title: Differentiation of common large sellar-suprasellar masses effect of artificial neural network on radiologists' diagnosis performance.
abstract: OBJECTIVE
  When pituitary adenoma, craniopharyngioma, and Rathke's cleft cyst grow in the sellar and suprasellar region, it is often difficult to differentiate among these three lesions on magnetic resonance (MR) images. The purpose of this study was to apply an artificial neural network (ANN) for differential diagnosis among these three lesions with MR images and retrospectively evaluate the effect of ANN output on radiologists' performance.
  
  
  METHODS
  Forty-three patients with sellar-suprasellar masses were studied. The ANN was designed to differentiate among pituitary adenoma, craniopharyngioma, and Rathke's cleft cyst by using patients' ages and nine MR image findings obtained by three neuroradiologists using a subjective rating scale. In the observer performance test, MR images were viewed by nine radiologists, including four neuroradiologists and five general radiologists, first without and then with ANN output. The radiologists' performance was evaluated using receiver-operating characteristic analysis with a continuous rating scale.
  
  
  RESULTS
  The ANN showed high performance in differentiation among the three lesions (area under the receiver-operating characteristic curve, 0.990). The average area under the curve for all radiologists for differentiation among the three lesions increased significantly from 0.910 to 0.985 (P = .0024) when they used the computer output. Areas under the curves for the general radiologists and neuroradiologists increased from 0.876 to 0.983 (P = .0083) and from 0.952 to 0.989 (P = .038), respectively.
  
  
  CONCLUSIONS
  In diagnostic performance for differentiation among pituitary macroadenoma, craniopharyngioma, and Rathke's cleft cyst with MR imaging, the ANN resulted in parity between neuroradiologists and general radiologists.
authors.affiliation: Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 860-8556, Japan.
authors.forename: Mika
authors.initials: M
authors.lastname: Kitajima
authors.forename: Toshinori
authors.initials: T
authors.lastname: Hirai
authors.forename: Shigehiko
authors.initials: S
authors.lastname: Katsuragawa
authors.forename: Tomoko
authors.initials: T
authors.lastname: Okuda
authors.forename: Hirofumi
authors.initials: H
authors.lastname: Fukuoka
authors.forename: Akira
authors.initials: A
authors.lastname: Sasao
authors.forename: Masuma
authors.initials: M
authors.lastname: Akter
authors.forename: Kazuo
authors.initials: K
authors.lastname: Awai
authors.forename: Yoshiharu
authors.initials: Y
authors.lastname: Nakayama
authors.forename: Ryuji
authors.initials: R
authors.lastname: Ikeda
authors.forename: Yasuyuki
authors.initials: Y
authors.lastname: Yamashita
authors.forename: Shigetoshi
authors.initials: S
authors.lastname: Yano
authors.forename: Jun-ichi
authors.initials: J
authors.lastname: Kuratsu
authors.forename: Kunio
authors.initials: K
authors.lastname: Doi
mesh_terms: D000236:Adenoma / Q000175:diagnosis*; D000465:Algorithms; D020863:Central Nervous System Cysts / Q000175:diagnosis*; D003397:Craniopharyngioma / Q000175:diagnosis*; D003937:Diagnosis, Differential; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods; D010911:Pituitary Neoplasms / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity