Incorporating Structured and Unstructured Data Sources to Identify and Characterize Hereditary Cancer Testing Among Veterans With Metastatic Castration-Resistant Prostate Cancer [0.03%]
结合结构化和非结构化数据来源识别并表征转移性去势抵抗性前列腺癌患者中的遗传性癌症检测
Danielle Candelieri-Surette,Anna Hung,Fatai Y Agiri et al.
Danielle Candelieri-Surette et al.
Purpose: This study introduces an integrated approach using structured and unstructured data from an electronic health record to identify and characterize patient utilization of hereditary cancer genetic testing among pat...
Advancements in Interoperability: Achieving Anatomic Pathology Reports That Adhere to International Standards and Are Both Human-Readable and Readily Computable [0.03%]
互操作性进展：实现符合国际标准的解剖病理报告，使之既适合人阅读也便于计算
Walter S Campbell,Brian A Rous,Stefan Dubois et al.
Walter S Campbell et al.
Purpose: Over the past 50 years, multiple pathology organizations worldwide have evolved in cancer histopathology reporting from subjective, narrative assessments to structured, synoptic formats using controlled vocabular...
Patient-Reported Outcomes: Comparing Functional Avoidance and Standard Thoracic Radiation Therapy in Lung Cancer [0.03%]
肺癌患者的报告结果：功能避免与标准支气管照射治疗的比较
Spencer J Poiset,Joseph Lombardo,Edward Castillo et al.
Spencer J Poiset et al.
Purpose: Novel methods generate functional images using image processing techniques combined with four-dimensional computed tomography (4DCT) data (4DCT-ventilation). 4DCT-ventilation was implemented in a phase II, multic...
Clinical Trial
JCO clinical cancer informatics. 2025 Feb:9:e2400202. DOI:10.1200/CCI-24-00202 2025
CFO: Calibration-Free Odds Bayesian Designs for Dose Finding in Clinical Trials [0.03%]
临床试验中的剂量寻找的无需校准的概率贝叶斯设计 CFO
Jialu Fang,Ninghao Zhang,Wenliang Wang et al.
Jialu Fang et al.
Purpose: Calibration-free odds type (CFO-type) designs have been demonstrated to be robust, model-free, and practically useful, which have become the state-of-the-art approach for dose finding. However, a key challenge fo...
Provision of Radiology Reports Simplified With Large Language Models to Patients With Cancer: Impact on Patient Satisfaction [0.03%]
大型语言模型简化癌症患者的放射学报告提供对其患者满意度的影响
Amit Gupta,Swarndeep Singh,Hema Malhotra et al.
Amit Gupta et al.
Purpose: To explore the perceived utility and effect of simplified radiology reports on oncology patients' knowledge and feasibility of large language models (LLMs) to generate such reports. ...
Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases [0.03%]
基于机器学习分类器和辅助数据集的影像组学预测肾上腺转移瘤磁共振引导放疗期间疾病进展
Jesutofunmi A Fajemisin,John M Bryant,Payman G Saghand et al.
Jesutofunmi A Fajemisin et al.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease...
Novel Use and Value of Contrast-Enhanced Susceptibility-Weighted Imaging Morphologic and Radiomic Features in Predicting Extremity Soft Tissue Undifferentiated Pleomorphic Sarcoma Treatment Response [0.03%]
对比增强磁敏感加权成像的形态学和影像组学特征在预测肢体软组织未分化多形性肉瘤治疗反应中的新型应用和价值
Raul F Valenzuela,Elvis de Jesus Duran Sierra,Mathew A Canjirathinkal et al.
Raul F Valenzuela et al.
Purpose: Undifferentiated pleomorphic sarcomas (UPSs) demonstrate therapy-induced hemosiderin deposition, granulation tissue formation, fibrosis, and calcification. We aimed to determine the treatment-assessment value of ...
Bias in Prediction Models to Identify Patients With Colorectal Cancer at High Risk for Readmission After Resection [0.03%]
结肠直肠癌患者再入院风险预测模型的偏倚评估研究
Mary M Lucas,Mario Schootman,Jonathan A Laryea et al.
Mary M Lucas et al.
Purpose: Machine learning algorithms are used for predictive modeling in medicine, but studies often do not evaluate or report on the potential biases of the models. Our purpose was to develop clinical prediction models f...
Toward a Computable Phenotype for Determining Eligibility of Lung Cancer Screening Using Electronic Health Records [0.03%]
基于电子健康档案的肺癌筛查适用性计算表型研究
Shuang Yang,Yu Huang,Xiwei Lou et al.
Shuang Yang et al.
Purpose: Lung cancer screening (LCS) has the potential to reduce mortality and detect lung cancer at its early stages, but the high false-positive rate associated with low-dose computed tomography (LDCT) for LCS acts as a...
Validation of Clinical Dynamic Contrast-Enhanced Magnetic Resonance Imaging Perfusion Modeling and Neoadjuvant Chemotherapy Response Prediction in Breast Cancer Using 18FDG and 64Cu-DOTA-Trastuzumab Positron Emission Tomography Studies [0.03%]
使用18FDG和64Cu-DOTA-曲妥珠单抗正电子发射断层扫描研究验证乳腺癌临床动态对比增强磁共振灌注建模及新辅助化疗反应预测能力
John Whitman,Vikram Adhikarla,Lusine Tumyan et al.
John Whitman et al.
Purpose: Perfusion modeling presents significant opportunities for imaging biomarker development in breast cancer but has historically been held back by the need for data beyond the clinical standard of care (SoC) and unc...