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CT Diagnosis of Lung Adenocarcinoma: Radiologic-Pathologic Correlation and Growth Rate

2020; Radiological Society of North America; Volume: 297; Issue: 1 Linguagem: Inglês

10.1148/radiol.2020202895

1527-1315

Keiko Kuriyama, Masahiro Yanagawa,

Medical Imaging Techniques and Applications

HomeRadiologyVol. 297, No. 1 PreviousNext Reviews and CommentaryFree AccessEditorialCT Diagnosis of Lung Adenocarcinoma: Radiologic-Pathologic Correlation and Growth RateKeiko Kuriyama , Masahiro YanagawaKeiko Kuriyama , Masahiro YanagawaAuthor AffiliationsFrom the Department of Radiology, National Hospital Organization/Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka 540-0006, Japan (K.K.); and Department of Radiology, Graduate School of Medicine/Faculty of Medicine, Osaka University, Osaka, Japan (M.Y.).Address correspondence to K.K. (e-mail: [email protected]).Keiko Kuriyama Masahiro YanagawaPublished Online:Aug 4 2020https://doi.org/10.1148/radiol.2020202895MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by de Margerie-Mellon et al in this issue.Dr Keiko Kuriyama is a clinical professor of radiology at the Graduate School of Medicine/Faculty of Medicine at Osaka University and a manager of the Department of Radiology at Osaka National Hospital. From 1985 to 2005, she was a chest radiologist at Osaka International Cancer Institute. Her principal interests include CT-pathologic correlations of lung cancers.Download as PowerPointOpen in Image Viewer Dr Masahiro Yanagawa is an associate professor of radiology at the Graduate School of Medicine/Faculty of Medicine at Osaka University. His research interests focus on the application of advanced quantitative CT techniques to characterize aspects of thoracic oncology. He has served as a principal investigator for several national grants, published more than 60 articles and multiple book chapters, and won 12 awardsDownload as PowerPointOpen in Image Viewer Lung cancer is the most common cause of cancer deaths worldwide in both men and women, and women are at a higher risk of developing cancer than men. In patients with early-stage disease, surgical resection is associated with the highest cure rate. According to the eighth edition of TNM classification for lung cancer, the 5-year survival rate of patients with stage IA1 disease (T1a ≤1 cm) is 92%. In early-stage lung cancers, the tumor size is associated with survival. Several large randomized trials of low-dose CT screening (National Lung Screening Trial in the United States and several European randomized trials) have shown reduced mortality in high-risk populations. These trials included large numbers of small lung nodules detected with CT, and they identified many noncancer nodules compared with asymptomatic lung cancers.In the small lung adenocarcinomas classification described by Noguchi et al (1) in 1995, tumors that showed lepidic growth (replacement growth of alveolar-lining epithelial cells) had better prognosis than tumors that showed hilic growth (compressive and expanding growth). In the 2011 International Association for Study of Lung Cancer/American Thoracic Society/European Respiratory Society lung adenocarcinoma classification, new entities of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma, and lepidic predominant adenocarcinoma were defined according to lepidic growth and the size of invasive components of the tumor (2). These definitions were later incorporated into the 2015 World Health Organization Classification. On thin-section CT images, adenocarcinomas appear as nodules with solid opacity, nodules exhibiting some ground-glass opacity (GGO) with some solid components (part-solid nodules), or nodules exhibiting complete GGO (ground-glass nodules). All subsolid nodules (part-solid nodules and ground-glass nodules) exhibit some degree of GGO. The CT findings of GGO versus solid component tend to correspond respectively to lepidic versus invasive component observed pathologically (3).Atypical adenomatous hyperplasia and AIS are classified as preinvasive lesions characterized by neoplastic pneumocytes growing along alveolar walls (lepidic growth). Atypical adenomatous hyperplasias are