Lung CAD helps, but performance may be spotty

December 1, 2006

Radiologists could optimize their diagnoses of lung abnormalities using computer-aided detection systems, provided they develop a better understanding of the strengths and shortcomings of every factor involved in the process. Learning this could save a day or two in court, according to studies presented at the RSNA meeting.

Radiologists could optimize their diagnoses of lung abnormalities using computer-aided detection systems, provided they develop a better understanding of the strengths and shortcomings of every factor involved in the process. Learning this could save a day or two in court, according to studies presented at the RSNA meeting.

"Detecting early lung cancer remains a significant challenge on chest x-rays, usually the initial study obtained, because such cancers are often small, subtle, or obscured by overlapping structures. Missed lung cancers on chest x-rays are second only to breast cancer for malpractice actions," said Dr. Charles S. White, a professor of radiology at the University of Maryland.

White released a retrospective review that he and his colleagues performed on an imaging database of 2100 patients diagnosed with lung cancer between 1993 and 2006. They assessed the utility of CAD to detect lesions overlooked in these patients' final reports.

The investigators found that CAD could detect many lesions overlooked by human readers on chest radiography. Although a lesion's size, location, and conspicuity did not generally affect the frequency of detection, CAD could miss lesions larger than the software is set to catch, White said.

The researchers found missed lung cancers in 88 patients. These lesions were mostly peripheral and ranged in size from 0.5 cm to 5.5 cm (mean 1.85 cm). CAD identified 33% and 37.7% of undetected lesions on a per-film and per-patient basis, respectively. CAD detected peripheral lesions just as frequently as central ones, providing an average rate of 4.5 false-positive results per radiograph.

In a separate study, Dr. David P. Naidich, a professor of radiology and medicine at New York University, presented results on a retrospective review of 200 chest CT studies from four different institutions. Naidich and colleagues assessed CAD's added value in identifying pulmonary nodules according to their size and conspicuity. Seventeen general radiologists reviewed a first round of all blinded studies with additional CAD-supplied findings. Five chest radiologists performed a blinded final round of reads.

The investigators found that CAD could bolster lung nodule detection as a second reader depending on the nodules' size and conspicuity. The more conspicuous the lesion, the easier it was for CAD to pick up. These conclusions got a bit tricky, however, since all the radiologists involved in the reading process defined conspicuity in different ways, Naidich said.

A study by Dr. Justus Roos, a radiologist at Stanford University, applied CAD to multislice CT scans from 20 patients with lung cancer. Roos and colleagues found that time defined radiologists' performance in their interaction with CAD. An initial period of upward performance in lung nodule detection became significantly degraded over time, as the radiologists tended to become complacent over false positives, Roos said.