streamlining the automation of radiotherapy treatment planning. However, traditional approaches depend exclusivel...
Authors: John Ginn, Zhuoyun Huang, Yongbok Kim, Ke Lu, Chunhao Wang, Yibo Xie, Zhenyu Yang, Jingtong Zhao
Affiliation: Duke University, Duke Kunshan University
Abstract Preview: Purpose: This study aims to develop and validate a machine learning model for predicting V60%, a critical dosimetric metric in LINAC-based Single-Isocenter-Multiple-Targets (SIMT) stereotactic radiosu...
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...
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: Lian Duan, Stephen F. Kry, Hunter S. Mehrens, Paige A. Taylor
Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center
Abstract Preview: Purpose: To develop a machine learning model for predicting dose delivery accuracy and identifying its key factors in IROC’s proton phantom program.
Methods: IROC’s proton QA program has six proton...
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...
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...
Authors: Lu Jiang, Ke Sheng
Affiliation: Department of Radiation Oncology, University of California at San Francisco, Department of Radiation Oncology, University of California, San Francisco
Abstract Preview: Purpose:
Conventional radiotherapy treatment planning is guided by a set of generic objectives that are unspecific to patient anatomy. Treatment planning thus heavily relies on the planner’s experi...
Authors: Matthew D Blackledge, Christopher M. Nutting, Anju Mohanan Kaimal, Justine Tyler, Konstantinos Zormpas-Petridis
Affiliation: Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Institute of Cancer Research, The Royal Marsden NHS Foundation Trust, The Institute of Cancer Research
Abstract Preview: Purpose: Swallowing dysfunction (dysphagia) is a common side effect of radiotherapy for oropharyngeal cancer, significantly affecting patient's quality of life. This study aims to investigate the rela...