Name
Title | ||
|---|---|---|
Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study. |
Abstract | ||
|---|---|---|
Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1117/12.2216922 | Proceedings of SPIE |
Keywords | Field | DocType |
Temporal ultrasound,cancer grading,class imbalance,SMOTE | Computer vision,Receiver operating characteristic,Feature selection,Pattern recognition,Unsupervised learning,Prostate cancer,Artificial intelligence,Classifier (linguistics),Random forest,Principal component analysis,Physics,Ultrasound | Conference |
Volume | ISSN | Citations |
9786 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 15 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sahar Ghavidel | 1 | 5 | 1.29 |
| Farhad Imani | 2 | 9 | 3.44 |
| Siavash Khallaghi | 3 | 44 | 6.33 |
| Eli Gibson | 4 | 188 | 23.91 |
| Amir Khojaste Galesh-Khale | 5 | 28 | 4.06 |
| Mena Gaed | 6 | 25 | 8.08 |
| Madeleine Moussa | 7 | 27 | 8.83 |
| Jose A Gomez | 8 | 43 | 7.53 |
| D Robert Siemens | 9 | 58 | 7.52 |
| Michael Leveridge | 10 | 0 | 0.68 |
| Silvia D Chang | 11 | 34 | 7.23 |
| Aaron Fenster | 12 | 270 | 67.27 |
| Aaron D Ward | 13 | 89 | 22.61 |
| Purang Abolmaesumi | 14 | 951 | 111.52 |
| Parvin Mousavi | 15 | 366 | 56.95 |