Erratum: Breast, Cervical, and Colorectal Cancer Screening Among New Jersey Medicaid Enrollees: 2017-2022 [0.03%]
更正：新泽西州医疗补助计划受益人的乳腺癌、宫颈癌和结直肠癌筛查：2017-2022年
Ann M Nguyen,Adriana Waldron-Corredor,Feng-Yi Liu et al.
Ann M Nguyen et al.
Published Erratum
JCO clinical cancer informatics. 2025 Sep:9:e2500256. DOI:10.1200/CCI-25-00256 2025
Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression [0.03%]
贝叶斯反事实机器学习个体化放射治疗选择以缓解免疫抑制
Duo Yu,Michael J Kane,Yiqing Chen et al.
Duo Yu et al.
Purpose: Lymphocytes play critical roles in cancer immunity and tumor surveillance. Radiation-induced lymphopenia (RIL) is a common side effect observed in patients with cancer undergoing chemoradiation therapy (CRT), lea...
Erratum: Risk Score Model of Aging-Related Genes for Bladder Cancer and Its Application in Clinical Prognosis [0.03%]
老年相关基因膀胱癌风险评分模型及其临床预后应用的更正注释
Kun Lu,Liu Chao,Jin Wang et al.
Kun Lu et al.
Published Erratum
JCO clinical cancer informatics. 2025 Aug:9:e2500251. DOI:10.1200/CCI-25-00251 2025
Enhancing Oncology-Specific Question Answering With Large Language Models Through Fine-Tuned Embeddings With Synthetic Data [0.03%]
利用合成数据通过微调嵌入增强大型语言模型的肿瘤学特定问答能力
Kun-Han Lu,Sina Mehdinia,Kingson Man et al.
Kun-Han Lu et al.
Purpose: The recent advancements of retrieval-augmented generation (RAG) and large language models (LLMs) have revolutionized the extraction of real-world evidence from unstructured electronic health records (EHRs) in onc...
Evaluating the Minimal Common Oncology Data Elements Suitability in Enhancing Clinical Observational Research [0.03%]
评估最小共同肿瘤数据元素在增强临床观察性研究中的适用性
May Terry,Janet L Espirito,Lisa Deister et al.
May Terry et al.
Purpose: This article explored how suitable the minimal Common Oncology Data Elements (mCODE) standard is for the real-world evidence research of cancer patient characterization, disease characterization, treatment patter...
Pragmatic Use of Minimal Common Oncology Data Elements and Observational Medical Outcomes Partnership at an Academic Medical Center [0.03%]
学术医学中心的最小常见肿瘤学数据元素和观察医疗结果合作伙伴关系的实用应用
Tatyana Sandler,Sarah Manglicmot,Katelyn M Mullen et al.
Tatyana Sandler et al.
Review and Commentary on Digital Pathology and Artificial Intelligence in Pathology [0.03%]
数字病理学和人工智能病理学的评论与述评
Sahussapont Joseph Sirintrapun
Sahussapont Joseph Sirintrapun
Purpose: This Special Article provides a comprehensive review and expert commentary on the prospective clinical implementation of artificial intelligence (AI) in the detection of prostate cancer from digital prostate biop...
Machine Learning Model Integrating Computed Tomography Image-Derived Radiomics and Circulating miRNAs to Predict Residual Teratoma in Metastatic Nonseminoma Testicular Cancer [0.03%]
基于影像组学和循环miRNAs的机器学习模型预测转移性非精原细胞睾丸癌残留畸胎瘤
Guliz Ozgun,Neda Abdalvand,Gizem Ozcan et al.
Guliz Ozgun et al.
Purpose: Chemotherapy is the primary treatment for metastatic nonseminomatous germ cell tumors (mNSGCTs), but patients often encounter postchemotherapy residual disease. Accurate noninvasive methods are needed to predict ...
Safe and Responsible Use of Artificial Intelligence in Health Care: Current Regulatory Landscape and Considerations for Regulatory Policy [0.03%]
医疗保健中人工智能的安全与负责任使用：现行监管格局及监管政策考量
Alaap Shah,Sekeithia Mitchell,Gary Coad et al.
Alaap Shah et al.
The integration of artificial intelligence (AI) into health care promises transformative advancements in diagnostics, treatment, and operational efficiency. However, this transformation introduces significant clinical, technical, and socioe...
Development of a Machine Learning Model for Aspyre Lung Blood: A New Assay for Rapid Detection of Actionable Variants From Plasma in Patients With Non-Small Cell Lung Cancer [0.03%]
Aspyre肺癌机器学习模型的开发：一种快速检测非小细胞肺癌患者血浆中可操作变异的新检测方法
Rebecca N Palmer,Sam Abujudeh,Magdalena Stolarek-Januszkiewicz et al.
Rebecca N Palmer et al.
Purpose: Aspyre Lung is a targeted biomarker panel of 114 genomic variants across 11 guideline-recommended genes with simultaneous DNA and RNA for non-small cell lung cancer (NSCLC). In this study, we developed a machine ...