Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. National Institute for Health and Care Research.

Background/Aims: Hepatocellular carcinoma (HCC) is a prevalent cancer that significantly contributes to mortality globally, primarily due to its late diagnosis. Early detection is crucial yet challenging. This study leverages the potential of deep learning (DL) technologies, employing the You Only Look Once (YOLO) architecture, to enhance the detection of HCC in computed tomography (CT) images, aiming to improve early diagnosis and thereby patient outcomes. Materials and methods: We used a dataset of 1290 CT images from 122 patients, segmented according to a standard 70:20:10 split for training, validation, and testing phases. The YOLO-based DL model was trained on these images, with subsequent phases for validation and testing to assess the model’s diagnostic capabilities comprehensively. Results: The model exhibited exceptional diagnostic accuracy, with a precision of 0.97216, recall of 0.919, and an overall accuracy of 95.35%, significantly surpassing traditional diagnostic approaches. It achieved a specificity of 95.83% and a sensitivity of 94.74%, evidencing its effectiveness in clinical settings and its potential to reduce the rate of missed diagnoses and unnecessary interventions. Conclusion: The implementation of the YOLO architecture for detecting HCC in CT scans has shown substantial promise, indicating that DL models could soon become a standard tool in oncological diagnostics. As artificial intelligence technology continues to evolve, its integration into healthcare systems is expected to advance the accuracy and efficiency of diagnostics in oncology, enhancing early detection and treatment strategies and potentially improving patient survival rates. Cite this article as: Şahin E, O, Tatar C, et al. Diagnostic performance of deep learning applications in hepatocellular carcinoma detection using computed tomography imaging. Turk J Gastroenterol. 2025;36(2):124-130.

Schizophrenia, an important brain neurodevelopmental disorder, is observed in 1% of the global population. New-generation antipsychotics have been developed as alternatives to typical antipsychotics for more effective and safe therapy. Chronic administration of asenapine and paliperidone compared to haloperidol on depression, anxiety and analgesy in the forced swimming test (FST), elevated plus maze (EPM) and hot plate tests were examined in mice. Moreover effects of drugs, on expression levels of brain neurotrophic factors [brain-derived neurotrophic factor (BDNF), cAMP response element binding protein (CREB),nerve growth factor (NGF), synapsin and fibroblast growth factor 2 (FGF2)] in the hippocampus of mice, neurogenesis and neurodegeneration, and blood enzyme levels were also investigated. In FST, haloperidol (0.25 mg/kg) significantly increased immobility time while both asenapine (0.075 mg/kg) and paliperidone (0.25 and 0.50 mg/kg) significantly diminished this parameter. In EPM test, haloperidol significantly increased both % time spent in open arms and % open arm entries. Asenapine (0.075 mg/kg) and paliperidone (0.50 mg/kg) significantly increased % time spent in the open arms. They also increased % open arm entries, but this parameter failed to reach a statistically significant value. In hot plate test, haloperidol (0.125 and 0.25 mg/kg) and paliperidone (0.25 and 0.50 mg/kg) significantly increased the latency to lick the hind paws but asenapine had no effect. Asenapine and paliperidone upregulated more neurotrophic factors in the brain and caused less neurodegeneration compared to haloperidol. Investigated drugs had no effect on liver enzymes and plasma glucose levels. Asenapine and paliperidone may be preferred over classical antipsychotics since they have antidepressant-like effect, upregulate more neurotrophic factors and cause less neurodegeneration in naive mice without having diabetogenic and liver damaging effects. Paliperidone seems to possess superior effects compared to asenapine since it also exerts analgesic-like effect.