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Quantification and Classification of Contrast Enhanced Ultrasound Breast Cancer Data: A Preliminary Study
| Content Provider | MDPI |
|---|---|
| Author | Ioannidis, Georgios S. Goumenakis, Michalis Stefanis, Ioannis Karantanas, Apostolos Marias, Kostas |
| Copyright Year | 2022 |
| Description | This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted $R^{2}$ metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted $R^{2}$ of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, $G_{mean}$, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy. |
| Starting Page | 425 |
| e-ISSN | 20754418 |
| DOI | 10.3390/diagnostics12020425 |
| Journal | Diagnostics |
| Issue Number | 2 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-06 |
| Access Restriction | Open |
| Subject Keyword | Diagnostics Oncology Radiology, Nuclear Medicine and Imaging Perfusion/models Breast Carcinoma Contrast Enhanced Ultrasonography Prognostic Factors Quantitative Analysis |
| Content Type | Text |
| Resource Type | Article |
