1. Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment
- Author
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Elisabeth Epstein, E. Smedberg, F. Christiansen, Kevin Smith, E. L. Epstein, and M. Åkerlund
- Subjects
medicine.medical_specialty ,ovarian neoplasm ,transfer learning ,Malignancy ,Sensitivity and Specificity ,Iota ,Diagnosis, Differential ,03 medical and health sciences ,Ovarian tumor ,0302 clinical medicine ,Obstetrics and gynaecology ,Image Processing, Computer-Assisted ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,030212 general & internal medicine ,Retrospective Studies ,Ovarian Neoplasms ,Original Paper ,030219 obstetrics & reproductive medicine ,Radiological and Ultrasound Technology ,business.industry ,Ultrasound ,Obstetrics and Gynecology ,deep learning ,Reproducibility of Results ,Ultrasonography, Doppler ,General Medicine ,Gold standard (test) ,ultrasonography ,medicine.disease ,Triage ,Original Papers ,computer‐aided diagnosis ,machine learning ,Reproductive Medicine ,classification ,Computer-aided diagnosis ,Adnexal Diseases ,ovarian tumor ,Female ,Radiology ,business - 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., This article's abstract has been translated into Spanish and Chinese. Follow the links from the abstract to view the translations.
- Published
- 2021