Utilization of Radiomics Features Extracted From Preoperative Medical Images to Detect Metastatic Lymph Nodes in Cholangiocarcinoma and Gallbladder Cancer Patients: A Systemic Review and Meta-analysis.

OBJECTIVES: This study aimed to determine the methodological quality and evaluate the diagnostic performance of radiomics features in detecting lymph node metastasis on preoperative images in patients with cholangiocarcinoma and gallbladder cancer. METHODS: Publications between January 2005 and October 2022 were considered for inclusion. Databases such as Pubmed/Medline, Scopus, Embase, and Google Scholar were searched for relevant studies. The quality of the methodology of the manuscripts was determined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2. Pooled results with corresponding 95% confidence intervals (CIs) were calculated using the DerSimonian-Liard method (random-effect model). Forest plots were used to visually represent the diagnostic profile of radiomics signature in each of the data sets pertaining to each study. Fagan plot was used to determine clinical applicability. RESULTS: Overall sensitivity was 0.748 (95% CI, 0.703-0.789). Overall specificity was 0.795 (95% CI, 0.742-0.839). The combined negative likelihood ratio was 0.299 (95% CI, 0.266-0.350), and the positive likelihood ratio was 3.545 (95% CI, 2.850-4.409). The combined odds ratio of the studies was 12.184 (95% CI, 8.477-17.514). The overall summary receiver operating characteristics area under the curve was 0.83 (95% CI, 0.80-0.86). Three studies applied nomograms to 8 data sets and achieved a higher pooled sensitivity and specificity (0.85 [0.80-0.89] and 0.85 [0.71-0.93], respectively). CONCLUSIONS: The pooled analysis showed that predictive models fed with radiomics features achieve good sensitivity and specificity in detecting lymph node metastasis in computed tomography and magnetic resonance imaging images. Supplementation of the models with biological correlates increased sensitivity and specificity in all data sets.

Does Adding the Pulmonary Infarction and Right Ventricle to Left Ventricle Diameter Ratio to the Qanadli Index (A Combined Qanadli Index) More Accurately, Predict Short-Term Mortality in Patients with Pulmonary Embolism?

Background The Qanadli index can be used to assess the severity of pulmonary arterial involvement in patients with acute pulmonary embolism. However, it seems that considering pulmonary infarction and right ventricle/left ventricle (RV/LV) ratio along with this index (called the combined Qanadli index) can provide a more accurate view of changes in cardiovascular parameters in these patients and help predict mortality in a better manner. In this regard, we evaluated the ability of the combined Qanadli index versus the Qanadli index in predicting short-term mortality in patients with pulmonary embolism. Methods This retrospective study enrolled 234 patients with acute pulmonary embolism. Patients were divided into two groups: those who expired in 30 days and who survived. Then they were evaluated by computed tomography angiography of pulmonary arteries. The RV/LV diameter ratio and also pulmonary artery obstruction index (PAOI) were calculated. The patient's computed tomography scans were reviewed for pulmonary infarction. By adding the RV/LV ratio and pulmonary infarction to PAOI, a new index called the modified Qanadli score was made. Univariable and multivariable logistic regression was done for finding predictors of mortality. Results Nine cases (40%) of patients in the mortality group and 42 (20%) of survivors had ischemic heart disease and the difference was significantly meaningful. The mean Qanadli index in the mortality group was 16.8 ± 8.45 and in survivors was 8.3 ± 4.2. By adding the pulmonary infarction score and PAOI score to RV/LV ratio score, the odds ratio (OR) for predicting mortality increased significantly to 13 and 16, respectively, which were significantly meaningful. Based on our findings, the highest OR for predicting short-term mortality was obtained through a combined Qanadli index (PAOI score + pulmonary infarction score + RV/LV score) that was 17 in univariable and 18 in multivariable logistic regression analysis ( p -value = 0.015). Conclusion The new combined Qanadli index has more ability than the Qanadli index and RV/LV ratio for predicting changes in cardiovascular parameters and short-term mortality in patients with pulmonary embolism.