A Preliminary Study on Exploring a potential Ultrasound Method for Predicting Cervical Cancer

Abstract

Background:The level of cervical cancer screening in underdeveloped countries is far behind that of developed countries mostly because current cervical cancer screening methods are difficult to implement in underdeveloped countries. The use of non-invasive, repeatable, and low-cost ultrasound needs to be accessed. Methods: The Canadian Sonix TOUCH ultrasound system and transvaginal ultrasound probe were used to record ultrasound radio frequency (RF) signals from cervical tissues of 69 patients with cervical cancer and 37 healthy women. The self-compiled RF time series signal analysis software was used to extract 3 different dimensions of parameters of the region of interest (ROI), including time domain, frequency domain, and fractal dimension (FD). Fourteen spectrum characteristic parameters were extracted, of which structure function method FD (SFD) and Higuchi FD belonged to FD parameters; slope, intercept, midbandfit, S1, S2, S3, and S4 were frequency domain parameters; and fuzzy entropy, kurtosis, peak, cross zero count, and cross zero standard deviation (Std) were time domain parameters. Results:The average values of the five time-domain characteristic parameters of cervical cancer tissues were smaller than those of normal cervical tissues (fuzzy entropy: 1.70±0.29 vs. 1.83±0.20; kurtosis: 0.347±0.03 vs. 0.350±0.02; peak: 1989.9±166.8 vs. 2024.69±187.5; cross zero count: 3.77±0.31 vs. 3.81±0.29; cross zero Std: 1.26±0.17 vs. 1.33±0.14), although the differences were not statistically significant (P = 0.130, 0.326, 0.618, 0.442, and 0.204, respectively). The average values of the two FD characteristic parameters and the seven frequency domain characteristic parameters of cervical cancer tissues were larger than those of normal tissues (SFD: 1.84±0.28 vs. 1.46±0.39; Higuchi FD: 1.71±0.30 vs. 1.28±0.30;

The detection of prostate cancer based on ultrasound RF signal

“…Because of the algorithm's complexity and time consumption, our method could still be used to accurately characterize prostate cancer and distinguish benign and malignant tumors. In Zheng and Lin's study (18), we used all their features with our data for comparison of results, and the comparison of the results is presented in Figure 6 and Table 5. The frequency domain and FD of RF time-series signals are parameters that reflect the complexity, roughness, and irregularity of the tissue surface, which may overlap with traditional gray-scale ultrasound imaging in identifying tissues.…”

“…Thus, our classification performance is better in comparison with theirs (AUC: 0.8, ACC: 0.78). Compared with Zheng and Lin’s study ( 18 ), they extracted FD features in addition to time domain features and frequency domain features, having good sensitivity and specificity for identifying cervical cancer. However, as to our data, no statistically significant differences about the FD features were found among benign and malignant cases with different degrees of differentiation.…”

“…In Khojaste and Imani’s paper ( 7 ), it proposes 60 spectral features of RF time series based on SVM with a radial basis kernel function for the detection of prostate cancer. In Zheng and Lin’s study ( 18 ), they extracted three different dimensions of parameters of the ROI, namely, time domain, frequency domain, and fractal dimension (FD), for cervical cancer diagnosis. Fourteen spectrum characteristic parameters were extracted, of which spectral fractal dimension (SFD) and Higuchi FD belong to FD parameters.…”