Evaluating the Diagnostic Performance of Contrast-Enhanced CT in Patients with Clinical Cystitis: A Retrospective Study

Evaluating the Diagnostic Performance of Contrast-Enhanced CT in Patients with Clinical Cystitis: A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the Diagnostic Performance of Contrast-Enhanced CT in Patients with Clinical Cystitis: A Retrospective Study Mohammed S. Almasri, Meatasem A. Alghofaili, Ahmed A. Alrizqi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7068075/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Cystitis is a common urinary tract infection typically diagnosed clinically. Radiological features suggestive of cystitis are frequently observed on computed tomography performed for various abdominopelvic complaints. The diagnostic value of these CT-based findings remains underinvestigated. This study aimed to evaluate the diagnostic performance of contrast-enhanced CT features in clinically confirmed cases of cystitis. Methods This retrospective, single-center study included 269 patients who underwent contrast-enhanced abdominopelvic CT between March 2023 and December 2024. Two radiologists independently assessed CT scans for predefined features, including mucosal enhancement, mural hypertrophy, mucosal irregularity, intraluminal debris, and perivesical fat stranding. Patients were categorized into diagnosed and non-diagnosed groups based on clinical confirmation. Logistic regression and receiver operating characteristic (ROC) analyses were used to evaluate diagnostic performance. A multivariable model was constructed to assess the independent contribution of significant radiological features. Results A total of 130 patients were clinically diagnosed with cystitis, with a mean age of 56.65 years and a predominance of males (61.5%). Perivesical fat stranding (AUC = 0.79), mucosal enhancement (AUC = 0.78), and mural hypertrophy exceeding the upper normal limit (AUC = 0.77) showed strong associations with clinical diagnosis (all p < 0.001) and remained significant in multivariable analysis. In contrast, mucosal irregularity and intraluminal debris showed limited diagnostic value. The combined multivariable model yielded excellent performance (AUC = 0.9316; sensitivity = 83.85%, specificity = 84.89%, accuracy = 84.39%). Conclusions Contrast-enhanced CT features, particularly perivesical fat stranding, mucosal enhancement, and significant mural hypertrophy, demonstrate strong diagnostic value in identifying clinically confirmed cystitis. Cystitis Urinary Tract Infections Radiology Sensitivity and Specificity ROC Curve INTRODUCTION Cystitis, defined as inflammation of the urinary bladder, represents a localized manifestation of urinary tract infection (UTI) and is one of its most common clinical subtypes [ 1 ]. UTIs are among the most prevalent bacterial infections globally, affecting millions of individuals annually, with a higher incidence in females [ 2 – 4 ]. Clinically, both uncomplicated and complicated forms of cystitis typically present with Lower Urinary Tract Symptoms (LUTS) such as dysuria, urgency, frequency, suprapubic discomfort, and, in some cases, hematuria [ 5 – 7 ]. The 2025 European Association of Urology (EAU) Guidelines define uncomplicated cystitis as a symptomatic bladder infection occurring in otherwise healthy, nonpregnant individuals without structural or functional urinary tract abnormalities [ 8 ]. Complicated cystitis, on the other hand, arises in the presence of risk factors including diabetes mellitus, immunosuppression, indwelling catheters, urinary tract obstruction, or recent urological procedures [ 8 , 9 ]. Diagnosis is primarily clinical, while laboratory investigations such as urinalysis and urine culture serve as supportive tools, especially in recurrent or treatment-resistant cases [ 1 , 9 ]. Although imaging is not recommended in the diagnostic workup of acute cystitis, it may be warranted in patients with diagnostic uncertainty, inadequate response to antimicrobial therapy, or underlying comorbidities that predispose to atypical infection [ 10 , 11 ]. In such contexts, contrast-enhanced computed tomography (CT) is commonly utilized due to its rapid acquisition, widespread availability, and comprehensive evaluation of both urological and non-urological structures [ 12 ]. Several CT features have been described in the context of bladder inflammation, although they are most often identified incidentally during imaging performed for unrelated clinical indications. Characteristic findings include diffuse or focal bladder wall thickening, urothelial mucosal enhancement, mucosal irregularity, and perivesical fat stranding, all of which suggest underlying inflammation [ 11 , 13 – 15 ]. Additional signs may include intraluminal debris or dependent fluid, reflecting pyuria or desquamated urothelial cells [ 11 , 14 ]. Despite their diagnostic utility, these imaging findings are nonspecific and may mimic other bladder pathologies, such as urothelial carcinoma, post-radiation changes, urinary tuberculosis, schistosomiasis, and rare benign inflammatory entities including eosinophilic cystitis and cystitis glandularis [ 13 , 15 , 16 ]. Bladder wall thickening or mural hypertrophy, remains one of the most frequently reported CT findings in suspected cystitis. However, most studies evaluating this feature have relied on ultrasonography, especially in pediatric populations and males with voiding dysfunction [ 17 – 19 ]. Although Fananapazir et al. proposed normative bladder wall thickness values on CT in a healthy adult population, standardized diagnostic thresholds remain undefined [ 20 ]. As a result, interpretation of bladder-related CT findings in the setting of suspected cystitis remains variable and lacks consistent reference ranges. While ultrasound and Magnetic Resonance Imaging (MRI) were used to evaluate the lower urinary tract inflammation, evidence regarding the diagnostic performance of CT in acute cystitis remains limited, especially in adult populations [ 21 , 22 ]. Existing studies focus on pediatric cohorts or patients with recurrent infections, limiting generalizability. To date, no study has systematically assessed the association between specific CT findings and the clinical diagnosis of cystitis in a diverse adult population. The primary aim of this retrospective single-center study is to evaluate the association between CT-based radiological features of cystitis, including mucosal enhancement, mucosal irregularity, intraluminal debris, mural hypertrophy, and perivesical fat stranding, and clinical diagnosis of cystitis. A secondary objective is to assess the diagnostic performance of these features, both individually and when combined. METHODS This study was approved with a waiver of informed consent due to the nature of the study by the Institutional Review Board (IRB) at King Saud Medical City (KSMC), Riyadh, Saudi Arabia (Registration number IORG0010374) on 10 October, 2024. Design and Patients This retrospective, cross-sectional study was conducted at King Saud Medical City (KSMC), a single tertiary hospital in Riyadh, Saudi Arabia. Patients who underwent contrast-enhanced abdominopelvic CT between March 2023 and November 2024 were screened for inclusion. A total of 375 patients aged 14 years or older with reported CT features suggestive of cystitis were initially identified based on finalized radiology reports. Patients were excluded if they had missing clinical data in the electronic medical record, underwent plain CT scans, or had alternative established urinary tract diagnoses such as urolithiasis, bladder abscess obstructive uropathy, bladder cancer, emphysematous cystitis, urethritis, pyelonephritis, prostatic abscess, or renal abscess. Clinical and Laboratory Data Collection Demographic and clinical data were retrospectively extracted from the electronic health record. Collected variables included age, sex, patient setting (inpatient, outpatient, or emergency), and comorbidities such as diabetes mellitus and hypertension. Cystitis symptoms including dysuria, frequency, urgency, suprapubic pain, and hematuria were documented when present. Additionally, risk factors including structural or functional urinary tract abnormalities and history of prior urological procedures were also recorded. Laboratory data included documentation of whether urinalysis and urine culture were performed. For urinalysis, the presence of nitrites, pyuria, and leukocyte esterase was noted. For urine cultures, the presence and identity of any isolated microorganisms were recorded. CT Imaging Protocol All CT scans were acquired using standardized protocols on two multidetector scanners: a 64 slice GE Discovery CT750 HD (GE Healthcare, USA) and a 128 slice Siemens SOMATOM Definition Flash (Siemens Healthineers, Germany). Acquisition parameters were harmonized across both scanners to reduce inter-scanner variability. Both scanners operated with tube voltages of 100 to 120 kVp and a table pitch of 0.9 to 1.0. Axial images were obtained using 5.0 mm collimation and reconstructed at 2.5 mm intervals. For multiplanar reconstruction, GE images were reformatted at 1.25 mm thickness with a 0.625 mm increment, while Siemens images were reconstructed at 1.0 mm with a 0.5 mm increment, both using standard soft-tissue kernels. Intravenous iodinated contrast was administered at a fixed flow rate of 3.0 mL/s. Agents used included iohexol (Omnipaque) and iodixanol (Visipaque), with contrast volumes ranging from 95 to 100 mL, adjusted for patient weight (1.5 mL/kg). Portal venous phase imaging was performed 70 seconds after injection, with patients scanned in the supine position. Image series were automatically transferred to the picture archiving and communication system (PACS; Philips Healthcare, USA). Image Review and Radiologic Feature Assessment All CT images were retrospectively reviewed on the institutional PACS (Philips Healthcare, USA) by two experienced radiologists. Each study was systematically evaluated for abdominopelvic abnormalities, with focused assessment and measurement of radiological features suggestive of cystitis. The following features were recorded: mucosal enhancement, mucosal irregularity, intraluminal debris, mural hypertrophy, and perivesical fat stranding. Bladder volumes were calculated using the ellipsoid formula, and wall thickness was measured using electronic calipers. Thickness values were stratified by bladder volume, following reference thresholds established by Fananapazir et al. [ 20 ]. Two categories of mural thickening were defined: (1) wall thickness greater than the average reference but below the upper limit, termed mural hypertrophy, and (2) wall thickness exceeding the upper limit, defined as mural hypertrophy exceeding the upper normal limit. A total of six radiological features were included in the final analysis. Additional findings, including Foley catheter placement, prostatic enlargement, and Double J (DJ) stent presence, were also documented. Definition of Clinical Cystitis Both uncomplicated and complicated forms of cystitis were included, encompassing male patients and those with recognized risk factors such as diabetes mellitus, urinary tract instrumentation, structural abnormalities, functional impairments, a history of urological procedures, or catheter-associated cystitis. However, complicated forms of cystitis involving specific pathological entities such as emphysematous cystitis, urinary schistosomiasis, tuberculous cystitis, cystitis cystica, and cystitis glandularis were excluded. A clinical diagnosis of cystitis was established based on the presence of suggestive urinary symptoms in conjunction with a positive urinalysis and/or urine culture, followed by treatment initiation. Patients classified in the control group had no cystitis-related symptoms, no laboratory evidence of infection, and no alternative urological diagnosis. Statistical Analysis The data were statistically analyzed with SPSS version 17.0 (SPSS Inc., Chicago, IL, USA). Continuous variables were reported as means and Standard Deviations (SD), while categorical variables were summarized using frequencies and percentages. Descriptive statistics were presented for the entire cohort and stratified by the presence or absence of a clinical diagnosis of cystitis. Between-group comparisons were conducted using the independent samples t-test for continuous variables and the Chi-square test for categorical variables. A p-value < 0.05 was considered statistically significant. Univariate logistic regression analyses were conducted to evaluate the association between individual radiological features and the clinical diagnosis of cystitis. Features with p < 0.05 in the univariate analysis were subsequently included in a multivariable logistic regression model to assess their independent effect. Both unadjusted and adjusted Odds Ratios (OR) with 95% Confidence Interval (CI) and corresponding p-values were reported. Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each radiological feature in predicting the clinical diagnosis of cystitis. For each radiological variable, predicted probabilities were derived from univariate logistic regression models, and corresponding ROC curves were constructed. Subsequently, a multivariable logistic regression model was constructed incorporating the radiological features that demonstrated statistically significant associations in univariate analysis. An AUC of 0.5 indicated no diagnostic ability, whereas values approaching 1.0 denoted excellent performance. In addition to AUC, diagnostic performance measures including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy, were computed using a threshold probability of 0.5. RESULTS Clinical Information Of the 375 initially identified patients, 52 were excluded due to technical imaging issues or the absence of intravenous contrast, 15 due to missing clinical or laboratory data, and 39 due to other established or associated urinary tract diagnoses. After applying the exclusion criteria, 269 patients were included in the final analysis. The mean age of the study population was 54.1 years (range, 15 to 107), with 174 males (64.7%) comprising the majority. Urinalysis and urine culture were performed in 188 patients (69.9%) and 170 patients (63.2%), respectively, with some undergoing both tests. Among the study population, 130 patients (48.3%) were clinically diagnosed with cystitis, while 139 (51.7%) had no clinical diagnosis of cystitis. Patients with cystitis had a higher mean age of 56.65 ± 20.68 years, compared to 51.73 ± 16.79 years in the non-cystitis group. Males accounted for 80 patients (61.5%) in the cystitis group, and 71 patients (54.6%) were scanned as inpatients. Most patients with cystitis had no history of diabetes (69, 53.1%) or hypertension (75, 57.7%). Cystitis symptoms were present in 82 patients (63.1%), with dysuria in 42 (32.3%) and hematuria in 41 (31.5%) being the most frequently reported. Structural urinary tract abnormalities were identified in 6 patients (4.6%) and functional abnormalities in 5 (3.8%). A history of urological procedures was noted in 16 patients (12.3%). Foley catheter placement was present in 65 patients (50.0%), and DJ stents in 2 (1.5%). Statistically significant differences between the cystitis and non-cystitis groups were observed in age, patient setting, presence of cystitis symptoms, Foley catheter placement, urinalysis, and urine culture (p < 0.05). No significant differences were found in gender, diabetes, hypertension, urinary tract abnormalities, or history of urological procedures. Detailed clinical and laboratory data are presented in Table 1 . Table 1 Patient characteristics include demographics, clinical presentation and laboratory investigations. Variable Not Diagnosed with Cystitis, n (%) Diagnosed with Cystitis, n (%) P-value Age, years (mean ± SD) 51.73 ± 16.79 56.65 ± 20.68 0.03* Gender 0.35 Female 45 (32.4%) 50 (38.5%) Male 94 (67.6%) 80 (61.5%) Patient Type < 0.0001* Outpatient 33 (23.7%) 9 (6.9%) Emergency 15 (10.8%) 50 (38.5%) Inpatient 91 (65.5%) 71 (54.6%) DM 0.88 No 76 (54.7%) 69 (53.1%) Yes 63 (45.3%) 61 (46.9%) HTN 0.15 No 93 (66.9%) 75 (57.7%) Yes 46 (33.1%) 55 (42.3%) Cystitis Symptoms < 0.0001* Not Present 137 (98.6%) 48 (36.9%) Present 2 (1.4%) 82 (63.1%) Dysuria 1 (0.7%) 42 (32.3%) Frequency 0 (0.0%) 6 (4.6%) Urgency 0 (0.0%) 6 (4.6%) Suprapubic Pain 0 (0.0%) 35 (26.9%) Hematuria 1 (0.7%) 41 (31.5%) Structural Urinary Tract Abnormalities 0.43 Not Present 136 (97.8%) 124 (95.4%) Present 3 (2.2%) 6 (4.6%) Functional Urinary Tract Abnormalities 0.39 Not Present 137 (98.6%) 125 (96.2%) Present 2 (1.4%) 5 (3.8%) Previous Urological Procedures 0.43 Not Present 127 (91.4%) 114 (87.7%) Present 12 (8.6%) 16 (12.3%) Foleys Catheter 0.01* Not inserted 92 (66.2%) 65 (50.0%) Inserted at the time of procedure 47 (33.8%) 65 (50.0%) DJ Stent 0.74 Not Present 135 (97.1%) 128 (98.5%) Inserted at the time of procedure 4 (2.9%) 2 (1.5%) Urinalysis 10) 10 (7.2%) 64 (49.2%) Leukocyte Esterase 13 (9.4%) 69 (53.1%) Urine Culture < 0.0001* Not performed 76 (54.7%) 23 (17.7%) Negative 63 (45.3%) 0 (0.0%) Positive 0 (0.0%) 107 (82.3%) DM diabetes mellites; HTN hypertension; DJ double J; WBC white blood count; *indicates P < 0.05 Radiological Characteristics In univariate logistic regression analysis, five radiological features demonstrated significant associations with clinical cystitis as illustrated in Table 2 . These included mucosal enhancement (OR = 18.06; 95% CI: 9.32–35.03; p < 0.0001), mucosal irregularity (OR = 3.67; 95% CI: 1.91–7.06; p = 0.0001), intraluminal debris (OR = 5.08; 95% CI: 1.66–15.52; p = 0.0044), mural hypertrophy exceeding the upper normal limit (OR = 12.18; 95% CI: 6.81–21.77; p < 0.0001), and perivesical fat stranding (OR = 16.54; 95% CI: 8.88–30.8; p < 0.0001). Mural hypertrophy did not show a statistically significant association (OR = 1.60; 95% CI: 0.80–3.21; p = 0.1850). Table 2 Univariate and multivariable logistic regression analysis of CT radiological characteristics for diagnosing Cystitis Variable Univariate Analysis Multivariate Analysis OR 95% CI P-value OR 95% CI P-value Mucosal Enhancement 18.06 [9.32–35.03] < 0.0001* 9.6979 [4.1976–22.4054] < 0.0001* Mucosal irregularity 3.67 [1.91–7.06] 0.0001* 2.408 [0.871–6.657] 0.09 Intraluminal Debris 5.08 [1.66–15.52] 0.004* 6.4279 [1.0973–37.6526] 0.03* Mural hypertrophy 1.6 [0.8–3.21] 0.18 Mural hypertrophy > Upper Limit 12.18 [6.81–21.77] < 0.0001* 7.8464 [3.6236–16.9901] < 0.0001* Perivesical Fat Stranding 16.54 [8.88–30.8] < 0.0001* 10.8848 [4.9148–24.1069] < 0.0001* OR odds ratio, CI confidence interval, *indicates P < 0.05. After adjustment in the multivariable logistic regression model, four features remained independently associated with clinical cystitis. These included mucosal