{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:00:44Z","timestamp":1781614844678,"version":"3.54.5"},"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The increasing complexity of diagnostic imaging often leads to misinterpretations and diagnostic errors, particularly in critical conditions such as pneumothorax. This study addresses the pressing need for improved diagnostic accuracy in CT scans by developing an intelligent model that leverages radiomics features and machine learning techniques. By enhancing the detection of pneumothorax, this research aims to mitigate diagnostic errors and accelerate the process of image interpretation, ultimately improving patient outcomes. Data used in this study was extracted from the medical records of 175 patients with suspected pneumothorax. The collected images were preprocessed in Matlab software. Radiomics features were extracted from each image and finally, the machine learning models were implemented on these features. The used machine learning algorithms are Gradient Tree Boosting (GBM), eXtreme Gradient Boosting (XGBoost), and Light GBM. To evaluate the performance of models, various evaluation criteria such as precision, accuracy, specificity, sensitivity, F1 score, Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), and misclassification were calculated. According to the calculated evaluation criteria, in terms of accuracy, the Gradient Boosting Machine (GBM) model achieved the highest performance with an accuracy of 98.97%, followed closely by the XGBoost model at 98.29%. For precision, the GBM model outperformed the other models, recording a precision value of 99.55%. Regarding sensitivity, all three models\u2014GBM, XGBoost, and LightGBM (LGBM)\u2014demonstrated strong performance, with sensitivity values of 99%, 99%, and 100%, respectively, indicating minimal variation among them. The artificial intelligence models used in this study have significant potential to enhance patient care by supporting radiologists and other clinicians in the diagnosis of pneumothorax. These models can facilitate the prioritization of positive cases, expedite evaluations, and ultimately improve patient outcomes.<\/jats:p>","DOI":"10.1371\/journal.pone.0314988","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T18:35:02Z","timestamp":1733769302000},"page":"e0314988","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Radiomics-based machine learning for automated detection of Pneumothorax in CT scans"],"prefix":"10.1371","volume":"19","author":[{"given":"Hanieh","family":"Alimiri Dehbaghi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3629-3389","authenticated-orcid":true,"given":"Karim","family":"Khoshgard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Sharini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samira","family":"Jafari Khairabadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhad","family":"Naleini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"pone.0314988.ref001","doi-asserted-by":"crossref","first-page":"108692","DOI":"10.1016\/j.ejrad.2019.108692","article-title":"Deep learning-enabled system for rapid pneumothorax screening on chest CT","volume":"120","author":"X Li","year":"2019","journal-title":"European journal of radiology"},{"key":"pone.0314988.ref002","first-page":"2017","article-title":"Primary and secondary spontaneous pneumothorax: prevalence, clinical features, and in-hospital mortality","author":"T Onuki","year":"2017","journal-title":"Canadian Respiratory Journal"},{"issue":"6","key":"pone.0314988.ref003","doi-asserted-by":"crossref","first-page":"792","DOI":"10.52547\/armaghanj.25.6.792","article-title":"Comparative Accuracy of FAST Sonography and CT Scan for Diagnosis Pneumothorax in Trauma Patients.","volume":"25","author":"M Tahmasebi","year":"2020","journal-title":"Armaghane Danesh."},{"issue":"5","key":"pone.0314988.ref004","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.jemermed.2021.07.006","article-title":"Traumatic pneumothorax: a review of current diagnostic practices and evolving management","volume":"61","author":"J Tran","year":"2021","journal-title":"The Journal of Emergency Medicine"},{"issue":"6","key":"pone.0314988.ref005","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.3390\/s22062278","article-title":"Deep learning-based computer-aided pneumothorax detection using chest X-ray images.","volume":"22","author":"P Malhotra","year":"2022","journal-title":"Sensors"},{"issue":"5","key":"pone.0314988.ref006","doi-asserted-by":"crossref","DOI":"10.1183\/13993003.02697-2020","article-title":"COVID-19 and pneumothorax: a multicentre retrospective case series","volume":"56","author":"AW Martinelli","year":"2020","journal-title":"European Respiratory Journal"},{"issue":"4","key":"pone.0314988.ref007","doi-asserted-by":"crossref","first-page":"9","DOI":"10.52547\/psj.17.4.9","article-title":"Comparison of the Diagnostic Values of Four-Point and Two-point Ultrasound Versus CT Scan in Determining Pneumothorax.","volume":"17","author":"D Farsi","year":"2019","journal-title":"Pajouhan Scientific