Dataset Information

Distinguishing between Thymic Epithelial Tumors and Benign Cysts via Computed Tomography.

ABSTRACT:

Objective

To investigate whether computed tomography (CT) and fluorine-18-labeled fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) may be applied to distinguish thymic epithelial tumors (TETs) from benign cysts in the anterior mediastinum.

Materials and methods

We included 262 consecutive patients with pathologically proven TETs and benign cysts 5 cm or smaller who underwent preoperative CT scans. In addition to conventional morphological and ancillary CT findings, the relationship between the lesion and the adjacent mediastinal pleura was evaluated qualitatively and quantitatively. Mean lesion attenuation was measured on CT images. The maximum standardized uptake value (SUVmax) was obtained with FDG-PET scans in 40 patients. CT predictors for TETs were identified with multivariate logistic regression analysis. For validation, we assessed the diagnostic accuracy and inter-observer agreement between four radiologists in a size-matched set of 24 cysts and 24 TETs using a receiver operating characteristic curve before and after being informed of the study findings.

Results

The multivariate analysis showed that post-contrast attenuation of 60 Hounsfield unit or higher (odds ratio [OR], 12.734; 95% confidence interval [CI], 2.506-64.705; p = 0.002) and the presence of protrusion from the mediastinal pleura (OR, 9.855; 95% CI, 1.749-55.535; p = 0.009) were the strongest CT predictors for TETs. SUVmax was significantly higher in TETs than in cysts (5.3 ± 2.4 vs. 1.1 ± 0.3; p < 0.001). After being informed of the study findings, the readers' area under the curve improved from 0.872-0.955 to 0.949-0.999 (p = 0.066-0.149). Inter-observer kappa values for protrusion were 0.630-0.941.

Conclusion

Post-contrast CT attenuation, protrusion from the mediastinal pleura, and SUVmax were useful imaging features for distinguishing TETs from cysts in the anterior mediastinum.

SUBMITTER: Lee SH

PROVIDER: S-EPMC6424822 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Distinguishing between Thymic Epithelial Tumors and Benign Cysts via Computed Tomography.

Lee Sang Hyup SH Yoon Soon Ho SH Nam Ju Gang JG Kim Hyung Jin HJ Ahn Su Yeon SY Kim Hee Kyung HK Lee Hyun Ju HJ Lee Hwan Hee HH Cheon Gi Jeong GJ Goo Jin Mo JM

Korean journal of radiology 20190401 4

<h4>Objective</h4>To investigate whether computed tomography (CT) and fluorine-18-labeled fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) may be applied to distinguish thymic epithelial tumors (TETs) from benign cysts in the anterior mediastinum.<h4>Materials and methods</h4>We included 262 consecutive patients with pathologically proven TETs and benign cysts 5 cm or smaller who underwent preoperative CT scans. In addition to conventional morphological and ancillary CT findings ...[more]

PMID: 30887749

Similar Datasets

Project description:ObjectiveThis study aimed to develop and validate an integrated prediction model based on clinicoradiological data and computed tomography (CT)-radiomics for differentiating between benign and malignant parotid gland (PG) tumors via multicentre cohorts.Materials and methodsA cohort of 87 PG tumor patients from hospital #1 who were diagnosed between January 2017 and January 2020 were used for prediction model training. A total of 378 radiomic features were extracted from a single tumor region of interest (ROI) of each patient on each phase of CT images. Imaging features were extracted from plain CT and contrast-enhanced CT (CECT) images. After dimensionality reduction, a radiomics signature was constructed. A combination model was constructed by incorporating the rad-score and CT radiological features. An independent group of 38 patients from hospital #2 was used to validate the prediction models. The model performances were evaluated by receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the models. The radiomics signature model was constructed and the rad-score was calculated based on selected imaging features from plain CT and CECT images.ResultsAnalysis of variance and multivariable logistic regression analysis showed that location, lymph node metastases, and rad-score were independent predictors of tumor malignant status. The ROC curves showed that the accuracy of the support vector machine (SVM)-based prediction model, radiomics signature, location and lymph node status in the training set was 0.854, 0.772, 0.679, and 0.632, respectively; specificity was 0.869, 0.878, 0.734, and 0.773; and sensitivity was 0.731, 0.808, 0.723, and 0.742. In the test set, the accuracy was 0.835, 0.771, 0.653, and 0.608, respectively; the specificity was 0.741, 0.889, 0.852, and 0.812; and the sensitivity was 0.818, 0.790, 0.731, and 0.716.ConclusionsThe combination model based on the radiomics signature and CT radiological features is capable of evaluating the malignancy of PG tumors and can help clinicians guide clinical tumor management.

Project description:In the present study, we aimed to construct a radiomics model using contrast‑enhanced computed tomography (CT) to predict the pathological invasiveness of thymic epithelial tumors (TETs). We retrospectively reviewed the records of 179 consecutive patients (89 females) with histologically confirmed TETs from two hospitals. The 82 low‑ and 97 high‑risk TETs were assigned to training (90 tumors), internal validation (49 tumors) and external validation (40 tumors) cohorts. Radiomics features extracted from preoperative contrast‑enhanced chest CT were selected using least absolute shrinkage and selection operator logistic regression. Three prediction models were developed using multivariate logistic regression analysis. Their performance and clinical utility were assessed using receiver operating characteristic curves and the DeLong test, respectively. Eight radiomics features with non‑zero coefficients were used to develop a radiomics score, which significantly differed between low‑ and high‑risk TETs (P<0.001). The subjective finding, infiltration, was independently associated with high‑risk TETs. Prediction models based on infiltration alone, the radiomics signature alone, and both these parameters showed diagnostic accuracies of 72.2% [area under curve (AUC), 0.731; 95% confidence interval (CI): 0.627‑0.819; sensitivity, 85.7%; specificity, 60.4%], 88.9% (AUC, 0.944; 95% CI: 0.874‑0.981; sensitivity, 92.9%; specificity, 85.4%), and 90.0% (AUC, 0.953; 95% CI: 0.887‑0.987; sensitivity, 92.9%; specificity, 87.5%), respectively. Decision‑curve analysis showed that the combined model added more net benefit than the single‑parameter models. In conclusion, a radiomics signature based on contrast‑enhanced CT has the potential to differentiate between low‑ and high‑risk TETs. The model incorporating the radiomics signature and subjective finding may facilitate the individualized, preoperative prediction of the pathological invasiveness of TETs.

Project description:ImportanceThe ability of computed tomography (CT) to distinguish between benign congenital lung malformations and malignant cystic pleuropulmonary blastomas (PPBs) is unclear.ObjectiveTo assess whether chest CT can detect malignant tumors among postnatally detected lung lesions in children.Design, setting, and participantsThis retrospective multicenter case-control study used a consortium database of 521 pathologically confirmed primary lung lesions from January 1, 2009, through December 31, 2015, to assess diagnostic accuracy. Preoperative CT scans of children with cystic PPB (cases) were selected and age-matched with CT scans from patients with postnatally detected congenital lung malformations (controls). Statistical analysis was performed from January 18 to September 6, 2020. Preoperative CT scans were interpreted independently by 9 experienced pediatric radiologists in a blinded fashion and analyzed from January 24, 2019, to September 6, 2020.Main outcomes and measuresAccuracy, sensitivity, and specificity of CT in correctly identifying children with malignant tumors.ResultsAmong 477 CT scans identified (282 boys [59%]; median age at CT, 3.6 months [IQR, 1.2-7.2 months]; median age at resection, 6.9 months [IQR, 4.2-12.8 months]), 40 cases were extensively reviewed; 9 cases (23%) had pathologically confirmed cystic PPB. The median age at CT was 7.3 months (IQR, 2.9-22.4 months), and median age at resection was 8.7 months (IQR, 5.0-24.4 months). The sensitivity of CT for detecting PPB was 58%, and the specificity was 83%. High suspicion for malignancy correlated with PPB pathology (odds ratio, 13.5; 95% CI, 2.7-67.3; P = .002). There was poor interrater reliability (κ = 0.36 [range, 0.06-0.64]; P < .001) and no significant difference in specific imaging characteristics between PPB and benign cystic lesions. The overall accuracy rate for distinguishing benign vs malignant lesions was 81%.Conclusions and relevanceThis study suggests that chest CT, the current criterion standard imaging modality to assess the lung parenchyma, may not accurately and reliably distinguish PPB from benign congenital lung malformations in children. In any cystic lung lesion without a prenatal diagnosis, operative management to confirm pathologic diagnosis is warranted.

Project description:Purpose: The purpose of this study was to investigate the feasibility of Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) image-based radiomics in differentiating bone metastases from benign bone lesions in patients with tumors. Methods: A total of 192 lesions from 132 patients (134 in the training group, 58 in the validation group) diagnosed with vertebral bone metastases or benign bone lesions were enrolled. All images were evaluated and diagnosed independently by two physicians with more than 20 years of diagnostic experience for qualitative classification, the images were imported into MaZda software in Bitmap (BMP) format for feature extraction. All radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation algorithms after the process of normalization and correlation analysis. Based on these selected features, two models were established: The CT model and SPECT model (radiomics features were derived from CT and SPECT images, respectively). In addition, a combination model (ComModel) combined CT and SPECT features was developed in order to better evaluate the predictive performance of radiomics models. Subsequently, the diagnostic performance between each model was separately evaluated by a confusion matrix. Results: There were 12, 13, and 18 features contained within the CT, SPECT, and ComModel, respectively. The constructed radiomics models based on SPECT/CT images to discriminate between bone metastases and benign bone lesions not only had high diagnostic efficacy in the training group (AUC of 0.894, 0.914, 0.951 for CT model, SPECT model, and ComModel, respectively), but also performed well in the validation group (AUC; 0.844, 0.871, 0.926). The AUC value of the human experts was 0.849 and 0.839 in the training and validation groups, respectively. Furthermore, both SPECT model and ComModel show higher classification performance than human experts in the training group (P = 0.021 and P = 0.001, respectively) and the validation group (P = 0.037 and P = 0.007, respectively). All models showed better diagnostic accuracy than human experts in the training group and the validation group. Conclusion: Radiomics derived from SPECT/CT images could effectively discriminate between bone metastases and benign bone lesions. This technique may be a new non-invasive way to help prevent unnecessary delays in diagnosis and a potential contribution in disease staging and treatment planning.

Dataset Information

Distinguishing between Thymic Epithelial Tumors and Benign Cysts via Computed Tomography.

Objective

Materials and methods

Results

Conclusion

Publications

Distinguishing between Thymic Epithelial Tumors and Benign Cysts via Computed Tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets