RATIONALE AND OBJECTIVES: This study aims to determine if half-dose gadoxetic acid-enhanced liver magnetic resonance imaging (MRI) at 5.0 T is feasible by comparing with full-dose MRI at 3.0 T. MATERIALS AND METHODS:19 patients with liver disease underwent both full-dose (0.025 mmol/kg) gadoxetic acid-enhanced 3.0-T MRI and half-dose (0.0125 mmol/kg) 5.0-T MRI. Quantitative image quality was assessed by measuring the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast enhancement index (CEI) in liver and hepatic lesions. Qualitative image quality and respiratory motion artifacts were evaluated. Statistical comparisons were performed using paired t-tests or the Wilcoxon signed-rank test. RESULTS:Half dose 5.0-T MRI yielded significantly higher SNRs in liver and hepatic lesions compared to full-dose 3.0-T MRI (all P < 0.001). The 15-minute hepatobiliary phase (HBP) at 5.0 T achieved image quality comparable to 20-minute HBP at 3.0 T. Specifically, at the 15-minute HBP, the 5.0-T protocol demonstrated significantly higher liver SNR (77.24 ± 12.08 vs. 51.84 ± 8.18 at 20 min for 3.0 T; P < 0.001) and liver-to-lesion CNR (38.93 ± 15.56 vs. 25.37 ± 9.43; P < 0.01). Additionally, respiratory motion artifacts in the early arterial phase were significantly reduced at 5.0 T (1.12 ± 0.33 vs. 1.62 ± 0.86; P < 0.05). CONCLUSION:Liver 5.0-T MRI with a half-dose of gadoxetic acid maintains diagnostic image quality while enabling a 25% reduction in HBP acquisition time and mitigating motion artifacts. This technique represents a viable strategy to improve scanning efficiency and patient safety in gadoxetic acid-enhanced liver imaging.

Background Intersphincteric resection (ISR) is a procedure aimed at preserving the anus during the treatment of ultra-low rectal cancer (ULRC). However, the absence of an effective predictive model for selectively identifying patients suitable for laparoscopic ISR (LISR) operations persists, primarily owing to factors related to pelvic anatomy. Methods The present study encompassed individuals diagnosed with ULRC, who underwent LISR between January 2017 and August 2022. These ULRC patients were stratified into difficult or non-difficult LISR groups using recognized and widely accepted scoring criteria. Following the identification of crucial variables, five machine learning (ML) models—Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and the Random Forest (RF) algorithm—were employed to predict surgical difficulty for LISR. Ultimately, area Under the Curve (AUC) and other indices were utilized to evaluate the predictive performance of these ML models. Results In adherence to the inclusion and exclusion criteria, finally, 163 patients diagnosed with ULRC were included in the present study. Among these, 36 patients (22.1%) were categorized into the difficult ISR group, while the remaining 127 patients (77.9%) were classified as belonging to the non-difficult ISR group. Using Lasso regression and binary logistic regression analysis, nine variables were selected for constructing the ML model. To enhance the reliability and predictive accuracy of this study, we established two types of ML models, each incorporating either nine variables or all variables, respectively. One category preserved all variables, with RF yielding the best performance (accuracy: 0.878, PPV: 1, NPV: 0.867, sensitivity: 0.4, specificity: 1, AUC: 0.877), while the other category retained the screened 9 variables, with SVM demonstrating superior performance (accuracy: 0.857, PPV: 0.636, NPV: 0.921, sensitivity: 0.7, specificity: 0.897, AUC: 0.854). Conclusions The proposed ML model offers a dependable and precise approach to classify ULRC patients undergoing LISR using preoperative pelvimetry imaging data. These models can assist clinicians in better preparing for challenging ISR cases and optimizing treatment plans for individual ULRC patients.

