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Deep learning-based segmentation of multisite disease in ovarian cancer
Univ Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England.;Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Hosp Hamburg Eppendorf, Dept Diagnost & Intervent Radiol & Nucl Med, Hamburg, Germany.;Jung Diagnost GmbH, Hamburg, Germany..
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Univ Salerno, Dept Informat & Elect Engn & Appl Math, Fisciano, Italy..
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Danube Private Univ, Dept Med, Krems, Austria..
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England..
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2023 (English)In: EUROPEAN RADIOLOGY EXPERIMENTAL, ISSN 2509-9280, Vol. 7, no 1, article id 77Article in journal (Refereed) Published
Abstract [en]

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. Graphical Abstract: [Figure not available: see fulltext.]
Place, publisher, year, edition, pages
Springer Nature , 2023. Vol. 7, no 1, article id 77
Keywords [en]
Deep learning, Omentum, Ovarian Neoplasms, Tomography (x-ray computed), Pelvis
National Category
Cancer and Oncology Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-341569DOI: 10.1186/s41747-023-00388-zISI: 001116858300001PubMedID: 38057616Scopus ID: 2-s2.0-85178885749OAI: oai:DiVA.org:kth-341569DiVA, id: diva2:1822309
Note

QC 20231222

Available from: 2023-12-22 Created: 2023-12-22 Last updated: 2023-12-22Bibliographically approved

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Öktem, Ozan

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