To discuss the feasibility of low-dose whole-pancreas imaging utilizing 640-slice dynamic volume CT.80 patients (40 cases of normal pancreas and 40 patients supposed of having pancreatic carcinoma or focal pancreatic space-occupying lesions were mainly refered) referred for CT pancreas perfusion were enrolled in the study. 80 patients randomly assigned to 3 groups: Group ① (whole sequence). Group ② (odd number sequence). Group ③ (even number group)(Compared to ①, the scanning times and effective radiate dose of ② and ③ decreased about 50% respectively). The head, body, tail of each normal pancreas without any pancreatic disease, lesion and lesion-surrounding areas of each pancreatic cancer were selected as ROI, and tissue peak, blood flow are measured.According to pathology and clinical materials, 27 patients were diagnosed as pancreatic cancer; 40 patients were diagnosed as normal pancreas. The tissue peak and blood flow of the head, body, tail of normal pancreas without any pancreatic disease are 109.63 ± 16.60 and 131.90 ± 41.61, 104.38 ± 19.39 and 127.78 ± 42.52, 104.55 ± 15. 44 and 123.50 ± 33.44 respectively. The tissue peak and blood flow of pancreatic cancer is 59.59 ± 18.20 and 60.00 ± 15.36. For and between each group, there is no significant statistical difference for the tissue peak and blood flow of normal areas of the head, body, tail of normal pancreas. There is statistical difference for the tissue peak and blood flow of lesion and lesion-surrounding areas of pancreatic cancer in each group. However, there is no statistical difference for the tissue peak and blood flow of normal and diseasing areas between 3 groups.Low-dose whole-pancreas perfusion with 640-slice dynamic volume CT is feasible.

Background: Spectral computed tomography (CT) is considered a useful tool for predicting tumor treatment response and survival. However, its role in lymphoma treatment remains unclear. The purpose of this study was to develop spectral CT-based nomogram for predicting response and prognosis in lymphoma patients after first-line therapy. Methods: The baseline clinical and spectral CT data from 91 patients with lymphoma were retrospectively analyzed. Treatment response was assessed according to the Lugano criteria, with patients classified into complete response (CR) and non-CR groups. Spectral CT parameters were measured from the largest lesion in the arterial and venous phases. Univariable and multivariable logistic regression analyses were performed to identify predictors of non-CR and to construct clinical, spectral CT, and combined models. The final combined model was internally validated using 1000 bootstrap resamples without repeating variable selection. Model performance was assessed using ROC analysis, calibration analysis, and decision curve analysis. Median follow up was estimated using the reverse Kaplan Meier method, while Kaplan Meier analysis and Cox regression were used to evaluate progression-free survival (PFS) and overall survival (OS). Results: In the multivariable analysis, B symptoms, Ki67, and arterial enhancement fraction based on IC (AEF IC) were identified as predictors of non-CR. The combined nomogram showed an apparent AUC of 0.866 (95% CI: 0.791, 0.938), which was higher than that of the clinical model but not significantly different from that of the spectral CT model. Risk stratification based on the clinical, spectral CT, and combined models was associated with PFS, with the combined model showing the strongest association with shorter PFS (HR: 4.05; 95% CI: 1.85, 8.88). Conclusions: Spectral CT and clinical models showed potential for predicting treatment response and PFS in patients with lymphoma. Integrating spectral CT parameters with clinical characteristics in a nomogram may improve response prediction and support exploratory risk stratification. Further external validation is required.