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)

ISRCTN ISRCTN51927471
DOI https://doi.org/10.1186/ISRCTN51927471
Submission date
16/07/2020
Registration date
24/07/2020
Last edited
02/09/2024
Recruitment status
No longer recruiting
Overall study status
Completed
Condition category
Cancer
Prospectively registered
Protocol
Statistical analysis plan
Results
Individual participant data
Record updated in last year

Plain English Summary

Background and study aims
Expert ultrasound examination has become the main imaging technique for assessing ovarian lesions. While the diagnostic accuracy is higher in experts than in less experienced doctors, there is a shortage of expert examiners. Every year approximately 10,000 ovarian surgical procedures are performed in Sweden. We believe that up to a quarter of these are unnecessary procedures that could be avoided if expert ultrasound assessment would be available. AI approaches have gained interest in several medical fields where experts visually assess images. Automated imaging AI tools have matched or even surpassed experts. Our own recent data show that artificial intelligence (AI), using deep neural networks (DNN), can discriminating between benign and malignant ovarian tumors with performance on par with ultrasound experts.
Aim: To externally validate our DNN models, and to compare the results to the assessment made by expert ultrasound examiners, in a large international multicentre setting.

Who can participate?
Any secondary/tertiary gynecological/gyneoncological ultrasound referral centre using high-end ultrasound systems (GE Voluson E8, GE Voluson E10, Philips IU22, Philips EPIQ, or similar), that can provide at least 100 consecutive cases (50 benign and 50 malignant) with at least 3 good quality, representative ultrasound images per case.

What does the study involve?
This study involves the validation and the comparison of machine learning models to human experts with regard to assessing ovarian tumours as benign or malignant.

What are the possible benefits and risks of participating?
None

Where is the study run from?
Karolinska Institutet (Sweden)

When is the study starting and how long is it expected to run for?
July 2020 to December 2020

Who is funding the study?
SLL: Innovations fonden, ALF-medicin (Sweden)

Who is the main contact?
1. Elisabeth Epstein, Department of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden,
Elisabeth.epstein@ki.se
2. Filip Christiansen, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden,
filipchr@kth.se
3. Elliot Epstein, School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden,
elliotepstein14@gmail.com

Contact information

Prof Elisabeth Epstein
Public

Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet
Sjukusbacken 10
Stockholm
11883
Sweden

ORCiD logoORCID ID 0000-0003-2298-7785
Phone +46 706699019
Email Elisabeth.epstein@ki.se

Study information

Study designObservational retrospective study
Primary study designObservational
Secondary study designCross sectional study
Study setting(s)Other
Study typeDiagnostic
Participant information sheet No participant information sheet available
Scientific titleExternal validation of the deep learning models Ovry-Dx1 and Ovry-Dx2, applied on ultrasound images, to discriminate benign and malignant ovarian tumours. An external international multicentre validation study by the Ovarian Tumour Machine Learning Collaboration (OMLC)
Study acronymOMLC validation study
Study hypothesisBased on our preliminary findings we hypothesize that DNN models can discriminate between benign and malignant ovarian tumors with performance similar to ultrasound experts, and this performance generalizes to a large scale multicenter setting including images of varying quality. We anticipate that DNN models can be used in the triage of women with ovarian tumours, aiding and improving clinical decision making. Especially in the case of non-expert examiners, an autonomous AI clinical decision support tool is expected to result in higher detection of ovarian cancer, at a lower rate of false positives, and thus a more cost-effective utilization of healthcare resources and reduced morbidity among patients.
Ethics approval(s)Approved 10/11/2020, Swedish Ethical Review Authority (Etikprövningsmyndigheten, Box 2110,
750 02, Uppsala, Sweden; +46 10-475 08 00; registrator@etikprovning.se), ref: DNR 2020-04090
ConditionOvarian tumours
InterventionObservational study: Multi-centre (n=22) study, including at least 6,000 images from at least 2,000 cases (1,000 benign and 1,000 malignant) of adnexal lesions, with known histological outcome from surgery. Subjective classification of tumours prior to surgery; benign or malignant and the certainty in the assessment will be used for comparative analysis.
All cases will also undergo external review by 3 experts from other centres, evaluating tumours as benign or malignant based on the available images from each case. Images and questionnaires will be made available on a web-based platform.
Intervention typeOther
Primary outcome measureDiagnostic 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 measuresData collected from patient records:
1. Case ID
2. Subjective expert assessment prior to surgery
3. Classification of tumours (benign, borderline or malignant)
4. The certainty in the assessment (uncertain vs. certain)
5. Histological outcome (benign/malignant)
6. Specific histological diagnosis form surgery
7. Date of examination
8. Ultrasound system used
Overall study start date16/07/2020
Overall study end date31/12/2022

Eligibility

Participant type(s)Patient
Age groupAll
SexFemale
Target number of participantsat least 1,600
Total final enrolment3657
Participant inclusion criteria1. 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
Participant exclusion criteriaDoes not meet inclusion criteria
Recruitment start date31/07/2020
Recruitment end date30/04/2021

