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Published in:
01-12-2021 | Lung Cancer | Imaging Informatics and Artificial Intelligence
AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset
Authors: Hyunsuk Yoo, Sang Hyup Lee, Chiara Daniela Arru, Ruhani Doda Khera, Ramandeep Singh, Sean Siebert, Dohoon Kim, Yuna Lee, Ju Hyun Park, Hye Joung Eom, Subba R. Digumarthy, Mannudeep K. Kalra
Published in: European Radiology | Issue 12/2021
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Objective
Assess if deep learning–based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs).
Methods
This reader study included 173 images from cancer-positive patients (n = 98) and 346 images from cancer-negative patients (n = 196) selected from National Lung Screening Trial (NLST). Eight readers, including three radiology residents, and five board-certified radiologists, participated in the observer performance test. AI algorithm provided image-level probability of pulmonary nodule or mass on CXRs and a heatmap of detected lesions. Reader performance was compared with AUC, sensitivity, specificity, false-positives per image (FPPI), and rates of chest CT recommendations.
Results
With AI, the average sensitivity of readers for the detection of visible lung cancer increased for residents, but was similar for radiologists compared to that without AI (0.61 [95% CI, 0.55–0.67] vs. 0.72 [95% CI, 0.66–0.77], p = 0.016 for residents, and 0.76 [95% CI, 0.72–0.81] vs. 0.76 [95% CI, 0.72–0.81, p = 1.00 for radiologists), while false-positive findings per image (FPPI) was similar for residents, but decreased for radiologists (0.15 [95% CI, 0.11–0.18] vs. 0.12 [95% CI, 0.09–0.16], p = 0.13 for residents, and 0.24 [95% CI, 0.20–0.29] vs. 0.17 [95% CI, 0.13–0.20], p < 0.001 for radiologists). With AI, the average rate of chest CT recommendation in patients positive for visible cancer increased for residents, but was similar for radiologists (54.7% [95% CI, 48.2–61.2%] vs. 70.2% [95% CI, 64.2–76.2%], p < 0.001 for residents and 72.5% [95% CI, 68.0–77.1%] vs. 73.9% [95% CI, 69.4–78.3%], p = 0.68 for radiologists), while that in cancer-negative patients was similar for residents, but decreased for radiologists (11.2% [95% CI, 9.6–13.1%] vs. 9.8% [95% CI, 8.0–11.6%], p = 0.32 for residents and 16.4% [95% CI, 14.7–18.2%] vs. 11.7% [95% CI, 10.2–13.3%], p < 0.001 for radiologists).
Conclusions
AI algorithm can enhance the performance of readers for the detection of lung cancers on chest radiographs when used as second reader.
Key Points
• Reader study in the NLST dataset shows that AI algorithm had sensitivity benefit for residents and specificity benefit for radiologists for the detection of visible lung cancer.
• With AI, radiology residents were able to recommend more chest CT examinations (54.7% vs 70.2%, p < 0.001) for patients with visible lung cancer.
• With AI, radiologists recommended significantly less proportion of unnecessary chest CT examinations (16.4% vs. 11.7%, p < 0.001) in cancer-negative patients.
