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

Humans Female Deep Learning Ovarian Cysts Ovarian Neoplasms / diagnostic imaging Neural Networks, Computer Tomography, X-Ray Computed
Deep learning Omentum Ovarian Neoplasms Pelvis Tomography (x-ray computed)

Journal

European radiology experimental
ISSN: 2509-9280
Titre abrégé: Eur Radiol Exp
Pays: England
ID NLM: 101721752

Informations de publication

Date de publication:
07 12 2023
Historique:
received: 21 09 2022
accepted: 21 09 2023
medline: 11 12 2023
pubmed: 7 12 2023
entrez: 6 12 2023
Statut: epublish

Résumé

To 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. A 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. Our 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 Automated 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. Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. • 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.

Identifiants

DOI: 10.1186/s41747-023-00388-z PMID: 38057616 PMC: PMC10700248
pubmed: 38057616
doi: 10.1186/s41747-023-00388-z
pii: 10.1186/s41747-023-00388-z
pmc: PMC10700248
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

77

Subventions

Organisme : Wellcome Trust
ID : RG98755
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 15601
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 22905
Pays : United Kingdom
Organisme : Wellcome Trust
ID : EP/R511675/1
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : 75N91019D00024
Pays : United States

Informations de copyright

© 2023. The Author(s).

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Auteurs

Thomas Buddenkotte (T)

Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany.
jung diagnostics GmbH, Hamburg, Germany.

Leonardo Rundo (L)

Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy.

Ramona Woitek (R)

Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Department of Medicine, Danube Private University, Krems, Austria.

Lorena Escudero Sanchez (L)

Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.

Lucian Beer (L)

Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria.

Mireia Crispin-Ortuzar (M)

Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
Department of Oncology, University of Cambridge, Cambridge, UK.

Christian Etmann (C)

Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

Subhadip Mukherjee (S)

Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

Vlad Bura (V)

Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca-Napoca, Romania.

Cathal McCague (C)

Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.

Hilal Sahin (H)

Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Department of Radiology, Tepecik Training and Research Hospital, Izmir, Turkey.

Roxana Pintican (R)

Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca-Napoca, Romania.
Department of Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca-Napoca, Romania.

Marta Zerunian (M)

Department of Medical-Surgical and Translational Medicine-Radiology Unit, Sapienza University of Rome, Sant'Andrea Hospital, Rome, Italy.

Iris Allajbeu (I)

Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.

Naveena Singh (N)

Department of Clinical Pathology, Barts Health NHS Trust, London, UK.

Anju Sahdev (A)

Department of Radiology, Barts Health NHS Trust, London, UK.

Laura Havrilesky (L)

Duke University Medical Center, Durham, NC, USA.

David E Cohn (DE)

Departmant 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 Bateman (NW)

Department 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.
The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA.

Thomas P Conrads (TP)

Department 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.
The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA.
Department of Obstetrics and Gynecology, Inova Fairfax Medical Campus, Falls Church, VA, USA.
Inova Center for Personalized Health, Inova Schar Cancer Institute, Falls Church, VA, USA.

Kathleen M Darcy (KM)

Department 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.
The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA.

G Larry Maxwell (GL)

Department 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.
The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA.
Department of Obstetrics and Gynecology, Inova Fairfax Medical Campus, Falls Church, VA, USA.

John B Freymann (JB)

Cancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.

Ozan Öktem (O)

Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden.

James D Brenton (JD)

Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.

Evis Sala (E)

Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK. evis.sala@policlinicogemelli.it.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK. evis.sala@policlinicogemelli.it.
Dipartimento Di Scienze Radiologiche Ed Ematologiche, Universita Cattolica del Sacro Cuore, Rome, Italy. evis.sala@policlinicogemelli.it.
Dipartimento Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy. evis.sala@policlinicogemelli.it.

Carola-Bibiane Schönlieb (CB)

Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Deep learning-based segmentation of multisite...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › Apprentissage profond › Deep learning-based segmentation of multisite...
questionsmedicales.fr › Maladies endocriniennes › Troubles gonadiques › Maladies ovariennes › Kystes de l'ovaire › Deep learning-based segmentation of multisite...
questionsmedicales.fr › Maladies endocriniennes › Troubles gonadiques › Maladies ovariennes › Tumeurs de l'ovaire › Deep learning-based segmentation of multisite...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › Deep learning-based segmentation of multisite...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Tomographie à rayons X › Tomodensitométrie › Deep learning-based segmentation of multisite...