Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model.

Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator.

Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9–54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809–0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]).

The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

While metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with obesity, the cause of its rapidly rising prevalence is not well understood. In this study, we aimed to examine the association between arsenic exposure and MASLD in humans.

Urinary inorganic arsenic data from the National Health and Nutrition Examination Survey, 2011–2020, were used. These were combined with death certificate data from the National Death Index of the National Center for Health Statistics to ascertain mortality rates. Weighted linear regression and chi-squared analysis were performed.

The analysis included 6,386 participants after exclusions. The mean urinary arsenic level was 5.92 µg/L in participants with MASLD versus 5.59 µg/L in those without. Alanine aminotransferase levels exhibited a statistically significant increasing trend across both continuous arsenic levels and arsenic quintiles. A statistically significant upward trend was observed for the income-to-poverty ratio and body mass index but not for education status. MASLD prevalence was highest among the white population, while an increasing trend was observed in the Hispanic population over the years (p < 0.001). The proportion of Mexican Americans increased to 12.6% in the MASLD group versus 8.09% in the non-MASLD cohort (p < 0.001). There was a statistically significant increase in the odds of MASLD across arsenic exposure levels, with individuals in the highest quintile having a 32% greater likelihood compared to those in the lowest quintile (p-trend = 0.002). The odds further increased to 55% in the highest quintile (odds ratio 1.55, 95% confidence interval: 1.19–2.03; p-trend < 0.001). MASLD was more prevalent in females than males (57.9% vs. 47.6%; p < 0.001), and the mean age increased from 46.9 years to 49.9 years (p = 0.016).

Our findings reveal a positive association between urinary arsenic exposure and MASLD, with increasing trends particularly observed among Hispanics and those with higher income-to-poverty ratios and body mass index.

Giant invasive spinal schwannoma (GISS) is a rare benign tumor that extends over two or more vertebral levels with myofascial invasion. No previous case of GISS with vertebral body collapse has been reported. A 44-year-old man presented with a one-year history of progressive limb weakness and difficulty with defecation. He was initially misdiagnosed with a metastatic spinal tumor. Imaging revealed a large extradural mass with C4 vertebral body collapse. Histological examination of tumor tissue from both operations confirmed the diagnosis of schwannoma. The postoperative course was uneventful, and the patient’s limb weakness gradually improved. One year after surgery, the patient was able to walk and write independently. Muscle strength recovered to 4/5 in the upper extremities and 5/5 in the lower extremities, with a modified Japanese Orthopaedic Association score of 15/15. The patient’s neurological function improved significantly, and one-year follow-up showed no recurrence and stable spinal fixation. Currently, the patient’s bowel function has improved; however, the patient still requires defecation in bed. When magnetic resonance imaging reveals giant spinal tumors with imaging features suggestive of malignancy, GISS should be considered. Preoperative biopsy is essential for accurate diagnosis.