Usefulness of artificial neural network for differential diagnosis of hepatic masses on CT images.

[1]  K. Ohtomo,et al.  Noninvasive diagnosis of small cavernous hemangioma of the liver: advantage of MRI. , 1985, AJR. American journal of roentgenology.

[2]  K. Doi,et al.  Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis. , 2002, AJR. American journal of roentgenology.

[3]  H. Honda,et al.  Intrahepatic Peripheral Cholangiocarcinoma: Two‐Phased Dynamic Incremental CT and Pathologic Correlation , 1993, Journal of computer assisted tomography.

[4]  J. Gurney,et al.  Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis. , 1995, Radiology.

[5]  C J Vyborny,et al.  Artificial neural networks in chest radiography: application to the differential diagnosis of interstitial lung disease. , 1999, Academic radiology.

[6]  G. W. Gross,et al.  Neural networks in radiologic diagnosis. II. Interpretation of neonatal chest radiographs. , 1990, Investigative radiology.

[7]  Kunio Doi,et al.  Use of an artificial neural network to determine the diagnostic value of specific clinical and radiologic parameters in the diagnosis of interstitial lung disease on chest radiographs. , 2002, Academic radiology.

[8]  K. Doi,et al.  Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs. , 1996, Radiology.

[9]  Y Himeno,et al.  Hepatic hemangioma: findings with two-phase CT. , 1995, Radiology.

[10]  M. Houghton,et al.  A high prevalence of antibody to the hepatitis C virus in patients with hepatocellular carcinoma in Japan , 1991 .

[11]  J. Haaga,et al.  CT and MR Imaging of the Whole Body , 2002 .

[12]  Erik Schrumpf,et al.  Diagnosis and treatment of cholangiocarcinoma , 2004, Current gastroenterology reports.

[13]  Y. Wu,et al.  Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. , 1993, Radiology.

[14]  E. Fishman,et al.  Intrahepatic cholangiocarcinoma: the role of imaging in detection and staging. , 1997, Critical reviews in diagnostic imaging.

[15]  C. Metz ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[16]  R. Pugh,et al.  Transection of the oesophagus for bleeding oesophageal varices , 1973, The British journal of surgery.

[17]  C. Metz,et al.  Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. , 1998, Statistics in medicine.

[18]  K Nakamura,et al.  Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. , 2000, Radiology.

[19]  D. Naidich,et al.  Solitary Pulmonary Nodules: CT-Bronchoscopic Correlation , 1988 .

[20]  S. Hirohashi,et al.  Early advanced hepatocellular carcinoma: evaluation of CT and MR appearance with pathologic correlation. , 1994, Radiology.

[21]  H. Honda,et al.  Differential diagnosis of hepatic tumors (hepatoma, hemangioma, and metastasis) with CT: value of two-phase incremental imaging. , 1992, AJR. American journal of roentgenology.

[22]  Kunio Doi,et al.  Artificial neural networks (ANNs) for differential diagnosis of interstitial lung disease : results of a simulation test with actual clinical cases1 , 2004 .

[23]  C E Metz,et al.  Some practical issues of experimental design and data analysis in radiological ROC studies. , 1989, Investigative radiology.

[24]  Kunio Doi,et al.  Application of an artificial neural network to high-resolution CT: usefulness in differential diagnosis of diffuse lung disease. , 2004, AJR. American journal of roentgenology.

[25]  A. Margulis,et al.  Hepatic Mass Lesions , 1985 .

[26]  J. Figueras,et al.  Intrahepatic peripheral cholangiocarcinoma: CT evaluation , 2000, Abdominal Imaging.

[27]  Y. Wu,et al.  Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: pilot study. , 1990, Radiology.

[28]  Wei Li,et al.  Radiologists' performance in the diagnosis of liver tumors with central scars by using specific CT criteria. , 2002, Radiology.

[29]  K Nakamura,et al.  Effect of an artificial neural network on radiologists' performance in the differential diagnosis of interstitial lung disease using chest radiographs. , 1999, AJR. American journal of roentgenology.