Comparison of the diagnostic performance of a CAD system for automatic detection of pulmonary nodules with single and double reading and its dependency on nodule size

Objective: Assess the performance of a computer aided diagnosis (CAD) system for automatic detection of pulmonary nodules at CT scans compared to single and double reading by radiologists. Material and methods: A nodule detection CAD system (Siemens LungCare NEV VB10) was applied to low-dose-CT (LDCT) scans of nine patients with pulmonary metastases and compared to findings of three radiologists; standard-dose-CT (SDCT) was acquired simultaneously to establish ground truth. Study design was approved by the Institutional Review Board and the appropriate German authorities. Ground truth was established by fusion of sets of detected nodules from independent reading by three radiologists at LDCT and SDCT scans and CAD results. Special focus was taken on the size of nodules detected only by CAD compared to the size of all detected nodules. Results: Average sensitivity of 54% (range 51-55%) was observed for single reading by one radiologist. Application of the CAD system demonstrated a similar sensitivity of 55%. Double reading by two radiologists increased sensitivity to an average of 67% (range 67-68%). The difference to single reading was significant (p<0.001). Use of CAD as second opinion after single reading increased the sensitivity to 79% (range 77-81%) which proved to be significantly better than double reading (p<0.001). 11% of nodules with a size of more than 4 mm were detected only by CAD. Conclusion: CAD as second reader offered a significant increase in sensitivity compared to conventional double reading. Therefore, CAD is a valuable second opinion for the detection of pulmonary nodules.

[1]  Martin Fiebich,et al.  Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system , 2002, European Radiology.

[2]  R Kikinis,et al.  Spiral CT of the chest: comparison of cine and film-based viewing. , 1995, Radiology.

[3]  W. Heindel,et al.  Detection of pulmonary nodules at multirow-detector CT: effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest CT , 2004, European Radiology.

[4]  S. Armato,et al.  Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. , 2002, Radiology.

[5]  O. Miettinen,et al.  Early Lung Cancer Action Project: overall design and findings from baseline screening , 1999, The Lancet.

[6]  S. Swensen,et al.  Screening for lung cancer with low-dose spiral computed tomography. , 2000, American journal of respiratory and critical care medicine.

[7]  K. Awai,et al.  Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. , 2004, Radiology.

[8]  Noriyuki Tomiyama,et al.  Detection of pulmonary metastases with multi-detector row CT scans of 5-mm nominal section thickness: autopsy lung study. , 2003, Radiology.

[9]  O. Miettinen,et al.  CT screening for lung cancer: suspiciousness of nodules according to size on baseline scans. , 2004, Radiology.

[10]  B Gosselin,et al.  Sliding thin slab, minimum intensity projection technique in the diagnosis of emphysema: histopathologic-CT correlation. , 1996, Radiology.

[11]  S. Armato,et al.  Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. , 2002, Radiology.

[12]  H. Scheld,et al.  Helical CT of pulmonary nodules in patients with extrathoracic malignancy: CT-surgical correlation. , 1999, AJR. American journal of roentgenology.

[13]  Masumi Kadoya,et al.  Indeterminate solitary pulmonary nodules revealed at population-based CT screening of the lung: using first follow-up diagnostic CT to differentiate benign and malignant lesions. , 2003, AJR. American journal of roentgenology.

[14]  Jane P. Ko,et al.  Interobserver variations on interpretation of multislice CT lung cancer screening studies, and the implications for computer-aided diagnosis , 2002, SPIE Medical Imaging.

[15]  Binsheng Zhao,et al.  Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. , 2000, Radiology.

[16]  W. Heindel,et al.  Low-dose CT: new tool for screening lung cancer? , 2001, European Radiology.