TY - JOUR
T1 - Toward clinically usable CAD for lung cancer screening with computed tomography
AU - Brown, Matthew S.
AU - Lo, Pechin
AU - Goldin, Jonathan G.
AU - Barnoy, Eran
AU - Kim, Grace Hyun J.
AU - McNitt-Gray, Michael F.
AU - Aberle, Denise R.
N1 - Publisher Copyright:
© 2014, European Society of Radiology.
PY - 2014/11
Y1 - 2014/11
N2 - Objectives: 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.
AB - Objectives: 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.
KW - Computer-assisted diagnosis
KW - Early detection of cancer
KW - Lung cancer
KW - Multiple pulmonary nodules
KW - X-ray computerized axial tomography
UR - http://www.scopus.com/inward/record.url?scp=84930978244&partnerID=8YFLogxK
U2 - 10.1007/s00330-014-3329-0
DO - 10.1007/s00330-014-3329-0
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C2 - 25052078
AN - SCOPUS:84930978244
SN - 0938-7994
VL - 24
SP - 2719
EP - 2728
JO - European Radiology
JF - European Radiology
IS - 11
ER -
