Natural Language Processing to Ascertain Cancer Outcomes From Medical Oncologist Notes [0.03%]
从肿瘤内科医生的笔记中通过自然语言处理技术来确定癌症结果
Kenneth L Kehl,Wenxin Xu,Eva Lepisto et al.
Kenneth L Kehl et al.
Purpose: Cancer research using electronic health records and genomic data sets requires clinical outcomes data, which may be recorded only in unstructured text by treating oncologists. Natural language processing (NLP) co...
Provider Engagement in Radiation Oncology Data Science: Workshop Report [0.03%]
放射肿瘤学数据科学中的供应商参与：工作坊报告
Anshu K Jain,Sanjay Aneja,Clifton D Fuller et al.
Anshu K Jain et al.
Generative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67 [0.03%]
用于Ki-67免疫组化组织图像中细胞核定量的生成对抗领域适应方法
Xuhong Zhang,Toby C Cornish,Lin Yang et al.
Xuhong Zhang et al.
Purpose: We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry (IHC)-stained pancreatic neuroendocrine tumor (NET) images. We hypothesize that deep learning-b...
Jack W London,Elnara Fazio-Eynullayeva,Matvey B Palchuk et al.
Jack W London et al.
Purpose: While there are studies under way to characterize the direct effects of the COVID-19 pandemic on the care of patients with cancer, there have been few quantitative reports of the impact that efforts to control th...
Comparative Study
JCO clinical cancer informatics. 2020 Jul:4:657-665. DOI:10.1200/CCI.20.00068 2020
Electronic Patient-Reported Outcome-Based Interventions for Palliative Cancer Care: A Systematic and Mapping Review [0.03%]
基于电子患者报告结果的姑息癌症护理介入措施：系统回顾和地图研究
Christina Karamanidou,Pantelis Natsiavas,Lefteris Koumakis et al.
Christina Karamanidou et al.
Purpose: Capitalizing on the promise of patient-reported outcomes (PROs), electronic implementations of PROs (ePROs) are expected to play an important role in the development of novel digital health interventions targetin...
Machine Learning-Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival [0.03%]
基于机器学习的前列腺癌生存率的非线性相互作用的解释与可视化
Richard Li,Ashwin Shinde,An Liu et al.
Richard Li et al.
Purpose: Shapley additive explanation (SHAP) values represent a unified approach to interpreting predictions made by complex machine learning (ML) models, with superior consistency and accuracy compared with prior methods...
HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy [0.03%]
HLA平台：一个用于癌症免疫疗法的肽-HLA复合物结构建模和分析的可定制环境
Dinler A Antunes,Jayvee R Abella,Sarah Hall-Swan et al.
Dinler A Antunes et al.
Purpose: HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural fea...
Assessing Genitourinary Cancer Clinical Trial Accrual Sufficiency Using Archived Trial Data [0.03%]
基于存档试验数据评估泌尿生殖系统癌症临床试验的入组情况
Kristian Stensland,Samuel Kaffenberger,David Canes et al.
Kristian Stensland et al.
Purpose: Clinical trials often fail to reach their anticipated end points, most frequently because of poor accrual. Prior studies have analyzed trial termination, but it has not been easy to assess accrual estimates using...
Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices [0.03%]
通过虚拟分子肿瘤委员会协作评估癌症变异以指导本地临床实践
Shruti Rao,Beth Pitel,Alex H Wagner et al.
Shruti Rao et al.
Purpose: The cancer research community is constantly evolving to better understand tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been hi...
Li-Xuan Qin,Jian Zou,Jiejun Shi et al.
Li-Xuan Qin et al.
Purpose: Methods for depth normalization have been assessed primarily with simulated data or cell-line-mixture data. There is a pressing need for benchmark data enabling a more realistic and objective assessment, especial...