Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging.

BACKGROUND: The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging. METHODS: The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857). CONCLUSION: The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.

Cross-border Activities Associated With Pattern of Amphetamine-type Stimulants Usage Among Drug Users in Southwest of China.

Cross-border activities are possibly associated with the use of amphetamine-type stimulants (ATS), this study was to explore poly-substance of ATS use and influencing factors among ATS use populations in southwest China. A cross-sectional study was conducted by response driven and continuous samplings from January to July 2021. Descriptive, univariate and logistic regression were carried out. ATS users accounted for 95.6% of the target population, of whom one-third had cross-border experiences with 4.1% of the cross-border purchase of drugs. ATS users were mainly over 31 years old (53.9%), male (98.7%), minority (79.1%), and unmarried (72.7%). Cross-border users consumed more ketamine (8%) and methamphetamine (40%) (P < .05). After adjusting for socioeconomic-demographic factors, cross-border activity [OR: 0.336 (0.141, 0.799)], occupation [OR: 0.273 (0.080, 0.929)], injecting drug behavior [OR: 6.239 (1. 087, 35.811)], frequency [OR: 0.251 (0.073, 0.859)], and ATS use location [OR: 2.915 (1.040, 8.168)] were possible factors influencing ATS use patterns (P < .05). Cross-border activity may be associated with polydrug use, especially predominantly methamphetamine use, among ATS users along the Southwest border. It implied that the focus of drug prevention and control in border areas should be on cross-border populations.