Gianluca Milanese1,2, Mario Silva1,2, Thomas Frauenfelder3, Matthias Eberhard3, Federica Sabia2, Chiara Martini4, Alfonso Marchianò5, Mathias Prokop6, Nicola Sverzellati1, Ugo Pastorino2. 1. Division of Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy. 2. Department of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milan, Italy. 3. Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland. 4. Department of Medicine and Surgery, University of Parma, Parma, Italy. 5. Department of Radiology, IRCCS Istituto Nazionale Tumori, Milan, Italy. 6. Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Abstract
PURPOSE: To test ultra-low-dose computed tomography (ULDCT) scanning protocols for the detection of pulmonary nodules (PN). METHODS: A chest phantom containing 19 solid and 11 subsolid PNs was scanned on a third-generation dual-source computed tomography (CT) scanner. Five ULDCT scans (Sn100kVp and 120, 70, 50, 30, and 20 reference mAs, using tube current modulation), reconstructed with iterative reconstruction (IR) algorithm at strength levels 2, 3, 4, and 5, were compared with standard CT (120kVp, 150 reference mAs, using tube current modulation). PNs were subjectively assessed according to a 4-point scale: 0, nondetectable nodule; 1, detectable nodule, very unlikely to be correctly measured; 2, detectable nodule, likely to be correctly measured; 3, PN quality equal to standard of reference. PN scores were analysed according to the Lung Imaging Reporting and Data System (Lung-RADS), simulating detection of nodules at baseline and incidence screening round. RESULTS: For the baseline round, there were 17 Lung-RADS 2, 4 Lung-RADS 3, 8 Lung-RADS 4A, and 1 Lung-RADS 4B PNs. They were detectable in any ULDCT protocol, with the exception of 1 nondetectable part-solid nodule in 1 scanning protocol (120 reference mAs; IR strength: 3). For the incidence round, there were 4 Lung-RADS 2, 14 Lung-RADS 3, 2 Lung-RADS 4A, and 10 Lung-RADS 4B PNs. Ten were nondetectable in at least one ULDCT dataset; however, they were at least detectable in ULDCT with 70 reference mAs (IR strength: 4 and 5). CONCLUSIONS: ULDCT scanning protocols allowing the detection of PNs can be proposed for the purpose of lung cancer screening.
PURPOSE: To test ultra-low-dose computed tomography (ULDCT) scanning protocols for the detection of pulmonary nodules (PN). METHODS: A chest phantom containing 19 solid and 11 subsolid PNs was scanned on a third-generation dual-source computed tomography (CT) scanner. Five ULDCT scans (Sn100kVp and 120, 70, 50, 30, and 20 reference mAs, using tube current modulation), reconstructed with iterative reconstruction (IR) algorithm at strength levels 2, 3, 4, and 5, were compared with standard CT (120kVp, 150 reference mAs, using tube current modulation). PNs were subjectively assessed according to a 4-point scale: 0, nondetectable nodule; 1, detectable nodule, very unlikely to be correctly measured; 2, detectable nodule, likely to be correctly measured; 3, PN quality equal to standard of reference. PN scores were analysed according to the Lung Imaging Reporting and Data System (Lung-RADS), simulating detection of nodules at baseline and incidence screening round. RESULTS: For the baseline round, there were 17 Lung-RADS 2, 4 Lung-RADS 3, 8 Lung-RADS 4A, and 1 Lung-RADS 4B PNs. They were detectable in any ULDCT protocol, with the exception of 1 nondetectable part-solid nodule in 1 scanning protocol (120 reference mAs; IR strength: 3). For the incidence round, there were 4 Lung-RADS 2, 14 Lung-RADS 3, 2 Lung-RADS 4A, and 10 Lung-RADS 4B PNs. Ten were nondetectable in at least one ULDCT dataset; however, they were at least detectable in ULDCT with 70 reference mAs (IR strength: 4 and 5). CONCLUSIONS: ULDCT scanning protocols allowing the detection of PNs can be proposed for the purpose of lung cancer screening.