Low-Dose Chest Ct To Predict Disease-Free Survival For Early-Stage Node-Negative Centrally Located Lung Adenocarcinoma

HomeRadiologyVol. 299, No. 2 PreviousNext Reviews and CommentaryFree AccessEditorialLow-Dose Chest CT to Predict Disease-Free Survival for Early-Stage Node-Negative Centrally Located Lung AdenocarcinomaJohn Wandtke , Susan K. HobbsJohn Wandtke , Susan K. HobbsAuthor AffiliationsFrom the Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642.Address correspondence to J.W. (e-mail: [email protected]).John Wandtke Susan K. HobbsPublished Online:Feb 23 2021https://doi.org/10.1148/radiol.2021210219MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by Choi et al in this issue.John C. Wandtke, MD, is a professor in the Department of Imaging Sciences, University of Rochester Medical Center. He has served for many years as vice chairman of the department and was the director of thoracic radiology. He was an associate editor of Radiology and is a reviewer for Cardiothoracic Imaging and Artificial Intelligence. He was an active member of the Society of Thoracic Radiology and has served on several committees. His research interest is in innovations in medical imaging.Download as PowerPointOpen in Image Viewer Susan Hobbs, MD, PhD, is an associate professor in the Department of Imaging Sciences, University of Rochester Medical Center. She is the vice chair for education, director of the cardiothoracic division, and director of residency program. She is an active member of the Society of Thoracic Radiology, NASCI, and the American College of Radiology. Dr Hobbs has received numerous awards for teaching and service from the department of imaging sciences.Download as PowerPointOpen in Image Viewer Lung cancer remains the leading cause of cancer death in the United States, with a 5-year survival rate for early, localized, stage 1 non–small cell lung cancer of only 59% according to the National Institutes of Health National Cancer Institute (1). The advent of lung cancer screening with low radiation dose technique (<3 mGy for a typical patient) has led to an increase in the detection of early-stage lung cancer. These early-stage lung cancers detected with low-dose CT lung cancer screening are most likely to benefit from early curative treatment. The standard treatment for stage 1 lung cancer is lobar resection with the possible addition of adjuvant chemotherapy.Pretreatment staging in central lung cancers (CLCs) includes interventional nodal assessment. The optimal definition of CLC is important because CLCs are known to have more frequent mediastinal metastasis and bone metastasis. There have been several studies that show that although the frequency of positive mediastinal nodes is relatively low in CLC, it is more common than with peripheral early lung cancers. Therefore, mediastinal staging is important, especially when patients are unable to undergo surgical resection (2). The determination of a central tumor location is a challenge for radiologists because there are conflicting definitions of “central” (3,4).In this issue of Radiology, Choi et al (3) performed an analysis using three definitions of CLC. The first qualitative definition by Casal et al (4) described a CLC as being located in the inner one-third of the lung, with concentric lines arising from the hilum. A second qualitative definition by Shin et al (5) described a CLC as being in the inner one-third of the lung defined by concentric lines from the midline based on axial CT scans. In addition to these two qualitative definitions, Choi et al proposed quantitative definitions of a location index, which is a ratio of an intersecting point between the horizontal midline of the thorax and mediastinum and the distance of the tumor from the hilum. The authors evaluated these different definitions to address which definition of CLC best aids in the prediction of disease-free survival (DFS).This research describes how 436 patients with CT scans of early-stage node-negative lung adenocarcinoma were evaluated to determine if centrally located tumors had a worse prognosis than peripherally located tumors. The CT nodule patterns included 44 pure ground-glass, 201 part-solid, and 191 solid nodules. The median follow-up was 3.2 years.The differentiation of early lung cancer in central and peripheral tumor locations was reported to result in a different DFS rate in the study by Choi et al (3). The authors found adverse survival of five events among 34 patients with central tumors (15%) compared with only 27 events among 402 patients with peripheral tumors (6.7%), with an adjusted hazard ratio of 2.90 (95% CI: 1.06, 7.96; P = .04).The 3-year DFS with use of definition 1 was 87.4% for CLC compared with 93.7% for peripheral cancers. The 3-year DFS with definition 2 was 92.1% for CLC compared with 93.4% peripheral cancer. The results obtained with the quantitative location index showed that CLC was associated with significantly shorter DFS, with 16 of 130 (12.3%) recurrences of tumor or metastasis for CLC compared with 16 of 306 (5.2%) for peripheral lung cancers.The study also evaluated ease of use of the location index. Two radiologists used each of the definitions of CLC, and interreader agreement was measured. The Cohen κ statistic was 0.52 (95% CI: 0.37, 0.68; moderate agreement) for definition 1 and 0.86 (95% CI: 0.80, 0.91; almost perfect agreement) for definition 5 (inner two-thirds based on the location index).The current research has several limitations. All patients came from the same medical