Computer-aided diagnosis as a second reader: spectrum of findings in CT studies of the chest interpreted as normal.
OBJECTIVE
To assess the performance of an automated computer-aided detection (CAD) system as a second reader on chest CT studies interpreted as normal at routine clinical interpretation.


METHODS
Chest CT studies were processed using a prototype CAD system for automated detection of lung lesions. Three experienced radiologists analyzed each CAD finding and confirmed or dismissed the marked image features as lung lesions. Noncalcified, focal lung lesions were classified according to size as being of high (> or = 10 mm), intermediate (5 to 9 mm), or low (< or = 4 mm) significance.


METHODS
Two sub-specialized academic tertiary referral centers in the United States and Germany.


METHODS
Chest CT studies were performed in 100 patients, with results initially reported as normal at clinical double reading. Indications for chest CT were suspected pulmonary embolism (PE) [n = 33], lung cancer screening in a high-risk population (n = 28), or follow-up for a cancer history (n = 39).


METHODS
Reevaluation of all chest CT studies for focal lung lesions with the CAD system as a second reader.


METHODS
Prevalence and spectrum of lung lesions missed at routine clinical interpretation but found by the CAD system.


RESULTS
In 33% (33 of 100 patients), CAD detected significant lung lesions that were not previously reported. Fifty-three significant lesions were detected (mean, 1.6 lesions per case), of which 5 lesions (9.4%) were of high significance, 21 lesions (39.6%) were of intermediate significance, and 27 lesions (50.9%) were of low significance. In the PE group, the lung cancer screening group, and the group with a cancer history, four patients (12.1%), six patients (21.4%), and nine patients (23.1%), respectively, had focal lung lesions of high and/or intermediate significance. The false-positive rate of the CAD system was an average of 1.25 per case (range, 0 to 11).


CONCLUSIONS
Significant lung lesions are frequently missed at routine clinical interpretation of chest CT studies but may be detected if CAD is used as an additional reader.
Peldschus
Kersten
K
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Herzog
Peter
P
Wood
Susan A
SA
Cheema
Jugesh I
JI
Costello
Philip
P
Schoepf
U Joseph
UJ
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003936:Diagnosis, Computer-Assisted; D003951:Diagnostic Errors / Q000517:prevention & control*; D005260:Female; D006801:Humans; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted; D013902:Radiography, Thoracic / Q000379:methods / Q000592:standards*; D014057:Tomography, X-Ray Computed