title: Toward clinically usable CAD for lung cancer screening with computed tomography.
abstract: OBJECTIVE
  The purpose of this study was to define clinically appropriate, computer-aided lung nodule detection (CAD) requirements and protocols based on recent screening trials. In the following paper, we describe a CAD evaluation methodology based on a publically available, annotated computed tomography (CT) image data set, and demonstrate the evaluation of a new CAD system with the functionality and performance required for adoption in clinical practice.
  
  
  METHODS
  A new automated lung nodule detection and measurement system was developed that incorporates intensity thresholding, a Euclidean Distance Transformation, and segmentation based on watersheds. System performance was evaluated against the Lung Imaging Database Consortium (LIDC) CT reference data set.
  
  
  RESULTS
  The test set comprised thin-section CT scans from 108 LIDC subjects. The median (±IQR) sensitivity per subject was 100 (±37.5) for nodules ≥ 4 mm and 100 (±8.33) for nodules ≥ 8 mm. The corresponding false positive rates were 0 (±2.0) and 0 (±1.0), respectively. The concordance correlation coefficient between the CAD nodule diameter and the LIDC reference was 0.91, and for volume it was 0.90.
  
  
  CONCLUSIONS
  The new CAD system shows high nodule sensitivity with a low false positive rate. Automated volume measurements have strong agreement with the reference standard. Thus, it provides comprehensive, clinically-usable lung nodule detection and assessment functionality.
  
  
  CONCLUSIONS
  • CAD requirements can be based on lung cancer screening trial results. • CAD systems can be evaluated using publically available annotated CT image databases. • A new CAD system was developed with a low false positive rate. • The CAD system has reliable measurement tools needed for clinical use.
authors: Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA, mbrown@mednet.ucla.edu.
authors: Matthew S
authors: MS
authors: Brown
authors: Pechin
authors: P
authors: Lo
authors: Jonathan G
authors: JG
authors: Goldin
authors: Eran
authors: E
authors: Barnoy
authors: Grace Hyun J
authors: GH
authors: Kim
authors: Michael F
authors: MF
authors: McNitt-Gray
authors: Denise R
authors: DR
authors: Aberle
mesh_terms: D003937:Diagnosis, Differential; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D012372:ROC Curve; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*