• From the Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, MA (J.S., A.G., F.J.F.); Harvard Medical School, Boston, MA (J.S., A.E.B.C., S.J.S., L.V.S., F.J.F.); Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA (P.M.); Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA (P.M.); Division of Hematology/Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA (A.E.B.C., L.V.S.); Department of Medicine, MGH Biostatistics, Massachusetts General Hospital, Boston MA (S.J.S.); and Multidisciplinary Thoracic Oncology Program, Baptist Cancer Center, Memphis, TN (R.U.O.).

PURPOSE: Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT scanner manufacturer on Sybil's performance.

MATERIALS AND METHODS: Using LDCTs of a subset of the National Lung Screening Trial participants, which we previously used for internal validation of the Sybil algorithm (test set), we ran the Sybil algorithm on LDCT series pairs matched on kilovoltage peak, milliampere-seconds, reconstruction interval, reconstruction diameter, and either reconstruction filter or axial slice thickness. We also evaluated the cumulative effect of these parameters by combining the best- and the worst-performing parameters. A subanalysis compared Sybil's performance by CT manufacturer. We considered any LDCT positive if future lung cancer was subsequently confirmed by biopsy or surgical resection. The areas under the curve (AUCs) for each series pair were compared using DeLong's test.

RESULTS: There was no difference in Sybil's performance between 1049 pairs of standard versus bone reconstruction filter (AUC at 1 year 0.84 [95% confidence interval (CI): 0.70-0.99] vs 0.86 [95% CI: 0.75-0.98], P = 0.87) and 1961 pairs of standard versus lung reconstruction filter (AUC at 1 year 0.98 [95% CI: 0.97-0.99] vs 0.98 [95% CI: 0.96-0.99], P = 0.81). Similarly, there was no difference in 1288 pairs comparing 2-mm versus 5-mm axial slice thickness (AUC at 1 year 0.98 [95% CI: 0.94-1.00] vs 0.99 [95% CI: 0.97-0.99], P = 0.68). The best-case scenario combining a lung reconstruction filter with 2-mm slice thickness compared with the worst-case scenario combining a bone reconstruction filter with 2.5-mm slice thickness uncovered a significantly different performance at years 2-4 (P = 0.03). Subanalysis showed no significant difference in performance between Siemens and Toshiba scanners.

CONCLUSIONS: Sybil's predictive performance for future lung cancer risk is robust across different reconstruction filters and axial slice thicknesses, demonstrating its versatility in various imaging settings. Combining favorable reconstruction parameters can significantly enhance predictive ability at years 2-4. The absence of significant differences between Siemens and Toshiba scanners further supports Sybil's versatility.