Background Gastrointestinal stromal tumors (GISTs) and gastrointestinal leiomyomas (GILs) are the most common subepithelial lesions (SELs). All GISTs have malignant potential; however, GILs are considered benign. Current imaging cannot effectively distinguish GISTs from GILs. We aimed to develop an artificial intelligence (AI) system to differentiate these tumors using endoscopic ultrasonography (EUS). Methods The AI system was based on EUS images of patients with histologically-confirmed GISTs or GILs. Participants from four centers were collected to develop and retrospectively evaluate an AI-based system. The system was used when endosonographers considered SELs as GISTs or GILs. We used the system in a multicenter prospective diagnostic test to clinically explore whether joint diagnoses by endosonographers and the AI system can distinguish between GISTs and GILs to improve the total diagnostic accuracy of SELs. Results The AI system was developed using 10439 EUS images from 752 participants with GISTs or GILs. In the prospective test, 132 participants were histologically-diagnosed (36 GISTs, 44 GILs, and 52 other types of SELs) among 508 consecutive subjects. Through joint diagnoses, the total accuracy of endosonographers in diagnosing the 132 histologically-confirmed participants increased from 69.7% (95% confidence interval [CI]: 61.4–76.9%) to 78.8% (95% CI: 71–84.9%; p =0.012). The accuracy of endosonographers in diagnosing the 80 participants with GISTs or GILs increased from 73.8% (95% CI: 63.1–82.2%) to 88.8% (95% CI: 79.8–94.2%; p =0.012). Conclusions We developed an AI-based EUS diagnostic system that can effectively distinguish GISTs from GILs and improve the diagnostic accuracy of SELs.

Background: Standard liver volume (SLV) is important in risk assessment for major hepatectomy. We aimed to investigate the growth patterns of normal liver volume with age and body weight (BW) and summarize formulae for calculating SLV in children.Methods: Overall, 792 Chinese children (<18 years of age) with normal liver were enrolled. Liver volumes were measured using computed tomography. Correlations between liver volume and BW, body height (BH), and body surface area (BSA) were analyzed. New SLV formulae were selected from different regression models; they were assessed by multicentral validations and were compared.Results: The growth patterns of liver volume with age (1 day−18 years) and BW (2–78 kg) were summarized. The volume grows from a median of 139 ml (111.5–153.6 in newborn) to 1180.5 ml (1043–1303.1 at 16–18 years). Liver volume was significantly correlated with BW (r = 0.95, P < 0.001), BH (r = 0.92, P < 0.001), and BSA (r = 0.96, P < 0.001). The effect of sex on liver volume increases with BW, and BW of 20 kg was identified as the optimal cutoff value. The recommended SLV formulae were BW≤20 kg: SLV = 707.12 × BSA1.09; BW>20 kg, males: SLV = 691.90 × BSA1.06; females: SLV = 663.19 × BSA1.04.Conclusions: We summarized the growth patterns of liver volume and provided formulae predicting SLV in Chinese children, which is useful in assessing the safety of major hepatectomies.