Percutaneous cholecystostomy as a definitive treatment for acute acalculous cholecystitis: clinical outcomes and risk factors for recurrent cholecystitis.

OBJECTIVE: To investigate the outcomes of percutaneous cholecystostomy (PC) as a definitive treatment for acute acalculous cholecystitis (AAC) and to identify the risk factors for cholecystitis recurrence after catheter removal. METHODS: Between January 2008 and December 2017, 124 patients who had undergone PC as definitive treatment for moderate or severe AAC. The initial clinical success, complications, and recurrent cholecystitis after PC removal were retrospectively assessed. Twenty-one relevant variables were analyzed to identify risk factors for recurrent cholecystitis. RESULTS: Clinical effectiveness was achieved in 107 patients (86.3%) at 3 days and in all patients (100%) at 5 days after PC placement. Six Grade 2 adverse events occurred, including catheter dislodgement (n = 3) and clogging (n = 3), which required catheter exchange. The PC catheter was removed in 123 patients (99.2%), with a median indwelling duration of 18 days (range 5-116 days). During the follow-up period (median, 1624 days; range, 40-4945 days), five patients experienced recurrent cholecystitis (4.1%). The cumulative recurrence rates were 3.3%, 4.1%, and 4.1% at 6 months, 1 year, and 5 years, respectively. Multivariate analysis revealed that an age-adjusted Charlson comorbidity index (aCCI)≥7 positively correlated with recurrence (OR, 1.97; 95% confidence interval, 1.07-3.64; p = 0.029). CONCLUSIONS: Definitive PC is a safe and effective treatment option for patients with AAC. The PC catheters can be safely removed in most patients. An aCCI≥7 was a risk factor for cholecystitis recurrence after catheter removal. ADVANCES IN KNOWLEDGE: 1. Percutaneous cholecystostomy (PC) is a safe and effective as a definitive treatment in patients with acute acalculous cholecystitis (AAC).2. PC can be safely removed after recover from AAC in the majority of patients (99.2%) with low rate of recurrence of cholecystitis (4.1%).3. Age-adjusted Charlson comorbidity index ≥7 was a risk factor for recurrence of cholecystitis after PC removal.

Predictive accuracy of T2-FLAIR mismatch sign for the IDH-mutant, 1p/19q noncodeleted low-grade glioma: An updated systematic review and meta-analysis.

BACKGROUND: The T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, has been considered a highly specific imaging biomarker of IDH-mutant, 1p/19q noncodeleted low-grade glioma. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of T2-FLAIR mismatch sign for prediction of a patient with IDH-mutant, 1p/19q noncodeleted low-grade glioma, and identify the causes responsible for the heterogeneity across the included studies. METHODS: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before November 17, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Meta-regression analyses were also performed to determine factors influencing heterogeneity. RESULTS: For all the 10 included cohorts from 8 studies, the pooled sensitivity was 40% (95% confidence interval [CI] 28-53%), and the pooled specificity was 100% (95% CI 95-100%). In the hierarchic summary receiver operating characteristic curve, the difference between the 95% confidence and prediction regions was relatively large, indicating heterogeneity among the studies. Higgins I2 statistics demonstrated considerable heterogeneity in sensitivity (I2 = 83.5%) and considerable heterogeneity in specificity (I2 = 95.83%). Among the potential covariates, it seemed that none of factors was significantly associated with study heterogeneity in the joint model. However, the specificity was increased in studies with all the factors based on the differences in the composition of the detailed tumors. CONCLUSIONS: The T2-FLAIR mismatch sign is near-perfect specific marker of IDH mutation and 1p/19q noncodeletion.

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography.

The present study evaluated the diagnostic performance of artificial intelligence-based computer-aided diagnosis (AI-CAD) compared to that of dedicated breast radiologists in characterizing suspicious microcalcification on mammography. We retrospectively analyzed 435 unilateral mammographies from 420 patients (286 benign; 149 malignant) undergoing biopsy for suspicious microcalcification from June 2003 to November 2019. Commercial AI-CAD was applied to the mammography images, and malignancy scores were calculated. Diagnostic performance was compared between radiologists and AI-CAD using the area under the receiving operator characteristics curve (AUC). The AUCs of radiologists and AI-CAD were not significantly different (0.722 vs. 0.745, p = 0.393). The AUCs of the adjusted category were 0.726, 0.744, and 0.756 with cutoffs of 2%, 10%, and 38.03% for AI-CAD, respectively, which were all significantly higher than those for radiologists alone (all p < 0.05). None of the 27 cases downgraded to category 3 with a cutoff of 2% were confirmed as malignant on pathological analysis, suggesting that unnecessary biopsies could be avoided. Our findings suggest that the diagnostic performance of AI-CAD in characterizing suspicious microcalcification on mammography was similar to that of the radiologists, indicating that it may aid in making clinical decisions regarding the treatment of breast microcalcification.