Can artificial intelligence, applied on ultrasound images, discriminate benign and malignant ovarian tumours, and thus be used in the triage of women with these lesions? An external international multicentre validation study by the Ovarian Tumour Machine Learning Collaboration (OMLC)
Not Applicable
Completed
- Conditions
- Ovarian tumoursCancer
- Registration Number
- ISRCTN51927471
- Lead Sponsor
- Stockholm County Council
- Brief Summary
2020 Other publications in https://pubmed.ncbi.nlm.nih.gov/33142359/ development and testing ultrasound image analysis using deep neural networks (added 02/09/2024)
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Completed
- Sex
- Female
- Target Recruitment
- 3657
Inclusion Criteria
1. Women with adnexal lesions undergoing structured ultrasound examination prior to surgery
2. At least 3 good quality, representative ultrasound images per case
3. Histological outcome form surgery available
Exclusion Criteria
Does not meet inclusion criteria
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Diagnostic performance of the previously developed deep learning models (Ovry-Dx1 and Ovry-Dx2) in discriminating benign and malignant lesions. These models were created by 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. Using DNNs, tumours were classified as benign or malignant (Ovry-Dx1); or benign, inconclusive or malignant (Ovry-Dx2).
- Secondary Outcome Measures
Name Time Method Data collected from patient records:<br>1. Case ID<br>2. Subjective expert assessment prior to surgery<br>3. Classification of tumours (benign, borderline or malignant)<br>4. The certainty in the assessment (uncertain vs. certain)<br>5. Histological outcome (benign/malignant)<br>6. Specific histological diagnosis form surgery<br>7. Date of examination<br>8. Ultrasound system used