A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: Results of a multicentre validation study

Olivier Lucidarme, Jean Paul Akakpo, Seth Granberg, Mario Sideri, Hanoch Levavi, Achim Schneider, Philippe Autier, Dror Nir, Harry Bleiberg

Research output: Contribution to journalArticle

Abstract

Objectives: To prospectively assess an innovative computer-aided diagnostic technology that quantifies characteristic features of backscattered ultrasound and theoretically allows transvaginal sonography (TVS) to discriminate benign from malignant adnexal masses. Methods: Women (n=264) scheduled for surgical removal of at least one ovary in five centres were included. Preoperative three-dimensional (3D)-TVS was performed and the voxel data were analysed by the new technology. The findings at 3D-TVS, serum CA125 levels and the TVS-based diagnosis were compared with histology. Cancer was deemed present when invasive or borderline cancerous processes were observed histologically. Results: Among 375 removed ovaries, 141 cancers (83 adenocarcinomas, 24 borderline, 16 cases of carcinomatosis, nine of metastases and nine others) and 234 noncancerous ovaries (107 normal, 127 benign tumours) were histologically diagnosed. The new computer-aided technology correctly identified 138/141 malignant lesions and 206/234 non-malignant tissues (98% sensitivity, 88% specificity). There were no false-negative results among the 47 FIGO stage I/II ovarian lesions. Standard TVS and CA125 had sensitivities/specificities of 94%/66% and 89%/75%, respectively. Combining standard TVS and the new technology in parallel significantly improved TVS specificity from 66% to 92% (p

Original languageEnglish
Pages (from-to)1822-1830
Number of pages9
JournalEuropean Radiology
Volume20
Issue number8
DOIs
Publication statusPublished - Aug 2010

Keywords

  • Ovarian cancer diagnosis
  • Ovarian HistoScanning
  • Tissue characterisation
  • Ultrasound

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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    Lucidarme, O., Akakpo, J. P., Granberg, S., Sideri, M., Levavi, H., Schneider, A., Autier, P., Nir, D., & Bleiberg, H. (2010). A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: Results of a multicentre validation study. European Radiology, 20(8), 1822-1830. https://doi.org/10.1007/s00330-010-1750-6