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Likelihood ratio of computed tomography characteristics for diagnosis of malignancy in adrenal incidentaloma: systematic review and meta-analysis.

ABSTRACT: To propose an evidence based diagnostic algorithm using mass characteristics to determine malignancy in patients with adrenal incidentaloma by CTscan.A systematic review in Medline, Scopus, relevant reference books and desk searching was performed up to January 2016 with relevant reference checking. The summery estimates of sensitivity, specificity, positive and negative likelihood ratio of different characteristics were calculated in two groups of the articles investigating the cases without previous malignancy and the articles investigating the oncologic cases.Thirty six articles were included in this study. In the first group with no history of malignancy a positive and negative LR of 3.1 and 0.13 in 4 cm threshold and positive and negative LR of 2.85 and 0 in 10HU density were found. In the second group with history of malignancy positive and negative LR of 2.3 and 0.27 in 3 cm threshold and positive and negative LR of 3.6 and 0.08 in 20HU density were resulted.The results retrieved in this study considering the limitations show that adrenal incidentaloma with a size less than 4 cm or a mass larger than 4 cm with density less than 10HU in the first group can be managed with imaging follow up. For masses larger than 4 cm with density more than 10HU another diagnostic procedure should be performed. In the second group an adrenal mass larger than 3 cm or less than 3 cm with density more than 20HU should go under operation. But masses smaller than 3 cm with less than 20HU density can be followed by imaging.

SUBMITTER: Sabet FA

PROVIDER: S-EPMC4839087 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Project description:This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.

Project description:BackgroundWe evaluated malignancy according to the characteristics of pericardial fluid in symptomatic Japanese patients undergoing pericardiocentesis and computed tomography (CT).MethodsThis was a retrospective, single-center, observational study of 125 symptomatic patients undergoing pericardiocentesis. The patients were classified into two groups: a malignancy group and a non-malignancy group, according to the primary disease and cytology of the pericardial effusion (PE). We compared the pericardial fluid sample and CT measurements between both groups.ResultsAll patients were diagnosed as having exudative PE by Light's criteria. PE with malignant cells was demonstrated in 76.8% of the malignancy group patients. Pericardial to serum lactate dehydrogenase (LDH) ratio > 0.6, as one of Light's criteria, was associated with malignancy (p = 0.017). Lower serum brain natriuretic peptide (BNP) concentration was also associated with malignancy (BNP: 126.9 ± 89.8 pg/ml vs 409.2 ± 97.7 pg/ml, malignancy vs non-malignancy groups, respectively; p = 0.037). A significant difference was observed in pericardial fluid glucose level between the malignancy and non-malignancy groups (pericardial fluid glucose: 78.24 ± 48.29 mg/dl vs 98.41 ± 44.85, respectively; p = 0.048). Moreover, CT attenuation values (Hounsfield units (HU)) tended to be higher in the malignancy group vs the non-malignancy group (22.7 [interquartile range (IQR), 17.4-26.0] vs 17.4 [IQR, 13.7-26.4], respectively; p = 0.08). The sensitivity and specificity of pericardial fluid glucose level ≤ 70 mg/dl and CT attenuation values > 20 HU were 40.9% and 89.6%, respectively, in the malignancy group. The positive- and negative predictive values of pericardial fluid glucose level ≤ 70 mg/dl and CT attenuation values > 20 HU were 85.7% and 50.0%, respectively, in the malignancy group. Pericardial fluid glucose level ≤ 70 mg/dl and CT attenuation values > 20 HU were cutoff values associated with malignancy (p = 0.012).ConclusionsLower pericardial fluid glucose level with higher CT attenuation values may suggest malignancy-related PE.

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Likelihood ratio of computed tomography characteristics for diagnosis of malignancy in adrenal incidentaloma: systematic review and meta-analysis.

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