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Computer-Aided Screening Detects Missed Lung Cancers

Computer-Aided Screening Detects Missed Lung Cancers

CHICAGO—Screening for lung cancer with low-dose helical CT scans is
becoming increasingly popular. Computer programs to assist in the detection of
lung cancers appear to increase the accuracy of CT screening, said Samuel G.
Armato III, PhD, assistant professor of radiology, University of Chicago.

In a study he reported at the 87th Scientific Assembly and Annual Meeting of
the Radiological Society of North America (RSNA abstract 380), radiologists
missed 38 biopsy-confirmed lung cancers out of a total of 50 lung nodules
during patients’ initial clinical workup. When a computerized detection
program was applied, 32 (84%) of the previously missed cancers were found.

Dr. Armato explained that research involving the computerized lung cancer
detection system, which was developed at the University of Chicago, is still at
the experimental stage. "Our research at this point was not intended to
show how radiologists would use the technology clinically but to show how well
the computer itself performed in detecting missed lung cancer," he said.

Especially important, he added, was that the study targeted a set of
problematic clinical images. "This was a difficult database, by
definition, because these are all cancers that were missed by radiologists. So
a radiologist, in the category of detection errors, was batting zero percent
and our computer program was batting 78%. That’s a pretty good sensitivity
for this kind of situation," he said.

The computerized lung nodule detection system segments the lung images to
create a segmented lung volume, then thresholds the lung volume and identifies
three-dimensional contiguous structures in each thresholded lung volume data
set.

The system selects lesions as possible nodules on the basis of size
criteria. A rule-based approach is used to decrease the number of
false-positive nodules. An automated program distinguishes between true lesions
and normal anatomy in the remaining nodule candidates.

In the study, Dr. Armato categorized the 38 missed cancers as either
radiologist detection errors (23 cancers) or interpretation mistakes (15
cancers). The computer program found 18 (78%) of the 23 detection errors and 14
(93%) of the 15 misinterpreted lesions. In both categories, the computerized
program had a 1.6 false-positive rate per CT section image.

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