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• W3138361027 abstract "Improving stratification of patients with indeterminate pulmonary nodules (IPNs) can lead both to earlier diagnosis of lung cancer and to reduced scanning and reduced intervention in cases of benign disease. AI-based decision support software has been shown to outperform conventional risk models at classifying IPNs as low or high risk, but its performance in addition to clinician assessment has yet to be investigated. We report the results of a Multiple-Reader Multiple-Case reader evaluation comparing reader performance for both radiologists and pulmonologists on an IPN risk stratification task with and without AI assistance from the previously-published Lung Cancer Prediction Convolutional Neural Network (LCP-CNN). A pool of 12 readers interpreted 100 non-contrast chest CTs each with at least one IPN. The reader breakdown was 7 radiologists, 5 pulmonologists. 7 were UK and 5 US, with representation from both specialties in both geographic zones. They ranged in experience from registrar (resident) to consultant (attending). Readers interacted with viewing software in which they could scroll through axial slices, and adjust window/level settings, but were blinded to patient clinical information. Readers first estimated the likelihood of malignancy for each nodule independently (solo-LoM) as a percentage. The reader was then provided with the AI score as a number from 1-10 and allowed to update assessment of LoM (assisted-LoM). The dataset comprised 50 histologically-diagnosed primary lung cancers (median 10.1mm, IQR 8-13mm) and 50 benign nodules (median 8.8mm, IQR 7-11mm). The nodules were detected incidentally at six EU centres, and were all 5-15mm in size at detection, in patients of 18+ years without a history of malignancy in the past 5 years. Performance was analysed comparing the Area Under the ROC curve (AUC) for the solo-LoM and assisted-LoM. CIs and P-values were calculated using bootstrapping. The average pre-AI AUC over all readers was 76.6 (95%CI 68.7-83.7), and 84.3 (77.2-90.6) when assisted by AI (P<.0001). The mean improvement over the set of readers is 7.7 points of AUC (95%CI 4.6-11.0). Table 1 shows the performance on a per-reader basis. All readers improved when assisted by the AI, and the improvement was significant in 10/12 readers at the 0.05 level.Table 1Performance on per-reader basisReader #AUC pre-AIAUC post-AIAUC improvementP value181.1 (72.1-89.1)88.0 (81.0-93.8)6.9 (1.8-12.4).004274.5 (64.5-83.5)81.9 (73.4-89.2)7.4 (3.2-12.3)<.001380.7 (71.3-89.1)83.1 (74.0-91.2)2.5 (0.0-5.4).024476.5 (66.4-85.7)87.6 (80.3-93.7)11.2 (4.3-18.6)<.001580.2 (71.3-87.9)82.1 (73.6-89.3)1.9 (-0.6-4.4).066671.6 (61.2-81.0)83.7 (75.5-90.7)12.1 (6.7-18.3)<.001770.6 (60.1-80.0)85.5 (77.8-92.2)14.9 (7.3-23.3)<.001876.4 (66.6-85.1)85.2 (77.4-91.9)8.8 (1.2-16.7).012980.3 (71.3-88.3)82.3 (73.8-89.8)2.0 (-1.2-5.1).1051078.1 (68.6-86.5)85.4 (77.5-92.1)7.3 (2.6-12.3)<.0011172.0 (61.6-81.3)82.6 (74.1-90.1)10.6 (5.0-16.8)<.0011276.8 (67.2-85.5)84.1 (76.0-91.1)7.3 (2.9-12.2)<.001 Open table in a new tab Radiologists and pulmonologists were able to significantly improve their assessment of the likelihood of malignancy for an IPN when assisted by AI score." @default.
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• W3138361027 date "2021-03-01" @default.
• W3138361027 modified "2023-09-26" @default.
• W3138361027 title "P42.01 AI Assistance for Pulmonary Nodule Stratification: An Multiple-Reader Multiple-Case Study" @default.
• W3138361027 doi "https://doi.org/10.1016/j.jtho.2021.01.825" @default.
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