Differentiation of Common Large Sellar-Suprasellar Masses: Effect of Artificial Neural Network on Radiologists' Diagnosis Performance

Rationale and Objectives. When pituitary adenoma, craniopharyngioma, and Ratlike's cleft cyst grow in the sellar and suprasellar region, it is often difficult to differentiate among these three lesions oil 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 Oil radiologists performance. Materials and Methods. Forty-three patients with sellar-suprasellar masses were Studied. The ANN was designed to differentiate among pituitary adenoma, craniopharyngioma, and Ratlike'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). Me 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. Conclusion. In diagnostic performance for differentiation among pituitary macroadenoma, craniopharyngioma, and Ratlike's cleft cyst with MR imaging, the ANN resulted in parity between neuroradiologists and general radiologists.