Radiomics and Deep Learning to Predict Pulmonary Nodule Metastasis at CT

HomeRadiologyVol. 311, No. 1 PreviousNext Reviews and CommentaryEditorialRadiomics and Deep Learning to Predict Pulmonary Nodule Metastasis at CTJae Ho Sohn , Brandon K. K. FieldsJae Ho Sohn , Brandon K. K. FieldsAuthor AffiliationsFrom the Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging and Division of Cardiothoracic Imaging, University of California San Francisco (UCSF), 185 Berry St, Ste 350, San Francisco, CA 94107.Address correspondence to J.H.S. (email: [email protected]).Jae Ho Sohn Brandon K. K. FieldsPublished Online:Apr 9 2024https://doi.org/10.1148/radiol.233356See also the article by Pan and Hu et al in this issue.MoreSectionsFull textPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookXLinked In References1. Adams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. Lancet 2023;401(10374):390–408. Crossref, Medline, Google Scholar2. Cardillo G, Petersen RH, Ricciardi S, et al. European guidelines for the surgical management of pure ground-glass opacities and part-solid nodules: Task Force of the European Association of Cardio-Thoracic Surgery and the European Society of Thoracic Surgeons. Eur J Cardiothorac Surg 2023;64(4):ezad222. Crossref, Medline, Google Scholar3. Travis WD, Brambilla E, Noguchi M, et al. International association for the study of lung cancer/American thoracic society/European respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 2011;6(2):244–285. Crossref, Medline, Google Scholar4. Pan Z, Hu G, Zhu Z, et al. Predicting invasiveness of lung adenocarcinoma at chest CT with deep learning ternary classification models. Radiology 2024;311(1):e232057. Google Scholar5. Polikar R. Ensemble learning. In: Cha Zhang, Ma Y, eds. Ensemble machine learning: methods and applications. New York, NY: Springer Science+Business Media,2012; 1–34. Google Scholar6. Kitami A, Sano F, Hayashi S, et al. Correlation between histological invasiveness and the computed tomography value in pure ground-glass nodules. Surg Today 2016;46(5):593–598. Crossref, Medline, Google Scholar7. Lee SM, Park CM, Goo JM, Lee HJ, Wi JY, Kang CH. Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as ground-glass nodules: differentiation by using CT features. Radiology 2013;268(1):265–273. Link, Google Scholar8. Feng H, Shi G, Xu Q, Ren J, Wang L, Cai X. Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas. Insights Imaging 2023;14(1):24. Crossref, Medline, Google Scholar9. Yoon HJ, Choi J, Kim E, et al. Deep learning analysis to predict EGFR mutation status in lung adenocarcinoma manifesting as pure ground-glass opacity nodules on CT. Front Oncol 2022;12:951575. Crossref, Medline, Google Scholar10. Varghese BA, Fields BKK, Hwang DH, Duddalwar VA, Matcuk GR Jr, Cen SY. Spatial assessments in texture analysis: what the radiologist needs to know. Front Radiol 2023;3:1240544. Crossref, Medline, Google ScholarArticle HistoryReceived: Dec 11 2023Revision requested: Dec 21 2023Revision received: Dec 27 2023Accepted: Jan 2 2024Published online: Apr 09 2024 FiguresReferencesRelatedDetailsAccompanying This ArticlePredicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification ModelsApr 9 2024RadiologyRecommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 311, No. 1 Metrics Altmetric Score PDF download