Clinical experience with a computer-aided diagnosis system for automatic detection of pulmonary nodules at spiral CT of the chest

Dag Wormanns; Martin Fiebich; Mustafa Saidi; Stefan Diederich; Walter Heindel

doi:10.1117/12.428047

28 May 2001 Clinical experience with a computer-aided diagnosis system for automatic detection of pulmonary nodules at spiral CT of the chest

Dag Wormanns, Martin Fiebich, Mustafa Saidi, Stefan Diederich, Walter Heindel

Author Affiliations +

Proceedings Volume 4319, Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures; (2001) https://doi.org/10.1117/12.428047
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

The purpose of the study was to evaluate a computer aided diagnosis (CAD) workstation with automatic detection of pulmonary nodules at low-dose spiral CT in a clinical setting for early detection of lung cancer. Two radiologists in consensus reported 88 consecutive spiral CT examinations. All examinations were reviewed using a UNIX-based CAD workstation with a self-developed algorithm for automatic detection of pulmonary nodules. The algorithm was designed to detect nodules with at least 5 mm diameter. The results of automatic nodule detection were compared to the consensus reporting of two radiologists as gold standard. Additional CAD findings were regarded as nodules initially missed by the radiologists or as false positive results. A total of 153 nodules were detected with all modalities (diameter: 85 nodules <5mm, 63 nodules 5-9 mm, 5 nodules >= 10 mm). Reasons for failure of automatic nodule detection were assessed. Sensitivity of radiologists for nodules >=5 mm was 85%, sensitivity of CAD was 38%. For nodules >=5 mm without pleural contact sensitivity was 84% for radiologists at 45% for CAD. CAD detected 15 (10%) nodules not mentioned in the radiologist's report but representing real nodules, among them 10 (15%) nodules with a diameter $GREW5 mm. Reasons for nodules missed by CAD include: exclusion because of morphological features during region analysis (33%), nodule density below the detection threshold (26%), pleural contact (33%), segmentation errors (5%) and other reasons (2%). CAD improves detection of pulmonary nodules at spiral CT significantly and is a valuable second opinion in a clinical setting for lung cancer screening. Optimization of region analysis and an appropriate density threshold have a potential for further improvement of automatic nodule detection.

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Dag Wormanns, Martin Fiebich, Mustafa Saidi, Stefan Diederich, and Walter Heindel "Clinical experience with a computer-aided diagnosis system for automatic detection of pulmonary nodules at spiral CT of the chest", Proc. SPIE 4319, Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures, (28 May 2001); https://doi.org/10.1117/12.428047

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