Assessing PANACHE's Style and Substance for Deep Learning Prognostication in Pancreatic Cancer [0.03%]
评估PANACHE在胰腺癌深度学习预测中的风格与实质
Joey Lew,Ayman Ali,Daniel P Nussbaum
Joey Lew
Deep Learning of Histopathology Predicts Outcomes After Surgery for Pancreatic Cancer [0.03%]
基于深度学习的组织病理学预测胰腺癌手术后预后的研究
Avelyn Wong,Taib A Bourega,Rémy Nicolle et al.
Avelyn Wong et al.
Purpose: Predicting recurrence of pancreatic cancer after surgery could inform clinical decision making, including adjuvant therapies and follow-up. This study aimed to develop and validate a deep learning model using dig...
Clinicogenomic Real-World Data Enable Prediction of Hospital Readmissions at a Comprehensive Cancer Center [0.03%]
临床组学真实世界数据用于预测全面癌症中心的再入院率
Nikitha V Kalahasti,Gayathri Donepudi,Sean Bauersfeld et al.
Nikitha V Kalahasti et al.
Purpose: Curating high-quality clinical and genomic data sets from patients with cancer to predict hospital readmission using machine learning (ML) models. ...
Multicentric Validation of a Complete Blood Count-Based Model for Breast Cancer Risk Stratification in Women Age 40-49 Years [0.03%]
基于女性40至49岁乳腺癌风险分层的完整血细胞计数模型的多中心验证
Daniella Castro Araújo,Bruno Aragão Rocha,Suzylaine da Silva Lima et al.
Daniella Castro Araújo et al.
Purpose: Recent changes in breast cancer (BC) screening guidelines have extended eligibility to women age 40-49 years, yet risk stratification in this group remains challenging. This study evaluated the performance of a c...
Multicenter Study
JCO clinical cancer informatics. 2026 Apr;10(2):e2500347. DOI:10.1200/CCI-25-00347 2026
Validation of a Composite Mortality End Point in a Large Clinicogenomic Real-World Database of Patients With Advanced Cancer [0.03%]
验证大型临床基因组学真实世界数据库中晚期癌症患者复合死亡终点的有效性
Joshuah Kapilivsky,Farahnaz Islam,Emma K Roth et al.
Joshuah Kapilivsky et al.
Purpose: Real-world data from electronic health records and next-generation sequencing are used to study treatment effectiveness in molecularly refined patient populations. Incomplete mortality data can overestimate survi...
Erratum: Novel R Shiny Tool for Survival Analysis With Time-Varying Covariate in Oncology Studies: Overcoming Biases and Enhancing Collaboration [0.03%]
生存分析中带有时变协变量的肿瘤学研究中的R_shiny工具的错误修正：克服偏差和增强协作
Yimei Li,Yang Qiao,Fei Gao et al.
Yimei Li et al.
Published Erratum
JCO clinical cancer informatics. 2026 Apr;10(2):e2600072. DOI:10.1200/CCI-26-00072 2026
Performance of an Artificial Intelligence Foundation Model for Prostate Radiotherapy Segmentation [0.03%]
人工智能基础模型在前列腺放射治疗分割中的性能评估
Matthew Doucette,Chien-Yi Liao,Mu-Han Lin et al.
Matthew Doucette et al.
Purpose: Radiation therapy is a major treatment modality for localized prostate cancer, and accurate target segmentation is a critical aspect of radiation delivery that can directly affect patient outcomes. We evaluated t...
Feasibility of Automated Laboratory Data Ascertainment and Transfer From Hospitals Into Medidata Rave Across Pediatric National Cancer Institute-Supported Cooperative Groups [0.03%]
从医院自动获取实验室数据并将其传输到Medidata Rave的可行性研究——基于美国国家癌症研究所支持的儿科协作组的研究
Tamara P Miller,Richard Aplenc,Charles Minard et al.
Tamara P Miller et al.
Purpose: Electronic data capture (EDC) has the potential to improve trial data accuracy and efficiency. The Children's Oncology Group Pediatric Early Phase Clinical Trials Network (PEP-CTN) and Pediatric Brain Tumor Conso...
Erratum: Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer [0.03%]
贝叶斯网络预测非小细胞肺癌系统治疗患者的急诊就诊的订正案研究
Brian D Gonzalez,Xiaoyin Li,Lisa M Gudenkauf et al.
Brian D Gonzalez et al.
Published Erratum
JCO clinical cancer informatics. 2026 Apr;10(2):e2600084. DOI:10.1200/CCI-26-00084 2026
Childhood Cancer Data Initiative Participant Index: Mapping Pediatric Cancer Data to Facilitate Cross-Study Integrated Analysis [0.03%]
儿童癌症数据计划参与者索引：映射儿科癌症数据以促进跨研究综合分析
Subhashini Jagu,Jaime M Guidry Auvil,Mark D Cunningham et al.
Subhashini Jagu et al.
Purpose: To facilitate integrated multimodal data analysis, it is critical to connect data from multiple sources to address multifaceted research questions, better understand disease biology and natural history, develop n...