Name
Abstract | ||
|---|---|---|
Prostate cancer is the most prevalently diagnosed and the second cause of cancer-related death in North American men. Several approaches have been proposed to augment detection of prostate cancer using different imaging modalities. Due to advantages of ultrasound imaging, these approaches have been the subject of several recent studies. This paper presents the results of a feasibility study on differentiating between lower and higher grade prostate cancer using ultrasound RF time series data. We also propose new spectral features of RF time series to highlight aggressive prostate cancer in small ROIs of size 1 mm x 1 mm in a cohort of 19 ex vivo specimens of human prostate tissue. In leave-one-patient-out cross-validation strategy, an area under accumulated ROC curve of 0.8 has been achieved with overall sensitivity and specificity of 81% and 80%, respectively. The current method shows promising results on differentiating between lower and higher grade of prostate cancer using ultrasound RF time series. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2082663 | Proceedings of SPIE |
Keywords | Field | DocType |
ultrasound RF data,RF time series,tissue classification,prostate cancer | Computer vision,Ultrasonography,Imaging modalities,Ultrasound imaging,Artificial intelligence,Prostate,Prostate cancer,Radiology,Cancer,Ultrasound,Physics | Conference |
Volume | ISSN | Citations |
9414 | 0277-786X | 3 |
PageRank | References | Authors |
0.45 | 2 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Khojaste Galesh-Khale | 1 | 28 | 4.06 |
| Farhad Imani | 2 | 53 | 7.40 |
| Mehdi Moradi | 3 | 219 | 31.03 |
| David M. Berman | 4 | 42 | 7.00 |
| D Robert Siemens | 5 | 58 | 7.52 |
| eric e sauerberi | 6 | 3 | 0.45 |
| Alexander Boag | 7 | 59 | 4.73 |
| Purang Abolmaesumi | 8 | 951 | 111.52 |
| Parvin Mousavi | 9 | 366 | 56.95 |