Details

Autor(en) / Beteiligte

• Săftoiu, Adrian
• Vilmann, Peter
• Gorunescu, Florin
• Janssen, Jan
• Hocke, Michael
• Larsen, Michael
• Iglesias–Garcia, Julio
• Arcidiacono, Paolo
• Will, Uwe
• Giovannini, Marc
• Dietrich, Cristoph F
• Havre, Roald
• Gheorghe, Cristian
• McKay, Colin
• Gheonea, Dan Ionuţ
• Ciurea, Tudorel

Titel

Efficacy of an Artificial Neural Network–Based Approach to Endoscopic Ultrasound Elastography in Diagnosis of Focal Pancreatic Masses

Ist Teil von

• Clinical gastroenterology and hepatology, 2012, Vol.10 (1), p.84-90.e1

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2012

Quelle

MEDLINE

Beschreibungen/Notizen

• Background & Aims By using strain assessment, real-time endoscopic ultrasound (EUS) elastography provides additional information about a lesion's characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural network analysis. Methods We performed a prospective, blinded, multicentric study at of 258 patients (774 recordings from EUS elastography) who were diagnosed with chronic pancreatitis (n = 47) or pancreatic adenocarcinoma (n = 211) from 13 tertiary academic medical centers in Europe (the European EUS Elastography Multicentric Study Group). We used postprocessing software analysis to compute individual frames of elastography movies recorded by retrieving hue histogram data from a dynamic sequence of EUS elastography into a numeric matrix. The data then were analyzed in an extended neural network analysis, to automatically differentiate benign from malignant patterns. Results The neural computing approach had 91.14% training accuracy (95% confidence interval [CI], 89.87%–92.42%) and 84.27% testing accuracy (95% CI, 83.09%–85.44%). These results were obtained using the 10-fold cross-validation technique. The statistical analysis of the classification process showed a sensitivity of 87.59%, a specificity of 82.94%, a positive predictive value of 96.25%, and a negative predictive value of 57.22%. Moreover, the corresponding area under the receiver operating characteristic curve was 0.94 (95% CI, 0.91%–0.97%), which was significantly higher than the values obtained by simple mean hue histogram analysis, for which the area under the receiver operating characteristic was 0.85. Conclusions Use of the artificial intelligence methodology via artificial neural networks supports the medical decision process, providing fast and accurate diagnoses.