An artificial intelligence system for distinguishing between gastrointestinal stromal tumors and leiomyomas using endoscopic ultrasonography.

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 the AI-based system. The system was used when endosonographers considered SELs to be GISTs or GILs. It was then used 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 for SELs. RESULTS: The AI system was developed using 10 439 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 71.0 %-84.9 %; P = 0.01). 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 = 0.01). CONCLUSIONS: We developed an AI-based EUS diagnostic system that can effectively distinguish GISTs from GILs and improve the diagnostic accuracy of SELs.

Standard Liver Volume-Predicting Formulae Derived From Normal Liver Volume in Children Under 18 Years of Age.

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 × BSA 1.09; BW>20 kg, males: SLV = 691.90 × BSA 1.06; females: SLV = 663.19 × BSA 1.04. 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.

Bioactivity-guided fast screen and identification of cancer metastasis chemopreventive components from raw extracts of Murraya exotica.

Murraya exotica is a traditional Chinese medicine (TCM) widely grown in southeast China. We herein proposed a fast strategy for separation and identification of active components of cancer metastatic chemopreventives from the root, leaf, twig and stem bark extracts that were obtained by reflux in 80% acidic ethanol and then liquid-liquid extraction. High performance liquid chromatography (HPLC) analysis showed that the extract mixtures from leaf, bark and twig were similar, while the root extract contained a characteristic component (CM1). Bioactivity assays revealed that the root extract contained some active components that significantly inhibited cancer cell viability and migration. Ultra performance liquid chromatography coupled with diode array detection and electrospray ionization mass spectrometry (UPLC-DAD-ESI-MS) analysis indicated the existence of coumarins in the root and leaf extracts. Semi-preparative chromatographic separation and physicochemical characterization indicated that CM1 was a novel coumarin derivative that warrants further chemopreventive studies on cancer metastasis. The present phytochemical and phytopharmacological studies exemplify a fast strategy for screening and identifying active component(s) from raw extracts of phytomedicines.