Discrimination between benign and malignant adnexal masses by specialist ultrasound examination versus serum CA-125

Research output: Contribution to journalArticle

Abstract

Background Subjective evaluation of gray-scale and Doppler ultrasound findings (i. e., pattern recognition) by an experienced examiner and preoperative serum levels of CA-125 can both discriminate benign from malignant adnexal ( i. e., ovarian, paraovarian, or tubal) masses. We compared the diagnostic performance of these methods in a large multicenter study. Methods In a prospective multicenter study-the International Ovarian Tumor Analysis-1066 women with a persistent adnexal mass underwent transvaginal gray-scale and color Doppler ultrasound examinations by an experienced examiner within 120 days of surgery. Pattern recognition was used to classify a mass as benign or malignant. Of these women, 809 also had blood collected preoperatively for measurement of serum CA-125. Various levels of CA-125 were used as cutoffs to classify masses. Results from both assays were then compared with histologic findings after surgery. Results Pattern recognition correctly classified 93% (95% confidence interval [CI]=90.9% to 94.6%) of the tumors as benign or malignant. Serum CA-125 correctly classified at best 83% ( 95% CI=80.3% to 85.6%) of the masses. Histologic diagnoses that were most often misclassified by CA-125 were fibroma, endometrioma, and abscess ( false-positive results) and borderline tumor ( false-negative results). Pattern recognition correctly classified 86% ( 95% CI=81.1% to 90.4%) of masses of these four histologic types as being benign or malignant, whereas a serum CA-125 at a cutoff of 30 U/mL correctly classified 41% ( 95% CI=34.4% to 47.5%) of them. Pattern recognition assigned a correct specific histologic diagnosis to 333 (59%, 95% CI=54.5% to 62.8%) of the 567 benign lesions. Conclusion Pattern recognition was superior to serum CA-125 for discrimination between benign and malignant adnexal masses.

Details

Authors
  • Ben Van Calster
  • Dirk Timmerman
  • Tom Bourne
  • Antonia Carla Testa
  • Caroline Van Holsbeke
  • Ekaterini Domali
  • Davor Jurkovic
  • Patrick Neven
  • Sabine Van Huffel
  • Lil Valentin
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Cancer and Oncology
Original languageEnglish
Pages (from-to)1706-1714
JournalJournal of the National Cancer Institute
Volume99
Issue number22
Publication statusPublished - 2007
Publication categoryResearch
Peer-reviewedYes