{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T07:39:38Z","timestamp":1771918778021,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T00:00:00Z","timestamp":1633564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the \u201cblack-box\u201d nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.<\/jats:p>","DOI":"10.3390\/s21196655","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"6655","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6691-9739","authenticated-orcid":false,"given":"Michael","family":"Horry","sequence":"first","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia"},{"name":"IBM Australia Ltd., Sydney, NSW 2000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0102-5424","authenticated-orcid":false,"given":"Subrata","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia"},{"name":"Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6870-5056","authenticated-orcid":false,"given":"Manoranjan","family":"Paul","sequence":"additional","affiliation":[{"name":"Machine Vision and Digital Health (MaViDH), School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Douglas","family":"Gomes","sequence":"additional","affiliation":[{"name":"Machine Vision and Digital Health (MaViDH), School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5145-7276","authenticated-orcid":false,"given":"Anwaar","family":"Ul-Haq","sequence":"additional","affiliation":[{"name":"Machine Vision and Digital Health (MaViDH), School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,7]]},"reference":[{"key":"ref_1","unstructured":"(2020, December 08). Lung Cancer Statistics. Available online: https:\/\/www.wcrf.org\/dietandcancer\/cancer-trends\/lung-cancer-statistics."},{"key":"ref_2","unstructured":"(2021, October 06). World Lung Cancer Day 2020 Fact Sheet\u2014American College of Chest Physicians. Available online: https:\/\/www.chestnet.org\/newsroom\/chest-news\/2020\/07\/world-lung-cancer-day-2020-fact-sheet."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1097\/00004424-199202000-00005","article-title":"Computerized scheme for the detection of pulmonary nodules: A nonlinear filtering technique","volume":"27","author":"Yoshimura","year":"1992","journal-title":"Investig. Radiol."},{"key":"ref_4","unstructured":"Gaol, F.L. (2010, January 28\u201330). 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