Dataset Information

Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.

ABSTRACT:

Objectives

To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between benign and malignant ovarian tumors and to compare its diagnostic accuracy with that of subjective assessment (SA) by an ultrasound expert.

Methods

We included 3077 (grayscale, n?=?1927; power Doppler, n?=?1150) ultrasound images from 758 women with ovarian tumors, who were classified prospectively by expert ultrasound examiners according to IOTA (International Ovarian Tumor Analysis) terms and definitions. Histological outcome from surgery (n?=?634) or long-term (??3?years) follow-up (n?=?124) served as the gold standard. The dataset was split into a training set (n?=?508; 314 benign and 194 malignant), a validation set (n?=?100; 60 benign and 40 malignant) and a test set (n?=?150; 75 benign and 75 malignant). We used transfer learning on three pre-trained DNNs: VGG16, ResNet50 and MobileNet. Each model was trained, and the outputs calibrated, using temperature scaling. An ensemble of the three models was then used to estimate the probability of malignancy based on all images from a given case. The DNN ensemble classified the tumors as benign or malignant (Ovry-Dx1 model); or as benign, inconclusive or malignant (Ovry-Dx2 model). The diagnostic performance of the DNN models, in terms of sensitivity and specificity, was compared to that of SA for classifying ovarian tumors in the test set.

Results

At a sensitivity of 96.0%, Ovry-Dx1 had a specificity similar to that of SA (86.7% vs 88.0%; P?=?1.0). Ovry-Dx2 had a sensitivity of 97.1% and a specificity of 93.7%, when designating 12.7% of the lesions as inconclusive. By complimenting Ovry-Dx2 with SA in inconclusive cases, the overall sensitivity (96.0%) and specificity (89.3%) were not significantly different from using SA in all cases (P?=?1.0).

Conclusion

Ultrasound image analysis using DNNs can predict ovarian malignancy with a diagnostic accuracy comparable to that of human expert examiners, indicating that these models may have a role in the triage of women with an ovarian tumor. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

SUBMITTER: Christiansen F

PROVIDER: S-EPMC7839489 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.

Christiansen F F Epstein E L EL Smedberg E E Åkerlund M M Smith K K Epstein E E

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology 20210101 1

<h4>Objectives</h4>To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between benign and malignant ovarian tumors and to compare its diagnostic accuracy with that of subjective assessment (SA) by an ultrasound expert.<h4>Methods</h4>We included 3077 (grayscale, n = 1927; power Doppler, n = 1150) ultrasound images from 758 women with ovarian tumors, who were classified prospectively by expert ultrasound examiners accor ...[more]

PMID: 33142359

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Project description:ObjectivesThe use of magnetic resonance (MR) imaging in differentiation between benign and malignant adnexal masses in children and adolescents might be of great value in the diagnostic workup of sonographically indeterminate masses, since preserving fertility is of particular importance in this population. This systematic review evaluates the diagnostic value of MR imaging in children with an ovarian mass.MethodsThe review was made according to the PRISMA Statement. PubMed and EMBASE were systematically searched for studies on the use of MR imaging in differential diagnosis of ovarian masses in both adult women and children from 2008 to 2018.ResultsSixteen paediatric and 18 adult studies were included. In the included studies, MR imaging has shown good diagnostic performance in differentiating between benign and malignant ovarian masses. MR imaging techniques including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging seem to further improve the diagnostic performance.ConclusionThe addition of DWI with apparent diffusion coefficient (ADC) values measured in enhancing components of solid lesions and DCE imaging may further increase the good diagnostic performance of MR imaging in the pre-operative differentiation between benign and malignant ovarian masses by increasing specificity. Prospective age-specific studies are needed to confirm the high diagnostic performance of MR imaging in children and adolescents with a sonographically indeterminate ovarian mass.Key points• MR imaging, based on several morphological features, is of good diagnostic performance in differentiating between benign and malignant ovarian masses. Sensitivity and specificity varied between 84.8 to 100% and 20.0 to 98.4%, respectively. • MR imaging techniques like diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging seem to improve the diagnostic performance. • Specific studies in children and adolescents with ovarian masses are required to confirm the suggested increased diagnostic performance of DWI and DCE in this population.

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

Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.

Objectives

Methods

Results

Conclusion

Publications

Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.

Similar Datasets

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