title: The influence of the b-value combination on apparent diffusion coefficient based differentiation between malignant and benign tissue in cervical cancer.
abstract: OBJECTIVE
  To analyze the influence of different b-value combinations on apparent diffusion coefficient (ADC)-based differentiation of known malignant and benign tissue in cervical cancer patients.
  
  
  METHODS
  A total of 35 patients with stage IB1, IB2, IIA cervical cancer underwent a 3.0T MRI scan prior to radical hysterectomy and pelvic lymph node dissection. Conventional T1- and T2-weighted sequences and a diffusion-weighted sequence (b = 0, 150, 500, 1000 seconds/mm(2)) were performed. Regions-of-interest (ROI) were drawn on ADC maps derived from five different b-value combinations (0, 500; 0, 150, 500; 0, 1000; 0, 150, 500, 1000; 150, 500, 1000 seconds/mm(2)). The influence of the b-value combination on ADC-based differentiation of benign and malignant tissue was analyzed using receiver-operating-characteristics curves.
  
  
  RESULTS
  For all b-value combinations, ADCs were significantly lower (P < 0.001) in cervical malignancies (1.15 +/- 0.21.10(-3); 1.10 +/- 0.21.10(-3); 0.97 +/- 0.18.10(-3); 0.97 +/- 0.23.10(-3) and 0.85 +/- 0.18.10(-3) mm(2)/second respectively to the aforementioned b-value combinations) than in benign cervix (2.08 +/- 0.31.10(-3); 2.00 +/- 0.29.10(-3); 1.62 +/- 0.23.10(-3); 1.54 +/- 0.21.10(-3) and 1.42 +/- 0.22.10(-3) mm(2)/second respectively). The diagnostic accuracy was high for all b-value combinations and without statistical differences between the combinations.
  
  
  CONCLUSIONS
  ADC-based differentiation of benign from malignant cervical tissue is independent of the tested b-value combinations. The results support the inclusion and possible pooling of studies using different b-value combinations in meta-analyses on ADC-based tissue differentiation in cervical cancer.
authors.affiliation: Gynaecological Oncology, University Medical Center Utrecht, Utrecht, The Netherlands. jphoogendam@gmail.com
authors.forename: Jacob P
authors.initials: JP
authors.lastname: Hoogendam
authors.forename: Wenche M
authors.initials: WM
authors.lastname: Klerkx
authors.forename: Gerard A P
authors.initials: GA
authors.lastname: de Kort
authors.forename: Shandra
authors.initials: S
authors.lastname: Bipat
authors.forename: Ronald P
authors.initials: RP
authors.lastname: Zweemer
authors.forename: Daisy M D S
authors.initials: DM
authors.lastname: Sie-Go
authors.forename: René H M
authors.initials: RH
authors.lastname: Verheijen
authors.forename: Willem P T M
authors.initials: WP
authors.lastname: Mali
authors.forename: Wouter B
authors.initials: WB
authors.lastname: Veldhuis
mesh_terms: D000328:Adult; D000465:Algorithms; D002584:Cervix Uteri / Q000473:pathology*; D005188:False Negative Reactions; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008495:Medical Oncology / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D012372:ROC Curve; D015203:Reproducibility of Results; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000473:pathology*