Background Although transverse colon ptosis (TCP) is commonly diagnosed in patients with constipation, it has not attracted significant attention in the evaluation of constipation. Herein, we assessed the correlation between TCP-related radiological parameters and the severity of slow transit constipation (STC). Methods This study was a single-center retrospective cohort study, with participants enrolled between 2012 and 2020 in Zhongnan Hospital of Wuhan University, China. STC was diagnosed according to Rome IV criteria and results of colonic transit test (CTT); healthy volunteers were also recruited as controls. All participants were examined using abdominal X-rays (AXRs) to acquire the radiological parameters related to TCP. Among these parameters, the degree of TCP (DTCP) was defined as the vertical distance from the top of the splenic flexure to the lowest point of the reverse colon. The Wexner Constipation Score and Hospital Anxiety and Depression Scale were used to assess clinical severity. After multivariable linear regression, the correlations between radiological parameters and severity of STC were investigated. We also explored the differences in radiological parameters between the operation and the conservative group. Results The study included 139 patients with STC and 125 healthy people in as the normal control (NC). Patients with STC probably had larger DTCPs than those in the NC group (242.27±25.86 vs. 93.00±32.57 mm; P<0.001). Pearson correlation analysis showed that TCP-related parameters were consistent with the symptom severity of STC [e.g., parameter DTCP was strongly correlated with Wexner Constipation Score, with a β coefficient (95% CI) of 8.63 (8.24–9.02), P<0.001]. Multivariable linear regression models showed that patients with a larger DTCP were more likely to undergo surgery (23.67; 95% CI: 1.40–45.94; P=0.04). Conclusions TCP-related parameters, especially the DTCP, may serve as novel and feasible alternative indices for the assessment of STC. However, the potential value of DTCP in assisting the evaluation of STC needs to be confirmed in study with a larger sample size.

Abstract Aim Intersphincteric resection (ISR) is an anus-preserving procedure for the treatment of low rectal cancer. However, some patients have difficult ISR procedures due to pelvic stenosis. We aim to build a machine learning (ML) model to predict the difficulty of ISR.Methods We retrospectively collected information of 163 patients with low rectal cancer who underwent laparoscopic ISR from January 2017 to August 2022. The prediction models of surgical difficulty were constructed by five MLs. External validation of the European MRI and Rectal Cancer Surgery (EuMaRCS) score was also performed.Results Of 163 patients,36 (22.1%) were assessed as difficult, and 127 (77.9%) were assessed as non-difficult. 9 variables were finally included through lasso regression and binary logistic regression. Two main types of models were constructed, with one retaining all variables, with random forest (RF) performing best (accuracy, 0.878; positive predictive value [PPV], 1; negative predictive value [NPV], 0.867; sensitivity, 0.4; specificity, 1; area under the curve [AUC], 0.877; 95% confidence interval [CI], 0.732–1). The other category retained the 9 variables screened, with support vector machine (SVM) performing best(accuracy, 0.857; PPV, 0.636; NPV, 0.921; sensitivity, 0.7; specificity, 0.897; AUC, 0.854; 95% CI, 0.698–1). The EuMaRCS score did not show a better predictive performance in our study.Conclusions The ML models we developed were found to be more accurate in comparison to the EuMaRCS score. The pelvimetry-based ML model can be used as an effective predictive tool for identifying the difficulty of ISR for low rectal cancer.

慢性便秘(chronic constipation,CC)已经成为影响人们现代生活质量的主要因素之一.同时,随着社会生活水平的逐步提高,以及健康管理的理念更新,越来越多的CC 病人面临诊断和治疗的困惑,尤其是难以寻求直观准确的病因诊断[1 ].CC 分为慢传输型便秘(slow transit constipation,STC )、出口梗阻型便秘(out-let obstructive constipation,OOC)和混合型便秘.相比之下,OOC的病因诊断和治疗更加复杂,从病因诊断标准来看,目前尚缺乏金标准与高级别的临床研究证据,外科治疗方面也面临术前评估、术式选择、术后疗效评价等方面缺乏权威参考的问题[2].因此,有必要对OOC的病因学诊断进行系统回顾.

Ultrasonography and magnetic resonance imaging (MRI) are used more commonly in diagnosing obstetrics disorders as non-invasive and non-ionizing methods. Ultrasonography is a safe and available modality that provides real-time images in multiple planes. MRI can perform as a subsequent evaluation for cases in which ultrasonography is doubtful. This systematic study aimed to identify the current knowledge regarding the use of ultrasonography and MRI in diagnosing obstetrics cardiac disorders, their medical applications, safety issues, limitations, and future applications.

BackgroundThe ongoing coronavirus disease 2019 (COVID-19) pandemic has put radiologists at a higher risk of infection during the computer tomography (CT) examination for the patients. To help settling these problems, we adopted a remote-enabled and automated contactless imaging workflow for CT examination by the combination of intelligent guided robot and automatic positioning technology to reduce the potential exposure of radiologists to 2019 novel coronavirus (2019-nCoV) infection and to increase the examination efficiency, patient scanning accuracy and better image quality in chest CT imaging .MethodsFrom February 10 to April 12, 2020, adult COVID-19 patients underwent chest CT examinations on a CT scanner using the same scan protocol except with the conventional imaging workflow (CW group) or an automatic contactless imaging workflow (AW group) in Wuhan Leishenshan Hospital (China) were retrospectively and prospectively enrolled in this study. The total examination time in two groups was recorded and compared. The patient compliance of breath holding, positioning accuracy, image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and compared between the two groups.ResultsCompared with the CW group, the total positioning time of the AW group was reduced ((118.0 ± 20.0) s vs. (129.0 ± 29.0) s, P = 0.001), the proportion of scanning accuracy was higher (98% vs. 93%), and the lung length had a significant difference ((0.90±1.24) cm vs. (1.16±1.49) cm, P = 0.009). For the lesions located in the pulmonary centrilobular and subpleural regions, the image noise in the AW group was significantly lower than that in the CW group (centrilobular region: (140.4 ± 78.6) HU vs. (153.8 ± 72.7) HU, P = 0.028; subpleural region: (140.6 ± 80.8) HU vs. (159.4 ± 82.7) HU, P = 0.010). For the lesions located in the peripheral, centrilobular and subpleural regions, SNR was significantly higher in the AW group than in the CW group (centrilobular region: 6.6 ± 4.3 vs. 4.9 ± 3.7, P = 0.006; subpleural region: 6.4 ± 4.4 vs. 4.8 ± 4.0, P < 0.001).ConclusionsThe automatic contactless imaging workflow using intelligent guided robot and automatic positioning technology allows for reducing the examination time and improving the patient's compliance of breath holding, positioning accuracy and image quality in chest CT imaging.

Objective To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. Materials and methods One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups. Results The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas. Conclusion The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest. Key Points • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae.

本文报道了1例MRI产前诊断胼胝体周围脂肪瘤并生后随访的病例。该病例2019年2月就诊于武汉大学中南医院，孕31周 +6、38周 +3及生后2 d和3个月共进行了4次胎儿/新生儿MRI检查，使用了多种MRI序列和成像方法。MRI显示胼胝体发育正常，病灶位于胼胝体周围，呈T1加权成像高信号T2加权成像中高信号，抑脂序列信号减低，磁敏感加权成像及时间飞越技术磁共振血管成像未见异常畸形血管。综合产前及生后MRI表现，临床诊断为胼胝体周围脂肪瘤。

Objective: To investigate the value of multi-parameter quantitative CT combining with HRCT in evaluating the prognosis of coronavirus disease 2019 (COVID-19) Methods: Sixty-nine cases were included in this retrospective study All of the cases were primarily diagnosed with common type of COVID-19 when admitted to hospital According to the clinical prognosis and the changes of image appearance, three groups were divided Group A with decreasing extent of lesions (21 cases) and group B with increasing extent of lesions (25 cases) were both common type of COVID-19 without converting into severe type Group C (23 cases) converted common type into severe type during hospitalization Multiple CT parameters among the groups were compared Results: The changes of the multiple parameters of CT after treatment, such as the total volume of lesions, GGO volume, and the volume of the consolidation and MLA, had a significant difference among the three groups When the cut-off values for the increasing total volume, GGO volume, consolidation volume, and MLA were determined to be 231 46 cm3, 168 58 cm3, 74 46 cm3, and 57 Hu, the sensitivity (95 7%, 82 6%, 73 9%, and 78 3%) and specificity (91 3%, 91 3%, 93 5%, and 87%) of the diagnosis of common type converting into severe type were optimal and the areas under ROC curve were 0 961, 0 914, 0 885, and 0 885 Conclusion: The changes of the volumes of total lesions, GGO and consolidation and the change of MLA between follow-up CT and first CT are important prognostic factors in patients with COVID-19 Common type COVID-19 patients with total volume of lesions increasing >231 46 cm3, GGO volume increasing >168 58 cm3, consolidation volume increasing >74 46 cm3, or MLA increasing >57 Hu are more likely to convert into severe type The multiparameter quantitative CT combined with HRCT has an effect on evaluating the prognosis of COVID-19 © 2021, Editorial Board of Medical Journal of Wuhan University All right reserved

Objective: To analyze and compare the imaging workflow, radiation dose and image quality for COVID-19 patients examined using either the conventional manual positioning method or an AI-based positioning method. Materials and Methods: 127 adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based positioning method (AP group) for the follow-up scan. Radiation dose, patient off-center distance, examination and positioning time of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups. Results: The AP group reduced the total positioning time and examination time by 28% and 8%, respectively compared with the MP group. Compared with the MP group, AP group had significantly less patient off-center distance (AP:1.56cm ± 0.83 vs. MP: 4.05cm ± 2.40, p <0.001) and higher proportion of positioning accuracy (AP: 99% vs. MP: 92%), resulted in 16% radiation dose reduction (AP: 6.1mSv ± 1.3 vs. MP: 7.3mSv ± 1.2, p< 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas. Conclusion: The AI-based positioning and centering in CT imaging is a promising new technique for reducing radiation dose, optimizing imaging workflow and image quality in imaging the chest. This technique has important added clinical value in imaging COVID-19 patients to reduce the cross-infection risks.

Yadong Gang Zhongnan Hospital of Wuhan University Xiongfeng Chen Puren Hospital a liated to Wuhan University of Science and Technology Huan Li Zhongnan Hospital of Wuhan University Hanlun Wang Zhongnan Hospital of Wuhan University Jianying Li Computed Tomography Research Center Ying Guo Computed Tomography Research Center Junjie Zeng Zhongnan Hospital of Wuhan University Qiang Hu Zhongnan Hospital of Wuhan University Jinxiang Hu Zhongnan Hospital of Wuhan University Haibo Xu (  xuhaibo1120@hotmail.com ) Zhongnan Hospital of Wuhan University

This retrospective report, detailed the volunteer work of the radiology department in Wuhan Leishenshan Hospital at the COVID-19 epidemic period.Under the background of the lack of medical staff, we pointed out that the special hospital is not restricted in one pattern, and, which has laid a solid foundation for the effective development of the medical treatment.During the past two months, the volunteers showed strong humanistic care and selfl ess dedication, which provided manpower guarantee for spreading positive energy and conquering the COVID-19 epidemic as soon as possible.Such a program could serve as a model for common reference throughout the world.The author summarizes the report and pay tribute to the volunteers from all over the world.

Objective To determine the characteristics of type 2 diabetes mellitus (T2DM)–related acute pancreatitis (AP) on magnetic resonance imaging (MRI). Methods Retrospectively studied 262 patients with AP were admitted to our institution and underwent MRI. Diagnosis of T2DM-related AP was based on clinical manifestations, laboratory tests, and MRI. Pancreatic/peripancreatic changes were assessed on MRI. Results Fifty-three (20.2%) patients with T2DM-related AP and 209 (79.8%) with nondiabetic AP were enrolled. On MRI, a higher prevalence of necrotizing pancreatitis (P < 0.001), pancreatic necrosis >30% (57.5% vs 29.2%; P = 0.006), hemorrhage (35.8% vs 19.1%; P = 0.009), abdominal wall edema (67.9% vs 46.8%; P = 0.006), walled-off necrosis (43.2% vs 14.6%; P < 0.001), and infected collections (P < 0.001) were registered in T2DM with AP. T2DM-related AP sustained greater magnetic resonance severity index (mean, 5.1 [range, 2–10] vs 3.4 [range, 1–10]; P < 0.001), higher incidence of moderate and severe pancreatitis (69.8% vs 40.2%; P < 0.001), higher organ failure (45.3% vs 22%; P = 0.001), and prolonged hospitalization (mean, 25.2 [range, 10–63] vs 16 [range, 5–48] days; P < 0.001). Conclusions Type 2 diabetes mellitus–related AP is more moderate-to-severe pancreatitis, and it correlates with MRI characteristics of the pancreas itself, hemorrhage, abdominal wall, and infected collections.

ABSTRACT BACKGROUND Assessment of colonic transit tend to be more subjective and qualitative. This study aimed to evaluate the capability of our new quantitative scale to predict the subtypes of constipation and assess symptom severity of patients with slow transit constipation. METHODS A retrospective cohort population was assembled, consisting of adult patients with chronic constipation who underwent both colonic transit test and defecography between 2012 and 2019. Radiological parameters were measured on AXRs. The Luojia score was introduced to convey the vertical distance from the splenic flexure to the lowest point of the transverse colon, representing the degree of transverse colon ptosis. Patients with slow transit constipation only were especially required to complete the Wexner Constipation Scale (WCS) and Hospital Anxiety and Depression Scale (HADS) for clinical severity assessment. FINDINGS Of 368 patients, 191 patients (51·9%) showed slow colonic transit, and patients with slow colonic transit were more likely to have severe ptosis of the transverse colon on AXRs. Patients with slow colonic transit had a significantly higher Luojia score than those with normal colonic transit (p˂0·001). A cut-off of 195 mm was used to distinguish slow colonic transit. A significant difference in Luojia score was also found between patients with obstructed defecation syndrome and normal patients, and a cut-off of 140 mm was identified. In patients with slow transit constipation, there was a strong correlation between Luojia score and WCS (r=0·618) and a moderate correlation between Luojia score and HADS-Anxiety (r=0·507). These results indicated that the Luojia score is a reliable predictor of symptom severity and psychological condition in patients with slow transit constipation. INTERPRETATION The Luojia score might be a new quantitative, precise method in the assessment of constipation. FUNDING The National Natural Science Foundation of China and the Clinical Research Special Fund of Wu Jieping Medical Foundation. Research in context Evidence before this study We searched PubMed for papers published between Feb 1, 2000, and Jan 1, 2019, with the keywords “transverse colonic ptosis” OR “abdominal x-ray” AND “constipation” OR “colonic transit”. No restrictions on study type or language were implemented. Our search retrieved studies on the use of stool burden score on AXR in the assessment of constipation but no studies to use transverse colonic ptosis to evaluate colonic transit. Added value of this study We established a Luojia score which was defined as the vertical distance from the splenic flexure to the lowest point of transverse colon on the abdominal x-ray (AXR) that representing the degree of transverse colon ptosis. A retrospective cohort study of 368 patients proved that Luojia score has high sensitivity and specificity in distinguishing slow colonic transit and normal colonic transit as well as obstructed defecation syndrome and normal group. We were satisfied to found that in patients with slow transit constipation, there was a strong correlation between Luojia score and WCS (r=0·618) and a mediate correlation between Luojia score and HADS-A (r=0·507). Implications of all the available evidence Precise assessment and evaluation of colonic transit play an important role in clinical diagnosis and treatment of constipation patients. Our result proved that Luojia Score is a simple and effective assessment system of certain clinic value in in identifying patients with constipation and is a potential predictor of symptom severity.

BackgroundIn the past four months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health threat. In the context of the coronavirus disease 2019 (COVID-19) epidemic, pneumonia is a critical disease that threatens the health of pregnant women and fetuses. We aimed to evaluate the quantitative parameters of CT scans performed on pregnant women with COVID-19 who had different reverse transcription-polymerase chain reaction (RT-PCR) results.MethodsPregnant women with suspected cases of COVID-19 pneumonia (confirmed by next-generation sequencing or RT-PCR) who underwent high-resolution lung CT scans were retrospectively enrolled. Patients were grouped based on the results of the RT-PCR and the first CT scan: group 1 (double positive patients; positive RT-PCR and CT scan) and group 2 (negative RT-PCR and positive CT scan). The imaging features and their distributions were extracted and compared between the two groups.ResultsSeventy-eight patients were admitted to the hospital between Dec 20, 2019, and Feb 29, 2020. The mean age of the patients was 31.82 years (SD 4.1, ranged from 21 to 46 years). The cohort included 14 (17.95%) patients with a positive RT-PCR test and 64 (82.05%) with a negative RT-PCR test, there were 37 (47.44%) patients with a positive CT scan, and 41 (52.56%) patients with a negative CT scan. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of CT-based diagnosis of COVID-19 were 85.71%, 60.94%, 32.40%, 95.12% and 65.38%, respectively. COVID-19 pneumonia mainly involved the right lower lobe of the lung. There were 53 semi-quantitative and 59 quantitative parameters, which were compared between the two groups. There were no significant differences in the quantitative parameters. However, the Hellinger distance was significantly different between the two groups, albeit with a limited diagnostic value (AUC = 0.63).ConclusionsPregnant women with pneumonia usually present with typical abnormal signs on CT. Although multidimensional CT quantitative parameters are somewhat different between groups of patients with different RT-PCR results, it is still impossible to accurately predict whether the RT-PCR will be positive, which would allow for the earlier detection of SARS-CoV-2 infection.

