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Results for "lasso regression": 10 found

A Combination of Radiomics and Dosiomics for Gross Tumor Volume Regression in 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 novel ablative radiation dosing scheme developed by our institution. This study aims to establish a regression...

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...

AI-Driven Drug Discovery through an Interactive Analysis of Radiomics and Biological Insights in Glioblastoma

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...

Binary Classification of Lymphedema in 3DCRT Patients Using Machine Learning on 3D Dose Distribution Data

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 ...

Developing a Comprehensive Multi-Modal Framework for Population-Scale Liver Volumetry: Insights and Predictive Models

Authors: Mustafa Bashir, Diana Kadi, Kyle J. Lafata, Jacob A. Macdonald, Mark Martin, Yuqi Wang, Marilyn Yamamoto

Affiliation: Duke University, Department of Radiation Oncology, Duke University, Department of Electrical and Computer Engineering, Duke University, Department of Radiology, Duke Unversity

Abstract Preview: Purpose: To develop a high-throughput, automated-data-interrogation pipeline for integrating imaging and clinical information to identify key determinants of liver volume (LV), enabling population-sca...

Development of a Comprehensive Thoracic Re-Irradiation Database and Investigation of Time-Dependent Dose-Recovery Dynamics for Toxicity Modeling

Authors: Victoria Doss, Tsion Gebre, Rachel B. Ger, Esi A Hagan, Elaina Hales, Russell K Hales, Xun Jia, Heng Li, Dezhi Liu, Todd R. McNutt, Meti Negassa, Anas Obaideen, Tinker Trent, K. Ranh Voong, Cecilia FPM de Sousa

Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Johns Hopkins University

Abstract Preview: Purpose: As cancer care advances, more patients require re-irradiation, yet evidence-based data is lacking. This study aimed to develop a thoracic re-irradiation database and explore time-dependent re...

Early GU Toxicity Prediction in Prostate SBRT Using Delivered Dosimetry Via Long Short-Term Memory Model

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...

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...

Multimodal Framework for Predicting Radiation-Induced Severe Acute Esophagitis in Esophageal Cancer

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...

Using Machine Learning to Predict Esophagitis Risk in Lung Cancer Radiotherapy Based on Clinical and Dosimetric Factors

Authors: Ibtisam Almajnooni, Siyong Kim, Nathaniel Miller, Elisabeth Weiss, Lulin Yuan

Affiliation: Virginia Commonwealth University

Abstract Preview: Purpose: Radiation-induced esophagitis (RE) is a common concern in lung cancer IMRT. Recent studies have indicated that the risk of radiation side effects varies greatly with patients’ baseline clinic...