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  • Buddenkotte, ThomasUniv 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. (author)

Deep learning-based segmentation of multisite disease in ovarian cancer

  • Article/chapterEnglish2023

Publisher, publication year, extent ...

  • Springer Nature,2023
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:kth-341569
  • https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-341569URI
  • https://doi.org/10.1186/s41747-023-00388-zDOI

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  • Language:English
  • Summary in:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • QC 20231222
  • 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.]

Subject headings and genre

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  • Rundo, LeonardoUniv 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. (author)
  • Woitek, RamonaUniv 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. (author)
  • Sanchez, Lorena EscuderoUniv Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England. (author)
  • Beer, LucianUniv 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. (author)
  • Crispin-Ortuzar, MireiaUniv Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Univ Cambridge, Canc Res UK Cambridge Inst, Cambridge, England.;Univ Cambridge, Dept Oncol, Cambridge, England. (author)
  • Etmann, ChristianUniv Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England. (author)
  • Mukherjee, SubhadipUniv Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England. (author)
  • Bura, VladUniv 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. (author)
  • McCague, CathalUniv Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England.;Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England. (author)
  • Sahin, HilalUniv 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. (author)
  • Pintican, RoxanaCty Clin Emergency Hosp, Dept Radiol & Med Imaging, Cluj Napoca, Romania.;Iuliu Hatieganu Univ Med & Pharm, Dept Radiol, Cluj Napoca 400012, Romania. (author)
  • Zerunian, MartaSapienza Univ Rome, St Andrea Hosp, Dept Med Surg & Translat Med, Radiol Unit, Rome, Italy. (author)
  • Allajbeu, IrisUniv Cambridge, Dept Radiol, Box 218,Cambridge Biomed Campus, Cambridge CB2 0QQ, England. (author)
  • Singh, NaveenaDept Clin Pathol, Barts Hlth NHS Trust, London, England. (author)
  • Sahdev, AnjuBarts Hlth NHS Trust, Dept Radiol, London, England. (author)
  • Havrilesky, LauraDuke Univ, Med Ctr, Durham, NC USA. (author)
  • Cohn, David E.Ohio State Univ, Coll Med, Div Gynecol Oncol, Dept Obstet & Gynecol,Comprehens Canc Ctr, Columbus, OH USA. (author)
  • Bateman, Nicholas W.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. (author)
  • Conrads, Thomas P.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. (author)
  • Darcy, Kathleen M.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. (author)
  • Maxwell, G. LarryUniformed 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. (author)
  • Freymann, John B.Frederick Natl Lab Canc Res, Canc Imaging Informat Lab, Frederick, MD USA. (author)
  • Öktem, Ozan,1969-KTH,Matematik (Avd.)(Swepub:kth)u1q7d7sf (author)
  • Brenton, James D.Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England.;Univ Cambridge, Canc Res UK Cambridge Inst, Cambridge, England. (author)
  • Sala, EvisUniv 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. (author)
  • Schonlieb, Carola-BibianeUniv Cambridge, Dept Appl Math & Theoret Phys, Cambridge, England. (author)
  • 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)

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  • In:EUROPEAN RADIOLOGY EXPERIMENTAL: Springer Nature7:12509-9280

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