enhancement (OR = 9.70; 95% CI: 4.20–22.41; p < 0.0001), intraluminal debris (OR = 6.43; 95% CI: 1.10–37.65; p = 0.0391), mural hypertrophy exceeding the upper normal limit (OR = 7.85; 95% CI: 3.62–16.99; p < 0.0001), and perivesical fat stranding (OR = 10.88; 95% CI: 4.91–24.11; p < 0.0001). Mucosal irregularity did not retain statistical significance in the adjusted model (OR = 2.41; 95% CI: 0.87–6.66; p = 0.0903). Diagnostic Performance of Radiological Features When evaluating the diagnostic performance of each individual radiological feature, as shown in Table 3 , perivesical fat stranding demonstrated the highest overall performance with an Area Under the Curve (AUC) of 0.7941, sensitivity of 86.2%, specificity of 66.9%, accuracy of 76.2%, and Negative Predictive Value (NPV) of 84.9%. Mucosal enhancement followed closely, achieving an AUC of 0.7843, with the highest specificity at 89.9% and the highest Positive Predictive Value (PPV) at 86.1%, alongside a sensitivity of 64.6% and an accuracy of 77.7%. Similarly, mural hypertrophy exceeding the upper normal limit also showed favorable diagnostic utility, with an AUC of 0.7746, sensitivity of 81.5%, specificity of 73.4%, accuracy of 77.3%, and NPV of 80.9%. Table 3 Performance of different radiological features in diagnosing Cystitis and a combined fused model using the multivariate logistic regression analysis. Feature AUC Accuracy Sensitivity Specificity PPV NPV Mucosal Enhancement 0.7843 0.7881 0.6692 0.8993 0.8614 0.744 Mucosal irregularity 0.5999 0.6097 0.3077 0.8921 0.7273 0.5794 Intraluminal Debris 0.551 0.5651 0.1308 0.9712 0.8095 0.5444 Mural hypertrophy 0.5286 0.5167 0.8846 0.1727 0.5 0.6154 Mural hypertrophy > Upper Limit 0.7746 0.7732 0.8154 0.7338 0.7413 0.8095 Perivesical Fat Stranding 0.7941 0.7918 0.8615 0.7266 0.7467 0.8487 Multivariable fused model 0.9316 0.8439 0.8385 0.8489 0.8385 0.8489 AUC the area under the receiver operating characteristic curve; PPV positive predictive value; NPV negative predictive value In contrast, mucosal irregularity demonstrated limited performance, with an AUC of 0.5999, low sensitivity of 30.8%, high specificity of 89.2%, and an accuracy of 63.6%. Intraluminal debris showed the a lower overall utility, with an AUC of 0.5510, the lowest sensitivity at 13.1%, albeit the highest specificity at 97.1%, and an accuracy of 58.0%. Non-thresholded mural hypertrophy showed the weakest diagnostic value, with an AUC of 0.5286, despite a high sensitivity of 88.5%; however, its specificity was only 17.3%, resulting in the lowest overall accuracy at 56.5%. Subsequently, a fused model combining four radiological features including mucosal enhancement, mural hypertrophy exceeding the upper normal limit, intraluminal debris, and perivesical fat stranding demonstrated the highest overall diagnostic performance. This model achieved an AUC of 0.9316, with an accuracy of 84.4%, sensitivity of 83.9%, specificity of 84.9%, PPV of 83.9%, and NPV of 84.9%, outperforming all individual features across all evaluated metrics. DISCUSSION This study assessed the diagnostic performance of CT-based radiological features in identifying clinically confirmed cystitis. Our results indicate that several individual imaging findings, particularly perivesical fat stranding, mucosal enhancement, and significant mural hypertrophy beyond the upper normal limit, are strongly associated with clinical diagnosis and provide robust diagnostic value. The strongest independent predictor in our analysis was perivesical fat stranding, with an AUC of 0.7941, high sensitivity (86.15%) and NPV (84.87%). These values are consistent with previous reports showing that fat stranding adjacent to hollow pelvic organs often reflects inflammatory extension into surrounding soft tissue [ 13 , 14 ]. Similarly, mucosal enhancement demonstrated strong diagnostic performance (AUC = 0.7843), with a specificity of 89.93% and PPV of 86.14%, supporting its known association with active urothelial hyperemia in acute infectious cystitis [ 15 , 21 ]. Mural hypertrophy exceeding the upper normal limit was significantly associated with cystitis (adjusted OR = 6.88, p < 0.001) and demonstrated a diagnostic AUC of 0.7746. These findings highlight the importance of applying standardized volume-adjusted reference thresholds [ 20 ]. Non-thresholded mural thickening was not independently associated with cystitis, reinforcing that subtle thickening, especially in underfilled bladders, may not be diagnostically specific [ 15 , 19 , 20 ]. Conversely, mucosal irregularity and intraluminal debris, while recognized in infectious bladder processes, showed limited discriminatory power (AUCs of 0.5999 and 0.551 respectively). The low sensitivity and specificity of these findings in our cohort suggest they may occur in various other bladder diagnosis or reactive conditions, limiting their value as standalone markers [ 11 , 14 ]. Notably, our multivariable predictive model, which integrated the most diagnostically relevant CT features, achieved excellent performance (AUC = 0.9316) with sensitivity (83.85%), specificity (84.89%) and diagnostic accuracy (84.39%). This supports a multi-feature approach in interpreting bladder findings rather than reliance on a single variable. Demographic and clinical variables yielded several notable observations. Older age was significantly associated with cystitis, consistent with well-established epidemiological trends linking aging to increased UTI susceptibility due to comorbidities and reduced immune function [ 3 , 23 , 24 ]. In contrast, male gender, diabetes, structural or functional urinary tract abnormalities, and prior urological procedures did not show significant associations. These findings likely reflect imaging referral patterns rather than a lack of clinical relevance. Men with urinary symptoms are more likely to undergo CT to rule out obstruction or prostatitis [ 25 , 26 ], while diabetic patients and inpatients are often treated empirically without imaging unless complications arise [ 10 , 11 , 27 , 28 ]. Additionally, the low prevalence of structural risk factors in our sample may have limited statistical power to detect their effects [ 6 , 8 , 30 , 31 ]. The presence of a Foley catheter demonstrated a statistically significant association with cystitis, aligning with the known pathophysiology of Catheter-Associated Urinary Tract Infections (CAUTI), which involve ascending infection and biofilm formation [ 32 – 34 ]. These findings reaffirm the role of catheterization as a modifiable risk factor in cystitis development and support current guidelines advocating for minimal use and early removal of indwelling urinary devices [ 34 ]. Laboratory results, including urinalysis and urine culture, were strongly associated with clinical diagnosis. Despite few positive urinalysis results in the non-cystitis group, likely due to asymptomatic bacteriuria or false positives, the strong correlation in the cystitis group confirms their continued diagnostic value, particularly when interpreted alongside imaging and clinical findings [ 5 , 35 ]. From a clinical perspective, these findings reinforce that radiologists should report the presence of perivesical fat stranding, mucosal enhancement, and bladder wall thickening exceeding normal limits when evaluating abdominopelvic CTs in patients with possible infection. These findings, especially when present in combination, are highly suggestive of cystitis and can provide crucial diagnostic support, particularly in patients with atypical presentations or equivocal laboratory results. LIMITATIONS Our study has several limitations. First, due to its retrospective design, selection bias is inherent, as only patients who underwent contrast-enhanced CT were included. This likely resulted in a predominance of complex or atypical cases, while uncomplicated cases of cystitis were underrepresented. From a methodological standpoint, this bias is difficult to avoid, as uncomplicated cases are rarely evaluated with CT. Even in prospective designs, eliminating this bias would require systematic imaging in clinically diagnosed uncomplicated cystitis, which are typically managed based on clinical suspicion. Such an approach would also result in unnecessary radiation exposure in otherwise straightforward cases. Another limitation is the absence of universally accepted reference thresholds for bladder wall thickness on CT, which limits the interpretability of this feature in our analysis. This is particularly relevant when referring to mural hypertrophy as a diagnostic radiological marker. Finally, the use of two different CT scanners may have introduced inter-scanner variability, although acquisition parameters were standardized to minimize this effect. Future studies may consider the use of a single high-resolution CT scanner to improve consistency. CONCLUSION This study demonstrates that specific CT-based radiological features, particularly perivesical fat stranding, mucosal enhancement, and bladder wall thickening exceeding volume-adjusted upper limits, are strongly associated with clinically diagnosed cystitis and exhibit high diagnostic accuracy. When combined, these features provide excellent discriminatory performance, supporting their utility as reliable imaging markers in patients undergoing abdominopelvic CT for various clinical indications. Although CT is not routinely indicated for such diagnosis, our findings reinforce the concept that cystitis may be confidently suggested when multiple hallmark features are present. Future prospective studies are warranted to validate these results, refine diagnostic thresholds, and compare imaging findings across differential diagnoses to aid in distinguishing cystitis from malignancy and other mimicking conditions. Declarations Author Contribution Conceptualization, M.S., M.A. and A.R.; Data Collection, M.S. and M.A.; Methodology, M.S., M.A. and A.R.; Formal analysis, M.S.; Investigation, M.S., M.A. and A.R.; Writing– original draft & review & editing, M.S., M.A. and A.R.; Supervision, A.R. DATA AVAILABILITY All data will be shared upon reasonable request from the corresponding authors. References Lala, Vasimahmed, Stephen W. Leslie, and David A. Minter. “Acute Cystitis.” StatPearls, edited by Justin B. Dimick et al., StatPearls Publishing, 2023, https://www.ncbi.nlm.nih.gov/books/NBK459322/. Flores-Mireles, Ana L et al. “Urinary tract infections: epidemiology, mechanisms of infection and treatment options.” Nature reviews. 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Bright, Elizabeth et al. “Ultrasound estimated bladder weight and measurement of bladder wall thickness--useful noninvasive methods for assessing the lower urinary tract?.” The Journal of urology vol. 184,5 (2010): 1847-54. doi:10.1016/j.juro.2010.06.006. Fananapazir, Ghaneh et al. “Normal reference values for bladder wall thickness on CT in a healthy population.” Abdominal radiology (New York) vol. 43,9 (2018): 2442-2445. doi:10.1007/s00261-018-1463-x. Ghobrial, Emad E et al. “Value of Ultrasound in Detecting Urinary Tract Anomalies After First Febrile Urinary Tract Infection in Children.” Clinical pediatrics vol. 55,5 (2016): 415-20. doi:10.1177/0009922815590224. Fananapazir, Ghaneh et al. “Bladder debris on ultrasound in the emergency department: correlation with urinalysis.” Abdominal radiology (New York) vol. 43,9 (2018): 2462-2466. doi:10.1007/s00261-018-1513-4. Rowe, Theresa A, and Manisha Juthani-Mehta. “Urinary tract infection in older adults.” Aging health vol. 9,5 (2013): 10.2217/ahe.13.38. doi:10.2217/ahe.13.38. Magill, Shelley S et al. “Changes in Prevalence of Health Care-Associated Infections in U.S. Hospitals.” The New England journal of medicine vol. 379,18 (2018): 1732-1744. doi:10.1056/NEJMoa1801550. Nickel, J Curtis. “Management of urinary tract infections: historical perspective and current strategies: Part 2--Modern management.” The Journal of urology vol. 173,1 (2005): 27-32. doi:10.1097/01.ju.0000141497.46841.7a. Köves, Béla, and Björn Wullt. “The Roles of the Host and the Pathogens in Urinary Tract Infections.” European Urology Supplements, vol. 15, no. 4, July 2016, pp. 88–94. European Urology Supplements, doi:10.1016/j.eursup.2016.04.005. Geerlings, Suzanne E. “Urinary tract infections in patients with diabetes mellitus: epidemiology, pathogenesis and treatment.” International journal of antimicrobial agents vol. 31 Suppl 1 (2008): S54-7. doi:10.1016/j.ijantimicag.2007.07.042. Ronald, Allan. “The etiology of urinary tract infection: traditional and emerging pathogens.” Disease-a-month : DM vol. 49,2 (2003): 71-82. doi:10.1067/mda.2003.8. Cortes-Penfield, Nicolas W et al. “Urinary Tract Infection and Asymptomatic Bacteriuria in Older Adults.” Infectious disease clinics of North America vol. 31,4 (2017): 673-688. doi:10.1016/j.idc.2017.07.002 Fihn, Stephan D. “Clinical practice. Acute uncomplicated urinary tract infection in women.” The New England journal of medicine vol. 349,3 (2003): 259-66. doi:10.1056/NEJMcp030027. Nicolle, Lindsay E. “Catheter-related urinary tract infection.” Drugs & aging vol. 22,8 (2005): 627-39. doi:10.2165/00002512-200522080-00001. Tambyah, P A, and D G Maki. “Catheter-associated urinary tract infection is rarely symptomatic: a prospective study of 1,497 catheterized patients.” Archives of internal medicine vol. 160,5 (2000): 678-82. doi:10.1001/archinte.160.5.678. Saint, Sanjay, and Carol E Chenoweth. “Biofilms and catheter-associated urinary tract infections.” Infectious disease clinics of North America vol. 17,2 (2003): 411-32. doi:10.1016/s0891-5520(03)00011-4. Hooton, Thomas M et al. “Diagnosis, prevention, and treatment of catheter-associated urinary tract infection in adults: 2009 International Clinical Practice Guidelines from the Infectious Diseases Society of America.” Clinical infectious diseases : an official publication of the Infectious Diseases Society of America vol. 50,5 (2010): 625-63. doi:10.1086/650482. Khasriya, Rajvinder et al. “The inadequacy of urinary dipstick and microscopy as surrogate markers of urinary tract infection in urological outpatients with lower urinary tract symptoms without acute frequency and dysuria.” The Journal of urology vol. 183,5 (2010): 1843-7. doi:10.1016/j.juro.2010.01.008. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7068075","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482605912,"identity":"a45c25f6-049f-4844-9f72-32d638016242","order_by":0,"name":"Mohammed S. Almasri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACgwMMjAdADDb2NphYAn4tlkD1EC08x4jUYg/TwiCRRqQWswPMDw7dqKnL55N8lvzpRg1DND97AvOHH3i1sBkczjl22LJNOu2YdM4xhtyZPQ/YJHvwamEAamE7YMAmnd7GnNvAkLvhRgIbAw8eLQYH2D8czvlXZ8Amebz5M0jL/hsJzB//4NXCY3A4t43ZgE2C7YA02BaJBAZpvLYc5ik4nNt32ICNJy0N6BeJ3BlnHrZJy+DTcrx94+Ocb3UG8u3HjD/n1Njk9rcnH/74Bo8WBmZUrgQQMzbg0zAKRsEoGAWjgAgAAJLkUG1GCiVlAAAAAElFTkSuQmCC","orcid":"","institution":"King Saud Medical City","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"S.","lastName":"Almasri","suffix":""},{"id":482605913,"identity":"a5f8638d-4926-4cc2-a19f-581226a1853f","order_by":1,"name":"Meatasem A. Alghofaili","email":"","orcid":"","institution":"King Saud Medical City","correspondingAuthor":false,"prefix":"","firstName":"Meatasem","middleName":"A.","lastName":"Alghofaili","suffix":""},{"id":482605914,"identity":"45e079c6-adcd-429c-adf4-5ecbbf8ccc5f","order_by":2,"name":"Ahmed A. Alrizqi","email":"","orcid":"","institution":"King Saud Medical City","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"A.","lastName":"Alrizqi","suffix":""}],"badges":[],"createdAt":"2025-07-07 18:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7068075/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7068075/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88820388,"identity":"c795aaac-7327-4516-a494-1c15477657c4","added_by":"auto","created_at":"2025-08-11 17:23:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":818980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7068075/v1/67e2a420-060c-4063-8193-30ff91ceffa6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEvaluating the Diagnostic Performance of Contrast-Enhanced CT in Patients with Clinical Cystitis: A Retrospective Study\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCystitis, defined as inflammation of the urinary bladder, represents a localized manifestation of urinary tract infection (UTI) and is one of its most common clinical subtypes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. UTIs are among the most prevalent bacterial infections globally, affecting millions of individuals annually, with a higher incidence in females [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Clinically, both uncomplicated and complicated forms of cystitis typically present with Lower Urinary Tract Symptoms (LUTS) such as dysuria, urgency, frequency, suprapubic discomfort, and, in some cases, hematuria [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe 2025 European Association of Urology (EAU) Guidelines define uncomplicated cystitis as a symptomatic bladder infection occurring in otherwise healthy, nonpregnant individuals without structural or functional urinary tract abnormalities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Complicated cystitis, on the other hand, arises in the presence of risk factors including diabetes mellitus, immunosuppression, indwelling catheters, urinary tract obstruction, or recent urological procedures [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Diagnosis is primarily clinical, while laboratory investigations such as urinalysis and urine culture serve as supportive tools, especially in recurrent or treatment-resistant cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough imaging is not recommended in the diagnostic workup of acute cystitis, it may be warranted in patients with diagnostic uncertainty, inadequate response to antimicrobial therapy, or underlying comorbidities that predispose to atypical infection [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In such contexts, contrast-enhanced computed tomography (CT) is commonly utilized due to its rapid acquisition, widespread availability, and comprehensive evaluation of both urological and non-urological structures [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral CT features have been described in the context of bladder inflammation, although they are most often identified incidentally during imaging performed for unrelated clinical indications. Characteristic findings include diffuse or focal bladder wall thickening, urothelial mucosal enhancement, mucosal irregularity, and perivesical fat stranding, all of which suggest underlying inflammation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additional signs may include intraluminal debris or dependent fluid, reflecting pyuria or desquamated urothelial cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Despite their diagnostic utility, these imaging findings are nonspecific and may mimic other bladder pathologies, such as urothelial carcinoma, post-radiation changes, urinary tuberculosis, schistosomiasis, and rare benign inflammatory entities including eosinophilic cystitis and cystitis glandularis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBladder wall thickening or mural hypertrophy, remains one of the most frequently reported CT findings in suspected cystitis. However, most studies evaluating this feature have relied on ultrasonography, especially in pediatric populations and males with voiding dysfunction [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Although Fananapazir et al. proposed normative bladder wall thickness values on CT in a healthy adult population, standardized diagnostic thresholds remain undefined [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. As a result, interpretation of bladder-related CT findings in the setting of suspected cystitis remains variable and lacks consistent reference ranges.\u003c/p\u003e\u003cp\u003eWhile ultrasound and Magnetic Resonance Imaging (MRI) were used to evaluate the lower urinary tract inflammation, evidence regarding the diagnostic performance of CT in acute cystitis remains limited, especially in adult populations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Existing studies focus on pediatric cohorts or patients with recurrent infections, limiting generalizability. To date, no study has systematically assessed the association between specific CT findings and the clinical diagnosis of cystitis in a diverse adult population.\u003c/p\u003e\u003cp\u003eThe primary aim of this retrospective single-center study is to evaluate the association between CT-based radiological features of cystitis, including mucosal enhancement, mucosal irregularity, intraluminal debris, mural hypertrophy, and perivesical fat stranding, and clinical diagnosis of cystitis. A secondary objective is to assess the diagnostic performance of these features, both individually and when combined.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e This study was approved with a waiver of informed consent due to the nature of the study by the Institutional Review Board (IRB) at King Saud Medical City (KSMC), Riyadh, Saudi Arabia (Registration number IORG0010374) on 10 October, 2024.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign and Patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective, cross-sectional study was conducted at King Saud Medical City (KSMC), a single tertiary hospital in Riyadh, Saudi Arabia. Patients who underwent contrast-enhanced abdominopelvic CT between March 2023 and November 2024 were screened for inclusion. A total of 375 patients aged 14 years or older with reported CT features suggestive of cystitis were initially identified based on finalized radiology reports.\u003c/p\u003e\u003cp\u003ePatients were excluded if they had missing clinical data in the electronic medical record, underwent plain CT scans, or had alternative established urinary tract diagnoses such as urolithiasis, bladder abscess obstructive uropathy, bladder cancer, emphysematous cystitis, urethritis, pyelonephritis, prostatic abscess, or renal abscess.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical and Laboratory Data Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDemographic and clinical data were retrospectively extracted from the electronic health record. Collected variables included age, sex, patient setting (inpatient, outpatient, or emergency), and comorbidities such as diabetes mellitus and hypertension. Cystitis symptoms including dysuria, frequency, urgency, suprapubic pain, and hematuria were documented when present.\u003c/p\u003e\u003cp\u003eAdditionally, risk factors including structural or functional urinary tract abnormalities and history of prior urological procedures were also recorded. Laboratory data included documentation of whether urinalysis and urine culture were performed. For urinalysis, the presence of nitrites, pyuria, and leukocyte esterase was noted. For urine cultures, the presence and identity of any isolated microorganisms were recorded.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCT Imaging Protocol\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll CT scans were acquired using standardized protocols on two multidetector scanners: a 64 slice GE Discovery CT750 HD (GE Healthcare, USA) and a 128 slice Siemens SOMATOM Definition Flash (Siemens Healthineers, Germany). Acquisition parameters were harmonized across both scanners to reduce inter-scanner variability. Both scanners operated with tube voltages of 100 to 120 kVp and a table pitch of 0.9 to 1.0. Axial images were obtained using 5.0 mm collimation and reconstructed at 2.5 mm intervals. For multiplanar reconstruction, GE images were reformatted at 1.25 mm thickness with a 0.625 mm increment, while Siemens images were reconstructed at 1.0 mm with a 0.5 mm increment, both using standard soft-tissue kernels.\u003c/p\u003e\u003cp\u003eIntravenous iodinated contrast was administered at a fixed flow rate of 3.0 mL/s. Agents used included iohexol (Omnipaque) and iodixanol (Visipaque), with contrast volumes ranging from 95 to 100 mL, adjusted for patient weight (1.5 mL/kg). Portal venous phase imaging was performed 70 seconds after injection, with patients scanned in the supine position. Image series were automatically transferred to the picture archiving and communication system (PACS; Philips Healthcare, USA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eImage Review and Radiologic Feature Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll CT images were retrospectively reviewed on the institutional PACS (Philips Healthcare, USA) by two experienced radiologists. Each study was systematically evaluated for abdominopelvic abnormalities, with focused assessment and measurement of radiological features suggestive of cystitis. The following features were recorded: mucosal enhancement, mucosal irregularity, intraluminal debris, mural hypertrophy, and perivesical fat stranding.\u003c/p\u003e\u003cp\u003eBladder volumes were calculated using the ellipsoid formula, and wall thickness was measured using electronic calipers. Thickness values were stratified by bladder volume, following reference thresholds established by Fananapazir et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Two categories of mural thickening were defined: (1) wall thickness greater than the average reference but below the upper limit, termed mural hypertrophy, and (2) wall thickness exceeding the upper limit, defined as mural hypertrophy exceeding the upper normal limit. A total of six radiological features were included in the final analysis. Additional findings, including Foley catheter placement, prostatic enlargement, and Double J (DJ) stent presence, were also documented.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDefinition of Clinical Cystitis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBoth uncomplicated and complicated forms of cystitis were included, encompassing male patients and those with recognized risk factors such as diabetes mellitus, urinary tract instrumentation, structural abnormalities, functional impairments, a history of urological procedures, or catheter-associated cystitis. However, complicated forms of cystitis involving specific pathological entities such as emphysematous cystitis, urinary schistosomiasis, tuberculous cystitis, cystitis cystica, and cystitis glandularis were excluded. A clinical diagnosis of cystitis was established based on the presence of suggestive urinary symptoms in conjunction with a positive urinalysis and/or urine culture, followed by treatment initiation. Patients classified in the control group had no cystitis-related symptoms, no laboratory evidence of infection, and no alternative urological diagnosis.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe data were statistically analyzed with SPSS version 17.0 (SPSS Inc., Chicago, IL, USA). Continuous variables were reported as means and Standard Deviations (SD), while categorical variables were summarized using frequencies and percentages. Descriptive statistics were presented for the entire cohort and stratified by the presence or absence of a clinical diagnosis of cystitis. Between-group comparisons were conducted using the independent samples t-test for continuous variables and the Chi-square test for categorical variables. A p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eUnivariate logistic regression analyses were conducted to evaluate the association between individual radiological features and the clinical diagnosis of cystitis. Features with p \u0026lt; 0.05 in the univariate analysis were subsequently included in a multivariable logistic regression model to assess their independent effect. Both unadjusted and adjusted Odds Ratios (OR) with 95% Confidence Interval (CI) and corresponding p-values were reported.\u003c/p\u003e\u003cp\u003eReceiver Operating Characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each radiological feature in predicting the clinical diagnosis of cystitis. For each radiological variable, predicted probabilities were derived from univariate logistic regression models, and corresponding ROC curves were constructed. Subsequently, a multivariable logistic regression model was constructed incorporating the radiological features that demonstrated statistically significant associations in univariate analysis. An AUC of 0.5 indicated no diagnostic ability, whereas values approaching 1.0 denoted excellent performance. In addition to AUC, diagnostic performance measures including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy, were computed using a threshold probability of 0.5.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eClinical Information\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOf the 375 initially identified patients, 52 were excluded due to technical imaging issues or the absence of intravenous contrast, 15 due to missing clinical or laboratory data, and 39 due to other established or associated urinary tract diagnoses. After applying the exclusion criteria, 269 patients were included in the final analysis. The mean age of the study population was 54.1 years (range, 15 to 107), with 174 males (64.7%) comprising the majority. Urinalysis and urine culture were performed in 188 patients (69.9%) and 170 patients (63.2%), respectively, with some undergoing both tests.\u003c/p\u003e\u003cp\u003eAmong the study population, 130 patients (48.3%) were clinically diagnosed with cystitis, while 139 (51.7%) had no clinical diagnosis of cystitis. Patients with cystitis had a higher mean age of 56.65\u0026thinsp;\u0026plusmn;\u0026thinsp;20.68 years, compared to 51.73\u0026thinsp;\u0026plusmn;\u0026thinsp;16.79 years in the non-cystitis group. Males accounted for 80 patients (61.5%) in the cystitis group, and 71 patients (54.6%) were scanned as inpatients. Most patients with cystitis had no history of diabetes (69, 53.1%) or hypertension (75, 57.7%). Cystitis symptoms were present in 82 patients (63.1%), with dysuria in 42 (32.3%) and hematuria in 41 (31.5%) being the most frequently reported. Structural urinary tract abnormalities were identified in 6 patients (4.6%) and functional abnormalities in 5 (3.8%). A history of urological procedures was noted in 16 patients (12.3%). Foley catheter placement was present in 65 patients (50.0%), and DJ stents in 2 (1.5%).\u003c/p\u003e\u003cp\u003eStatistically significant differences between the cystitis and non-cystitis groups were observed in age, patient setting, presence of cystitis symptoms, Foley catheter placement, urinalysis, and urine culture (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were found in gender, diabetes, hypertension, urinary tract abnormalities, or history of urological procedures. Detailed clinical and laboratory data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics include demographics, clinical presentation and laboratory investigations.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Diagnosed with Cystitis, n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDiagnosed with Cystitis, n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.73\u0026thinsp;\u0026plusmn;\u0026thinsp;16.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.65\u0026thinsp;\u0026plusmn;\u0026thinsp;20.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (38.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (67.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (61.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatient Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutpatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmergency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (38.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInpatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 (65.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (54.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (54.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (53.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (45.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (46.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHTN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (66.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (57.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (33.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (42.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCystitis Symptoms\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (98.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (36.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (63.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDysuria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (32.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrgency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuprapubic Pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematuria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStructural Urinary Tract Abnormalities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (97.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124 (95.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFunctional Urinary Tract Abnormalities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (98.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (96.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrevious Urological Procedures\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127 (91.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114 (87.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFoleys Catheter\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot inserted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (66.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInserted at the time of procedure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (33.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDJ Stent\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (97.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (98.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInserted at the time of procedure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrinalysis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot performed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (35.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (53.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (63.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePyuria (WBC\u0026thinsp;\u0026gt;\u0026thinsp;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (49.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocyte Esterase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (53.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrine Culture\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot performed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (54.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (45.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (82.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eDM diabetes mellites; HTN hypertension; DJ double J; WBC white blood count; *indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRadiological Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn univariate logistic regression analysis, five radiological features demonstrated significant associations with clinical cystitis as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These included mucosal enhancement (OR\u0026thinsp;=\u0026thinsp;18.06; 95% CI: 9.32\u0026ndash;35.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), mucosal irregularity (OR\u0026thinsp;=\u0026thinsp;3.67; 95% CI: 1.91\u0026ndash;7.06; p\u0026thinsp;=\u0026thinsp;0.0001), intraluminal debris (OR\u0026thinsp;=\u0026thinsp;5.08; 95% CI: 1.66\u0026ndash;15.52; p\u0026thinsp;=\u0026thinsp;0.0044), mural hypertrophy exceeding the upper normal limit (OR\u0026thinsp;=\u0026thinsp;12.18; 95% CI: 6.81\u0026ndash;21.77; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and perivesical fat stranding (OR\u0026thinsp;=\u0026thinsp;16.54; 95% CI: 8.88\u0026ndash;30.8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Mural hypertrophy did not show a statistically significant association (OR\u0026thinsp;=\u0026thinsp;1.60; 95% CI: 0.80\u0026ndash;3.21; p\u0026thinsp;=\u0026thinsp;0.1850).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariable logistic regression analysis of CT radiological characteristics for diagnosing Cystitis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate Analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariate Analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMucosal Enhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[9.32\u0026ndash;35.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.6979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[4.1976\u0026ndash;22.4054]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMucosal irregularity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.91\u0026ndash;7.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[0.871\u0026ndash;6.657]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraluminal Debris\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[1.66\u0026ndash;15.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.4279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[1.0973\u0026ndash;37.6526]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMural hypertrophy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.8\u0026ndash;3.21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMural hypertrophy\u0026thinsp;\u0026gt;\u0026thinsp;Upper Limit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[6.81\u0026ndash;21.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.8464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[3.6236\u0026ndash;16.9901]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerivesical Fat Stranding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[8.88\u0026ndash;30.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.8848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[4.9148\u0026ndash;24.1069]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eOR odds ratio, CI confidence interval, *indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter adjustment in the multivariable logistic regression model, four features remained independently associated with clinical cystitis. These included mucosal enhancement (OR\u0026thinsp;=\u0026thinsp;9.70; 95% CI: 4.20\u0026ndash;22.41; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), intraluminal debris (OR\u0026thinsp;=\u0026thinsp;6.43; 95% CI: 1.10\u0026ndash;37.65; p\u0026thinsp;=\u0026thinsp;0.0391), mural hypertrophy exceeding the upper normal limit (OR\u0026thinsp;=\u0026thinsp;7.85; 95% CI: 3.62\u0026ndash;16.99; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and perivesical fat stranding (OR\u0026thinsp;=\u0026thinsp;10.88; 95% CI: 4.91\u0026ndash;24.11; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Mucosal irregularity did not retain statistical significance in the adjusted model (OR\u0026thinsp;=\u0026thinsp;2.41; 95% CI: 0.87\u0026ndash;6.66; p\u0026thinsp;=\u0026thinsp;0.0903).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnostic Performance of Radiological Features\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhen evaluating the diagnostic performance of each individual radiological feature, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, perivesical fat stranding demonstrated the highest overall performance with an Area Under the Curve (AUC) of 0.7941, sensitivity of 86.2%, specificity of 66.9%, accuracy of 76.2%, and Negative Predictive Value (NPV) of 84.9%. Mucosal enhancement followed closely, achieving an AUC of 0.7843, with the highest specificity at 89.9% and the highest Positive Predictive Value (PPV) at 86.1%, alongside a sensitivity of 64.6% and an accuracy of 77.7%. Similarly, mural hypertrophy exceeding the upper normal limit also showed favorable diagnostic utility, with an AUC of 0.7746, sensitivity of 81.5%, specificity of 73.4%, accuracy of 77.3%, and NPV of 80.9%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerformance of different radiological features in diagnosing Cystitis and a combined fused model using the multivariate logistic regression analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMucosal Enhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMucosal irregularity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5794\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraluminal Debris\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMural hypertrophy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6154\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMural hypertrophy\u0026thinsp;\u0026gt;\u0026thinsp;Upper Limit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerivesical Fat Stranding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultivariable fused model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8489\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eAUC the area under the receiver operating characteristic curve; PPV positive predictive value; NPV negative predictive value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn contrast, mucosal irregularity demonstrated limited performance, with an AUC of 0.5999, low sensitivity of 30.8%, high specificity of 89.2%, and an accuracy of 63.6%. Intraluminal debris showed the a lower overall utility, with an AUC of 0.5510, the lowest sensitivity at 13.1%, albeit the highest specificity at 97.1%, and an accuracy of 58.0%. Non-thresholded mural hypertrophy showed the weakest diagnostic value, with an AUC of 0.5286, despite a high sensitivity of 88.5%; however, its specificity was only 17.3%, resulting in the lowest overall accuracy at 56.5%.\u003c/p\u003e\u003cp\u003eSubsequently, a fused model combining four radiological features including mucosal enhancement, mural hypertrophy exceeding the upper normal limit, intraluminal debris, and perivesical fat stranding demonstrated the highest overall diagnostic performance. This model achieved an AUC of 0.9316, with an accuracy of 84.4%, sensitivity of 83.9%, specificity of 84.9%, PPV of 83.9%, and NPV of 84.9%, outperforming all individual features across all evaluated metrics.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study assessed the diagnostic performance of CT-based radiological features in identifying clinically confirmed cystitis. Our results indicate that several individual imaging findings, particularly perivesical fat stranding, mucosal enhancement, and significant mural hypertrophy beyond the upper normal limit, are strongly associated with clinical diagnosis and provide robust diagnostic value. The strongest independent predictor in our analysis was perivesical fat stranding, with an AUC of 0.7941, high sensitivity (86.15%) and NPV (84.87%). These values are consistent with previous reports showing that fat stranding adjacent to hollow pelvic organs often reflects inflammatory extension into surrounding soft tissue [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, mucosal enhancement demonstrated strong diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.7843), with a specificity of 89.93% and PPV of 86.14%, supporting its known association with active urothelial hyperemia in acute infectious cystitis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMural hypertrophy exceeding the upper normal limit was significantly associated with cystitis (adjusted OR\u0026thinsp;=\u0026thinsp;6.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and demonstrated a diagnostic AUC of 0.7746. These findings highlight the importance of applying standardized volume-adjusted reference thresholds [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Non-thresholded mural thickening was not independently associated with cystitis, reinforcing that subtle thickening, especially in underfilled bladders, may not be diagnostically specific [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Conversely, mucosal irregularity and intraluminal debris, while recognized in infectious bladder processes, showed limited discriminatory power (AUCs of 0.5999 and 0.551 respectively). The low sensitivity and specificity of these findings in our cohort suggest they may occur in various other bladder diagnosis or reactive conditions, limiting their value as standalone markers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, our multivariable predictive model, which integrated the most diagnostically relevant CT features, achieved excellent performance (AUC\u0026thinsp;=\u0026thinsp;0.9316) with sensitivity (83.85%), specificity (84.89%) and diagnostic accuracy (84.39%). This supports a multi-feature approach in interpreting bladder findings rather than reliance on a single variable.\u003c/p\u003e\u003cp\u003eDemographic and clinical variables yielded several notable observations. Older age was significantly associated with cystitis, consistent with well-established epidemiological trends linking aging to increased UTI susceptibility due to comorbidities and reduced immune function [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, male gender, diabetes, structural or functional urinary tract abnormalities, and prior urological procedures did not show significant associations. These findings likely reflect imaging referral patterns rather than a lack of clinical relevance. Men with urinary symptoms are more likely to undergo CT to rule out obstruction or prostatitis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], while diabetic patients and inpatients are often treated empirically without imaging unless complications arise [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Additionally, the low prevalence of structural risk factors in our sample may have limited statistical power to detect their effects [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe presence of a Foley catheter demonstrated a statistically significant association with cystitis, aligning with the known pathophysiology of Catheter-Associated Urinary Tract Infections (CAUTI), which involve ascending infection and biofilm formation [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings reaffirm the role of catheterization as a modifiable risk factor in cystitis development and support current guidelines advocating for minimal use and early removal of indwelling urinary devices [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLaboratory results, including urinalysis and urine culture, were strongly associated with clinical diagnosis. Despite few positive urinalysis results in the non-cystitis group, likely due to asymptomatic bacteriuria or false positives, the strong correlation in the cystitis group confirms their continued diagnostic value, particularly when interpreted alongside imaging and clinical findings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, these findings reinforce that radiologists should report the presence of perivesical fat stranding, mucosal enhancement, and bladder wall thickening exceeding normal limits when evaluating abdominopelvic CTs in patients with possible infection. These findings, especially when present in combination, are highly suggestive of cystitis and can provide crucial diagnostic support, particularly in patients with atypical presentations or equivocal laboratory results.