typically smaller than 0.5 cm in diameter, and there may be multiple lesions. At CT, atypical adenomatous hyperplasias appear as small round nodules with complete GGO and lower CT attenuation. AISs are defined as small (≤3 cm) adenocarcinomas exhibiting lepidic growth that lacks stromal, vascular, or pleural invasion. They are typically nonmucinous tumors consisting of type II pneumocytes and/or Clara cell differentiation. A diagnosis of AIS can only be made if the entire tumor is resected and available for pathologic examination (2). At CT, they usually appear as pure ground-glass nodules but occasionally as part-solid nodules with a small solid component resulting from focal alveolar collapse (4). Pseudocavitation representing cystic air-filled areas may manifest within the tumor. The disease-free 5-year survival rate is 100% after resection.Minimally invasive adenocarcinoma is pathologically defined as a small adenocarcinoma (≤3 cm) with a predominantly lepidic pattern and invasion of at least 0.5 cm. It often resembles subsolid nodules on CT images. Minimally invasive adenocarcinomas were introduced in the 2011 lung adenocarcinoma classification (2). The disease-free 5-year survival rate is nearly 100% after complete resection. Lepidic predominant adenocarcinomas are invasive adenocarcinomas with a lepidic pattern and an invasion greater than 0.5 cm. At CT, they may resemble part-solid nodules, but the solid component appears larger than 0.5 cm.In clinical practice and CT screening, many persistent subsolid nodules exhibiting pathologic characteristics consistent with adenocarcinomas are encountered. Subsolid nodules have a slower growth rate and are associated with a higher risk of malignancy than small solid nodules. Therefore, the recommended total follow-up period for persistent subsolid nodules has been increased to 5 years or more. From a clinical practice perspective, sequential CT follow-up of small lung nodules to assess the volume doubling time is the most practical and least invasive modality for diagnosis (5). Two-dimensional caliper measurements and three-dimensional volumetric measurements of part-solid nodules have been used to estimate volume doubling times with respect to tumor growth (5–8). Both the total nodule diameter (including GGO) and the solid component diameter should be measured separately in accordance with the 2015 World Health Organization classification of lung cancer recommendations (4).In this issue of Radiology, de Margerie-Mellon et al attempted to collect evidence pertaining to the use of an exponential growth model in the context of subsolid nodules that incorporated the Akaike Information Criterion. Although linear and quadratic models are relatively simple in terms of explaining tumor growth, the exponential model was better than either of these two models (9). In addition to the total volume, solid component volume is an important parameter when addressing the growth of a subsolid nodule. These counterintuitive results may be due to selection bias because only resected nodules in patients who had undergone at least three CT examinations before surgery were included.In the study by de Margerie-Mellon et al, all subsolid nodules were surgically resected and confirmed to be adenocarcinomas at pathologic analysis. Thus, a possibility exists of some degree of selection bias because slow-growing nodules are unlikely to be resected, whereas rapidly growing nodules are likely to be resected. Long-term follow-up is required because pure ground-glass nodules exhibit almost no change over time. The optimal management method that will result in reduction in the lung cancer mortality rate remains unclear. Elucidation of the natural history of ground-glass nodules may be important. Notably, however, quantitative evaluation incorporating the tumor growth rate will facilitate the detection of changes in the size and attenuation at CT and will help determine management strategies for subsolid nodules.In conclusion, the growth pattern of adenocarcinomas manifesting as subsolid nodules at CT is better represented by an exponential model. The use of volume doubling time for the growth assessment of subsolid nodules is justified (9). Quantification using the growth rate and/or the application of artificial intelligence may improve the measurement of pulmonary nodules in the follow-up process (10). More reproducible and accurate evaluations could become possible in patients with subsolid nodules. Careful system standardization is of utmost importance to put quantitative tools into practical use.Disclosures of Conflicts of Interest: K.K. disclosed no relevant relationships. M.Y. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution has received reimbursement from Canon Medical Systems for travel/accommodations/meeting expenses. Other relationships: disclosed no relevant relationships.References1. Noguchi M, Morikawa A, Kawasaki M, et al. Small adenocarcinoma of the lung. Histologic characteristics and prognosis. Cancer 1995;75(12):2844–2852. Crossref, Medline, Google Scholar2. 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 Scholar3. Kuriyama K, Seto M, Kasugai T, et al. Ground-glass opacity on thin-section CT: value in differentiating subtypes of adenocarcinoma of the lung. AJR Am J Roentgenol 1999;173(2):465–469. Crossref, Medline, Google Scholar4. Travis WD, Asamura H, Bankier AA, et al. The IASLC Lung Cancer Staging Project: Proposals for Coding T Categories for Subsolid Nodules and Assessment of Tumor Size in Part-Solid Tumors in the Forthcoming Eighth Edition of the TNM Classification of Lung Cancer. J Thorac Oncol 2016;11(8):1204–1223. Crossref, Medline, Google Scholar5. Hasegawa M, Sone S, Takashima S, et al. Growth rate of small lung cancers detected on mass CT screening. Br J Radiol 2000;73(876):1252–1259. Crossref, Medline, Google Scholar6. Ko JP, Berman EJ, Kaur M, et al. Pulmonary Nodules: growth rate assessment in patients by using serial CT and three-dimensional volumetry. Radiology 2012;262(2):662–671. Link, Google Scholar7. Yanagawa M, Tanaka Y, Leung AN, et al. Prognostic importance of volumetric measurements in stage I lung adenocarcinoma. Radiology 2014;272(2):557–567. Link, Google Scholar8. Song YS, Park CM, Park SJ, Lee SM, Jeon YK, Goo JM. Volume and mass doubling times of persistent pulmonary subsolid nodules detected in patients without known malignancy. Radiology 2014;273(1):276–284. Link, Google Scholar9. de Margerie-Mellon C, Ngo LH, Gill RR, et al. The growth rate of subsolid lung adenocarcinoma nodules at chest CT. Radiology 2020;297:189–198. Medline, Google Scholar10. Ohno Y, Aoyagi K, Yaguchi A, et al. Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT. Radiology 2020;296(2):432–443. Link, Google ScholarArticle HistoryReceived: June 28 2020Revision requested: July 8 2020Revision received: July 21 2020Accepted: July 22 2020Published online: Aug 04 2020Published in print: Oct 2020 FiguresReferencesRelatedDetailsAccompanying This ArticleThe Growth Rate of Subsolid Lung Adenocarcinoma Nodules at Chest CTAug 4 2020RadiologyRecommended Articles Lung Adenocarcinomas: Can Volume Doubling Time Aid Management?Radiology2020Volume: 295Issue: 3pp. 713-714Visualization of the Associations between the CT Features Extracted from a Deep Learning Survival Prediction Model and Histopathologic Risk FactorsRadiology2022Volume: 305Issue: 2pp. 452-453High-Spatial-Resolution CT Offers New Opportunities for Discovery in the LungRadiology2020Volume: 297Issue: 2pp. 472-473Lung Adenocarcinoma: Correlation of Quantitative CT Findings with Pathologic FindingsRadiology2016Volume: 280Issue: 3pp. 931-939Over- and Underdiagnosis in Lung Cancer: Searching for a "Solid" DiagnosisRadiology2016Volume: 280Issue: 3pp. 655-658See More RSNA Education Exhibits Developments in Lung Cancer - What Radiologists Should Know About the WHO Classification Updates and Developments in Molecular Biology ResearchDigital Posters2022Imaging Diagnosis as Biomarkers for Lung Cancer with Driver Oncogene Mutation / Gene Translocation in the Era of Personalized MedicineDigital Posters2022Malignant Masquerader: The Many Faces of Lung AdenocarcinomaDigital Posters2020 RSNA Case Collection Diffuse idiopathic pulmonary neuroendocrine cell hyperplasiaRSNA Case Collection2020Small cell lung carcinomaRSNA Case Collection2020Thoracic splenosisRSNA Case Collection2020 Vol. 297, No. 1 Metrics Altmetric Score PDF download