Development, External Validation, and Deployment of RFAN-ML: A Machine Learning Model to Estimate Renal Function After Nephrectomy [0.03%]
RFAN-ML模型的开发、外部验证和应用：一种估算肾切除术后肾功能的机器学习模型
Jesse Persily,Steven L Chang,Chen Chen et al.
Jesse Persily et al.
Purpose: Partial nephrectomy has been advocated as the preferred surgical approach for small kidney tumors over total nephrectomy. However, partial nephrectomy is associated with increased perioperative risk. Estimating r...
Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer [0.03%]
人工智能纵向合成数据生成加速乳腺癌临床与转化研究
Elena Zazzetti,Saverio D&#x;Amico,Flavia Jacobs et al.
Elena Zazzetti et al.
Purpose: Real-world data (RWD) are critical for breast cancer (BC) research but are limited by privacy concerns, missing information, and data fragmentation. This study explores synthetic data (SD) generated through advan...
Reasoning Models for Text Mining in Oncology: A Comparison Between o1 Preview, GPT-4o, and GPT-5 at Different Reasoning Levels [0.03%]
肿瘤学文本挖掘的推理模型比较：在不同层次上比较o1 Preview、GPT-4o和GPT-5的能力
Paul Windisch,Fabio Dennstädt,Julia Weyrich et al.
Paul Windisch et al.
Purpose: Chain-of-thought prompting is a method to make large language models generate intermediate reasoning steps when solving a complex problem. OpenAI's o1 preview and GPT-5 have been trained to create such a chain of...
Comparative Study
JCO clinical cancer informatics. 2025 Nov:9:e2400311. DOI:10.1200/CCI-24-00311 2025
Augmenting Large Language Models With National Comprehensive Cancer Network Guidelines for Improved and Standardized Adjuvant Therapy Recommendations in Postoperative Breast Cancer Cases [0.03%]
利用国家综合癌症网络指南增强大型语言模型以改善和标准化乳腺癌术后辅助治疗建议
Serene Si Ning Goh,Ragunathan Mariappan,Grace Soo Woon Tan et al.
Serene Si Ning Goh et al.
Purpose: Multidisciplinary breast tumor boards (MTBs) are essential for optimizing breast cancer treatment but face challenges related to logistics, variability in expertise, and lack of standardization. Large language mo...
Artificial Intelligence System for Psychospiritual Distress in Family Caregivers of Patients With Terminal Cancer: A Retrospective Study [0.03%]
晚期癌症患者家庭照顾者的心理精神困扰人工智能系统：一项回顾性研究
Kento Masukawa,Ryusho Suzuki,Momoka Tanno et al.
Kento Masukawa et al.
Purpose: Family caregivers of patients with terminal cancer need psychospiritual care. The assessment of their psychospiritual distress is challenging. An automated system can be used to detect psychospiritual distress fr...
Integrating a Shareable Artificial Intelligence Model Into Clinical Research for Cancer Recurrence in Patients With Breast and Colorectal Cancer [0.03%]
将共享型人工智能模型整合应用于乳腺癌和结直肠癌患者癌症复发的临床研究
Anlan Cao,Kristina L Johnson,Ijeamaka Anyene Fumagalli et al.
Anlan Cao et al.
Purpose: Cancer recurrence in clinical settings is documented in unstructured text, requiring labor-intensive manual record review to extract this outcome. A shareable natural language processing model developed at Dana-F...
Rapid Growth in Patient Portal Messages Underscores the Need for Actionable Paths Forward [0.03%]
患者门户消息的快速增长凸显了采取行动路径的重要性
A Jay Holmgren
A Jay Holmgren
Measuring the Association Between the COVID-19 Pandemic and Cancer Incidence by Sex Using a Quasi-Experimental Study Design [0.03%]
利用准实验研究设计通过性别衡量COVID-19大流行与癌症发病率之间的联系
Kathleen M Decker,Allison Feely,Iresha Ratnayake et al.
Kathleen M Decker et al.
Purpose: This study examined the association between COVID-19 and cancer incidence by sex in Manitoba, Canada. Methods: We used a popul...
Machine Learning Designed for Any Hematologic Flow Cytometry Data Set [0.03%]
适用于任何血液流式细胞仪数据集的人工智能算法设计
Johannes Mammen,Calin-Petru Manta,Sarah Richter et al.
Johannes Mammen et al.
Purpose: Flow cytometry is a key diagnostic technique in hematology that provides protein information at a single-cell level. Traditionally interpreted manually in a sequence of two-dimensional plots, automated analysis t...
Leveraging Centralized Health System Data Management and Large Language Model-Based Data Preprocessing to Identify Predictors for Radiation Therapy Interruption [0.03%]
利用集中化卫生系统数据管理和基于大型语言模型的数据预处理识别放射治疗中断的预测因子
Fekede Asefa Kumsa,Christopher L Brett,Soheil Hashtarkhani et al.
Fekede Asefa Kumsa et al.
Purpose: Unplanned treatment interruptions represent an important care quality shortfall for patients undergoing cancer radiotherapy. This study aimed to evaluate use of a centralized electronic health record warehouse an...