Evaluating Cancer Screening in the Era of Advanced Causal Inference Methods: Innovation, Adherence, and Health Equity Considerations [0.03%]
先进因果推断方法时代的癌症筛查评价：创新、依从性与卫生公平性考量
Rebecca A Miksad,Somnath Sarkar
Rebecca A Miksad
ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach [0.03%]
IMPACT项目：利用人工智能改善维多利亚临床试验的可及性
Maria L Bechelli,Kris Ivanova,Suan Siang Tan et al.
Maria L Bechelli et al.
Purpose: Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to use artificial intelligence (AI) applied in the Victorian Cancer Regist...
Development and Validation of Emoji Response Scales for Assessing Patient-Reported Outcomes [0.03%]
表情符号响应量表的开发及验证：评估患者报告结果的新工具
Carrie A Thompson,Paul J Novotny,Kathleen Yost et al.
Carrie A Thompson et al.
Purpose: Emoji are digital images or icons used to express an idea or emotion in electronic communication. The purpose of this study was to develop and evaluate the psychometric properties of two patient-reported scales t...
Explainable Machine Learning to Predict Treatment Response in Advanced Non-Small Cell Lung Cancer [0.03%]
解释性机器学习在预测晚期非小细胞肺癌治疗反应中的应用
Vinayak S Ahluwalia,Ravi B Parikh
Vinayak S Ahluwalia
Purpose: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients...
Feasibility and Acceptability of Collecting Passive Smartphone Data for Potential Use in Digital Phenotyping Among Family Caregivers and Patients With Advanced Cancer [0.03%]
在家庭护理人员和晚期癌症患者中收集被动智能手机数据以供数字表型研究使用的方法可行性和接受度研究
J Nicholas Odom,Kyungmi Lee,Erin R Currie et al.
J Nicholas Odom et al.
Purpose: Modeling passively collected smartphone sensor data (called digital phenotyping) has the potential to detect distress among family caregivers and patients with advanced cancer and could lead to novel clinical mod...
Observational Study
JCO clinical cancer informatics. 2025 Jan:9:e2400201. DOI:10.1200/CCI-24-00201 2025
Machine Learning to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer Undergoing Neoadjuvant Systemic Treatment [0.03%]
预测新辅助系统治疗乳腺癌患者发生临床意义毒性个体风险的机器学习模型
Lie Cai,Thomas M Deutsch,Chris Sidey-Gibbons et al.
Lie Cai et al.
Purpose: Toxicity to systemic cancer treatment represents a major anxiety for patients and a challenge to treatment plans. We aimed to develop machine learning algorithms for the upfront prediction of an individual's risk...
Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing [0.03%]
基于自然语言处理的乳腺癌计算机断层扫描报告中癌症复发的自动识别技术
Jaimie J Lee,Andres Zepeda,Gregory Arbour et al.
Jaimie J Lee et al.
Purpose: Breast cancer relapses are rarely collected by cancer registries because of logistical and financial constraints. Hence, we investigated natural language processing (NLP), enhanced with state-of-the-art deep lear...
Real-World Outcomes in Patients With Metastatic Renal Cell Carcinoma Treated With First-Line Nivolumab Plus Ipilimumab in the United States [0.03%]
纳武利尤单抗联合伊匹木单抗一线治疗美国转移性肾细胞癌患者的真实世界结局
Gurjyot K Doshi,Andrew J Osterland,Ping Shi et al.
Gurjyot K Doshi et al.
Purpose: Nivolumab plus ipilimumab (NIVO + IPI) is a first-in-class combination immunotherapy for the treatment of intermediate- or poor (I/P)-risk advanced or metastatic renal cell carcinoma (mRCC). Currently, there are ...