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Lung Cancers Missed on Chest Radiographs: Results Obtained with a Commercial Computer-aided Detection Program¹

¹ From the Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC-2026, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637. From the 2005 RSNA Annual Meeting. Received October 27, 2006; revision requested January 5, 2007; revision received January 22; accepted March 1; final version accepted June 1. Supported in part by USPHS grants CA61625, CA09119, and EB00341 and a research grant from Riverain Medical. Address correspondence to F.L. (e-mail: feng{at}uchicago.edu).

Purpose: To retrospectively determine the sensitivity of and number of false-positive marks made by a commercially available computer-aided detection (CAD) system for identifying lung cancers previously missed on chest radiographs by radiologists, with histopathologic results as the reference standard.

Materials and Methods: Institutional review board approval was obtained for this HIPAA-compliant study; the requirement for informed patient consent was waived. A CAD nodule detection program was applied to 34 posteroanterior digital chest radiographs obtained in 34 patients (21 men, 13 women; mean age, 69 years). All 34 radiographs showed a nodular lung cancer that was apparent in retrospect but had not been mentioned in the report. Two radiologists identified these radiologist-missed cancers on the chest radiographs and graded them for visibility, location, subtlety (extremely subtle to extremely obvious on a 10-point scale), and actionability (actionable or not actionable according to whether the radiologists probably would have recommended follow-up if the nodule had been detected). The CAD results were analyzed to determine the numbers of cancers and false-positive nodules marked and to correlate the CAD results with the nodule grades for subtlety and actionability. The ² test or Fisher exact test for independence was used to compare CAD sensitivity between the very subtle (grade 1–3) and relatively obvious (grade > 3) cancers and between the actionable and not actionable cancers.

Results: The CAD program had an overall sensitivity of 35% (12 of 34 cancers), identifying seven (30%) of 23 very subtle and five (45%) of 11 relatively obvious radiologist-missed cancers (P = .21) and detecting two (25%) of eight missed not actionable and ten (38%) of 26 missed actionable cancers (P = .33). The CAD program made an average of 5.9 false-positive marks per radiograph.

Conclusion: The described CAD system can mark a substantial proportion of visually subtle lung cancers that are likely to be missed by radiologists.

© RSNA, 2008

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