急性胰腺炎是消化系统的常见病、多发病,因其严重程度不一、并发症多样,故具有潜在的致死风险.随着2012新亚特兰大指南的广泛应用,对于急性胰腺炎的影像学报告提出了新的要求.笔者着重介绍四川省医学影像重点实验室、川北医学院附属医院的急性胰腺炎影像结构化报告,皆在提高对本病的系统化认识,并规范地书写CT/MRI报告.

入科教育是指在住院医师入科时,由专人对住院医师介绍轮转计划、培养目标和要求、科室的人员设备情况、管理规定及影像检查安全性等内容的教学活动.入科教育是住院医师规范化培训的第一个环节,规范有效的入科教育对于提高住院医师规范化培训的教学质量至关重要.放射科是以放射影像诊断为主的专业性很强的平台科室,放射科基地除了有放射专业住院医师轮转,还承担众多非放射专业住院医师轮转的教学任务.如何分层分类进行住院医师规范化培训,提高教学质量,一直是实施住院医师规范化培训以来的难点.本文结合在放射科住院医师规范化培训教学中的实践经验,采用幻灯片讲解、视频录制播放、情景演示、实地参观、小测试等多种教学方法 ,提出兼顾模式化和专业个性化的入科教育的规范与流程,以提高放射科住院医师规范化培训的教学质量.

BACKGROUND: Assessment of colonic transit tend to be more subjective and qualitative. This study aimed to evaluate the capability of our new quantitative scale to predict the subtypes of constipation and assess symptom severity of patients with slow transit constipation. METHODS: A retrospective cohort population was assembled, consisting of adult patients with chronic constipation who underwent both colonic transit test and defecography between 2012 and 2019. Radiological parameters were measured on AXRs. The Luojia score was introduced to convey the vertical distance from the splenic flexure to the lowest point of the transverse colon, representing the degree of transverse colon ptosis. Patients with slow transit constipation only were especially required to complete the Wexner Constipation Scale (WCS) and Hospital Anxiety and Depression Scale (HADS) for clinical severity assessment. FINDINGS: Of 368 patients, 191 patients (51·9%) showed slow colonic transit, and patients with slow colonic transit were more likely to have severe ptosis of the transverse colon on AXRs. Patients with slow colonic transit had a significantly higher Luojia score than those with normal colonic transit (p˂0·001). A cut-off of 195 mm was used to distinguish slow colonic transit. A significant difference in Luojia score was also found between patients with obstructed defecation syndrome and normal patients, and a cut-off of 140 mm was identified. In patients with slow transit constipation, there was a strong correlation between Luojia score and WCS (r=0·618) and a moderate correlation between Luojia score and HADS-Anxiety (r=0·507). These results indicated that the Luojia score is a reliable predictor of symptom severity and psychological condition in patients with slow transit constipation. INTERPRETATION: The Luojia score might be a new quantitative, precise method in the assessment of constipation. FUNDING: This project was funded by grants from the National Natural Science Foundation of China [No. 81570492], the National Natural Science Foundation of China [No. 81500505] and the Clinical Research Special Fund of Wu Jieping Medical Foundation [No. 320.6750.18467].DECLARATION OF INTERESTS: CJ reports grants from Clinical Center of Intestinal and Colorectal Diseases of Hubei Province, Colorectal and Anal Disease Research Center in the Medical School of Wuhan University, Quality Control Center of Colorectal and Anal Surgery in the Health Commission of Hubei Province, the National Natural Science Foundation of China, during the conduct of the study. QQ reports grant from and the Clinical Research Special Fund of Wu Jieping Medical Foundation. All authors declare that they have no financial or personal relationship which could present a potential conflict of interestsETHICS APPROVAL STATEMENT: This study was approved by the medical ethics committee of Zhongnan Hospital of Wuhan University.