Abstract:
:Aim: Dedicated brain imaging is advocated by the National Comprehensive Cancer Network guidelines for newly diagnosed non-small-cell cancer (NSCLC) patients beyond stage I. The current study assessed the performance characteristics of this recommendation. Methods: Through accessing the Surveillance, Epidemiology and End Points (SEER) registry (2010-2015), all patients (regardless of stage) with newly diagnosed NSCLC and complete information about TN stages and presence or absence of brain metastases were extracted. In the current study, the following performance characteristics of the above recommendation were assessed: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), number needed to investigate (NNI) and accuracy. Results: A total of 182,977 NSCLC patients were included. For the overall cohort, PPV (for the recognition of brain metastases) was 13.8% and NNI to detect one case of brain metastasis was 7.2. Likewise, NPV (for the exclusion of brain metastases) was 97%, sensitivity was 92.1%, specificity was 31.1% and overall accuracy was 37.6%. When stratified by histology, patients with adenocarcinoma have PPV of 17.2% and NNI to detect one case with brain metastasis of 5.8. NPV (for the exclusion of brain metastases) was 97%, sensitivity of 91.4%, specificity of 35.4% and overall accuracy of 32.6%. On the other hand, patients with squamous cell carcinoma have PPV of 6.3% and NNI to detect one case with brain metastasis of 15.8. NPV (for the exclusion of brain metastases) was 98.9%, sensitivity of 94.6%, specificity of 26.3% and overall accuracy of 29.7%. Conclusion: In view of the poor specificity, the current study calls for reconsideration of the universal recommendation of dedicated brain imaging (in addition to PET/CT scan) among NSCLC patients beyond stage I.
journal_name
J Comp Eff Resjournal_title
Journal of comparative effectiveness researchauthors
Abdel-Rahman Odoi
10.2217/cer-2018-0148subject
Has Abstractpub_date
2019-06-01 00:00:00pages
569-575issue
8eissn
2042-6305issn
2042-6313journal_volume
8pub_type
杂志文章abstract::The 11 big health data networks participating in the AcademyHealth Electronic Data Methods Forum represent cutting-edge efforts to harness the power of big health data for research and quality improvement. This paper is a comparative case study based on site visits conducted with a subset of these large infrastructure...
journal_title:Journal of comparative effectiveness research
pub_type: 杂志文章
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
pub_type: 评论,杂志文章
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
pub_type: 杂志文章,meta分析,评审
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
pub_type: 杂志文章,meta分析
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
pub_type: 杂志文章
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
pub_type: 杂志文章
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journal_title:Journal of comparative effectiveness research
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journal_title:Journal of comparative effectiveness research
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