A study evaluated a computer-aided diagnosis (CADx) system designed to assess lung nodule malignancy risk on routine thoracic CT scans, comparing its performance to the British Thoracic Society (BTS) pulmonary nodule management guidelines. The analysis included a large dataset of incidentally discovered lung nodules, reflecting real-world clinical practice in a tertiary radiology center. CADx risk scores were compared with BTS guideline–based risk categories, and outcomes (nodule growth or confirmed malignancy) were followed using serial imaging or histopathology where available.
The CADx tool demonstrated superior risk stratification, with higher discrimination between benign and malignant nodules than the BTS guideline categories. Specifically, it reduced the number of false positives by downgrading low-risk cases and more accurately identified high-risk nodules warranting follow-up or further testing. This resulted in potentially fewer unnecessary scans and more targeted surveillance.
The authors conclude that integrating CADx into routine CT reporting may optimize lung nodule management, reduce resource burden, and support radiologists in making more precise follow-up recommendations. However, they emphasize that clinical oversight remains essential, and further validation in larger, multi-center cohorts is needed before widespread implementation.
A CADx tool improves lung nodule risk stratification when compared to British Thoracic Society guidelines on routine computed tomography (CT)
Clinical Radiology, 2025
Abstract
Aim
The aim of this study was to evaluate whether a computed tomography (CT) image–based lung nodule artificial intelligence (AI) classifier can improve lung nodule risk classification and clinical management decisions relative to British Thoracic Society (BTS) 2015 guidelines.
Materials and Methods
This is a retrospective single-site case-control study of incidental lung nodules identified from routine clinical CT scans. An AI-based classification tool (RevealAI-Lung) was used to compute a malignancy similarity index (mSI) of lung nodules from CT scans of patients with known definitive diagnoses. For comparison to current clinical best practices, nodules were retrospectively classified for follow-up using the BTS 2015 guidelines by a re-review of all cases. The mSI was used to adjust BTS recommendations (msi > 0.9: upgrade, mSI < 0.1: downgrade, and 0.1 < mSI < 0.9: maintain), and performance of the mSI reclassification against BTS guidelines was compared.
Results
A total of 100 control patients and 100 cancer patients were analysed from an UK National Health Service (NHS) referral population who had proven diagnosis (benign or malignant). Forty-five of 100 cancer patients would have received interval scans before eventual malignant diagnosis. mSI would have upgraded 44% (n=20/45) of these cases and downgraded none, reducing delayed cancer diagnoses from 45% to 25% (P<0.001). A total 100 control patients would have received followup scans before eventual benign diagnosis. mSI would have downgraded 7% (n=7/100) of these cases to no follow-up and upgraded 7% (n=7/100) from an interval scan to an immediate positron emission tomography (PET)-CT.
Conclusion
mSI significantly improved nodule classification in incidental lung nodules when combined with current best practice BTS guidelines.
