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Kavita Garg



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    ES 02 - Diagnostic and Interventional Radiology in Lung Cancer: Update 2017 (ID 511)

    • Event: WCLC 2017
    • Type: Educational Session
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      ES 02.03 - Radiologic Implications of the WHO Classification for Lung Cancer (ID 8026)

      11:30 - 11:45  |  Presenting Author(s): Kavita Garg

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      Abstract:
      Marked heterogeneity exists in clinical, radiologic, molecular, and pathologic features among adenocarcinoma cases. Therefore, a new Classification of Lung Adenocarcinoma was proposed by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society in 2011 (1). The 2011 classification addressed three important weaknesses in the previous classification. First, it eliminated the term bronchioloalveolar carcinoma (BAC). Second, it added new terminologies of carcinoma-in-situ (CIS), and minimally invasive adenocarcinoma (MIA) to recognize that minimal invasion (< 5mm) had nearly similar clinical outcome as noninvasive nodules. Third, it replaced the terminology of mixed subtype of adenocarcinoma. In this revised classification, invasive lung ADCs were divided into the five subtypes; lepidic, acinar, solid, papillary, and micropapillary patterns primarily based on histologic features. The term predominant is appended to all categories of invasive ADC, as most of these tumors consist of mixtures of the subtypes (1). The widespread availability of MDCT and abundance of new information obtained especially from low-dose CT lung cancer screening programs, have increased our understanding of the types and management of small peripheral lung nodules encountered in daily clinical practice, in particular, the importance and prevalence of subsolid pulmonary nodules (atypical adenomatous hyperplasia (AAH), ground glass nodules (GGN) and part-solid nodules). Thin-section CT has emerged as a new biomarker for lung adenocarcinoma subtypes. The approval of CT as a screening tool for lung cancer was based primarily on National Lung Screening Trial (NLST) results. The NLST recently found that Low Dose Helical Computed Tomography (LDCT) reduces lung cancer specific mortality by 20% relative to chest x-ray screening in a cohort at high risk of lung cancer (2). However, significant concerns remain regarding its high false positive rate, overdiagnosis, cost effectiveness and concerns related to radiation burden from repeat CT screens. There is a trade-of between early detection of lung cancer vs unnecessary work-up of indeterminate nodules resulting in many side effects including anxiety, radiation exposure from CT follow-up to assess for growth, cost and morbidity and mortality related to biopsy or resection of a benign nodule. It is expected that false positive rate would decrease by 50% using more accurate phenotyping of a nodule using the lung CT reporting and data system (Lung-RADS) appropriately (3). One of the major changes proposed in Lung-RADS is the size threshold for positive screen, from 4 mm in NLST to 6 mm for solid nodules and 20 mm for nonsolid nodules. Tissue sampling would be used primarily for larger than 15 mm solid nodules or PET positive nodules with larger than 8 mm solid component. False positive rate would still be likely not acceptable for an individual using this approach. There is need for more accurate nodule assessment and risk stratification as given our current understanding that genetic make-up of a nodule is the ultimate determinant of clinical outcome (4). Further improvements in stage discrimination and management of lung nodules could be expected in the future, as more robust data related to texture analyses of tumors, their genetic profiles and impact of those on clinical outcome becomes available (5-8). Simple measuring the tumor size with one-dimentional (Response Evaluation Criteria in Solid Tumors (or RECIST) long-axis measurements do not reflect the complexity of tumor morphology or behavior. Also, it may not be predictive of therapeutic benefit. In contrast, the emerging field of radiomics is a high-throughput process in which a large number of shape, edge, and texture imaging features are extracted, quantified, and stored in databases in an objective, reproducible, and mineable form. Once transformed into a quantifiable form, radiologic tumor properties can be linked to underlying genetic alterations and to medical outcomes. Marked heterogeneity in genetic properties of different cells in the same tumor is typical and reflects ongoing intratumoral evolution. Clinical imaging is well suited to measure temporal and spatial heterogeneity. Subjective imaging descriptors of cancers are inadequate to capture this heterogeneity and must be replaced by quantitative metrics that enable statistical comparisons between features describing intratumoral heterogeneity and clinical outcomes and molecular properties. A recent study adds further support toward taking a conservative approach in the management and treatment of patients with part-solid nodules especially when the solid component is small. This strategy is already reflected in the Lung-RADS guidelines, which recommend focusing on the size of the solid component in the part-solid nodule instead of on the overall nodule size. For the future, the critical issue will be further refinements for the follow-up of nonsolid and part-solid nodules based on the size or volume that allow a process of shared decision making in selecting appropriate management and treatment (9-10). This lecture will address the radiologic implications of this new lung adenocarcinoma classification. References: 1. Travis W, Brambilla E, Noguchi M, et al. IASLC/ATS/ERS International multidisciplinary classification of lung adenocarcinoma. J Thoracic Oncol 2011;6:244-285 2. Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395-409 3. American College of Radiology: Lung-RADS Version 1.0 Assessment Categories Release date: April 28, 2014. Accessed on 17 March, 2015 4. McWilliams, A. et al. Probability of cancer in pulmonary nodules detected on first screening CT. The New England journal of medicine 2013;369: 910-919, doi:10.1056/NEJMoa1214726 5. Lambin P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48 (4):441-446 6. Gatenby RA, Grove O, Gillies RJ. Radiology 2013;269:8-15 7. Bartholmai BJ, Koo CW, Johnson GB, et al. Pulmonary nodule characterization including computer analysis and quantitative features. J Thorac Imaging 2015;30 (2) 139-156 8. Song SH, Park H, Lee G, et al. Imaging phenotyping using Radiomics to predict micropapillary pattern within lung adenocarcinoma. JTO 2017;12:624-632 9. Rowena Yip, Henschke CI, Xu DM, et al. Lung cancers manifesting as part-solid nodules in the National Lung Screening Trial. AJR 2017;208:1011-1021 10. American College of Radiology website. Lung CT Screening Reporting and Data System (Lung-RADS). Accessed January 11, 2016 11. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: FROM THE Fleischner Society 2017. Radiology 2017;284:228-243

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