| Autor: |
Tianlei Xiao, Weiwei Shen, Qingming Wang, Guoqing Wu, Jinhua Yu, Ligang Cui |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Frontiers in Oncology, Vol 12 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2234-943X |
| DOI: |
10.3389/fonc.2022.946965 |
| Popis: |
ObjectiveThe diagnosis of prostate cancer has been a challenging task. Compared with traditional diagnosis methods, the radiofrequency (RF) signal is not only non-invasive but also rich in microscopic lesion information. This paper proposes a novel and accurate method for detecting prostate cancer based on the ultrasound RF signal.MethodOur approach is based on low-dimensional features in the frequency domain and high-throughput features in the spatial domain. The whole process could be divided into two parts: first, we calculate three feature maps from the ultrasound original RF signal, and 1,050 radiomics features are extracted from the three feature maps; second, we extracted 37 spectral features from the normalized frequency spectrum after Fourier transform.ResultsWe use LASSO regression as the method for feature selection; moreover, we use support vector machine (SVM) for classification 10-fold cross-validation for examining the classification performance of the SVM. An AUC (area under the receiver operating characteristic curve) of 0.84 was obtained on 71 subjects.ConclusionsOur method is feasible to detect prostate cancer based on the ultrasound RF signal with superior classification performance. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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