Objective:To construct and validate a clinical model combining CYP11B2 gene polymorphisms with clinical parameters to predict complete postoperative hypertension remission in primary aldosteronism patients.Methods:The clinical data of a total of 116 patients with primary aldosteronism who underwent unilateral adrenalectomy from April 2018 to August 2024 were retrospectively included. There were 63 males and 53 females，with a body mass index（BMI）of（25.50 ± 2.03）kg/m 2. Genomic DNA was extracted from venous blood leukocytes before surgery，and polymerase chain reaction-restriction fragment length polymorphisms（PCR-RFLP）were used to detect CYP11B2（rs1799998）promoter region 344（C > T）base substitution. The follow-up duration was more than 6 months，with the following parameters recorded at the last follow-up：plasma aldosterone，renin，serum potassium，and sodium levels. Blood pressure progression and antihypertensive medication usage were also assessed. The postoperative outcome was determined according to the Primary Aldosteronism Surgical Outcome score（PASO）for primary aldosteronism，and the specific criteria were as follows. ① Clinical complete remission：the patient's blood pressure returned to normal（< 140/90 mmHg，1 mmHg = 0.133 kPa）and all antihypertensive drugs were discontinued；②Partial clinical remission：blood pressure returns to normal，and the number or dose of antihypertensive drugs is reduced compared with before；③Clinical non-remission：blood pressure does not drop and antihypertensive drugs do not change or increase compared with before surgery. Patients were divided into complete and incomplete remission groups. The chi-square test was used for univariate analysis，followed by binary logistic forward conditional regression for multivariate analysis，and a variety of machine learning algorithms such as random forest，logistic regression，support vector machine and gradient lifter were integrated，and the results of multivariate analysis were included to construct a postoperative blood pressure outcome model，and the predictive performance of the model was evaluated by using receiver operating characteristic（ROC）curve，calibration curve and clinical decision curve. Results:The PCR-RFLP detection results of 116 cases showed the genotype distribution of CYP11B2（344C > T）（rs1799998）as follows：CC type in 50 cases（43.1%），CT type in 46 cases（39.7%），and TT type in 20 cases（17.3%）. There were 74 cases in the complete remission group and 42 cases in the incomplete remission group，and the rate of complete remission with hypertension at the end of the operation was 63.8%. Univariate analysis showed that the the differences between complete remission group and incomplete remission group in body mass index［（24.27 ± 2.90）kg/m 2 vs.（26.98 ± 3.17）kg/m 2， P<0.001］，preoperative hypertension grade（grade 1/2/3：29/29/16 cases vs. 9/13/20 cases， P = 0.012），preoperative antihypertensive drugs（0/1/≥ 2：25/32/17 cases vs. 7/15/20 cases， P = 0.016），and CYP11B2（344C > T）（CC/TT + CT：39/35 cases vs. 11/31 cases， P = 0.006）were statistically significant. Multivariate analysis showed that the type of preoperative antihypertensive drugs［≥ 2： OR = 5.26（95% CI 1.12?24.61， P = 0.016；1： OR = 4.55（95% CI 1.23?22.47）， P = 0.025］was the strongest independent predictor，followed by CYP11B2（344C > T）［ OR = 4.02（95% CI 1.16?13.82）， P = 0.028］and BMI［ OR = 3.96（95% CI 2.26?6.92）， P < 0.001］. Comparing the receiver operating feature（ROC）curves of the four types of machine learning models，the best model was the support vector machine model with an area under the curve（AUC）of 0.88（95% CI 0.82?0.95），followed by the gradient elevator model of 0.83（95% CI 0.76?0.91），the logistic regression model of 0.78（95% CI 0.68?0.88），and the random forest model of 0.77（95% CI 0.68?0.86）. The optimal threshold of the Yoden index of the support vector machine model was 0.588，with a sensitivity of 78.5% and a specificity of 86.5%. The clinical decision curve and calibration curve show that the support vector machine model has a higher net benefit and acceptable stability and reliability. Conclusions:The support vector machine model incorporating CYP11B2 gene polymorphisms，BMI，and types of preoperative antihypertensive medications could effectively predict postoperative hypertension remission in primary aldosteronism patients，providing new evidence for personalized treatment strategies

Objective:To construct and validate a clinical model combining CYP11B2 gene polymorphisms with clinical parameters to predict complete postoperative hypertension remission in primary aldosteronism patients.Methods:The clinical data of a total of 116 patients with primary aldosteronism who underwent unilateral adrenalectomy from April 2018 to August 2024 were retrospectively included. There were 63 males and 53 females，with a body mass index（BMI）of（25.50 ± 2.03）kg/m 2. Genomic DNA was extracted from venous blood leukocytes before surgery，and polymerase chain reaction-restriction fragment length polymorphisms（PCR-RFLP）were used to detect CYP11B2（rs1799998）promoter region 344（C > T）base substitution. The follow-up duration was more than 6 months，with the following parameters recorded at the last follow-up：plasma aldosterone，renin，serum potassium，and sodium levels. Blood pressure progression and antihypertensive medication usage were also assessed. The postoperative outcome was determined according to the Primary Aldosteronism Surgical Outcome score（PASO）for primary aldosteronism，and the specific criteria were as follows. ① Clinical complete remission：the patient's blood pressure returned to normal（< 140/90 mmHg，1 mmHg = 0.133 kPa）and all antihypertensive drugs were discontinued；②Partial clinical remission：blood pressure returns to normal，and the number or dose of antihypertensive drugs is reduced compared with before；③Clinical non-remission：blood pressure does not drop and antihypertensive drugs do not change or increase compared with before surgery. Patients were divided into complete and incomplete remission groups. The chi-square test was used for univariate analysis，followed by binary logistic forward conditional regression for multivariate analysis，and a variety of machine learning algorithms such as random forest，logistic regression，support vector machine and gradient lifter were integrated，and the results of multivariate analysis were included to construct a postoperative blood pressure outcome model，and the predictive performance of the model was evaluated by using receiver operating characteristic（ROC）curve，calibration curve and clinical decision curve. Results:The PCR-RFLP detection results of 116 cases showed the genotype distribution of CYP11B2（344C > T）（rs1799998）as follows：CC type in 50 cases（43.1%），CT type in 46 cases（39.7%），and TT type in 20 cases（17.3%）. There were 74 cases in the complete remission group and 42 cases in the incomplete remission group，and the rate of complete remission with hypertension at the end of the operation was 63.8%. Univariate analysis showed that the the differences between complete remission group and incomplete remission group in body mass index［（24.27 ± 2.90）kg/m 2 vs.（26.98 ± 3.17）kg/m 2， P<0.001］，preoperative hypertension grade（grade 1/2/3：29/29/16 cases vs. 9/13/20 cases， P = 0.012），preoperative antihypertensive drugs（0/1/≥ 2：25/32/17 cases vs. 7/15/20 cases， P = 0.016），and CYP11B2（344C > T）（CC/TT + CT：39/35 cases vs. 11/31 cases， P = 0.006）were statistically significant. Multivariate analysis showed that the type of preoperative antihypertensive drugs［≥ 2： OR = 5.26（95% CI 1.12?24.61， P = 0.016；1： OR = 4.55（95% CI 1.23?22.47）， P = 0.025］was the strongest independent predictor，followed by CYP11B2（344C > T）［ OR = 4.02（95% CI 1.16?13.82）， P = 0.028］and BMI［ OR = 3.96（95% CI 2.26?6.92）， P < 0.001］. Comparing the receiver operating feature（ROC）curves of the four types of machine learning models，the best model was the support vector machine model with an area under the curve（AUC）of 0.88（95% CI 0.82?0.95），followed by the gradient elevator model of 0.83（95% CI 0.76?0.91），the logistic regression model of 0.78（95% CI 0.68?0.88），and the random forest model of 0.77（95% CI 0.68?0.86）. The optimal threshold of the Yoden index of the support vector machine model was 0.588，with a sensitivity of 78.5% and a specificity of 86.5%. The clinical decision curve and calibration curve show that the support vector machine model has a higher net benefit and acceptable stability and reliability. Conclusions:The support vector machine model incorporating CYP11B2 gene polymorphisms，BMI，and types of preoperative antihypertensive medications could effectively predict postoperative hypertension remission in primary aldosteronism patients，providing new evidence for personalized treatment strategies

Objective To estimate the impact of the image quality of conebeam CT on the accelerator for the Bowtie filter. Methods This study used the system of conebeam CT on the accelerator,and scaned the phantom of Catphan 500 in the multiplicate scanning conditions with and without the Bowtie filter. The scanning conditions were : voltage :120 kV, 100 kV, 80 kV, electricity:25 mA, 40 mA, 64 mA,time of exposure :40 MHz, collimator: M20, L20, scanning angle :358°. We transmitted the scanning data in the treatment planning systems of Philips Pinnacle 8. 0, and estimated the influence of the image quality.Results With and without the Bowtie filter, all of the images had high geometric precision, and the maximum difference of the nominal and actual distance at the horizontal, vertical and arrowy directions in the transection were 0. 50% and 0. 56% ( t = 0. 44, P ＞ 0. 05 ), 0. 44% and 0. 48% ( t = 1.01, P ＞ 0. 05 ),0. 50% and 0. 56% (t = 1.06,P ＞0. 05 ), respectively. The spatial resolution was 0. 167 cm, immutably.The ununiformity with the Bowtie filter was decreased of 28.0% -76.0% (t=2.53,P＜0. 05)、2.8%-50.0% (t= 3.02,P＜0.01 ) than those without the Bowtie filter, and the contrast-to-noise ratio was increased of 4.0% -41.6% (t=2.70,P＜0.05)、4.0% -50.0% (t=2.14,P＜0.05), using the collimators of M20 and L20, respectively. The Bowtie filter could display clearly the edge of scanning phantom, and improve the CT number of the image edge of phantom. And the linearity of CT number was also improved. Couclusions With the Bowtie filter, the image uniformity, the precision and linearity of CT number and the contrast-to-noise ratio were improved obviously, the edge artifacts were decreased, and the geometric precision and the spacial resolution did not change.