Toward Automating the Summarization of Cancer Pathology Reports Using Large Language Models to Improve Clinical Usability [0.03%]
基于大型语言模型的癌症病理报告自动摘要研究以提高临床实用性
Yirong Liu,Jacob John,Sagnik Sarkar et al.
Yirong Liu et al.
Purpose: Reviewing pathology reports requires physicians to integrate complex histopathologic, immunohistochemical, and molecular findings from multiple reports and institutions, often under time constraints that increase...
Leveraging Population-Level and Multipayer Claims Data to Estimate Changes in Prostate Cancer Screening at the Small-Area Level [0.03%]
利用人口水平和多方索赔数据估计小区域前列腺癌筛查的变化
Rifei Liang,Elizabeth Juarez-Colunga,Marcelo C Perraillon
Rifei Liang
Purpose: In 2018, the US Preventive Services Task Force (USPSTF) updated its prostate cancer screening recommendations for men age 55-69 years from grade D, discouraging screening, to grade C, supporting individualized de...
Identification of Barriers to Digital Patient Portal Adoption Among Patients With Prostate Cancer Undergoing Curative Radiotherapy at a Canadian Provincial Cancer Program: A Quality Improvement Study [0.03%]
一项质量改进研究：识别加拿大省级癌症项目中接受根治性放射治疗的前列腺癌患者使用数字患者门户的障碍
Nikunj Patil,Rashmi Koul,Jennifer Moyer et al.
Nikunj Patil et al.
Purpose: Digital patient portals (DPPs) provide patients a direct electronic link with their care teams allowing secure, rapid symptom reporting and the capture of electronic patient-reported outcome questionnaires. A pro...
Observational Study
JCO clinical cancer informatics. 2026 Apr;10(2):e2500162. DOI:10.1200/CCI-25-00162 2026
Association Between Obesity and Sex-Related Survival Difference in Lung Cancer [0.03%]
肥胖与肺癌性别生存差异的关系研究
Alexey Ryzhenkov,Salma Rachidi,Valtteri Nieminen et al.
Alexey Ryzhenkov et al.
Purpose: Survival discrepancy between male and female patients in lung cancer is a well-known, but still poorly understood phenomenon. Previous studies have used different patient cohorts and clinical covariates and have ...
Generative Artificial Intelligence for Medical Summarization in Prostate Cancer: Comparative Evaluation by Physicians and Patient Advocates-A Pilot Study [0.03%]
用于前列腺癌医学总结的生成型人工智能：由医师和患者权益倡导者进行的比较评估——一项试点研究
Charles Raynaud,Loris Dematini,Jean-Emmanuel Bibault
Charles Raynaud
Purpose: The exponential growth of scientific publications presents increasing challenges for clinicians and patients seeking to access up-to-date medical information. Language models (LMs) have emerged as powerful tools ...
Comparative Study
JCO clinical cancer informatics. 2026 Apr:10:e2500316. DOI:10.1200/CCI-25-00316 2026
Study of the Perceptions and Concerns of a Single Health System Hematology and Oncology Workforce About Artificial Intelligence in Clinical Practice and Medical Education [0.03%]
单一卫生系统血液学和肿瘤学劳动力对人工智能在临床实践和医学教育中的感知和关注的研究
Guilherme Sacchi de Camargo Correia,Antonious Ziad Hazim,Binbin Zheng-Lin et al.
Guilherme Sacchi de Camargo Correia et al.
Purpose: Artificial intelligence (AI) has been rapidly evolving in medicine. While there are existing data about the perceptions and concerns of health care workers regarding AI, those of the hematology and oncology (HemO...
Influence of Device and Format: A Randomized Controlled Trial of an Electronic Health Record-Integrated Symptom Screening Questionnaire for Pediatric Patients With Cancer [0.03%]
一种儿科癌症患者的电子健康记录整合症状筛查问卷：设备和格式的影响：随机对照试验研究
Adam Paul Yan,Julia Shannon,Emily Saso et al.
Adam Paul Yan et al.
Purpose: The Symptom Screening in Pediatrics Tool (SSPedi) was integrated into the Epic electronic health records differently on the basis of completion using a computer or phone. Objectives were to determine whether comp...
Randomized Controlled Trial
JCO clinical cancer informatics. 2026 Mar:10:e2500282. DOI:10.1200/CCI-25-00282 2026
Web-Based User Interface for Fam3PRO: A Multigene, Multicancer Risk Prediction Model for Families With Cancer History [0.03%]
基于网络的用户界面Fam3PRO：一种针对有癌症家族史的多基因、多癌种风险预测模型
Xueying Chen,Jianfeng Ke,Lauren Flynn et al.
Xueying Chen et al.
Purpose: Hereditary cancer risk is key to guiding screening and prevention strategies. Cancer risks can vary by individual because of the presence or absence of high- and moderate-risk pathogenic variants (PVs) in cancer-...
Machine Learning Model for Predicting Severe Adverse Events in Oncology Patients Using the US Food and Drug Administration Adverse Event Reporting System [0.03%]
基于美国食品药品管理局不良事件数据库预测肿瘤患者严重不良事件的机器学习模型
Luke Xiyu Zhao,Catherine Wang,Jonathan Zou et al.
Luke Xiyu Zhao et al.
Purpose: Predicting severe adverse events (SAEs) in oncology is challenging because of complex therapies and patient heterogeneity. Traditional pharmacovigilance methods often fail to capture multifactorial risk patterns....
Spatial Access to Cancer Care Providers in National Cancer Institute-Designated Cancer Center Catchment Areas [0.03%]
国家癌症研究所指定的癌症中心服务区的癌症护理提供者空间可达性
R Blake Buchalter,Johnie Rose,Jarrod E Dalton et al.
R Blake Buchalter et al.
Purpose: Access to care is an important component of cancer center catchment area (CA) analytics, where CAs are defined as the geographic scope of cancer center operations. Spatial access to care is one piece of the acces...