Xiao Tianlei, Shen Weiwei, Wang Qingming, Wu Guoqing, Yu Jinhua, Cui Ligang
School of Information Science and Technology, Fudan University, Shanghai, China.
Department of Ultrasound, Peking University Third Hospital, Beijing, China.
Front Oncol. 2022 Dec 12;12:946965. doi: 10.3389/fonc.2022.946965. eCollection 2022.
The 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.
Our 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.
We 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.
Our method is feasible to detect prostate cancer based on the ultrasound RF signal with superior classification performance.
前列腺癌的诊断一直是一项具有挑战性的任务。与传统诊断方法相比，射频（RF）信号不仅是非侵入性的，而且富含微观病变信息。本文提出了一种基于超声RF信号检测前列腺癌的新颖且准确的方法。
我们的方法基于频域中的低维特征和空间域中的高通量特征。整个过程可分为两部分：首先，我们从超声原始RF信号计算三个特征图，并从这三个特征图中提取1050个放射组学特征；其次，我们在傅里叶变换后的归一化频谱中提取37个光谱特征。
我们使用LASSO回归作为特征选择方法；此外，我们使用支持向量机（SVM）进行分类，并采用10折交叉验证来检验SVM的分类性能。在71名受试者上获得了0.84的曲线下面积（AUC）。
我们基于超声RF信号检测前列腺癌的方法是可行的，具有卓越的分类性能。
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