Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization

ObjectiveRadiology is a finite health care resource in high demand at most health centers. However, anticipating fluctuations in demand is a challenge because of the inherent uncertainty in disease prognosis. The aim of this study was to explore the potential of natural language processing (NLP) to predict downstream radiology resource utilization in patients undergoing surveillance for hepatocellular carcinoma (HCC).Materials and MethodsAll HCC surveillance CT examinations performed at our institution from January 1, 2010, to October 31, 2017 were selected from our departmental radiology information system. We used open source NLP and machine learning software to parse radiology report text into bag-of-words and term frequency–inverse document frequency (TF-IDF) representations. Three machine learning models—logistic regression, support vector machine (SVM), and random forest—were used to predict future utilization of radiology department resources. A test data set was used to calculate accuracy, sensitivity, and specificity in addition to the area under the curve (AUC).ResultsAs a group, the bag-of-word models were slightly inferior to the TF-IDF feature extraction approach. The TF-IDF + SVM model outperformed all other models with an accuracy of 92%, a sensitivity of 83%, and a specificity of 96%, with an AUC of 0.971.ConclusionsNLP-based models can accurately predict downstream radiology resource utilization from narrative HCC surveillance reports and has potential for translation to health care management where it may improve decision making, reduce costs, and broaden access to care.     

The Policy of Co–First Authorship and Co–Senior Authorship in Radiology Journals

ObjectiveIncreasingly, medical journals are recognizing “equally credited authors” (ECA) in the primary and senior authorship of articles. The aim of this study was to assess the policies of co–first authorship, co–senior authorship, and designation of a corresponding author in the radiology literature.MethodsWe identified 29 radiology journals based on impact factor ranking. Journal offices were contacted by phone and e-mail to ascertain their practices on first and senior authorship ECA designations. We surveyed the March, June, and December 2018 issues of each journal (when available) to assess the utilization of the co-designations in articles.ResultsTwenty-five of 29 journals responded to our survey (response rate: 86.2%). Of 25 journals, 20 (80%) allowed co–first authorship. Among these, 4 of 25 journals (16%) allowed more than two co–first authors. Among the 25 responses, 14 journals (56%) allowed co–senior authorship. Among the 24 journals who responded to this specific question, 23 (96%) approved designation of a corresponding author, different from the first or senior author. The review of March, June, December 2018 editions found co–first authorship and co–senior authorship ECA rates of 8.6% (range 0.0%-22.7%) and 1.8% (range 0.0%-13.3%), respectively. A corresponding author other than first or senior author was noted in 13.3% (range 0.0%-34.7%).DiscussionThere has been widespread acceptance of the concept of ECA in the policies of the top cited imaging journals particularly for first authors (80%). However, the utilization of these designations is uncommon for first authorship (8.6%) and rare (1.8%) for senior authorship based on our 2018 sampling.     

Developing an international standard for the classification of surface anatomic location for use in clinical practice and epidemiologic research

1. Download : Download high-res image (305KB)
2. Download : Download full-size image

     

解脲脲支原体多带抗原基因分型鉴定及其临床应用研究

目的应用PCR技术对解脲脲支原体(Uu)多带抗原进行分型鉴定,探讨Uu各种基因型与临床病原学之间的关系. 方法 根据解脲脲支原体多带抗原基因(MBA)与16S rRNA基因和尿素酶基因结构设计10对引物,采用PCR基因扩增技术,对 384例非淋菌性尿道炎和其他泌尿生殖道感染患者的临床标本,进行解脲脲支原体生物变种和基因型分型鉴定,并与Uu培养法作比较. 结果 384例性病门诊患者临床标本中,检测Uu培养阳性 218例,阳性率56.8%;PCR基因扩增检测Uu-DNA阳性 208例,总阳性率为54.2%:其中生物变种1(biovar 1) 143例,占37.2%, 生物变种2 (biovar 2) 65例,占16.9%;基因分型结果:生物变种1血清变种1 (serovar 1) 50例,占13.0%,血清变种3/14 (serovars 3/14) 64例, 占16.7%, 血清变种6 (serovar 6) 29例,占7.6%;生物变种2 亚型1 (subtype 1) 25例,占6.5%, 亚型2 (subtype 2) 30例,占7.8%, 亚型3 (subtype 3) 10例,占2.6%. 结论 Uu是性病的重要病原体,MBA多带抗原PCR基因分型鉴定具有简便、快速、敏感、特异之优点.     

Monoliths with chiral surface functionalization for enantioselective capillary electrochromatography

The state-of-the-art in CEC enantiomer separations with monolithic capillary columns is comprehensively reviewed. The various types of monolithic columns comprising in situ organic polymer monoliths, molecularly imprinted polymer (MIP) monoliths, silica monoliths and monoliths made from particles are discussed with a focus on materials’ synthesis, chemistry and properties as well as column aspects. Monolithic MIP-type porous layer open-tubular (PLOT) columns are treated herein as well. From this survey of the literature, the authors come to the conclusion that monolithic silica capillaries appear to become the preferred column type for CEC enantiomer separations of low-molecular drugs and other chiral pharmaceuticals or chemicals.