Feasibility of Rapid Reading of CT Lung Cancer Screening with Computer-Aided Detection Support

B. van Ginneken, C. Jacobs, E.T. Scholten, M. Prokop and P.A. de Jong

in: Annual Meeting of the Radiological Society of North America, 2014

Abstract

PURPOSE: The reading effort associated with CT lung cancer screening programs is substantial. We investigated the performance of rapid reading of chest CT scans with integrated CAD support, with the goal of quickly assigning a subject to either regular one-year follow-up, short-term follow-up or immediate work-up. METHOD AND MATERIALS: From the baseline round of a large randomized controlled low-dose CT lung cancer screening trial, randomly 23 cases were selected from each of the three categories used in the trial: 1) no significant nodules, 1 year follow-up CT; 2) nodule 50-500 mm3, 3 month follow-up CT; 3) nodule >500 mm3, referral to pulmonologist. All 69 cases were pre-processed with three different CAD systems aimed at detecting both solid and subsolid lesions and set to operate at high sensitivity. CAD marks were merged and presented in a prototype software environment optimized for rapid reading that includes one-click immediate volumetric segmentation and study preloading to navigate to the next case in the worklist without delay. Seven blinded readers read all cases in random order in a single session as follows. First, CAD marks were inspected and accepted or rejected. Next, readers quickly inspected the scan and added relevant nodules if CAD had not identified these. Finally, readers assigned the scan to one of the three categories of the screening protocol. RESULTS: Cases had 5.1 CAD marks on average. 73±7% of cases (range 58-80%) were assigned to the correct category. 94% of discordances were between category 1 versus 2, or category 2 versus 3. In most cases the reason was that the volume of the most suspicious nodule was very close to the cutpoints used in the screening protocol. Of the 23 cases in category 3, 14 contained lung cancer. None of these were put in category 1 by any reader; only two of these were placed in category 2, each by only 1/7 readers. 2/9 of the benign category 3 cases were put in category 1: one case by 4/7 readers (a relatively large pleural lesion missed by CAD), and one by 1/7 readers. Total median reading time per case was 67±17 seconds. CONCLUSION: With the support of highly effective CAD systems, nodule volumetry, and an optimized reading environment, it is possible to accurately read lung cancer CT scans in around one minute per case. CLINICAL RELEVANCE/APPLICATION: An optimized reading environment is presented that can be used for large scale implementation of lung CT screening.