{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T15:59:41Z","timestamp":1762444781485},"reference-count":0,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,4,1]],"date-time":"2018-04-01T00:00:00Z","timestamp":1522540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Current Oncology"],"abstract":"<jats:p>Background: We aimed to develop a new EGFR mutation\u2013predictive scoring system to use in screening for EGFR-mutated lung adenocarcinomas (LACS). Methods: The study enrolled 279 patients with LAC, including 121 patients with EGFR wild-type tumours and 158 with EGFR-mutated tumours. The Student t-test, chi-square test, or Fisher exact test was applied to discriminate clinical and computed tomography (CT) features between the two groups. Using a principal component analysis (PCA) model, we derived predictive coefficients for the presence of EGFR mutation in LAC. Results: The EGFR mutation\u2013predictive score includes sex, smoking history, homogeneity, ground-glass opacity (GGO) on imaging, and the presence of pericardial effusion. The PCA predictive model took this form: sex \u00d7 16 + smoking history \u00d7 15 + GGO \u00d7 12 + pericardial effusion \u00d7 10 + emphysema \u00d7 11. Model scores ranged from 79 to 147. The area under the receiver operating characteristic curve was 0.752 [95% confidence interval (ci): 0.697 to 0.801] in the LAC population at the optimal cut-off value of 109, and the sensitivity and specificity were 68.4% (95% CI: 60.5% to 75.5%) and 74.4% (95% CI: 65.6% to 81.9%) respectively. Conclusions: The EGFR mutation risk scoring system based on clinical data and CT features is noninvasive and user-friendly. The model appears to frame a positive predictive value and was able to determine the value of repeating a biopsy if tissue is limited.<\/jats:p>","DOI":"10.3747\/co.25.3805","type":"journal-article","created":{"date-parts":[[2018,5,13]],"date-time":"2018-05-13T19:06:12Z","timestamp":1526238372000},"page":"132-138","source":"Crossref","is-referenced-by-count":5,"title":["A New Predictive Scoring System Based on Clinical Data and Computed Tomography Features for Diagnosing EGFR-mutated Lung Adenocarcinoma"],"prefix":"10.3390","volume":"25","author":[{"given":"Y.","family":"Cao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H.","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,1]]},"container-title":["Current Oncology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/current-oncology.com\/index.php\/oncology\/article\/viewFile\/3805\/2709","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/current-oncology.com\/index.php\/oncology\/article\/viewFile\/3805\/2692","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.mdpi.com\/1718-7729\/25\/2\/3805\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T13:19:15Z","timestamp":1613740755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1718-7729\/25\/2\/3805"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,1]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["co.25.3805"],"URL":"https:\/\/doi.org\/10.3747\/co.25.3805","relation":{},"ISSN":["1718-7729"],"issn-type":[{"value":"1718-7729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,1]]}}}