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Deep learning-based segmentation of multisite disease in ovarian cancer

Buddenkotte, Thomas (författare)
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.
Rundo, Leonardo (författare)
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.
Woitek, Ramona (författare)
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.
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Sanchez, Lorena Escudero (författare)
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.
Beer, Lucian (författare)
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Med Univ Vienna, Dept Biomed Imaging & Image Guided Therapy, Vienna, Austria.
Crispin-Ortuzar, Mireia (författare)
Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Univ Cambridge, Canc Res UK Cambridge Inst, Cambridge, England.;Univ Cambridge, Dept Oncol, Cambridge, England.
Etmann, Christian (författare)
Univ Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England.
Mukherjee, Subhadip (författare)
Univ Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England.
Bura, Vlad (författare)
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Cty Clin Emergency Hosp, Dept Radiol & Med Imaging, Cluj Napoca, Romania.
McCague, Cathal (författare)
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.
Sahin, Hilal (författare)
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Tepecik Training & Res Hosp, Dept Radiol, Izmir, Turkiye.
Pintican, Roxana (författare)
Cty Clin Emergency Hosp, Dept Radiol & Med Imaging, Cluj Napoca, Romania.;Iuliu Hatieganu Univ Med & Pharm, Dept Radiol, Cluj Napoca 400012, Romania.
Zerunian, Marta (författare)
Sapienza Univ Rome, St Andrea Hosp, Dept Med Surg & Translat Med, Radiol Unit, Rome, Italy.
Allajbeu, Iris (författare)
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.
Singh, Naveena (författare)
Dept Clin Pathol, Barts Hlth NHS Trust, London, England.
Sahdev, Anju (författare)
Barts Hlth NHS Trust, Dept Radiol, London, England.
Havrilesky, Laura (författare)
Duke Univ, Med Ctr, Durham, NC USA.
Cohn, David E. (författare)
Ohio State Univ, Coll Med, Div Gynecol Oncol, Dept Obstet & Gynecol,Comprehens Canc Ctr, Columbus, OH USA.
Bateman, Nicholas W. (författare)
Uniformed Serv Univ Hlth Sci, Dept Obstet & Gynecol, Gynecol Canc Ctr Excellence, Walter Reed Natl Mil Med Ctr, Bethesda, MD USA.;Walter Reed Natl Mil Med Ctr, John P Murtha Canc Ctr, Bethesda, MD USA.
Conrads, Thomas P. (författare)
Uniformed Serv Univ Hlth Sci, Dept Obstet & Gynecol, Gynecol Canc Ctr Excellence, Walter Reed Natl Mil Med Ctr, Bethesda, MD USA.;Walter Reed Natl Mil Med Ctr, John P Murtha Canc Ctr, Bethesda, MD USA.;Dept Obstet & Gynecol, Inova Fairfax Med Campus, Falls Church, VA USA.;Inova Ctr Personalized Hlth, Inova Schar Canc Inst, Falls Church, VA USA.
Darcy, Kathleen M. (författare)
Uniformed Serv Univ Hlth Sci, Dept Obstet & Gynecol, Gynecol Canc Ctr Excellence, Walter Reed Natl Mil Med Ctr, Bethesda, MD USA.;Walter Reed Natl Mil Med Ctr, John P Murtha Canc Ctr, Bethesda, MD USA.
Maxwell, G. Larry (författare)
Uniformed Serv Univ Hlth Sci, Dept Obstet & Gynecol, Gynecol Canc Ctr Excellence, Walter Reed Natl Mil Med Ctr, Bethesda, MD USA.;Walter Reed Natl Mil Med Ctr, John P Murtha Canc Ctr, Bethesda, MD USA.;Dept Obstet & Gynecol, Inova Fairfax Med Campus, Falls Church, VA USA.
Freymann, John B. (författare)
Frederick Natl Lab Canc Res, Canc Imaging Informat Lab, Frederick, MD USA.
Öktem, Ozan, 1969- (författare)
KTH,Matematik (Avd.)
Brenton, James D. (författare)
Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Univ Cambridge, Canc Res UK Cambridge Inst, Cambridge, England.
Sala, Evis (författare)
Univ Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Univ Cattolica Sacro Cuore, Dipartimento Sci Radiol & Ematol, Rome, Italy.;Policlin Univ A Gemelli IRCCS, Dipartimento Diagnost Immagini Radioterapia Oncol, Rome, Italy.
Schonlieb, Carola-Bibiane (författare)
Univ Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England.
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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. (creator_code:org_t)
Springer Nature, 2023
2023
Engelska.
Ingår i: EUROPEAN RADIOLOGY EXPERIMENTAL. - : Springer Nature. - 2509-9280. ; 7:1
Relaterad länk:
https://doi.org/10.1...
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https://urn.kb.se/re...
https://doi.org/10.1...
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  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.]

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)

Nyckelord

Deep learning
Omentum
Ovarian Neoplasms
Tomography (x-ray computed)
Pelvis

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