Feasibility of a Smartphone Application for Education and Symptom Management of Patients With Renal Cell Carcinoma on Combined Tyrosine Kinase and Immune Checkpoint Inhibitors [0.03%]
智能手机应用程序对接受靶免联合治疗的肾细胞癌患者教育及症状管理的可行性研究
Gabriel Roman Souza,Kea Turner,Keerthi Gullapalli et al.
Gabriel Roman Souza et al.
Purpose: Patients with advanced renal cell carcinoma (RCC) face significant challenges, stemming both from the complexities of the disease itself and the adverse effects of treatments. This study evaluated the feasibility...
Identification of Novel DNA Methylation Prognostic Biomarkers for AML With Normal Cytogenetics [0.03%]
正常细胞遗传学的急性髓系白血病的新DNA甲基化预后生物标志物识别
Cândida Cardoso,Daniel Pestana,Sreemol Gokuladhas et al.
Cândida Cardoso et al.
Purpose: AML is a hematologic cancer that is clinically heterogeneous, with a wide range of clinical outcomes. DNA methylation changes are a hallmark of AML but are not routinely used as a criterion for risk stratificatio...
Assessing Patient Perspectives and the Health Equity of a Digital Cancer Symptom Remote Monitoring and Management System [0.03%]
评估数字癌症症状远程监测和管理系统的患者视角和卫生公平性
Kathi Mooney,Susan L Beck,Christina Wilson et al.
Kathi Mooney et al.
Purpose: People with cancer experience poorly controlled symptoms that persist between treatment visits. Automated digital technology can remotely monitor and facilitate symptom management at home. Essential to digital in...
Randomized Controlled Trial
JCO clinical cancer informatics. 2024 Jul:8:e2300243. DOI:10.1200/CCI.23.00243 2024
Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System [0.03%]
退伍军人医疗系统中用于从肿瘤记录中提取多发性骨髓瘤分期的自然语言处理算法
Sergey D Goryachev,Cenk Yildirim,Clark DuMontier et al.
Sergey D Goryachev et al.
Purpose: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to e...
Potential Role of Generative Adversarial Networks in Enhancing Brain Tumors [0.03%]
生成对抗网络在增强脑肿瘤方面的潜在作用
Amr Muhammed,Rafaat A Bakheet,Karam Kenawy et al.
Amr Muhammed et al.
Purpose: Contrast enhancement is necessary for visualizing, diagnosing, and treating brain tumors. Through this study, we aimed to examine the potential role of general adversarial neural networks in generating artificial...
Predicting Benefit From FOLFOXIRI Plus Bevacizumab in Patients With Metastatic Colorectal Cancer [0.03%]
预测贝伐单抗联合FOLFOXIRI治疗转移性结直肠癌患者的效果
Marinde J G Bond,Maarten van Smeden,Koen Degeling et al.
Marinde J G Bond et al.
Purpose: Patient outcomes may differ from randomized trial averages. We aimed to predict benefit from FOLFOXIRI versus infusional fluorouracil, leucovorin, and oxaliplatin/fluorouracil, leucovorin, and irinotecan (FOLFOX/...
Development of a Web-Based Interactive Tool for Visualizing Breast Cancer Clinical Trial Tolerability Data [0.03%]
一种基于Web的交互式工具的发展，用于可视化乳腺癌临床试验耐受性数据
Michael Luu,Gillian Gresham,Lynn Henry et al.
Michael Luu et al.
Purpose: Longitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we dev...
External Validation and Update of the Risk Prediction Model for Denosumab-Induced Hypocalcemia Developed From a Hospital-Based Administrative Database [0.03%]
基于医院行政数据库的Denosumab致低钙血症风险预测模型的外部验证及更新研究
Keisuke Ikegami,Shungo Imai,Osamu Yasumuro et al.
Keisuke Ikegami et al.
Purpose: Denosumab is used to treat patients with bone metastasis from solid tumors, but sometimes causes severe hypocalcemia, so careful clinical management is important. This study aims to externally validate our previo...
Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records [0.03%]
基于深度学习的电子健康记录中卵巢癌患者真实世界静脉血栓栓塞症的动态风险预测模型研究
Dahhay Lee,Seongyoon Kim,Sanghee Lee et al.
Dahhay Lee et al.
Purpose: Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few m...
Clinical Validation of Mathematically Derived Early Tumor Dynamics for Solid Tumors in Response to Durvalumab [0.03%]
验证德瓦鲁单抗治疗实体瘤时早期肿瘤动力学的临床价值
Qin Li,Vittorio Cristini,Ashok Gupta et al.
Qin Li et al.
Purpose: Early prediction of response to immunotherapy may help guide patient management by identifying resistance to treatment and allowing adaptation of therapies. This analysis evaluated a mathematical model of respons...
Clinical Trial
JCO clinical cancer informatics. 2024 Jul:8:e2300254. DOI:10.1200/CCI.23.00254 2024