title: Role of Computer Aided Diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography.
abstract: OBJECTIVE
  To determine the overall performance of an existing CAD algorithm with thin-section computed tomography (CT) in the detection of pulmonary nodules and to evaluate detection sensitivity at a varying range of nodule density, size, and location.
  
  
  METHODS
  A cross-sectional prospective study was conducted on 20 patients with 322 suspected nodules who underwent diagnostic chest imaging using 64-row multi-detector CT. The examinations were evaluated on reconstructed images of 1.4 mm thickness and 0.7 mm interval. Detection of pulmonary nodules, initially by a radiologist of 2 years experience (RAD) and later by CAD lung nodule software was assessed. Then, CAD nodule candidates were accepted or rejected accordingly. Detected nodules were classified based on their size, density, and location. The performance of the RAD and CAD system was compared with the gold standard that is true nodules confirmed by consensus of senior RAD and CAD together. The overall sensitivity and false-positive (FP) rate of CAD software was calculated.
  
  
  RESULTS
  Of the 322 suspected nodules, 221 were classified as true nodules on the consensus of senior RAD and CAD together. Of the true nodules, the RAD detected 206 (93.2%) and 202 (91.4%) by the CAD. CAD and RAD together picked up more number of nodules than either CAD or RAD alone. Overall sensitivity for nodule detection with the CAD program was 91.4%, and FP detection per patient was 5.5%. The CAD showed comparatively higher sensitivity for nodules of size 4-10 mm (93.4%) and nodules in hilar (100%) and central (96.5%) location when compared to RAD's performance.
  
  
  CONCLUSIONS
  CAD performance was high in detecting pulmonary nodules including the small size and low-density nodules. CAD even with relatively high FP rate, assists and improves RAD's performance as a second reader, especially for nodules located in the central and hilar region and for small nodules by saving RADs time.
authors: Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal University, Manipal, Udupi, Karnataka, India.
authors: K
authors: K
authors: Prakashini
authors: Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal University, Manipal, Udupi, Karnataka, India.
authors: Satish
authors: S
authors: Babu
authors: Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal University, Manipal, Udupi, Karnataka, India.
authors: K V
authors: KV
authors: Rajgopal
authors: Consultant Radiologist, Jansons Health (P) Ltd., Erode, Tamil Nadu, India.
authors: K Raja
authors: KR
authors: Kokila
keywords: CAD; computed tomography; nodule density; pulmonary nodule