To delineate the features of multi-detector computed tomography (MDCT) images and clinical characteristics of pancreatic solid pseudopapillary tumors (SPTs) of the pancreas in asymptomatic patients and compare these features and characteristics between asymptomatic and symptomatic patients. This work is a retrospective study approved by our institutional review board. MDCT images and clinical data of 109 patients with pathologically proven SPTs obtained from October 2008 to October 2016 were reviewed. Patients were categorized into two groups: asymptomatic patients and patients with symptomatic disease. Cases were reviewed to determine the reason for detection, intervention, shape, diameter, location, calcification, encapsulation, internal composition, CT attenuation, enhancement pattern, and tumor pathology. Clinical factors and imaging features were also compared between groups. Statistical analysis was performed using χ2 and t-tests. Data from 49 asymptomatic and 60 symptomatic patients were collected. Asymptomatic SPTs were identified most frequently during routine health examination (18 patients, 36.7%), various screening purposes (12 patients, 24.5%), and traumatic injury (9 patients, 18.4%). Except for a smaller tumor size (5.8 cm in asymptomatic SPTs vs. 7.4 cm in symptomatic SPTs, P = 0.023), the clinical factors or imaging features of asymptomatic patients were very similar to those of symptomatic patients. The current research is the first single-center study to characterize SPTs in asymptomatic patients. Asymptomatic SPTs are gradually being identified with greater frequency. Although generally smaller in size than that in symptomatic patients, an asymptomatic pancreatic mass with the typical imaging features of SPT may be found, the treatment for which is similar to that for symptomatic patients. Evaluating asymptomatic SPTs requires further systematic and multi-center trials.

The discrepancy in reliability and evidence conflict in multimodal data fusion for medical image prediction significantly undermines the accuracy of clinical decision-making. To address this challenge, we propose an Adaptive Evidence-Gated Fusion Network (AEGFN) based on Dempster-Shafer (DS) evidence theory. This framework models the evidence quality and cognitive uncertainty of CT images, image sequences, and clinical data using the Dirichlet distribution. We innovatively introduce an Evidence-Attention Gate (EAG) to dynamically adjust fusion weights for high-conflict modalities (conflict > 0.6), enabling conflict-aware uncertainty compensation. Additionally, a hybrid loss function combining KL divergence regularization with uncertainty-weighted cross-entropy is designed to balance model confidence and generalization. Evaluated on colorectal cancer (656 cases) and radiation pneumonitis (117 cases) datasets for binary classification tasks (predicting patient death and predicting RP occurrence), AEGFN achieves classification accuracies of 95.04% (AUC 0.97) and 82.34% (AUC 0.8312), outperforming the state-of-the-art method DDEF by 0.66% and 1.94%, respectively. This work provides a robust and interpretable solution for multimodal medical prediction, enhancing the reliability of clinical decision support systems.

Objective: To assess the image quality of synthetic double inversion recovery (SyDIR) imaging and enhance the value of T2-weighted imaging (T2WI) in evaluating T stage for rectal cancer patients. Methods: A total of 112 pathologically confirmed rectal cancer patients were retrospectively selected after undergoing MRI, including synthetic MRI. The image quality of T2WI and SyDIR imaging was compared based on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall picture quality, presence of motion artifacts, lesion edge sharpness, and conspicuity. The concordance between MRI and pathological staging results, using T2WI alone and the combination of T2WI and SyDIR for junior and senior radiologists, was assessed using the Kappa test. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic efficacy of extramural infiltration in rectal cancer patients. Results: No significant differences in imaging quality were observed between conventional T2WI and SyDIR (p = 0.07–0.53). The combination of T2WI and SyDIR notably improved the staging concordance between MRI and pathology for both junior (kappa value from 0.547 to 0.780) and senior radiologists (kappa value from 0.738 to 0.834). In addition, the integration of T2WI and SyDIR increased the AUC for diagnosing extramural infiltration for both junior (from 0.842 to 0.918) and senior radiologists (from 0.917 to 0.938). Conclusion: The combination of T2WI and SyDIR increased the consistency of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration, which would benefit treatment selection. Critical relevance statement: SyDIR sequence provides additional diagnostic value for T2WI in the T staging of rectal cancer, improving the agreement of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration. Key Points: Synthetic double inversion recovery (SyDIR) and T2WI have comparable image quality. SyDIR provides rectal cancer anatomical features for extramural infiltration detections. The combination of T2WI and SyDIR improves the accuracy of T staging in rectal cancer. Graphical