lung nodules Archives - The Imaging Wire

A new study in Radiology on an AI algorithm for analyzing lung nodules on CT lung cancer screening exams shows that radiologists may be able to have their cake and eat it too: better identification of malignant nodules with lower false-positive rates.

The rising utilization of low-dose CT screening is great news for clinicians (and eligible patients), but managing suspicious nodules remains a major challenge, as false-positive findings expose patients to unnecessary biopsies and costs.

False-positive rates have come down somewhat from the high rates seen in the big lung cancer screening clinical trials like NLST and NELSON, but there is still room for improvement.

Dutch researchers applied AI to the problem, developing a deep learning algorithm trained on 16.1k NLST nodules that produces a score from 0% to 100% based on a nodule’s likelihood of malignancy.

They then tested the algorithm with baseline screening rounds of 4.1k patients from three datasets drawn from different lung cancer screening trials: NELSON, DLSCT in Denmark, and MILD in Italy.

The algorithm’s performance was compared to the Pan-Canadian Early Detection of Lung Cancer model, a widely used clinical guideline that uses patient characteristics like age and family history and nodule characteristics size and location to estimate risk.

Compared to PanCan, the deep learning algorithm…

Reduced false-positive findings sharply by classifying more benign cases as low risk (68% vs. 47%) when set at 100% sensitivity for cancers diagnosed within one year.
For all nodules, achieved comparable AUCs at one year (0.98 vs. 0.98), two years (0.96 vs. 0.94), and throughout screening (0.94 vs. 0.93).
For indeterminate nodules 5-15 mm, significantly outperformed PanCan at one year (0.95 vs. 0.91), two years (0.94 vs. 0.88), and throughout screening (0.91 vs. 0.86).

The model’s performance for indeterminate nodules is particularly intriguing, as these are challenging to manage due to their small size and can lead to unnecessary follow-up procedures.

The Takeaway

Using AI to differentiate malignant from benign nodules promises to make CT lung cancer screening more accurate and easier to perform than manual nodule classification methods – and should add to the exam’s growing momentum.

Making sure suspicious imaging findings are followed up appropriately is a key element in providing quality patient care. But a new study found that some suspicious findings aren’t being adequately tracked, especially when it comes to lung nodules.

Lung nodules are commonly detected on chest CT exams, and are often found incidentally, when patients are being examined for other reasons.

While most smaller nodules don’t represent a threat to patients, it’s important to work up the ones that could be clinically significant.

In the new paper, Japanese researchers studied 10.5k initial chest CT reports at their institution from 2020 to 2023.

They developed a natural language processing algorithm that analyzed free-text reports to see which ones recommended follow-up.

They determined that 1.5k reports (14%) recommended additional imaging with exams like chest CT or PET/CT; they then calculated whether these follow-up exams were conducted within 400 days of the initial exam. Further analysis indicated …

For 36% of exams (543) researchers could not confirm that follow-up imaging had taken place.
In a random sample of 42 of these patients, 40.5% (17) were not followed up appropriately.
For these cases, either no imaging was documented or no reason was given for the lack of follow-up.

The researchers clarified that they found no evidence of false negatives (missed cancers), as that wasn’t a goal of their study.

The Takeaway

The new findings indicate both the challenge and opportunity of follow-up management. While radiology must do better in tracking patients with suspicious findings, the study shows that software-based solutions could help, especially those that are automated to scan radiology reports and alert radiologists to cases that need their attention.

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