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLIMITATIONS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur study has several limitations. First, due to its retrospective design, selection bias is inherent, as only patients who underwent contrast-enhanced CT were included. This likely resulted in a predominance of complex or atypical cases, while uncomplicated cases of cystitis were underrepresented. From a methodological standpoint, this bias is difficult to avoid, as uncomplicated cases are rarely evaluated with CT. Even in prospective designs, eliminating this bias would require systematic imaging in clinically diagnosed uncomplicated cystitis, which are typically managed based on clinical suspicion. Such an approach would also result in unnecessary radiation exposure in otherwise straightforward cases. Another limitation is the absence of universally accepted reference thresholds for bladder wall thickness on CT, which limits the interpretability of this feature in our analysis. This is particularly relevant when referring to mural hypertrophy as a diagnostic radiological marker. Finally, the use of two different CT scanners may have introduced inter-scanner variability, although acquisition parameters were standardized to minimize this effect. Future studies may consider the use of a single high-resolution CT scanner to improve consistency.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrates that specific CT-based radiological features, particularly perivesical fat stranding, mucosal enhancement, and bladder wall thickening exceeding volume-adjusted upper limits, are strongly associated with clinically diagnosed cystitis and exhibit high diagnostic accuracy. When combined, these features provide excellent discriminatory performance, supporting their utility as reliable imaging markers in patients undergoing abdominopelvic CT for various clinical indications. Although CT is not routinely indicated for such diagnosis, our findings reinforce the concept that cystitis may be confidently suggested when multiple hallmark features are present. Future prospective studies are warranted to validate these results, refine diagnostic thresholds, and compare imaging findings across differential diagnoses to aid in distinguishing cystitis from malignancy and other mimicking conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, M.S., M.A. and A.R.; Data Collection, M.S. and M.A.; Methodology, M.S., M.A. and A.R.; Formal analysis, M.S.; Investigation, M.S., M.A. and A.R.; Writing\u0026ndash; original draft \u0026amp; review \u0026amp; editing, M.S., M.A. and A.R.; Supervision, A.R.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data will be shared upon reasonable request from the corresponding authors.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLala, Vasimahmed, Stephen W. Leslie, and David A. Minter. “Acute Cystitis.” StatPearls, edited by Justin B. Dimick et al., StatPearls Publishing, 2023, https://www.ncbi.nlm.nih.gov/books/NBK459322/.\u003c/li\u003e\n \u003cli\u003eFlores-Mireles, Ana L et al. “Urinary tract infections: epidemiology, mechanisms of infection and treatment options.” Nature reviews. Microbiology vol. 13,5 (2015): 269-84. doi:10.1038/nrmicro3432.\u003c/li\u003e\n \u003cli\u003eFoxman, Betsy. “The epidemiology of urinary tract infection.” Nature reviews. 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European Urology Supplements, doi:10.1016/j.eursup.2016.04.005.\u003c/li\u003e\n \u003cli\u003eGeerlings, Suzanne E. “Urinary tract infections in patients with diabetes mellitus: epidemiology, pathogenesis and treatment.” International journal of antimicrobial agents vol. 31 Suppl 1 (2008): S54-7. doi:10.1016/j.ijantimicag.2007.07.042.\u003c/li\u003e\n \u003cli\u003eRonald, Allan. “The etiology of urinary tract infection: traditional and emerging pathogens.” Disease-a-month : DM vol. 49,2 (2003): 71-82. doi:10.1067/mda.2003.8.\u003c/li\u003e\n \u003cli\u003eCortes-Penfield, Nicolas W et al. “Urinary Tract Infection and Asymptomatic Bacteriuria in Older Adults.” Infectious disease clinics of North America vol. 31,4 (2017): 673-688. doi:10.1016/j.idc.2017.07.002\u003c/li\u003e\n \u003cli\u003eFihn, Stephan D. “Clinical practice. Acute uncomplicated urinary tract infection in women.” The New England journal of medicine vol. 349,3 (2003): 259-66. doi:10.1056/NEJMcp030027.\u003c/li\u003e\n \u003cli\u003eNicolle, Lindsay E. “Catheter-related urinary tract infection.” Drugs \u0026amp; aging vol. 22,8 (2005): 627-39. doi:10.2165/00002512-200522080-00001.\u003c/li\u003e\n \u003cli\u003eTambyah, P A, and D G Maki. “Catheter-associated urinary tract infection is rarely symptomatic: a prospective study of 1,497 catheterized patients.” Archives of internal medicine vol. 160,5 (2000): 678-82. doi:10.1001/archinte.160.5.678.\u003c/li\u003e\n \u003cli\u003eSaint, Sanjay, and Carol E Chenoweth. “Biofilms and catheter-associated urinary tract infections.” Infectious disease clinics of North America vol. 17,2 (2003): 411-32. doi:10.1016/s0891-5520(03)00011-4.\u003c/li\u003e\n \u003cli\u003eHooton, Thomas M et al. “Diagnosis, prevention, and treatment of catheter-associated urinary tract infection in adults: 2009 International Clinical Practice Guidelines from the Infectious Diseases Society of America.” Clinical infectious diseases : an official publication of the Infectious Diseases Society of America vol. 50,5 (2010): 625-63. doi:10.1086/650482.\u003c/li\u003e\n \u003cli\u003eKhasriya, Rajvinder et al. “The inadequacy of urinary dipstick and microscopy as surrogate markers of urinary tract infection in urological outpatients with lower urinary tract symptoms without acute frequency and dysuria.” The Journal of urology vol. 183,5 (2010): 1843-7. doi:10.1016/j.juro.2010.01.008.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cystitis, Urinary Tract Infections, Radiology, Sensitivity and Specificity, ROC Curve","lastPublishedDoi":"10.21203/rs.3.rs-7068075/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7068075/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eCystitis is a common urinary tract infection typically diagnosed clinically. Radiological features suggestive of cystitis are frequently observed on computed tomography performed for various abdominopelvic complaints. The diagnostic value of these CT-based findings remains underinvestigated. This study aimed to evaluate the diagnostic performance of contrast-enhanced CT features in clinically confirmed cases of cystitis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective, single-center study included 269 patients who underwent contrast-enhanced abdominopelvic CT between March 2023 and December 2024. Two radiologists independently assessed CT scans for predefined features, including mucosal enhancement, mural hypertrophy, mucosal irregularity, intraluminal debris, and perivesical fat stranding. Patients were categorized into diagnosed and non-diagnosed groups based on clinical confirmation. Logistic regression and receiver operating characteristic (ROC) analyses were used to evaluate diagnostic performance. A multivariable model was constructed to assess the independent contribution of significant radiological features.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 130 patients were clinically diagnosed with cystitis, with a mean age of 56.65 years and a predominance of males (61.5%). Perivesical fat stranding (AUC\u0026thinsp;=\u0026thinsp;0.79), mucosal enhancement (AUC\u0026thinsp;=\u0026thinsp;0.78), and mural hypertrophy exceeding the upper normal limit (AUC\u0026thinsp;=\u0026thinsp;0.77) showed strong associations with clinical diagnosis (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and remained significant in multivariable analysis. In contrast, mucosal irregularity and intraluminal debris showed limited diagnostic value. The combined multivariable model yielded excellent performance (AUC\u0026thinsp;=\u0026thinsp;0.9316; sensitivity\u0026thinsp;=\u0026thinsp;83.85%, specificity\u0026thinsp;=\u0026thinsp;84.89%, accuracy\u0026thinsp;=\u0026thinsp;84.39%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eContrast-enhanced CT features, particularly perivesical fat stranding, mucosal enhancement, and significant mural hypertrophy, demonstrate strong diagnostic value in identifying clinically confirmed cystitis.\u003c/p\u003e","manuscriptTitle":"Evaluating the Diagnostic Performance of Contrast-Enhanced CT in Patients with Clinical Cystitis: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 16:10:12","doi":"10.21203/rs.3.rs-7068075/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c1374fd-e250-452e-afd0-82c5f09c29f5","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T17:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 16:10:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7068075","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7068075","identity":"rs-7068075","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}