Abstract：
:Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation, texture, shape, and tumor architecture to predict disease recurrence in early stage NSCLC from digitized H&E tissue microarray (TMA) slides. Using a retrospective cohort of early stage NSCLC patients (Cohort #1, n = 70), we constructed a supervised classification model involving the most predictive features associated with disease recurrence. This model was then validated on two independent sets of early stage NSCLC patients, Cohort #2 (n = 119) and Cohort #3 (n = 116). The model yielded an accuracy of 81% for prediction of recurrence in the training Cohort #1, 82% and 75% in the validation Cohorts #2 and #3 respectively. A multivariable Cox proportional hazard model of Cohort #2, incorporating gender and traditional prognostic variables such as nodal status and stage indicated that the computer extracted histomorphometric score was an independent prognostic factor (hazard ratio = 20.81, 95% CI: 6.42-67.52, P < 0.001).
journal_name
Sci Repjournal_title
Scientific reportsauthors
Wang X,Janowczyk A,Zhou Y,Thawani R,Fu P,Schalper K,Velcheti V,Madabhushi Adoi
10.1038/s41598-017-13773-7subject
Has Abstractpub_date
2017-10-19 00:00:00pages
13543issue
1issn
2045-2322pii
10.1038/s41598-017-13773-7journal_volume
7pub_type
杂志文章abstract：:In this paper, the annually average Defense Meteorological Satellite Program-Operational Linescan System (DMSP/OLS) night-time light data is first proposed as a surrogate indicator to mine and forecast the average housing prices in the inland capital cities of China. First, based on the time-series analysis of individ...
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pub_type： 杂志文章
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pub_type： 杂志文章,meta分析,评审
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