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SC24 - Management of Indeterminate Pulmonary Nodules (ID 348)
- Event: WCLC 2016
- Type: Science Session
- Track: Pulmonology
- Presentations: 1
SC24.02 - Radiological Techniques for the Evaluation of Pulmonary Nodules (ID 6701)
11:00 - 12:30 | Author(s): R. Munden
Radiologic Techniques for the Evaluation of Pulmonary Nodules The incidental detection of pulmonary nodules has increased with improved CT technology and thin section imaging techniques[,]. Adding to this increased detection of nodules is the heightened interest in the purposeful search for nodules such as in oncology patients and lung cancer screening programs. The management of CT detected nodules is a subject of much debate and dependent upon the clinical setting. For instance, in a lung cancer screening setting, there has been a large volume of investigation of solid, semi-solid and ground glass nodules that is the foundation of management recommendations such as LungRads. In patients with a known malignancy, there is minimal literature on management recommendations and thus more influenced by pulmonary metastatic potential of the malignancy and clinician experience. Finally incidentally detected nodule management is greatly influenced by cancer risk factors and nodule texture; for these situations, the Fleischner criteria have been the most widely used and accepted management guidelines. The radiologic evaluation of nodules most often utilizes conventional imaging techniques of chest radiographs, computed tomography (CT), PET/CT. Occasionally MRI and ultrasound may be employed. Most recent changes involve risk stratification, computer software applications to enhance nodule analysis such as nodule enhancement patterns, volumetric computations, and texture analysis[6-8]. Future directions include incorporation of genomics into imaging as well as radiomic analysis and machine learning[,]. This presentation will review the highlights of the radiologic methods for evaluating pulmonary nodules with a focus on current guidelines and future directions. Reference: 1. Frank L, Quint LE. Chest CT incidentalomas: thyroid lesions, enlarged mediastinal lymph nodes, and lung nodules. Cancer imaging : the official publication of the International Cancer Imaging Society 2012;12:41-8. 2. Jacobs PC, Mali WP, Grobbee DE, van der Graaf Y. Prevalence of incidental findings in computed tomographic screening of the chest: a systematic review. Journal of computer assisted tomography 2008;32:214-21. 3. Lung CT Screening Reporting and Data Systen (Lung-RADS). 2014. (Accessed March 27, 2015, at www.acr.org/Quality-Safety/Resources/LungRADS ) 4. Munden RF, Erasmus JJ, Wahba H, Fineberg NS. Follow-up of small (4 mm or less) incidentally detected nodules by computed tomography in oncology patients: a retrospective review. J Thorac Oncol 2010;5:1958-62. 5. McMahon PM, Meza R, Plevritis SK, et al. Comparing benefits from many possible computed tomography lung cancer screening programs: extrapolating from the National Lung Screening Trial using comparative modeling. PloS one 2014;9:e99978. 6. McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med 2013;369:910-9. 7. Revel MP, Merlin A, Peyrard S, et al. Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules. AJR Am J Roentgenol 2006;187:135-42. 8. Talwar A, Gleeson FV, Rahman NM, Pickup L, Gooding M, Kadir T. A Review Of The Use Of Computer Aided Texture Analysis For Pulmonary Nodules Classification. American journal of respiratory and critical care medicine 2015;191. 9. El-Zein RA, Lopez MS, D'Amelio AM, Jr., et al. The cytokinesis-blocked micronucleus assay as a strong predictor of lung cancer: extension of a lung cancer risk prediction model. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2014;23:2462-70. 10. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278:563-77.
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