Objective:To construct a prediction model of pancreatic fistula after pancreaticoduodenectomy and explore its application value.Methods:The retrospective case-control study was conducted. The clinicopathological data of 285 patients with periampullary diseases who underwent pancreaticoduodenectomy in the the First Affiliated Hospital of Harbin Medical University from January 2015 to September 2018 were collected. There were 183 males and 102 females, aged (56±14)years, with a range from 12 to 84 years. According to the random numbers showed in the computer, patients were randomly divided into training dataset consisting of 214 patients and validation dataset consisting of 71 patients, with a ratio of 3∶1. The training dataset was used to construct prediction model, and the validation dataset was used to evaluate performance of prediction model. Observation indicators: (1) incidence of postoperative pancreatic fistula; (2) construction of prediction model of pancreatic fistula after pancreaticoduodenectomy; (3) validation of prediction model of pancreatic fistula after pancreaticoduodenectomy. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed by the t test. Measurement data with skewed distribution were represented as M (range), and comparison between groups was analyzed using the Mann-Whitney rank sum test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. Univariate analysis and multivariate analysis were conducted using the Logistic regression model. The accuracy of prediction model was analyzed by drawing receiver operating characteristic curve and calculating area under curve (AUC). Results:(1) Incidence of postoperative pancreatic fistula: of 214 patients in the training dataset, 45 patients had postoperative pancreatic fistula, including 39 of grade B and 6 of grade C, respectively. (2) Construction of prediction model of pancreatic fistula after pancreaticoduodenectomy. Results of univariate analysis showed that body mass index(BMI), diameter of the main pancreatic duct on computed tomography (CT) scan, diameter of the main pancreatic duct by intraoperative exploration, pancreas texture, and level of amylase in ascites at the postoperative first day were related factors for pancreatic fistula after pancreaticoduodenectomy ( χ2=32.450, 15.789, 19.577, 4.559, Z=-7.962, P<0.05). Results of multivariate analysis showed that BMI>25 kg/m 2, diameter of the main pancreatic duct by intraoperative exploration <3 mm and level of amylase in ascites at the postoperative first day >2 651U/L were independent risk factors for pancreatic fistula after pancreaticoduodenectomy ( odds ratio=0.148, 4.286, 0.086, 95% confidence interval: 0.058-0.376, 1.736-10.580, 0.032-0.231, P<0.05). Based on results of multivariate analysis, a prediction model of pancreatic fistula after pancreaticoduodenectomy was built: the predicted value of pancreatic fistula=Exp[0.452-1.914(BMI)+ 1.455(diameter of the main pancreatic duct by intraoperative exploration)-2.451(level of amylase in ascites at the postoperative first day)]/1+ Exp[0.452-1.914(BMI)+ 1.455(diameter of the main pancreatic duct by intraoperative exploration)-2.451(level of amylase in ascites at the postoperative first day)]. The model had the AUC of 0.888 (95% confidence interval : 0.832-0.943, P<0.05). (3) Validation of prediction model of pancreatic fistula after pancreaticoduodenectomy: in the validation dataset, the prediction model of pancreatic fistula after pancreaticoduodenectomy had the AUC of 0.868 (95% confidence interval: 0.780-0.957, P<0.05). There was no significant difference in the AUC between the training dataset and validation dataset ( Z=0.514, P>0.05). Conclusions:BMI>25 kg/m 2, diameter of the main pancreatic duct by intraoperative exploration <3 mm and level of amylase in ascites at the postoperative first day >2 651 U/L are independent risk factors for pancreatic fistula after pancreaticoduodenectomy. Construction of a prediction model of pancreatic fistula after pancreaticoduo-denectomy can effectively predict the risks of postoperative pancreatic fistula.

Objective:To explore the application value of CT measurement of renal depth correction, optimized acquisition and post-processing in the measurement of renal glomerular filtration rate (GFR) by Gates renal dynamic imaging.Methods:From January 2018 to November 2019, 157 patients (102 males, 55 females, age (51.4±14.5) years) including 118 in normal renal area group (adults with normal renal position and morphology, and excluding hydronephrosis, renal occupation, retroperitoneal mass and other factors affecting renal depth) and 39 in abnormal renal area group (19 of transplanted kidney, 11 of horseshoe kidney and 9 of ectopic kidney), were retrospectively enrolled in Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. The GFR was measured by renal dynamic imaging Gates method. For the normal renal area group, the renal depth was calculated by CT method, the traditional Tonnesen formula or the Li Qian formula. For the abnormal renal area group, the GFR was measured by optimized acquisition and post-processing method (GFR optimization), the traditional post-processing method (GFR tradition), or Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula method (eGFR). The differences of the renal depth and corresponding GFR obtained by different methods were analyzed using one-way analysis of variance and the least significant difference (LSD) t test. The correlation was analyzed by Pearson correlation analysis, and the consistency was analyzed by Bland-Altman analysis. Results:In the normal renal area group, the left and right renal depth measured by CT were (7.40±1.43) and (7.51±1.37) cm. Tonnesen formula underestimated renal depth (left kidney: (6.03±0.82) cm, right kidney: (6.06±0.84) cm; F values: 64.145 and 68.567, both P<0.01), and the deviation increased with the increase of CT measured depth ( r values: 0.847 and 0.834, both P<0.01). The GFR measured by Tonnesen formula was (56.93±28.42) ml·min -1·1.73 m -2, and the difference was statistically significant compared with CT method ((73.43±36.56) ml·min -1·1.73 m -2; F=9.423, P<0.01). The renal left and right depth measured by Li Qian formula were (7.55±1.03) and (7.52±0.98) cm, and the total GFR was (73.65±34.50) ml·min -1·1.73 m -2 with no differences compared with CT method (all P>0.05). The GFR obtained by Li Qian formula had better correlation ( r=0.901, P<0.01) and consistency with CT method. In the abnormal renal area group, GFR optimization, GFR tradition and eGFR was (63.11±27.40), (48.40±25.45) and (59.89±32.24) ml·min -1·1.73 m -2, respectively, and the difference between GFR tradition and GFR optimization was statistically significant ( F=2.870, P=0.025). GFR optimization had better correlation ( r=0.941, P<0.01) and consistency with eGFR. Conclusions:Tonnesen formula underestimates the renal depth. Using CT to measure renal depth and perform depth correction can improve the accuracy of Gates method for GFR determination. For the special cases of transplanted kidney, horseshoe kidney, ectopic kidney and retroperitoneal mass, it is important to optimize acquisition scheme and post-processing method to obtain accurate GFR.

Objective:To evaluate the effects of different sphere volumes, target background ratio (T/B) and post-processing correction techniques on the quantitative results of 99Tc m SPECT/CT. Methods:Six spheres with different diameters (37, 28, 22, 17, 13, 10 mm) in National Electrical Manufacturers Association International Electrotechnical Commission (NEMA IEC) models were filled with a mixture of 0.54 MBq/ml 99Tc m and iodixanol. The mixture iodine content was about 0.3%(135 mg), which led to different T/B (32∶1, 16∶1, 8∶1, 4∶1) by changing the radioactivity concentration of the cylinder. Routine imaging was performed on different T/B phantoms which were scanned by SPECT/CT. The CT threshold method was used for the delineation of volume of interest (VOI). Then the same processing correction technique and ordered-subsets expectation maximization (OSEM) parameters were used to calculate the radioactivity concentrations of different spheres, and further compared with the true values, and the accuracies were calculated. Pearson correlation analysis was applied to evaluate the relationships between sphere volume, T/B and quantitative results. The sphere with T/B of 32∶1 and diameter of 37 mm were processed by 3 correction techniques (CT attenuation correction (CTAC)+ scatter correction (SC)+ resolution recovery (RR); CTAC+ SC; CTAC+ RR). One-way analysis of variance and the least significant difference t test were used to analyzed the effects of 3 correction techniques on the quantitative results and image contrasts. Results:There were significant relationships between the sphere volumes, T/B and the quantitative accuracy ( r values: 0.757, 0.409, both P<0.05). There were significant differences of 3 correction techniques on the quantitative results and image contrast ( F values: 139.665 and 38.905, both P<0.001). Among them, the quantitative error of CTAC+ SC+ RR was lower than that of CTAC+ SC ((9.63±8.82)% vs (38.89±2.17)%; P<0.001), and similar to that of CTAC+ RR ((8.70±6.64)%; P>0.05). The quantitative error of CTAC+ RR was lower than that of CTAC+ SC ( P<0.001). The image contrast of CTAC+ SC+ RR was higher than that of CTAC+ SC ((93.45±0.91)% vs (92.41±0.25)%; P<0.001) and the image contrast of CTAC+ SC was higher than that of CTAC+ RR ((91.37±0.87)%; P<0.001). Conclusions:The larger sphere volume and the higher T/B, the more quantitative accuracy. The volume has a more significant effect on quantitative accuracy than T/B. Choosing the appropriate correction technique is helpful to quantitative accuracy improvement. It is suggested to use CTAC+ SC+ RR in quantitative processing.