Locations

Countries of recruitment

  • Belgium
  • Czech Republic
  • Greece
  • Italy
  • Lithuania
  • Philippines
  • Poland
  • Spain
  • Sweden

Study participating centres

Södersjukhuset
Department of Obstetrics and Gynecology
Stockholm
11883
Sweden
European Institute of Oncology IRCCS
Preventive Gynaecology Unit
Division of Gynaecology
Milan
20141
Italy
Charles University and General University Hospital
Gynaecological Oncology Centre
Department of Obstetrics and Gynecology
First Faculty of Medicine
Prague
50005
Czech Republic
Alexandra Hospital
First Department of Obstetrics and Gynaecology
Athens
115 28
Greece
IRCCS “Burlo Garofolo”
Institute for Maternal and Child Health
Trieste
34137
Italy
Biomedical and Clinical Sciences Institute L. Sacco
Department of Obstetrics and Gynaecology
Milan
20157
Italy
Clinica Universidad de Navarra
Department of Obstetrics and Gynaecology
Pamplona
31008
Spain
San Gerardo Hospital
Clinic of Obstetrics and Gynaecology
Monza
20900
Italy
Policlinico Universitario Duilio Casula
Department of Obstetrics and Gynaecology
Monserrato
Cagliari
09042
Italy
S Orsola-Malpighi Hospital
Gynecology and Reproductive Medicine Unit
Bologna
40138
Italy
School of Health Sciences in Katowice
Department of Perinatology and Oncological Gynaecology
Katowice
40-055
Poland
Skåne University Hospital Lund
Department of Obstetrics and Gynaecology
Lund
22185
Sweden
Kaunas Medical University Hospital
Department of Obstetrics and Gynecology
Vilnius
44307
Lithuania
Third Faculty of Medicine, Charles University
Institute for the Care of Mother and Child
Prague
100 00
Czech Republic
Hospital Universitario Dexeus
Department of Obstetrics, Gynecology, and Reproduction
Barcelona
08028
Spain
Medical University of Lublin
First Department of Gynaecological Oncology and Gynaecology
Lublin
20-059
Poland
St Luke´s Medical Centre
Department of Obstetrics and Gynecology
Manila
1000
Philippines
Clinica Ostetrica e Ginecologica, Ospedale “G.Salesi"
Via F.Corridoni 11
Ancona
60123
Italy
Mater Olbia Hospital, Gynaecology and Breast care centre
Strada Statale 125 Orientale
Olbia
07026
Italy
Fondazione Poliambulanza
Via Bissolati 57
Brescia
25124
Italy

Sponsor information

Stockholm County Council
Government

Box 225 50
Stockholm
104 22
Sweden

Phone +46 72569 41 15
Email annette.alkebo@sll.se
Website https://forskningsstod.vmi.se/Ansokan/start.asp
ROR logo "ROR" https://ror.org/02zrae794
Stockholm County Council, ALF medicine
Government

Box 225 50
Stockholm
104 22
Sweden

Phone +4672598 12 65
Email kristin.blidberg@sll.se
Website https://forskningsstod.vmi.se/Ansokan/start.asp

Funders

Funder type

Government

SLL: Innovations fonden, ALF-medicin

No information available

Results and Publications

Intention to publish date31/12/2023
Individual participant data (IPD) Intention to shareNo
IPD sharing plan summaryStored in repository
Publication and dissemination planPlanned publication in high-impact peer-revewed journal within 1-1.5 years.
OMLC collaborators will be offered to use the image data set to validate their own AI-models.
IPD sharing planThe datasets generated during and/or analysed during the current study will be stored in a non-publically available repository.

Study outputs

Output type Details Date created Date added Peer reviewed? Patient-facing?
Protocol file version V4 11/12/2020 No No
Other publications development and testing ultrasound image analysis using deep neural networks 03/11/2020 02/09/2024 Yes No

Additional files

ISRCTN51927471_PROTOCOL_V4.pdf
uploaded 11/12/2020

Editorial Notes

02/09/2024: Publication reference added.
12/08/2024: The contact details were updated.
12/12/2022: The total final enrolment was changed from 2495 to 3657.
22/11/2021: The trial participating centres Skåne University Hospital and Ziekenhuis Oost-Limburg were removed.
17/11/2021: The trial participating centre Fondazione Poliambulanza was added.
26/08/2021: The following changes were made to the trial record:
1. The total final enrolment was added.
2. The trial participating centre Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica was removed.
24/08/2021: The trial participating centres "New York University School of Medicine, University College London Hospitals" were removed. The trial participating centres "Clinica Ostetrica e Ginecologica, Ospedale “G.Salesi, Mater Olbia Hospital, Gynaecology and Breast care centre" were added.
14/12/2020: The ethics approval was added.
11/12/2020: The following changes were made to the trial record:
1. The recruitment end date was changed from 31/12/2020 to 30/04/2021.
2. Uploaded protocol (not peer reviewed) Version 4, no date.
17/07/2020: Trial’s existence confirmed by SLL: Innovations fonden