Implementation Strategy for Artificial Intelligence in Radiotherapy: Can Implementation Science Help? [0.03%]
放射治疗中人工智能的实施策略：实施科学能起到什么作用？
Rachelle Swart,Liesbeth Boersma,Rianne Fijten et al.
Rachelle Swart et al.
Purpose: Artificial intelligence (AI) applications in radiotherapy (RT) are expected to save time and improve quality, but implementation remains limited. Therefore, we used implementation science to develop a format for ...
Prediction of Hepatocellular Carcinoma After Hepatitis C Virus Sustained Virologic Response Using a Random Survival Forest Model [0.03%]
应用随机生存森林模型预测丙型肝炎持续病毒学应答后肝细胞癌的发生风险
Hikaru Nakahara,Atsushi Ono,C Nelson Hayes et al.
Hikaru Nakahara et al.
Purpose: Postsustained virologic response (SVR) screening following clinical guidelines does not address individual risk of hepatocellular carcinoma (HCC). Our aim is to provide tailored screening for patients using machi...
Implementing Cancer Registry Data With the PCORnet Common Data Model: The Greater Plains Collaborative Experience [0.03%]
PCORnet通用数据模型中实施癌症登记处数据：大平原协作组经验
Bradley D McDowell,Michael A O&#x;Rorke,Mary C Schroeder et al.
Bradley D McDowell et al.
Purpose: Electronic health records (EHRs) comprise a rich source of real-world data for cancer studies, but they often lack critical structured data elements such as diagnosis date and disease stage. Fortunately, such con...
Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non-Small Cell Lung Cancer [0.03%]
基于深度学习的影像组学标志物预测晚期非小细胞肺癌PD-(L)1免疫检查点抑制剂疗效的真实世界和临床试验验证研究
Chiharu Sako,Chong Duan,Kevin Maresca et al.
Chiharu Sako et al.
Purpose: This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC) using real-world data (...
Measurement of Completeness and Timeliness of Linked Electronic Health Record Pharmacy Data for Early Detection of Nonadherence to Breast Cancer Adjuvant Endocrine Therapy [0.03%]
用于早期检测乳腺癌辅助内分泌治疗不依从的链接式电子健康档案药物数据的完整性和及时性测量
Chelsea McPeek,Shirlene Paul,Jordan Lieberenz et al.
Chelsea McPeek et al.
Purpose: This retrospective cohort study evaluated whether linked electronic health record (EHR) pharmacy data were adequately complete and timely to detect primary nonadherence to breast cancer adjuvant endocrine therapy...
Toward the Clinically Effective Evaluation of Artificial Intelligence-Generated Responses [0.03%]
迈向有效评估人工智能生成回复的临床方法
Silambarasan Anbumani,Ergun Ahunbay
Silambarasan Anbumani
Assessing Large Language Models for Oncology Data Inference From Radiology Reports [0.03%]
评估大型语言模型在放射学报告中提取肿瘤数据的性能
Li-Ching Chen,Travis Zack,Arda Demirci et al.
Li-Ching Chen et al.
Purpose: We examined the effectiveness of proprietary and open large language models (LLMs) in detecting disease presence, location, and treatment response in pancreatic cancer from radiology reports. ...
Comparative Analysis of Generative Pre-Trained Transformer Models in Oncogene-Driven Non-Small Cell Lung Cancer: Introducing the Generative Artificial Intelligence Performance Score [0.03%]
基于驱动基因的非小细胞肺癌中生成预训练 transformer 模型的比较分析：介绍生成式人工智能性能评分
Zacharie Hamilton,Aseem Aseem,Zhengjia Chen et al.
Zacharie Hamilton et al.
Purpose: Precision oncology in non-small cell lung cancer (NSCLC) relies on biomarker testing for clinical decision making. Despite its importance, challenges like the lack of genomic oncology training, nonstandardized bi...
Comparative Study
JCO clinical cancer informatics. 2024 Dec:8:e2400123. DOI:10.1200/CCI.24.00123 2024
Enhancing Thyroid Pathology With Artificial Intelligence: Automated Data Extraction From Electronic Health Reports Using RUBY [0.03%]
利用人工智能增强甲状腺病理学：使用RUBY从电子健康报告中自动提取数据
Dorian Culié,Renaud Schiappa,Sara Contu et al.
Dorian Culié et al.
Purpose: Thyroid nodules are common in the general population, and assessing their malignancy risk is the initial step in care. Surgical exploration remains the sole definitive option for indeterminate nodules. Extensive ...
Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning [0.03%]
基于深度学习的三维断层合成乳腺影像体积密度估计方法研究
Vinayak S Ahluwalia,Nehal Doiphode,Walter C Mankowski et al.
Vinayak S Ahluwalia et al.
Purpose: Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate ...