County medical community serves as pivotal instruments for the hierarchical diagnostic and treatment system. Since their nationwide implementation in December 2023, there has been a pressing demand for governance theories to direct practical applications. Based on polycentric-collaborative governance theory, this study articulated a framework that modeled county medical community as accountable care organizations, anchored by a foundation of primary health care and a polycentric-collaborative governance structure. The framework encompassed critical dimensions, including problem orientation, diverse governance actors, multi-dimensional governance mechanisms, governance objectives, and evaluative metrics. This research also introduced 17 governance instruments, delineated 13 priority action fields, and pinpointed 5 potential reform areas, with a pronounced focus on bolstering governance capacity through value-based payment reforms and technological underpinnings of digital innovation. The framework′s viability and efficacy have been substantiated through a case study in Zhejiang Province. The framework presented herein could offer a comprehensive and systematic guide for county medical community practice, capturing the essence of the reform through a problem-oriented lens, fostering multi-actor involvement, and ensuring the harmonization of multi-dimensional governance mechanisms, thereby fortifying the reform′s systematic, integrative, and collaborative attributes.

County medical community serves as pivotal instruments for the hierarchical diagnostic and treatment system. Since their nationwide implementation in December 2023, there has been a pressing demand for governance theories to direct practical applications. Based on polycentric-collaborative governance theory, this study articulated a framework that modeled county medical community as accountable care organizations, anchored by a foundation of primary health care and a polycentric-collaborative governance structure. The framework encompassed critical dimensions, including problem orientation, diverse governance actors, multi-dimensional governance mechanisms, governance objectives, and evaluative metrics. This research also introduced 17 governance instruments, delineated 13 priority action fields, and pinpointed 5 potential reform areas, with a pronounced focus on bolstering governance capacity through value-based payment reforms and technological underpinnings of digital innovation. The framework′s viability and efficacy have been substantiated through a case study in Zhejiang Province. The framework presented herein could offer a comprehensive and systematic guide for county medical community practice, capturing the essence of the reform through a problem-oriented lens, fostering multi-actor involvement, and ensuring the harmonization of multi-dimensional governance mechanisms, thereby fortifying the reform′s systematic, integrative, and collaborative attributes.

Objective To investigate a novel stroke recurrence risk prediction model,which utilized radiomics machine learning methods and specifically combined carotid computed tomography angiography(CT A)with the Essen stroke risk score(ESRS).Methods A total of 136 patients who underwent carotid CT A were analyzed retrospectively.The features of carotid plaque were extrac-ted by machine learning to construct a radiomics feature model,as well as combined with ESRS.Based on clinical outcomes at one-year follow-up,the stroke recurrence risk prediction model was constructed using the logistic regression(LR)machine learning model.To construct an effective and robust model,the dataset was divided into a training set and a validation set in a ratio of 7∶3.The performance of this model was evaluated using area under the curve(AUC)of receiver operating characteristic(ROC)curve,sensi-tivity and specificity.Results The model had strong predictive value.In the training set,AUC,sensitivity and specificity of this model were 0.903,0.796 and 0.761,respectively.In the validation set,AUC,sensitivity and specificity of this model were 0.869,0.667 and 0.850,respectively.Conclusion The stroke recurrence risk prediction model constructed based on radiomics analysis of carotid plaque characteristics in carotid CTA,in combination with the ESRS,can provide reliable predictions for stroke prognosis.

Objective To observe the value of augmented reality(AR)navigation system for assisting CT-guided puncture of pulmonary nodules in dog models.Methods Five healthy dogs were selected,and 4 target lung rings were implanted in each dog to build pulmonary nodule models.Deferring to crossover design,CT-guided punctures were performed with or without AR navigation 2 and 4 weeks after successful modeling,respectively,while punctures with AR navigation were regarded as AR group and the others as conventional group,respectively.The time duration of puncturing,the times of CT scanning,of needle adjustment,and the deviation distance between needle pinpoint to the center of pulmonary nodule shown on three-dimensional reconstruction were compared between groups.Results The duration time of puncture in AR group and conventional group was(13.62±5.11)min and(20.16±4.76)min,respectively.In AR group,the times of CT scanning,of needle adjustment,and the deviation distance was 2.40±0.50,2.75±0.44 and(2.94±1.92)mm,respectively,while in conventional group was 3.10±0.64,3.70±0.57 and(4.90±3.38)mm,respectively.The introduction of AR navigation was helpful to shortening the duration of puncture,reducing times of CT scanning and needle adjustment,also decreasing positioning error of needle pinpoint(all P＜0.05).In contrast,the variance of puncture sequences and dogs had no obvious effect on the results(both P＞0.05).Conclusion AR navigation system could improve accuracy and efficiency in CT-guided puncture of pulmonary nodules in dog models.

Medical service pricing system is the basic system of health economics, which has a profound impact on physicians′ medical behavior and the running mode of hospital. Rui′an City, Wenzhou City, Zhejiang Province, freed up the cost space of drug and consumables based on the reasonable diagnosis and treatment behavior of doctors and selection of drug and consumables in the hospital procurement link. 68% of the free space was used to improve the price of medical services. 248 medical service items with obvious cost inversion were selected, such as nursing, traditional Chinese medicine, diagnosis, etc., and the price adjustment range was determined according to the agreed free space limit. Meanwhile, the credit mechanism of Alipay was applied to synchronously implement " vacating space and adjusting structure" , so as to solve the game problem between medical treatment and medical insurance, broaden the path of the " translation compensation method" of medical service price reform, and produce policy superposition and linkage effect with the reform of medical insurance payment mode.

Objective:To evaluate the effectiveness of deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (Hybrid IR) in improving the image quality in chest low-dose CT (LDCT).Methods:Seventy-seven patients who underwent LDCT scan for physical examination or regular follow-up in Peking Union Medical College Hospital from October 2020 to March 2021 were retrospectively included. The LDCT images were reconstructed with Hybrid IR at standard level (Hybrid IR Stand) and DLR at standard and strong level (DLR Stand and DLR Strong). Regions of interest were placed on pulmonary lobe, aorta, subscapularis muscle and axillary fat to measure the CT value and image noise. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated. Subjective image quality was evaluated using Likert 5-score method by two experienced radiologists. The number and features of ground-glass nodule (GGN) were also assessed. If the scores of the two radiologists were inconsistent, the score was determined by the third radiologist. The objective and subjective image evaluation were compared using the Kruskal-Wallis test, and the Bonferroni test was used for multiple comparisons within the group.Results:Among Hybrid IR Stand, DLR Stand and DLR Strong images, the CT value of pulmonary lobe, aorta, subscapularis muscle and axillary fat had no significant differences (all P>0.05), but the image noise and SNR of pulmonary lobe, aorta, subscapularis muscle and axillary fat had significant differences(all P<0.05), and the CNR of images had significant difference( P<0.05), too. The CNR of Hybrid IR Stand images, DLR stand images and DLR strong images were 0.71 (0.49, 0.88), 1.06 (0.78, 1.32) and 1.14 (0.84, 1.48), respectively. Compared with Hybrid IR images, DLR images had lower objective and subjective image noise,higher SNR and CNR (all P<0.05). The scores of DLR images were superior to Hybrid IR images in identifying lung fissures, pulmonary vessels, trachea and bronchi, lymph nodes, pleura, pericardium and GGN (all P<0.05). Conclusions:DLR significantly reduced the image noise, and DLR images were superior to Hybrid IR images in identifying GGN in chest LDCT while maintaining superior image quality at relatively low radiation dose levels. Thus DLR images can improve the safety of lung cancer screening and pulmonary nodule follow-up by CT.

Objective:To explore application value of improving quality of the low dose pancreatic CT images by using deep learning reconstruction (DLR).Methods:From August to December 2020, 68 patients who underwent contrast-enhanced pancreatic CT were prospectively collected in Peking Union Medical College Hospital. All patients were randomly divided into routine dose group (34 patients, with tube voltage of 120 kV) and low dose group (34 patients, with tube voltage of 100 kV). All patients underwent non-contrast, arterial phase, parenchymal phase and delay phase scans. The four-phase images of low dose group were reconstructed by using filtered back projection (FBP), hybrid iterative reconstruction (AIDR) and DLR which were marked with LD-FBP, LD-AIDR and LD-DLR, respectively. The four-phase images of routine dose group were reconstructed by using AIDR algorithm which were marked with RD-AIDR. The CT value, image noise (SD), signal to noise ratio (SNR) and contrast to noise ratio (CNR) of pancreas were measured. The ANOVA test was performed in comparison with objective parameters of different reconstruction algorithms, and LSD test was performed in pairwise comparison. The subjective image scores were obtained and were compared using Kruskal-Wallis test.Results:CT value, SD, SNR and CNR of non-contrast, arterial phase, parenchymal phase and delay phase had significant difference among different reconstruction images of routine dose group and low dose group (all P<0.05). The CT value of LD-FBP, LD-AIDR, and LD-DLR images were significantly higher than those of RD-AIDR images in parenchymal phase and delay phase (all P<0.05). There were statistically significant differences in each pairwise comparison of SD and SNR of four phase images (all P<0.05). There were statistically significant differences of CNR among LD-FBP, LD-DLR and RD-AIDR in four phase images (all P<0.05). The CNR of RD-AIDR was better than that of LD-FBP, and CNR of LD-DLR was better than that of RD-AIDR. DLR algorithm improved the SD, SNR and CNR of four phases of pancreatic images. The improvement of SNR was more significant after contrast enhancement, and the improvement of CNR was more significant in the non-contrast and delay phases. Subjective image scores of different reconstruction images were statistically different in four phase images (all P<0.001). Overall image scores of LD-DLR and RD-AIDR had no significant differences in four phase ( Z value of four phases were 1.00, 2.24, 0.45 and 1.34, respectively; P value of four phases were 0.317, 0.025, 0.655 and 0.180, respectively). Conclusion:The DLR technology can decrease radiation dose of pancreatic CT, improve image quality and satisfy diagnostic requirement. The DLR technology can also reduce image noise, improve the SNR and CNR in low dose contrast-enhanced pancreatic CT.

Objective:To explore the effect of deep learning reconstruction (DLR) on radiation dosage reduction and image quality of CTPA compared with hybrid iterative reconstruction (HIR).Methods:A total of 100 patients with suspected pulmonary embolism (APE) or indications for CTPA due to other pulmonary artery diseases in Peking Union Medical College Hospital from December 2020 to April 2021 were prospectively enrolled and divided into HIR group and DLR group according to block randomization, with 50 cases in each group. The patient′s gender, age and body mass index (BMI) were recorded. HIR group and DLR group underwent standard deviation (SD)=8.8 and SD=15 CTPA protocols in combination with HIR and DLR algorithm respectively. Other scanning parameters and contrast medium injection plan were the same. The effective dose (ED) and size-specific dose estimate (SSDE) were calculated. Regions of interest (ROIs) were drawn in the lumen of Grade 1-3 pulmonary arteries and bilateral paravertebral muscles. The corresponding CT and SD values were recorded to acquire signal to noise ratio (SNR) and contrast noise ratio (CNR). Based on a double-blind method, two radiologists evaluated the subjective noise, visualization of pulmonary arteries, and diagnostic confidence of the two groups by 5-point Likert scales. The inconsistent results were judged comprehensively by the third radiologist. Independent samples t-test was used to compare the demographic data, radiation dosage and quantitative image quality of the two groups. Mann-Whitney U test was used to compare the subjective noise, visualization of pulmonary arteries and diagnostic confidence between the two groups. Linear weighted Kappa coefficient was calculated to analyze the consistency of the qualitative scores between the two radiologists. Results:There were no significant differences in gender, age and BMI between the two groups ( P>0.05). The CT values of Grade1-3 pulmonary arteries and paravertebral muscle had no significant differences ( P>0.05). Compared with HIR group, the ED and SSDE in DLR group decreased by about 35% to 1.3 mSv and 4.20 mGy respectively, while the SNR (30±5) and CNR (26±5) of CTPA images were higher in DLR group than those in HIR group (23±5 and 20±5, with t=-6.60 and -5.90, respectively, both P<0.001). The subjective noise score was higher in DLR group than that in HIR group ( Z=-7.34, P<0.001). In addition, two radiologists showed excellent interobserver agreement in DLR group (Kappa=0.847, 95%CI 0.553-1.000). No significant differences were found in visualization of pulmonary arteries and diagnostic confidence between the two groups ( P>0.05). Conclusion:DLR further reduced the radiation dosage and improved the image quality of CTPA, with no detriment to diagnostic confidence. Thus DLR is worthy of clinical promotion.

Objective:To investigate the feasibility of chest ultra-low dose CT (ULDCT) using deep learning reconstruction (DLR) for lung cancer screening, and to compare its image quality and nodule detection rate with ULDCT iterative reconstruction (Hybrid IR) and conventional dose CT (RDCT) Hybrid IR.Methods:The patients who underwent chest CT examination for pulmonary nodules in Peking Union Medical College Hospital from October 2020 to March 2021 were prospectively included and underwent chest RDCT (120 kVp, automatic tube current), followed by ULDCT (100 kVp, 20 mA). The RDCT images were reconstructed with Hybrid IR (adaptive iterative dose reduction 3D,AIDR 3D), and ULDCT was reconstructed with AIDR3D and DLR. Radiation dose parameters and nodule numbers were recorded. Image quality was assessed using objective noise, signal-to-noise ratio (SNR) of the main trachea and left upper lobe, subjective image scores of the lung and nodules. Subjective scores were scored by 2 experienced radiologists on a Likert 5-point scale. The difference of radiation dose was compared with paired t-test between ULDCT and RDCT.The differences of quantitative indexes, objective image noise and subjective scores of the three reconstruction methods were compared with one-way analysis of variance or Friedman test. Results:Forty-five patients were enrolled, including 17 males and 28 females, aged from 32 to 74 (55±11) years. The radiation dose of ULDCT was (0.17±0.01) mSv, which was significantly lower than that of RDCT [(1.35±0.41) mSv, t=15.46, P<0.001]. There were significant differences in the image noise and SNR in the trachea and lung parenchyma and in the CT value of the trachea among ULDCT-AICE, ULDCT-AIDR 3D and RDCT-AIDR 3D images ( P<0.05). Image noise in the trachea and lung parenchyma and CT value in the trachea of ULDCT-AICE were significantly lower than those of ULDCT-AIDR 3D ( P<0.05) and comparable to RDCT-AIDR 3D ( P>0.05). There were significant differences in subjective image scores of the lung and nodules among ULDCT-AICE, ULDCT-AIDR 3D and RDCT-AIDR 3D images (χ2=50.57,117.20, P<0.001). Subjective image scores of the lung and nodules for ULDCT-AICE were significantly higher than those of ULDCT-AIDR 3D ( P<0.05), and non-inferior to RDCT-ADIR 3D ( P>0.05). All 72 clinically significant nodules detected on RDCT-ADIR 3D were also noted on ULDCT-AICE and ULDCT-AIDR 3D images. Conclusions:Chest ULDCT using DLR can significantly reduce the radiation dose, and compared with Hybrid IR, it can effectively reduce the image noise and improve SNR, and display the pulmonary nodules well. The image quality and nodule detection are not inferior to RDCT Hybrid IR routinely used in clinical practice.

ObjectiveTo examine biochemical characteristics of the frontal lobe and hippocampus in unaffected parentsof schizophreniaprohandsusingprotonmagneticresonancespectroscopy(1H-MRS).Method 19 unaffected fathers of schizophrenia probands with matched 19 male healthy control subjects and 24 unaffected mothers of schizophrenia probands with matched 24 female healthy control subjects were included in the study.1 H-MRS was performed in two sides of the hippocampus and frontal lobes regions.The ratios of N-Acetylaspartate ( NAA ),choline (Cho) to creatine (Cr) were measured.One-sample T test and paired-samples t test were used for statistic process.ResultsUnaffected mothers of schizophrenia probands had a higher Cho/Cr ratio ( left ( 1.10 ± 0.13,right ( 1.08 ± 0.12 ) ) in the frontal white matter compared with matched female health control subjects(left( 1.03 ± 0.10),right( 1.02 ± 0.09 )).The NAA/Cr ratio was significantly reduced in the left frontal white matter of female health control subjects compared to right( 1.64 ± 0.12 vs 1.74 ± 0.13 ),but this difference was not observed in unaffected mothers of schizophrenia probands.There were no significant differences in metabolites for frontal lobe and hippocampus regions between unaffected fathers of schizophrenia probands and male healthy control subjects groups( all P＞0.05 ).ConclusionThe results implicate that the metabolic abnormalities and disappeared asymmetry of NAA/Cr might exist in the frontal white matter among unaffected mothers of schizophrenia probands.