center. Although the number of patients was reasonable, an even larger study population is ideal to make important clinical treatment decisions. A study with the addition of node-positive early lung adenocarcinoma would also be useful. While the study by Choi et al (3) followed up patients for 3 years of the DFS period, a 5-year study would be even more supportive for directing treatment of these patients.Nevertheless, this research is one of the newer attempts to evaluate low-dose CT lung cancer screening to determine if tumor features such as location can help predict which patients are more likely to have recurrent lung cancer after initial surgical resection. Artificial intelligence has already been shown to assist in better detection of disease and aid in predicting prognosis for lung infections and tumors. Computer-aided analysis of CT images to better understand the staging of lung cancer and predict which patients might benefit from new treatments is an area of medical research in which radiologists should be an important participant.According to the National Health Interview Survey (National Center for Health Statistics from the Centers for Disease Control and Prevention) (6), about 4.5% of adults aged 55–80 years currently undergo low-dose CT lung cancer screening.During low-dose CT lung cancer screening, the rate of stage 1 lung cancer is 63% in the United States, 67% in the United Kingdom, 62% in Korea, and 67% in Canada (7). With such a large number of early-stage lung cancers, improved CT analysis hopefully will result in better management and treatment protocols to improve on the 59% survival rate for stage 1 lung cancer. The study by Choi et al identified a better DFS for peripheral lung cancer compared with CLC. With larger research studies in additional medical centers, this worse prognosis for CLC might lead to better staging of early lung cancer and improve curative treatment approaches for CLC.Disclosures of Conflicts of Interest: J.W. disclosed no relevant relationships. S.K.H. disclosed no relevant relationships.References1. NIH/National Cancer Institute Surveillance, Epidemiology, and End Results Program. Cancer Stat Facts: Lung and Bronchus Cancer. https://seer.cancer.gov/statfacts/html/lungb.html. Accessed January 20, 2021. Google Scholar2. Casal RF, Sepesi B, Sagar AS, et al. Centrally located lung cancer and risk of occult nodal disease: an objective evaluation of multiple definitions of tumour centrality with dedicated imaging software. Eur Respir J 2019;53(5):1802220. Crossref, Medline, Google Scholar3. Choi H, Kim H, Park CM, Kim YT, Goo JM. Central Tumor Location at Chest CT Is an Adverse Prognostic Factor for Disease-Free Survival of Node-Negative, Early-Stage Lung Adenocarcinomas. Radiology 2021. https://doi.org/10.1148/radiol.2021203937. Published online February 23, 2021. Link, Google Scholar4. Casal RF, Vial MR, Miller R, et al. What Exactly Is a Centrally Located Lung Tumor? Results of an Online Survey. Ann Am Thorac Soc 2017;14(1):118–123. Crossref, Medline, Google Scholar5. Shin SH, Jeong DY, Lee KS, et al. Which definition of a central tumour is more predictive of occult mediastinal metastasis in nonsmall cell lung cancer patients with radiological N0 disease? Eur Respir J 2019;53(3):1801508. Crossref, Medline, Google Scholar6. Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services. Healthy People 2030: Increase the proportion of adults who get screened for lung cancer — C-03. https://health.gov/healthypeople/objectives-and-data/browse-objectives/cancer/increase-proportion-adults-who-get-screened-lung-cancer-c-03. Accessed February 12, 2021. Google Scholar7. Pinsky PF. Lung cancer screening with low-dose CT: a world-wide view. Transl Lung Cancer Res 2018;7(3):234–242. Crossref, Medline, Google ScholarArticle HistoryReceived: Jan 25 2021Revision requested: Feb 1 2021Revision received: Feb 2 2021Accepted: Feb 5 2021Published online: Feb 23 2021Published in print: May 2021 FiguresReferencesRelatedDetailsAccompanying This ArticleCentral Tumor Location at Chest CT Is an Adverse Prognostic Factor for Disease-Free Survival of Node-Negative Early-Stage Lung AdenocarcinomasFeb 23 2021RadiologyRecommended Articles Central Tumor Location at Chest CT Is an Adverse Prognostic Factor for Disease-Free Survival of Node-Negative Early-Stage Lung AdenocarcinomasRadiology2021Volume: 299Issue: 2pp. 438-447Importance of 68Ga-FAPI PET/CT for Detection of CancerRadiology2022Volume: 303Issue: 1pp. 200-201Pulmonary Fibrosis: A Guide for the PerplexedRadiology: Cardiothoracic Imaging2021Volume: 3Issue: 1Deep Learning Demonstrates Potential for Lung Cancer Detection in Chest RadiographyRadiology2020Volume: 297Issue: 3pp. 697-698Visualization of the Associations between the CT Features Extracted from a Deep Learning Survival Prediction Model and Histopathologic Risk FactorsRadiology2022Volume: 305Issue: 2pp. 452-453See More RSNA Education Exhibits Management of Solitary Pulmonary Nodules: Pushing the Limits Beyond the GuidelinesDigital Posters2019Developments in Lung Cancer - What Radiologists Should Know About the WHO Classification Updates and Developments in Molecular Biology ResearchDigital Posters2022Introduction to Artificial Intelligence and Big Data Research in Chest RadiologyDigital Posters2019 RSNA Case Collection Radioembolization of Liver Metastasis RSNA Case Collection2020Bronchial CarcinoidRSNA Case Collection2020Follicular Lymphoma on Ga68-Dotatate PET-CTRSNA Case Collection2020 Vol. 299, No. 2 Metrics Altmetric Score PDF download