Mario Silva1,2, Gianluca Milanese3,4, Stefano Sestini4, Federica Sabia4, Colin Jacobs5, Bram van Ginneken5, Mathias Prokop5, Cornelia M Schaefer-Prokop5,6, Alfonso Marchianò7, Nicola Sverzellati3, Ugo Pastorino4. 1. Section of Radiology (Pad. Barbieri), Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University Hospital of Parma, University of Parma, Via Gramsci 14, 43126, Parma, Italy. mario.silva@unipr.it. 2. Department of Thoracic Surgery, IRCCS Istituto Nazionale dei Tumori, Milan, Italy. mario.silva@unipr.it. 3. Section of Radiology (Pad. Barbieri), Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University Hospital of Parma, University of Parma, Via Gramsci 14, 43126, Parma, Italy. 4. Department of Thoracic Surgery, IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 5. Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands. 6. Department of Radiology, Meander Medical Centre, Amersfoort, The Netherlands. 7. Department of Radiology, IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Abstract
OBJECTIVES: The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS). METHODS: Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden. RESULTS: In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6-21.9% and a potential reduction of LDCT burden of 25.5-41%. CONCLUSIONS: Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible. KEY POINTS: • Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. • Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group. • Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5-41% reduction of LDCT burden, at the cost of 13.6-21.9% rate of delayed diagnosis.
OBJECTIVES: The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS). METHODS: Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden. RESULTS: In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6-21.9% and a potential reduction of LDCT burden of 25.5-41%. CONCLUSIONS: Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible. KEY POINTS: • Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. • Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group. • Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5-41% reduction of LDCT burden, at the cost of 13.6-21.9% rate of delayed diagnosis.
Authors: Lynn T Tanoue; Nichole T Tanner; Michael K Gould; Gerard A Silvestri Journal: Am J Respir Crit Care Med Date: 2015-01-01 Impact factor: 21.405
Authors: Marjolein A Heuvelmans; Joan E Walter; Rozemarijn Vliegenthart; Peter M A van Ooijen; Geertruida H De Bock; Harry J de Koning; Matthijs Oudkerk Journal: Thorax Date: 2017-10-22 Impact factor: 9.139
Authors: John R Goffin; William M Flanagan; Anthony B Miller; Natalie R Fitzgerald; Saima Memon; Michael C Wolfson; William K Evans Journal: Lung Cancer Date: 2016-09-28 Impact factor: 5.705
Authors: Nikolaus Becker; Erna Motsch; Anke Trotter; Claus P Heussel; Hendrik Dienemann; Philipp A Schnabel; Hans-Ulrich Kauczor; Sandra González Maldonado; Anthony B Miller; Rudolf Kaaks; Stefan Delorme Journal: Int J Cancer Date: 2019-06-20 Impact factor: 7.396
Authors: Anil K Chaturvedi; Hormuzd A Katki; Stephanie A Kovalchik; Martin Tammemagi; Christine D Berg; Neil E Caporaso; Tom L Riley; Mary Korch; Gerard A Silvestri Journal: N Engl J Med Date: 2013-07-18 Impact factor: 91.245
Authors: Nanda Horeweg; Joost van Rosmalen; Marjolein A Heuvelmans; Carlijn M van der Aalst; Rozemarijn Vliegenthart; Ernst Th Scholten; Kevin ten Haaf; Kristiaan Nackaerts; Jan-Willem J Lammers; Carla Weenink; Harry J Groen; Peter van Ooijen; Pim A de Jong; Geertruida H de Bock; Willem Mali; Harry J de Koning; Matthijs Oudkerk Journal: Lancet Oncol Date: 2014-10-01 Impact factor: 41.316
Authors: Harry J de Koning; Rafael Meza; Sylvia K Plevritis; Kevin ten Haaf; Vidit N Munshi; Jihyoun Jeon; Saadet Ayca Erdogan; Chung Yin Kong; Summer S Han; Joost van Rosmalen; Sung Eun Choi; Paul F Pinsky; Amy Berrington de Gonzalez; Christine D Berg; William C Black; Martin C Tammemägi; William D Hazelton; Eric J Feuer; Pamela M McMahon Journal: Ann Intern Med Date: 2014-03-04 Impact factor: 25.391
Authors: U Pastorino; M Silva; S Sestini; F Sabia; M Boeri; A Cantarutti; N Sverzellati; G Sozzi; G Corrao; A Marchianò Journal: Ann Oncol Date: 2019-07-01 Impact factor: 32.976
Authors: Edward F Patz; Erin Greco; Constantine Gatsonis; Paul Pinsky; Barnett S Kramer; Denise R Aberle Journal: Lancet Oncol Date: 2016-03-18 Impact factor: 41.316
Authors: Asha Bonney; Reem Malouf; Corynne Marchal; David Manners; Kwun M Fong; Henry M Marshall; Louis B Irving; Renée Manser Journal: Cochrane Database Syst Rev Date: 2022-08-03
Authors: Kaiyue Diao; Yuntian Chen; Ying Liu; Bo-Jiang Chen; Wan-Jiang Li; Lin Zhang; Ya-Li Qu; Tong Zhang; Yun Zhang; Min Wu; Kang Li; Bin Song Journal: Ann Transl Med Date: 2022-06