Evidence map›Paper›PMID 38057616›Full record

ArticleEuropean radiology experimental2023

Deep learning-based segmentation of multisite disease in ovarian cancer.

Thomas Buddenkotte, Leonardo Rundo, Ramona Woitek, Lorena Escudero Sanchez, Lucian Beer, Mireia Crispin-Ortuzar, Christian Etmann, Subhadip Mukherjee, Vlad Bura, Cathal McCague and 17 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European radiology experimental, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
5.2field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 19 citations in OpenAlex.

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  11. Interpretability of AI race detection model in medical imaging with saliency methods.Computational and structural biotechnology journal · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors at 15 institutions in 7 countries.

Thomas BuddenkotteDepartment, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Leonardo RundoDepartment of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Ramona WoitekDepartment of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Lorena Escudero SanchezDepartment of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Lucian BeerDepartment of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Mireia Crispin-OrtuzarCancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Christian EtmannDepartment, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Subhadip MukherjeeDepartment, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Vlad BuraDepartment of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Cathal McCagueDepartment of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Hilal SahinDepartment of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Roxana PinticanDepartment of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca-Napoca, Romania.
Marta ZerunianDepartment of Medical-Surgical and Translational Medicine-Radiology Unit, Sapienza University of Rome, Sant'Andrea Hospital, Rome, Italy.
Iris AllajbeuDepartment of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Naveena SinghDepartment of Clinical Pathology, Barts Health NHS Trust, London, UK.
Anju SahdevDepartment of Radiology, Barts Health NHS Trust, London, UK.
Laura HavrileskyDuke University Medical Center, Durham, NC, USA.
David E CohnDepartmant of Obstetrics and Gynecology, Division of Gynecologic Oncology, Ohio State University Comprehensive Cancer Center, Ohio State University College of Medicine, Columbus, OH, USA.
Nicholas W BatemanDepartment of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
Thomas P ConradsDepartment of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
Kathleen M DarcyDepartment of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
G Larry MaxwellDepartment of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
John B FreymannCancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Ozan ÖktemDepartment of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden.
James D BrentonCancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Evis Sala *Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK. evis.sala@policlinicogemelli.it.
Carola-Bibiane Schönlieb *Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
University of Cambridge · GBCancer Research UK Cambridge Center · GBBarts Health NHS Trust · GBWalter Reed National Military Medical Center · USDanube Private University · ATDuke Medical Center · USFrederick National Laboratory for Cancer Research · USInova Fairfax Hospital · USIuliu Hațieganu University of Medicine and Pharmacy · ROJungheinrich (Germany) · DEKTH Royal Institute of Technology · SEMedical University of Vienna · ATSapienza University of Rome · ITThe Ohio State University · USUniformed Services University of the Health Sciences · US

Funding

WORK ORDER 126643 B539 EXPAND IC SUITE75N91019D00024 · NIAID · LEIDOS BIOMEDICAL RESEARCH, INC. · PI PENDLETON, RICHARD · 2019 to 2025
$3932.6M
Cancer Research UK 15601Cancer Research UK 22905NCI NIH HHS 75N91019D00024Wellcome TrustWellcome Trust EP/R511675/1Wellcome Trust RG98755
6 · The paper itself

Abstract

purposeTo determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.

methodsA deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test.

resultsOur model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10

conclusionAutomated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. RELEVANCE STATEMENT: Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. KEY POINTS: • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.

Indexed as

Deep LearningOvarian CystsOvarian NeoplasmsFemaleHumansNeural Networks, ComputerTomography, X-Ray ComputedDeep learningOmentumOvarian NeoplasmsPelvisTomography (x-ray computed)

Identifiers

PMID38057616
PMCPMC10700248
OpenAlexW4389410414

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.