Dataset Information

Contrast-enhanced ultrasonography in differential diagnosis of benign and malignant ovarian tumors.

ABSTRACT:

Objective

To evaluate the accuracy of contrast-enhanced ultrasonography (CEUS) in differential diagnosis of benign and malignant ovarian tumors.

Methods

The scientific literature databases PubMed, Cochrane Library and CNKI were comprehensively searched for studies relevant to the use of CEUS technique for differential diagnosis of benign and malignant ovarian cancer. Pooled summary statistics for specificity (Spe), sensitivity (Sen), positive and negative likelihood ratios (LR+/LR-), and diagnostic odds ratio (DOR) and their 95%CIs were calculated. Software for statistical analysis included STATA version 12.0 (Stata Corp, College Station, TX, USA) and Meta-Disc version 1.4 (Universidad Complutense, Madrid, Spain).

Results

Following a stringent selection process, seven high quality clinical trials were found suitable for inclusion in the present meta-analysis. The 7 studies contained a combined total of 375 ovarian cancer patients (198 malignant and 177 benign). Statistical analysis revealed that CEUS was associated with the following performance measures in differential diagnosis of ovarian tumors: pooled Sen was 0.96 (95%CI = 0.92?0.98); the summary Spe was 0.91 (95%CI = 0.86?0.94); the pooled LR+ was 10.63 (95%CI = 6.59?17.17); the pooled LR- was 0.04 (95%CI = 0.02?0.09); and the pooled DOR was 241.04 (95% CI = 92.61?627.37). The area under the SROC curve was 0.98 (95% CI = 0.20?1.00). Lastly, publication bias was not detected (t = -0.52, P = 0.626) in the meta-analysis.

Conclusions

Our results revealed the high clinical value of CEUS in differential diagnosis of benign and malignant ovarian tumors. Further, CEUS may also prove to be useful in differential diagnosis at early stages of this disease.

SUBMITTER: Qiao JJ

PROVIDER: S-EPMC4357383 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Contrast-enhanced ultrasonography in differential diagnosis of benign and malignant ovarian tumors.

Qiao Jing-Jing JJ Yu Jing J Yu Zhe Z Li Na N Song Chen C Li Man M

PloS one 20150312 3

<h4>Objective</h4>To evaluate the accuracy of contrast-enhanced ultrasonography (CEUS) in differential diagnosis of benign and malignant ovarian tumors.<h4>Methods</h4>The scientific literature databases PubMed, Cochrane Library and CNKI were comprehensively searched for studies relevant to the use of CEUS technique for differential diagnosis of benign and malignant ovarian cancer. Pooled summary statistics for specificity (Spe), sensitivity (Sen), positive and negative likelihood ratios (LR+/LR ...[more]

PMID: 25764442

Similar Datasets

Project description:The aim of the present study was to assess the performance of contrast-enhanced ultrasound in distinguishing between malignant and benign breast lesions and the diagnostic value of its clinical application. A total of 52 cases with malignant breast tumors and 73 cases with benign breast lesions were included in the study. Time-intensity curves (TICs) for contrast-enhanced ultrasound were recorded, and the perfusion parameters were obtained and analyzed. Typical features of malignant breast tumors included irregular shape and vascular morphology, uneven contrast agent distribution, filling defects and contrast agent retention, 'fast-out' wash-out mode, unclear boundaries and uneven internal echo. Benign lesions were characterized by 'slow-out' or synchronous wash-out mode. Regarding perfusion, the starting time of the perfusion of the Sone-Vue microbubble contrast (always 20-30 sec) and time to peak (TTP) were significantly earlier for the malignant lesions, while the wash-out time was later. A significantly greater peak intensity, rising slope and area under the TIC were observed for the malignant breast lesions. All of the malignant breast lesions exhibited an enlarged focus scope on ultrasound, while no obvious focus scope enhancement was observed for benign breast lesions. Furthermore, the TICs of 88.4% of malignant breast lesions were of the fast-rising and slow-declining type, while the TICs of 75.3 and 17.8% of the benign breast lesions were of the slow-rising and fast-declining, and fast-rising and fast-declining type, respectively. Receiver operating characteristics analysis indicated that the TTP, wash-out time and rising slope might contribute to the differential diagnosis between malignant and benign breast lesions. In conclusion, TIC parameters of contrast-enhanced ultrasound have promising clinical value in differentiating between malignant and benign breast lesions. The TTP, wash-out time and rising slope may contribute to the diagnosis of patients with breast lesions to facilitate timely treatment and prognostication of breast cancer patients.

