LAUSR

To the Editors: Artificial intelligence (AI) radiomics-based tools demonstrate promise for indeterminate pulmonary nodule (PN) malignancy risk stratification. We performed a secondary analysis of a previous multi-reader, multi-case study to evaluate the effect of an AI tool on clinicians’ PN management decisions. The details of this study have been previously described. Briefly, 12 readers (6 radiologists, 6 pulmonologists) independently evaluated 300 indeterminate PN cases using solely axial CT chest scan imaging data. PNs were 5–30 mm in maximal diameter, and 50% were malignant. The AI tool assessed was the Lung Cancer Prediction Convolutional Neural Network (Virtual Nodule Clinic, version 2.0.0; Optellum Ltd, Oxford, UK). This tool calculates a Lung Cancer Prediction (LCP) score describing PN malignancy risk on a decile scale from 1 to 10 assuming a malignancy prevalence of 30%. For each case, each reader independently provided estimates of malignancy risk (0%–100%) and management decision (no follow-up, ≥6-month CT follow-up, 6-week to 6-month CT follow-up, immediate imaging follow-up, non-surgical biopsy, or surgical resection) before and after being shown the LCP score. We defined appropriate management of malignant PNs as non-surgical biopsy and surgical resection. For benign PNs, no follow-up or imaging follow-up were deemed appropriate. We classified immediate imaging as appropriate management for all PNs. The median LCP score for malignant PNs was 9 (IQR, 8–10) and 5 (IQR, 2–7) for benign PNs (p < 0.001). Among malignant PNs, the average reader malignancy risk estimate was 60.2% (SD, 31.7%) without the AI tool compared to 69.0% (SD, 28.6%) with it (p < 0.001). Among benign PNs, the average reader malignancy risk estimate was 23.4% (SD, 28.1%) without the AI tool compared to 21.0% (SD, 26.9%) with it (p = 0.01). The distributions of management decisions are displayed in Figure 1. Overall, the proportion of cases with appropriate management decisions increased from 79.5% (SD, 5.7%) to 84.1% (SD, 6.6%) with AI (p = 0.008). Among malignant PNs, on average readers selected immediate imaging, biopsy, or surgical resection in 71.9% (SD, 14.0%) of cases without use of AI compared to 81.4% (SD, 13.7%) with the AI tool (p < 0.001). Among benign PNs, on average readers selected no action, short-term, long-term, or immediate follow-up imaging in 87.2% (SD, 10.4%) of cases without and 88.7% (SD, 11.1%) with the AI tool, respectively (p = 0.19). We found that use of an AI tool was associated with an increase of the average proportion of cases with appropriate management decisions from 79.5% to 84.1%. This was largely driven by a 10 percentage point increase in malignant PNs appropriately managed with immediate imaging or tissue sampling. On the other hand, we did not observe a statistically significant difference in the management of benign PNs with use of the AI tool. Taken together, these results suggest that the previously demonstrated improvement in diagnostic accuracy with use of an AI tool may translate into meaningful changes in clinical management decisions and promote earlier diagnostic evaluation of malignant PNs, which may ultimately lead to increased timeliness of appropriate clinical treatment for thoracic malignancies.