WS04.02 Challenges in Imaging Evaluation to Enter Data in the IASLC Database

Abstract

Soon after the publication of the proposals for the 8thedition of the TNM staging system, the International Association for the Study of Lung Cancer (IASLC) opened a new database for data collection for the next iteration of the lung cancer staging system (1). The database contains more than 450 fields, which address data on patient characteristics, baseline laboratory values, as well as the results of pulmonary function, imaging, and pathological tests. As the database collects the data about lung cancer newly diagnosed between January 1, 2011, and December 31, 2019, the data entered might be predominantly retrospectively collected in some centers. From an imaging perspective, this might lead to a number of problems. First, at least some of the elements required by the database might not be mentioned in the radiological reports. This may necessitate a re-evaluation of the imaging studies by an experienced radiologist, which might not be possible in all centers. Furthermore, the levels of training of the radiologists and reporting across different countries may be variable and thus introduce some degree of imprecision into the quality of the data. As an example, in T staging, particular attention has to be paid to thorough measurements to ensure that the largest diameter in the axial, coronal, or sagittal plane is measured (2). Caution should also be taken not to overdiagnose additional pulmonary nodules as metastases. In N staging, particular attention has to be paid to the lymph node atlas-mining, particularly the well-known pitfall in the correct assignment of lymph nodes at levels 4 and 10 (3-5). Furthermore, the quality of imaging and the availability of more advanced/expensive imaging techniques may also vary from country to country. As an example, the availabilities of PET/CT and/or MRI are not homogenous all over the globe. However, if PET/CT is not performed routinely in operable patients, as many as 20% of unexpected distant metastases might be missed (6). Future staging projects should specifically address these issues in order to further improve the the quality of data necessary to improve the next iteration of the staging system. 1. Giroux DJ, Van Schil P, Asamura H, Rami-Porta R, Chansky K, Crowley JJ, et al. The IASLC Lung Cancer Staging Project: A Renewed Call to Participation. J Thorac Oncol. 2018;13(6):801-9. 2. Travis WD, Asamura H, Bankier AA, Beasley MB, Detterbeck F, Flieder DB, 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. 3. El-Sherief AH, Lau CT, Obuchowski NA, Mehta AC, Rice TW, Blackstone EH. Cross-Disciplinary Analysis of Lymph Node Classification in Lung Cancer on CT Scanning. Chest. 2017;151(4):776-85. 4. El-Sherief AH, Lau CT, Wu CC, Drake RL, Abbott GF, Rice TW. International association for the study of lung cancer (IASLC) lymph node map: radiologic review with CT illustration. Radiographics. 2014;34(6):1680-91. 5. Aviram G, Revel MP. Misclassification of Lymph Nodes in Lung Cancer Staging: Can We Improve? Chest. 2017;151(4):733-4. 6. Fischer B, Lassen U, Mortensen J, Larsen S, Loft A, Bertelsen A, et al. Preoperative Staging of Lung Cancer with Combined PET-CT. N Engl J Med. 2009;361(1):32-9. staging, TNM, Lung cancer