Project description:ObjectivesTo assess the value of conventional, Doppler and contrast-enhanced ultrasonography (CEUS) (conventional ultrasonography (US), Doppler US and CEUS) for diagnosing ovarian cancer.DesignSystematic review and meta-analysis.Data sourcesPubMed, Embase and the Cochrane Library were conducted for studies published until October 2021.Eligibility criteriaStudies assessed the diagnostic value of conventional US, Doppler US or CEUS for detecting ovarian cancer, with no restrictions placed on published language and status.Data extraction and synthesisThe study selection and data extraction were performed by two independent authors. The sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), diagnostic OR (DOR) and area under the receiver operating characteristic curve (AUC) were pooled using the bivariate generalised linear mixed model and random effects model.ResultsThe meta-analysis included 72 studies and involved 9296 women who presented with ovarian masses. The pooled sensitivity, specificity, PLR, NLR, DOR and AUC for conventional US were 0.91 (95% CI: 0.87 to 0.94) and 0.87 (95% CI: 0.82 to 0.91), 6.87 (95% CI: 4.98 to 9.49) and 0.10 (95% CI: 0.07 to 0.15), 57.52 (95% CI: 36.64 to 90.28) and 0.95 (95% CI: 0.93 to 0.97), respectively. The sensitivity, specificity, PLR, NLR, DOR and AUC for Doppler US were 0.93 (95% CI: 0.91 to 0.95) and 0.85 (95% CI: 0.80 to 0.89), 6.10 (95% CI: 4.59 to 8.11) and 0.08 (95% CI: 0.06 to 0.11), 61.76 (95% CI: 39.99 to 95.37) and 0.96 (95% CI: 0.94 to 0.97), respectively. The pooled sensitivity, specificity, PLR, NLR, DOR and AUC for CEUS were 0.97 (95% CI: 0.92 to 0.99) and 0.92 (95% CI: 0.85 to 0.95), 11.47 (95% CI: 6.52 to 20.17) and 0.03 (95% CI: 0.01 to 0.09), 152.11 (95% CI: 77.77 to 297.51) and 0.99 (95% CI: 0.97 to 0.99), respectively. Moreover, the AUC values for conventional US (p=0.002) and Doppler US (p=0.005) were inferior to those of CEUS.ConclusionsConventional US, Doppler US and CEUS have a relatively high differential diagnostic value for differentiating between benign and malignant ovarian masses. The diagnostic performance of CEUS was superior to that of conventional US and Doppler US.

Project description:BackgroundLiver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients' multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features.MethodsOur study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model .ResultsThe STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors' consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors' consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model's generalization ability.ConclusionsWe incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer.

Project description:BackgroundPleural disease is a prevalent condition. As precision therapy advances, noninvasive imaging modalities play even more important roles in the evaluation of pleural diseases. This study investigated the diagnostic capabilities of high-frequency B-mode ultrasound (US) and contrast-enhanced US (CEUS) in terms of differentiating between benign and malignant pleural diseases.MethodsPatients with unexplained thickened pleurae were prospectively analyzed via transthoracic US. High-frequency B-mode US was used to derive the pleural thickness, morphology, and echogenicity. We analyzed the high-frequency CEUS data including the enhancement mode and time intensity curve (TIC). The cause of pleural thickening was confirmed by pleural biopsy and follow-up after the biopsy. We analyzed the differences in various ultrasonic features between the malignant and benign groups. Moreover, we plotted receiver operator curves (ROCs) and obtained the area under the curves, sensitivities, and specificities of all significant continuous variables. Multivariate logistic regression was used to assess the combined usefulness of multiple US indicators in terms of predicting malignant pleurae.ResultsThirty malignant and twenty benign thickened pleurae were finally diagnosed via pleural biopsy and at least six months of follow-up. The pleural morphology and enhancement modes significantly differed between the two groups (all P<0.05). The thickness derived from B-mode US and CEUS were significantly thicker in the malignant group (both P<0.05). Arrival time (AT) and the time to peak (TTP) of TIC were significantly shorter in the malignant group, whereas peak intensity and the area under the TIC were significantly higher in the malignant group (all P<0.05). The area under the ROC for pleural thickness derived from B-mode US was 0.819; pleural thickness derived from CEUS was 0.848; AT was 0.804; TTP was 0.750; peak intensity was 0.745; the area under the TIC was 0.743; and the combined various B-mode and CEUS parameter was 0.975.ConclusionsPleural thickness, morphology, enhancement mode, and the TIC of high-frequency US aided the differentiation of benign from malignant pleural diseases. Combined analysis of US indicators further improved the diagnostic capability.

Dataset Information

Contrast-enhanced ultrasonography in differential diagnosis of benign and malignant ovarian tumors.

Objective

Methods

Results

Conclusions

Publications

Contrast-enhanced ultrasonography in differential diagnosis of benign and malignant ovarian tumors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets