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Results for "normalization selection": 5 found

A Radiomics and Dosomics-Based Approach for Predicting Hematologic Toxicity in Patients with Cervical or Endometrial Cancer

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...

Automated Framework for Predicting Tumour Growth in Vestibular Schwannomas Using Contrast-Enhanced T1-Weighted MRI

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...

Comparison of Radiomic Feature Normalizations, Feature Selection, and Modeling with Different Datasets

Authors: Eric N Carver, Julia Marks

Affiliation: Brown University

Abstract Preview: Purpose: The clinical applicability of radiomic features is hindered by challenges in stability and reproducibility. To address this, researchers are establishing image and feature standardizations an...

Graph-Based Feature Selection to Improve Stability and Reproducibility of CT-Based Radiomics in Head and Neck Squamous Cell Carcinoma: A Cross-Institutional Study

Authors: Daria Gaykalova, Ranee Mehra, Jason K Molitoris, Hajar Moradmand, Lei Ren, Amit Sawant, Phuoc Tran

Affiliation: University of Maryland School of Medicine, Maryland University Baltimore, University of Maryland, Department of Radiation Oncology, University of Maryland School of Medicine

Abstract Preview: Purpose: Radiomics extracts quantitative imaging biomarkers from medical images. However, maintaining the reproducibility and stability of selected features across institutions and parameter settings ...

Insights into Deep Learning Auto-Segmentation for Abdominal Organs in MR-Guided Adaptive Radiation Therapy: A Single-Institution CT-MR Comparison

Authors: Asma Amjad, Renae Conlin, Eric S. Paulson, Christina M. Sarosiek

Affiliation: Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose:
MR-guided adaptive radiation therapy (MRgART) is transforming clinical workflows, requiring fast, accurate organs-at-risk (OARs) contouring. While deep learning auto-segmentation (DLAS) of...