The use of artificial intelligence to aid the diagnosis of lung cancer – A retrospective-cohort study

Introduction

AI software in the form of deep learning–based automatic detection (DLAD) algorithms for chest X-ray (CXR) interpretation have shown success in early detection of lung cancer (LC), however, there remains uncertainty related to clinical validation.

Methods

CXRs and their corresponding chest-CT scans were retrospectively collated from a single institution between January 2019–2020. A commercially available AI software was used to evaluate 320 CXRs (<6 years prior-to-diagnosis) from 105 positive LC patients and 103 negative controls. Clinical reports were extracted and coded to correlate against AI findings.

Results

Of 105 LC patients, (57[55 %] men, median [IQR] age 73[68–83] years), clinical reports identified LC in 64 (61 %) whereas AI identified LC in 95 (90 %). AI diagnostic (image-level) and prognostic (patient-level) sensitivities were 57.6 % and 90.0 %, (81 % in correct location), respectively. On CXRs performed >12 months prior to LC diagnosis, the AI detected nodules in 24(23 %) cases of which 22/24 had negative clinical reports for lung nodule/mass. The potential median reduction in time-to-diagnosis for cases where AI identified nodule(s) on previous CXR, but clinical reports negative, was 193[IQR 42–598] days. Of the 103 ‘negative’ controls (48[47 %] men, median [IQR] age 69[61–77] years) 20 patients had a nodule abnormality score above the threshold, generating a false-positive rate of 19 %.

Conclusion

The AI software showed excellent performance in detecting LCs that initially went undetected on CXR. The algorithm has potential to increase LC detection rates and reduce time-to-diagnosis. Using the AI, in conjunction with a trained observer, could increase reporting accuracy and potentially improve clinical outcomes.

Implications for practice

This study demonstrated the benefits and pitfalls associated with using AI in a clinical setting. It provides further evidence for utilising decision-support aids within clinical practice.