Preoperative fistula diagnostics in male anorectal malformations after colostomy: a single-center experience.

BACKGROUND: Accurate preoperative fistula diagnostics in male anorectal malformations (ARM) after colostomy are of great significance. We reviewed our institutional experiences and explored methods for improving the preoperative diagnostic accuracy of fistulas in males with ARMs after colostomy. METHODS: A retrospective analysis was performed on males with ARMs after colostomy admitted to our hospital from January 2015 to June 2022. All patients underwent magnetic resonance imaging (MRI) and high-pressure colostogram (HPC) before anorectal reconstruction. Patients with no fistula as diagnosed by both modalities underwent a voiding cystourethrogram (VCUG). General information, imaging results and surgical results were recorded. RESULTS: Sixty-nine males with ARMs after colostomy were included. Age at the time of examination was 52 ~ 213 days, and the median age was 89 days. The Krickenbeck classification according to surgical results included rectovesical fistula (n = 19), rectoprostatic fistula (n = 24), rectobulbar fistula (n = 19) and no fistula (n = 7). There was no significant difference in the diagnostic accuracy between MRI and HPC for different types of ARMs. For determining the location of the fistula, compared to surgery, HPC (76.8%, 53/69) performed significantly better than MRI (60.9%, 42/69) (p = 0.043). Sixteen patients diagnosed as having no fistula by MRI or HPC underwent a VCUG, and in 14 patients, the results were comfirmed. However, there were 2 cases of rectoprostatic fistula that were not correctly diagnosed. CONCLUSION: High-pressure colostogram has greater accuracy than MRI in the diagnosis of fistula type in males with ARMs after colostomy. For patients diagnosed with no fistula by both methods, VCUG reduces the risk of false-negative exclusion, and rectoprostatic fistula should be considered during the operation.

A preoperative prediction of lymph node metastasis in early cervical squamous cell cancer with hematologica - based model.

Background: This study aimed to construct a preoperative model predicting lymph node metastasis (LNM) in IB1-IIA2 stage cervical squamous cell cancer (CSCC) based on hematological indexes. Merhods: Between February 2011 and February 2022, 463 patients with IB1-IIA2 stage CSCC underwent radical resection. Patients were allocated to either a model-development cohort (n=337) or a validation cohort (n=126). The final model was determined by comparing different methods of variable selection, and then its discrimination and calibration metrics were evaluated. A predicted probability of LNM < 5% was defined as low risk. ROC curves were used to define high risk. Results: Age, lactate dehydrogenase level, FIGO stage, squamous cell carcinoma antigen, cancer antigen 125, and cancer antigen 199 were identified as critical factors for the construction of the model. The model demonstrated good discrimination and calibration (concordance index, 0.761; 95% confidence interval, 0.666-0.884). In the validation cohort the discrimination accuracy was 0.821 (95% confidence interval, 0.714 - 0.927). In the model-development cohort, 11.9% were classified as low risk with a negative predictive value of 95.0%, and 24.9% were classified as high risk with a positive predictive value of 39.3%. Conclusion: A predictive model was developed and validated for LNM in IB1-IIA2 stage CSCC. The model will assist physicians in appraising the risk of LNM in preoperative patients and could aid in patient counseling and individualized clinical decision-making.

Differentiating cellular leiomyoma from uterine sarcoma and atypical leiomyoma using multi-parametric MRI.

Objectives: To evaluate the diagnostic performance of conventional magnetic resonance imaging (cMRI) combined with diffusion-weighted MRI (DWI) in discrimination of cellular leiomyoma, uterine sarcoma, and atypical leiomyoma. Methods: This retrospective study enrolled 106 patients with uterine masses, including 51 cellular leiomyomas (CLs), 32 uterine sarcomas (USs) and 23 degenerated leiomyomas (LMs) confirmed by histopathologic examination. Clinical data and imaging findings were assessed. Chi-squared test for qualitative variables and one way ANOVA analysis for quantitative variables were performed. Logistic regression analysis and the receiver operating characteristic (ROC) analysis were performed to determine the cut-off point and diagnostic performances for significant numeric values or multiple models. Results: Morphology (Odds ratio [OR] = 6.36) and margin (OR = 13.84) derived from cMRI were independent indicators for differentiating CLs from USs, and T2WI signal (OR = 0.23) were an independent indicator for differentiating CLs from degenerated LMs (all P < 0.05). The cutoff value of apparent diffusion coefficient (ADC) derived from DWI for differentiating CLs from USs was 839 ×10-6 mm2/sec and was 1239 ×10-6 mm2/sec for differentiating CLs from degenerated LMs. Compared with the use of cMRI features and ADC value alone, combination of independent indicators and ADC value achieved higher AUCs for both differentiations (all P < 0.05). Conclusions: cMRI is a reliable tool for differentiating CLs from USs and atypical leiomyoma, especially degenerated LMs. The combined use of cMRI and DWI can improve the differential diagnostic performance.

Association of gestational age with MRI-based biometrics of brain development in fetuses.

BACKGROUND: Reported date of last menstrual period and ultrasonography measurements are the most commonly used methods for determining gestational age in antenatal life. However, the mother cannot always determine the last menstrual period with certainty, and ultrasonography measurements are accurate only in the first trimester. We aimed to assess the ability of various biometric measurements on magnetic resonance imaging (MRI) in determining the accurate gestational age of an individual fetus in the second half of gestation. METHODS: We used MRI to scan a total of 637 fetuses ranging in age from 22 to 40 gestational weeks. We evaluated 9 standard fetal 2D biometric parameters, and regression models were fitted to assess normal fetal brain development. A stepwise linear regression model was constructed to predict gestational age, and measurement accuracy was determined in a held-out, unseen test sample (n = 49). RESULTS: A second-order polynomial regression model was found to be the best descriptor of biometric measures including brain bi-parietal diameter, head circumference, and fronto-occipital diameter in relation to normal fetal growth. Normal fetuses showed divergent growth patterns for the cerebrum and cerebellum, where the cerebrum undergoes rapid growth in the second trimester, while the cerebellum undergoes rapid growth in the third trimester. Moreover, a linear model based on biometrics of brain bi-parietal diameter, length of the corpus callosum, vermis area, transverse cerebellar diameter, and cerebellar area accurately predicted gestational age in the second and third trimesters (cross-validation R2 = 0.822, p < 0.001). CONCLUSIONS: These results support the use of MRI biometry charts to improve MRI evaluation of fetal growth and suggest that MRI biometry measurements offer a potential estimation model of fetal gestational age in the second half of gestation, which is vital to any assessment of pregnancy, fetal development, and neonatal care.