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

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

doi:10.1007/s00330-010-1750-6

Cited by 30 publications

(26 citation statements)

References 34 publications

(43 reference statements)

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“…1,2 US, CT, or magnetic resonance imaging can provide important information about the malignant potential of pelvic masses. [3][4][5] If a tissue diagnosis is preferred, however, percutaneous biopsy in this region may be difficult because of possible vital intervening structures. 6 EUS is a potentially useful modality to diagnose pelvic lesions in close proximity to the rectosigmoid colon.…”

mentioning

confidence: 99%

Utility of EUS-guided biopsy of extramural pelvic masses

Mohamadnejad

Al-Haddad

Sherman

et al. 2012

Gastrointestinal Endoscopy

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mentioning

confidence: 99%

Utility of EUS-guided biopsy of extramural pelvic masses

Mohamadnejad

Al-Haddad

Sherman

et al. 2012

Gastrointestinal Endoscopy

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“…An innovative CAD technology that quantifies characteristic features of backscattered US and theoretically allows transvaginal sonography (TVS) to discriminate benign from malignant adnexal masses was proposed. 57 This method correctly identified 138 of 141 malignant lesions and 206 of 234 nonmalignant tissues (98% sensitivity and 88% specificity) with an accuracy of 91.73%.…”

Section: Discussionmentioning

confidence: 94%

Ovarian Tissue Characterization in Ultrasound

Acharya

Molinari

Sree³

et al. 2014

Technol Cancer Res Treat

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Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computeraided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given.

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“…This CAD technology has been tested in clinical trials aimed to distinguish benign from malignant tissue in the breast and the ovary, but its widespread clinical use has not been adopted for these tissues [10][11][12]. According to published data, it has the potential to predict cancer lesion location within the prostate at 90% sensitivity and 72% specificity for tumors that are more than 0.2 cm 3 in volume compared to RP specimens [1,9].…”

Section: Introductionmentioning

confidence: 97%