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Results for "hematologic toxicity": 4 found

A Multi-Omics Approach for Predicting Acute Hematologic Toxicity in Patients with Cervical Cancer Undergoing External-Beam Radiotherapy

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...

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...

Evaluation of MR Proton Density Fat Fraction (PDFF) for Bone Marrow Protection in RT

Authors: Li Tong, Chuyan Wang, Zhengkui Wang, Yingli Yang, Jie Zhang

Affiliation: Shanghai United imaging Healthcare Advanced Technology Research Institute, Shanghai United Imaging Healthcare Co., LTD, Department of Radiology, Ruijin Hospital, Institute for Medical Imaging Technology, Ruijin Hospital

Abstract Preview: Purpose:
Pelvic radiotherapy (RT)-induced bone marrow (BM) damage affects patient prognosis by causing hematologic toxicity. However, consensus on BM-sparing (BMS) RT is still lacking, owing to the...

Predicting Hematologic Toxicity in Advanced Cervical Cancer Patients Using Interpretable Machine Learning Models Based on Radiomics and Dosimetrics

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...