Journal"},{"issue":"2","key":"pone.0314988.ref008","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1159\/000135932","article-title":"Pneumothorax","volume":"76","author":"M Noppen","year":"2008","journal-title":"Respiration"},{"issue":"2","key":"pone.0314988.ref009","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/0736-4679(93)90517-B","article-title":"CT detection of occult pneumothorax in multiple trauma patients","volume":"11","author":"KG Bridges","year":"1993","journal-title":"The Journal of emergency medicine"},{"issue":"2","key":"pone.0314988.ref010","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1111\/j.1440-1843.2004.00577.x","article-title":"Pneumothorax","volume":"9","author":"MH Baumann","year":"2004","journal-title":"Respirology"},{"issue":"5","key":"pone.0314988.ref011","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1097\/00003246-200005000-00018","article-title":"Evaluation of the utility of computed tomography in the initial assessment of the critical care patient with chest trauma.","volume":"28","author":"F Guerrero-L\u00f3pez","year":"2000","journal-title":"Critical care medicine."},{"key":"pone.0314988.ref012","first-page":"736320","article-title":"Automated Quantification of Pneumothorax in CT.","volume":"2012","author":"S Do","year":"2012","journal-title":"Computational and Mathematical Methods in Medicine."},{"issue":"3","key":"pone.0314988.ref013","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1378\/chest.11-0131","article-title":"Test characteristics of ultrasonography for the detection of pneumothorax: a systematic review and meta-analysis.","volume":"141","author":"K Alrajhi","year":"2012","journal-title":"Chest"},{"key":"pone.0314988.ref014","first-page":"62","article-title":"Comparative analysis of efficacy of chest X-ray and chest CT scan in patients with chest trauma: a retrospective study","volume":"2","author":"P. Kumari","year":"2017","journal-title":"Int J Contemp Med Surg Radiol"},{"issue":"3","key":"pone.0314988.ref015","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.jacr.2017.12.026","article-title":"Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success.","volume":"15","author":"JH Thrall","year":"2018","journal-title":"Journal of the American College of Radiology."},{"key":"pone.0314988.ref016","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s10140-020-01767-4","article-title":"Can AI outperform a junior resident? Comparison of deep neural network to first-year radiology residents for identification of pneumothorax.","volume":"27","author":"PH Yi","year":"2020","journal-title":"Emergency Radiology."},{"issue":"5","key":"pone.0314988.ref017","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/j.media.2012.02.005","article-title":"Machine learning and radiology","volume":"16","author":"S Wang","year":"2012","journal-title":"Med Image Anal"},{"issue":"3 Pt B","key":"pone.0314988.ref018","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.jacr.2017.12.028","article-title":"Machine Learning in Medical Imaging.","volume":"15","author":"ML Giger","year":"2018","journal-title":"J Am Coll Radiol."},{"issue":"8","key":"pone.0314988.ref019","first-page":"14","article-title":"A model for diagnosis of kidney disease using machine learning techniques","volume":"26","author":"SB Takhti","year":"2019","journal-title":"Razi Journal of Medical Sciences"},{"issue":"1","key":"pone.0314988.ref020","first-page":"34","article-title":"Applications of Machine Learning Approaches in Emergency Medicine; a Review Article.","volume":"7","author":"N Shafaf","year":"2019","journal-title":"Arch Acad Emerg Med."},{"key":"pone.0314988.ref021","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1007\/s10278-019-00299-9","article-title":"Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset","volume":"33","author":"RW Filice","year":"2020","journal-title":"Journal of digital imaging"},{"issue":"2","key":"pone.0314988.ref022","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learning for medical imaging.","volume":"37","author":"BJ Erickson","year":"2017","journal-title":"Radiographics"},{"issue":"1","key":"pone.0314988.ref023","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s41747-020-00152-7","article-title":"Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography.","volume":"4","author":"S R\u00f6hrich","year":"2020","journal-title":"European radiology experimental."