Effect of dual-energy subtraction on performance of a commercial computer-assisted diagnosis system in detection of pulmonary nodules

2005 ◽  
Author(s):  
Eliot Siegel ◽  
Bruce Reiner ◽  
Khan Siddiqui ◽  
Amy Musk ◽  
Susan Wood ◽  
...  
Keyword(s):  
Pulmonary Nodules ◽  
Dual Energy ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Computer Assisted Diagnosis ◽  
Energy Subtraction ◽  
Assisted Diagnosis

scholarly journals Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

2017 ◽  
Vol 30 (6) ◽  
pp. 796-811 ◽  
Author(s):  
Afsaneh Jalalian ◽  
Syamsiah Mashohor ◽  
Rozi Mahmud ◽  
Babak Karasfi ◽  
M. Iqbal Saripan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

Evaluation of a revised version of computer-assisted diagnosis system, BONENAVI version 2.1.7, for bone scintigraphy in cancer patients

2015 ◽  
Vol 29 (8) ◽  
pp. 659-665 ◽  
Author(s):  
Mitsuru Koizumi ◽  
Noriaki Miyaji ◽  
Taisuke Murata ◽  
Kazuki Motegi ◽  
Kenta Miwa ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Bone Scintigraphy ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

scholarly journals Detection and classification of contrast-enhancing masses by a fully automatic computer-assisted diagnosis system for breast MRI

2012 ◽  
Vol 35 (5) ◽  
pp. 1077-1088 ◽  
Author(s):  
Diane M. Renz ◽  
Joachim Böttcher ◽  
Felix Diekmann ◽  
Alexander Poellinger ◽  
Martin H. Maurer ◽  
...  
Keyword(s):  
Breast Mri ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Automatic Computer ◽  
Computer Assisted Diagnosis ◽  
Fully Automatic ◽  
Assisted Diagnosis

Characterization of Sonographically Indeterminate Ovarian Tumors with MR Imaging

1997 ◽  
Vol 38 (4) ◽  
pp. 572-577 ◽  
Author(s):  
Y. Yamashita ◽  
Y. Hatanaka ◽  
M. Torashima ◽  
M. Takahashi ◽  
K. Miyazaki ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Mr Imaging ◽  
Logistic Regression Analysis ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Computer Assisted Diagnosis ◽  
Contrast Enhanced ◽  
Malignant Lesions ◽  
Assisted Diagnosis

Purpose: The goal of this study was to maximize the discrimination between benign and malignant masses in patients with sonographically indeterminate ovarian lesions by means of unenhanced and contrast-enhanced MR imaging, and to develop a computer-assisted diagnosis system. Material and Methods: Findings in precontrast and Gd-DTPA contrast-enhanced MR images of 104 patients with 115 sonographically indeterminate ovarian masses were analyzed, and the results were correlated with histopathological findings. of 115 lesions, 65 were benign (23 cystadenomas, 13 complex cysts, 11 teratomas, 6 fibro-thecomas, 12 others) and 50 were malignant (32 ovarian carcinomas, 7 metastatic tumors of the ovary, 4 carcinomas of the fallopian tubes, 7 others). A logistic regression analysis was performed to discriminate between benign and malignant lesions, and a model of a computer-assisted diagnosis was developed. This model was pro-spectively tested in 75 cases of ovarian tumors found at other institutions. Results: From the univariate analysis, the following parameters were selected as significant for predicting malignancy (p<0.05): a solid or cystic mass with a large solid component or wall thickness greater than 3 mm; complex internal architecture; ascites; and bilaterality. Based on these parameters, a model of a computer-assisted diagnosis system was developed with the logistic regression analysis. To distinguish benign from malignant lesions, the maximum cut-off point was obtained between 0.47 and 0.51. In a prospective application of this model, 87% of the lesions were accurately identified as benign or malignant. Conclusion: Benign and malignant ovarian lesions can be distinguished in most sonographically indeterminate lesions by means of parameters obtained from contrast-enhanced MR imaging.

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