{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T05:08:25Z","timestamp":1725253705965},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2015,6,25]],"date-time":"2015-06-25T00:00:00Z","timestamp":1435190400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Respirology"],"published-print":{"date-parts":[[2015,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and objective<\/jats:title><jats:p>Expert analysis of endobronchial ultrasound mini probe (<jats:styled-content style=\"fixed-case\">EBUS\u2010MP<\/jats:styled-content>) images has established subjective criteria for discriminating benign and malignant disease. Minimal data are available for objective analysis of these images. The aim of this study was to determine if greyscale texture analysis could differentiate between benign and malignant lung lesions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Digital <jats:styled-content style=\"fixed-case\">EBUS\u2010MP<\/jats:styled-content> images with a gain setting of 10\/19 and contrast setting of 4\/8 from 2007 until 2012 inclusive were included. These images had an expert\u2010defined region of interest (<jats:styled-content style=\"fixed-case\">ROI<\/jats:styled-content>) mapped. <jats:styled-content style=\"fixed-case\">ROI<\/jats:styled-content> were analysed for the following greyscale texture features: mean pixel value, difference between maximum and minimum pixel value, standard deviation of the mean pixel value, entropy, correlation, energy and homogeneity. Significant greyscale texture features differentiating benign from malignant disease were used by two physicians to assess a validation set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 167 images were available. The first 85 lesions were used in the prediction set. Benign lesions had larger differences between maximum and minimum pixel values, larger standard deviations of the mean pixel values and higher entropy than malignant lesions (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.0001 for all values). A total of 82 peripheral lesions were in the validation set. Physician 1 correctly classified 63\/82 (76.8%) with a negative predictive value (<jats:styled-content style=\"fixed-case\">NPV<\/jats:styled-content>) for malignancy of 82% and positive predictive value (<jats:styled-content style=\"fixed-case\">PPV<\/jats:styled-content>) of 75%. Physician 2 correctly classified 62\/82 (75.6%) with a <jats:styled-content style=\"fixed-case\">NPV<\/jats:styled-content> of 100% and <jats:styled-content style=\"fixed-case\">PPV<\/jats:styled-content> of 71.0%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Greyscale texture analysis of <jats:styled-content style=\"fixed-case\">EBUS\u2010MP<\/jats:styled-content> images can help establish aetiology with a high <jats:styled-content style=\"fixed-case\">NPV<\/jats:styled-content> for malignancy.<\/jats:p><\/jats:sec>","DOI":"10.1111\/resp.12577","type":"journal-article","created":{"date-parts":[[2015,6,26]],"date-time":"2015-06-26T00:44:44Z","timestamp":1435279484000},"page":"960-966","source":"Crossref","is-referenced-by-count":16,"title":["Grey scale texture analysis of endobronchial ultrasound mini probe images for prediction of benign or malignant aetiology"],"prefix":"10.1111","volume":"20","author":[{"given":"Phan","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Thoracic Medicine The Royal Adelaide Hospital  Adelaide South Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farzad","family":"Bashirzadeh","sequence":"additional","affiliation":[{"name":"Department of Thoracic Medicine The Royal Brisbane and Women's Hospital  Brisbane Queensland Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Justin","family":"Hundloe","sequence":"additional","affiliation":[{"name":"Department of Thoracic Medicine The Royal Brisbane and Women's Hospital  Brisbane Queensland Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Salvado","sequence":"additional","affiliation":[{"name":"The Australian eHealth Research Centre CSIRO Information and Communication Technologies Centre  Brisbane Queensland 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