},{"issue":"2","key":"pone.0314988.ref024","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data.","volume":"278","author":"RJ Gillies","year":"2016","journal-title":"Radiology"},{"issue":"4","key":"pone.0314988.ref025","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to radiomics","volume":"61","author":"ME Mayerhoefer","year":"2020","journal-title":"Journal of Nuclear Medicine"},{"issue":"4","key":"pone.0314988.ref026","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"P Lambin","year":"2012","journal-title":"European journal of cancer"},{"issue":"1","key":"pone.0314988.ref027","doi-asserted-by":"crossref","first-page":"9866","DOI":"10.1038\/s41598-022-13379-8","article-title":"Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest","volume":"12","author":"H Naseri","year":"2022","journal-title":"Scientific Reports"},{"issue":"4","key":"pone.0314988.ref028","first-page":"106","article-title":"Classification of Lung nodules using textural and geometric features","volume":"27","author":"Ghahvechi Khaligh","year":"2020","journal-title":"Razi Journal of Medical Sciences"},{"issue":"5","key":"pone.0314988.ref029","first-page":"180","article-title":"Comparative study of different noise filtering techniques in digital images","volume":"3","author":"S Das","year":"2015","journal-title":"International Journal of Engineering Research and General Science"},{"key":"pone.0314988.ref030","article-title":"Social spider optimization based optimally weighted Otsu thresholding for image enhancement","author":"AK Bhandari","year":"2018","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"1","key":"pone.0314988.ref031","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201chow-to\u201d guide and critical reflection.","volume":"11","author":"JE van Timmeren","year":"2020","journal-title":"Insights into Imaging"},{"issue":"11","key":"pone.0314988.ref032","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1109\/34.244679","article-title":"Texture classification by wavelet packet signatures","volume":"15","author":"A Laine","year":"1993","journal-title":"IEEE Transactions on pattern analysis and machine intelligence"},{"issue":"2","key":"pone.0314988.ref033","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.","volume":"295","author":"A Zwanenburg","year":"2020","journal-title":"Radiology"},{"issue":"3","key":"pone.0314988.ref034","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TBME.2013.2284600","article-title":"Advanced statistical matrices for texture characterization: application to cell classification","volume":"61","author":"G Thibault","year":"2013","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"pone.0314988.ref035","volume-title":"Introduction to machine learning","author":"E. Alpaydin","year":"2020"},{"key":"pone.0314988.ref036","volume-title":"Inference, and Prediction","author":"T Hastie","year":"2009","edition":"2"},{"issue":"1","key":"pone.0314988.ref037","doi-asserted-by":"crossref","first-page":"44","DOI":"10.2214\/AJR.18.20260","article-title":"Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.","volume":"212","author":"Y Xie","year":"2019","journal-title":"American Journal of Roentgenology."},{"issue":"18","key":"pone.0314988.ref038","doi-asserted-by":"crossref","first-page":"2932","DOI":"10.3390\/electronics11182932","article-title":"Used Car Price Prediction Based on the Iterative Framework of XGBoost+LightGBM","volume":"11","author":"B Cui","year":"2022","journal-title":"Electronics"},{"key":"pone.0314988.ref039","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C, editors. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining; 2016.","DOI":"10.1145\/2939672.2939785"},{"key":"pone.0314988.ref040","first-page":"1","article-title":"Diagnosis of borderline personality disorder based on Cyberball social exclusion task and resting-state fMRI: using machine learning approach as an auxiliary tool","author":"S Jafari","year":"2022","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization"},{"key":"pone.0314988.ref041","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/978-1-62703-748-8_7","article-title":"Introduction to machine learning","volume":"1107","author":"Y Ba\u015ftanlar","year":"2014","journal-title":"Methods Mol Biol"},{"issue":"1","key":"pone.0314988.ref042","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","article-title":"Classification assessment methods.","volume":"17","author":"A. Tharwat","year":"2021","journal-title":"Applied Computing and Informatics"},{"issue":"3","key":"pone.0314988.ref043","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions.","volume":"2","author":"IH Sarker","year":"2021","journal-title":"SN Comput Sci."},{"issue":"1","key":"pone.0314988.ref044","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s40537-019-0219-y","article-title":"Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage","volume":"6","author":"IH Sarker","year":"2019","journal-title":"Journal of Big Data"},{"key":"pone.0314988.ref045","doi-asserted-by":"crossref","first-page":"101857","DOI":"10.1016\/j.media.2020.101857","article-title":"Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes","volume":"67","author":"RL Draelos","year":"2021","journal-title":"Med Image Anal"}],"updated-by":[{"DOI":"10.1371\/journal.pone.0321698","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000}}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0314988","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T18:35:15Z","timestamp":1733769315000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0314988"}},"subtitle":[],"editor":[{"given":"Robert Jeenchen","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,12,9]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12,9]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0314988","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,9]]}}}