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Deep learning-based segmentation of multisite disease in ovarian cancer

  1. Author:
    Buddenkotte, Thomas
    Rundo, Leonardo
    Woitek, Ramona
    Escudero Sanchez, Lorena
    Beer, Lucian
    Crispin-Ortuzar, Mireia
    Etmann, Christian
    Mukherjee, Subhadip
    Bura, Vlad
    McCague, Cathal
    Sahin, Hilal
    Pintican, Roxana
    Zerunian, Marta
    Allajbeu, Iris
    Singh, Naveena
    Sahdev, Anju
    Havrilesky, Laura
    Cohn, David E
    Bateman, Nicholas W
    Conrads, Thomas P
    Darcy, Kathleen M
    Maxwell, G Larry
    Freymann,John
    Öktem, Ozan
    Brenton, James D
    Sala, Evis
    Schönlieb, Carola-Bibiane

  2. Author Address

    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., Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK., Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy., Department of Medicine, Danube Private University, Krems, Austria., Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria., Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK., Department of Oncology, University of Cambridge, Cambridge, UK., Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca-Napoca, Romania., Department of Radiology, Tepecik Training and Research Hospital, Izmir, Turkey., Department of Radiology, Iuliu Ha?ieganu University of Medicine and Pharmacy, Cluj-Napoca-Napoca, Romania., Department of Medical-Surgical and Translational Medicine-Radiology Unit, Sapienza University of Rome, Sant 39;Andrea Hospital, Rome, Italy., Department of Clinical Pathology, Barts Health NHS Trust, London, UK., Department of Radiology, Barts Health NHS Trust, London, UK., Duke University Medical Center, Durham, NC, USA., Departmant of Obstetrics and Gynecology, Division of Gynecologic Oncology, Ohio State University Comprehensive Cancer Center, Ohio State University College of Medicine, Columbus, OH, USA., 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., Cancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden., 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.,
    1. Year: 2023
    2. Date: Dec 07
    3. Epub Date: 2023 12 07
  2. Journal: European Radiology Experimental
    1. 7
    2. 1
    3. Pages: 77
  4. Type of Article: Article
  5. Article Number: 77
  1. Abstract:

    Purpose: 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. 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. Results: 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 × 10-7, 3 × 10-4, 4 × 10-2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10-3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. Conclusion: 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. 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.

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External Sources

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  2. DOI: 10.1186/s41747-023-00388-z
  3. PMID: 38057616
  4. PMCID: PMC10700248
  5. PII : 10.1186/s41747-023-00388-z

Library Notes

  1. Fiscal Year: FY2023-2024
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