Ensuring Reliability of Curated Electronic Health Record-Derived Data: The Validation of Accuracy for Large Language Model-/Machine Learning-Extracted Information and Data (VALID) Framework [0.03%]
确保电子健康记录衍生数据的可靠性：大型语言模型/机器学习提取信息和数据的准确性验证框架（VALID）
Melissa Estevez,Nisha Singh,Lauren Dyson et al.
Melissa Estevez et al.
Large language models (LLMs) are increasingly used to extract clinical data from electronic health records, offering significant improvements in scalability and efficiency for real-world data (RWD) curation in oncology. However, the adoptio...
Automated Tumor International Classification of Diseases Coding of Real-World Pathology Reports Using Self-Hosted Large Language Models [0.03%]
使用自主托管的大规模语言模型对现实世界病理报告进行自动肿瘤疾病国际分类编码
Kamyar Arzideh,René Hosch,Amin Turki et al.
Kamyar Arzideh et al.
Purpose: Manual coding of pathology reports with International Classification of Diseases for Oncology (ICD-O)-3 codes is time-consuming, error-prone, and resource-intensive for health care institutions. To evaluate the p...
Abhishek Shivanna,Adam Spannaus,Jordan Tschida et al.
Abhishek Shivanna et al.
Purpose: Integrating artificial intelligence in cancer diagnostics has improved tumor classification beyond rule-based systems. Despite these advancements, these models may still encode demographic biases. We conducted a ...
Opportunities and Challenges in Implementing Large Language Models (LLMs) in Oncology [0.03%]
肿瘤学中实施大型语言模型（LLM）的机会与挑战
Christine Adams
Christine Adams
Large language models (LLMs) and artificial intelligence systems possess the transformative potential to revolutionize cancer care. However, their integration into oncology presents both extraordinary opportunities and challenges. Clinicall...
Cascade Chatbot: A Scalable Approach to Family-Based Genetic Testing for Hereditary Cancer Syndromes [0.03%]
cascade聊天机器人：家族遗传性癌症综合征基因检测的可扩展方法
Lauren B Davis Rivera,Lauren Mitchell,Muhammad Danyal Ahsan et al.
Lauren B Davis Rivera et al.
Purpose: Cascade genetic testing enables identification of relatives at risk of hereditary cancer syndromes, creating opportunities for early detection and prevention. However, uptake of cascade testing remains low, with ...
Leveraging Artificial Intelligence for Immune Checkpoint Inhibitor Safety: A Scoping Review of Current Applications [0.03%]
利用人工智能进行免疫检查点抑制剂的安全性研究：当前应用的范畴审查
Chin Hang Yiu,Edward C Y Lau,Charlotte Thuy Tien Le et al.
Chin Hang Yiu et al.
Purpose: To systematically map how artificial intelligence (AI) is being applied to immune-related adverse events (irAEs) induced by immune checkpoint inhibitors (ICIs), and to identify key knowledge gaps and future direc...
Analysis of Large Language Model Decision Making in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Early Breast Cancer [0.03%]
激素受体阳性/人类表皮生长因子受体阴性早期乳腺癌的大语义模型决策分析
Roberto Buonaiuto,Aldo Caltavituro,Rossana Di Rienzo et al.
Roberto Buonaiuto et al.
Purpose: To assess the ability of GPT-4o in adjuvant treatment decision making in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) early breast cancer by comparing its recommendati...
Multicenter Study
JCO clinical cancer informatics. 2026 Mar:10:e2500230. DOI:10.1200/CCI-25-00230 2026
Early-Stage Breast Cancer in Women Younger Than 50 Years: Comparing American Joint Committee on Cancer Anatomic and Prognostic Stages With Partitioning Around Medoids Clusters in SEER Data [0.03%]
基于SEER数据的AJCC解剖学和预后分期与层次聚类在年轻乳腺癌患者中的比较(年龄小于50岁)
Suvd Zulbayar,Jennifer Brooks,Arian Aminoleslami et al.
Suvd Zulbayar et al.
Purpose: Early-stage breast cancer (ESBC) in women younger than 50 years often presents with tumor features, including grade and hormone receptor and human epidermal growth factor receptor 2 (HER2) status different from o...
Comparative Study
JCO clinical cancer informatics. 2026 Mar:10:e2500173. DOI:10.1200/CCI-25-00173 2026
End-to-End Pretreatment Prediction of Radiation Pneumonitis in Patients With Non-Small Cell Lung Cancer Using Computed Tomography: A Vision Transformer Approach [0.03%]
基于计算机断层扫描的非小细胞肺癌患者放射性肺炎预处理的端到端预测：一种视觉变换器方法
Julie Midroni,Felipe S Torres,Jay Hennessy et al.
Julie Midroni et al.
Purpose: Radiation pneumonitis (RP) is the most common toxicity after thoracic radiotherapy. We develop an artificial intelligence model to predict RP in an institutional cohort of patients undergoing radiotherapy for non...