Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia [0.03%]
基于无监督聚类的机器学习模型可解释性的改进：在慢性淋巴细胞白血病中预测首次治疗时间
David Chen,Gaurav Goyal,Ronald S Go et al.
David Chen et al.
Purpose: Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although ...
Clinician Report of Oral Oncolytic Symptoms and Adherence Obtained via a Patient-Reported Outcome Measure (PROM) [0.03%]
基于患者报告结果测量（PROM）的口腔肿瘤内科症状及用药依从性调查
Victoria R Nachar,Karen Farris,Katie Beekman et al.
Victoria R Nachar et al.
Purpose: Patient-reported outcome measures (PROMs) for symptom monitoring during cancer therapy have been shown to have a positive impact on outcomes. These findings have primarily been shown for patients receiving intrav...
Identifying Smoking Status and Smoking Cessation Using a Data Linkage Between the Kentucky Cancer Registry and Health Claims Data [0.03%]
基于肯塔基州癌症登记处和医疗费用数据的链接识别吸烟状态及戒烟情况
Michael Shayne Gallaway,Bin Huang,Quan Chen et al.
Michael Shayne Gallaway et al.
Purpose: Linkage of cancer registry data with complementary data sources can be an informative way to expand what is known about patients and their treatment and improve delivery of care. The purpose of this study was to ...
Classifying Stage IV Lung Cancer From Health Care Claims: A Comparison of Multiple Analytic Approaches [0.03%]
利用医疗保健索赔数据诊断四期肺癌：多种分析方法的比较研究
Gabriel A Brooks,Savannah L Bergquist,Mary Beth Landrum et al.
Gabriel A Brooks et al.
Purpose: Cancer stage is a key determinant of outcomes; however, stage is not available in claims-based data sources used for real-world evaluations. We compare multiple methods for classifying lung cancer stage from clai...
Meta-Analysis of 1,200 Transcriptomic Profiles Identifies a Prognostic Model for Pancreatic Ductal Adenocarcinoma [0.03%]
基于1200个转录组谱的 meta-分析建立胰腺导管腺癌预后模型
Vandana Sandhu,Knut Jorgen Labori,Ayelet Borgida et al.
Vandana Sandhu et al.
Purpose: With a dismal 8% median 5-year overall survival, pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy. Only 10% to 20% of patients are eligible for surgery, and more than 50% of these patients wi...
Meta-Analysis
JCO clinical cancer informatics. 2019 May:3:1-16. DOI:10.1200/CCI.18.00102 2019
Improvement of Care in Patients With Colorectal Cancer: Influence of the Introduction of Standardized Structured Reporting for Pathology [0.03%]
结直肠癌患者的治疗改进：病理标准化结构报告介绍的影响分析
Caro E Sluijter,Frans van Workum,Theo Wiggers et al.
Caro E Sluijter et al.
Purpose: The use of standardized structured reporting (SSR) can improve communication between cancer specialists, which might improve clinical care; however, there are no reliable data on whether the introduction of SSR i...
Validity of Natural Language Processing for Ascertainment of EGFR and ALK Test Results in SEER Cases of Stage IV Non-Small-Cell Lung Cancer [0.03%]
自然语言处理确认SEER数据库中晚期非小细胞肺癌患者EGFR和ALK检测结果的有效性
Bernardo Haddock Lobo Goulart,Emily T Silgard,Christina S Baik et al.
Bernardo Haddock Lobo Goulart et al.
Purpose: SEER registries do not report results of epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutation tests. To facilitate population-based research in molecularly defined subgroups of no...
Radiation Records in the National Cancer Database: Variations in Coding and/or Practice Can Significantly Alter Survival Results [0.03%]
全国癌症数据库中的放射记录：编码和/或实践的差异可以显著改变生存结果
Corbin D Jacobs,David J Carpenter,Julian C Hong et al.
Corbin D Jacobs et al.
Purpose: The aim of the current work was to quantify internally inconsistent and anomalous radiation therapy (RT) data in the National Cancer Database (NCDB) and determine their association with overall survival (OS) usin...
Russell C Rockne,Jacob G Scott
Russell C Rockne
Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma [0.03%]
一种预测高级别浆液性卵巢癌分子亚型的平台独立分类系统
Arunima Shilpi,Manoj Kandpal,Yanrong Ji et al.
Arunima Shilpi et al.
Purpose: Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression-based classification system fo...