Ultrasonic RF time series for early assessment of the tumor response to chemotherapy

Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer

OBJECTIVES

To explore the clinical value of the nomogram based on spectral Doppler ultrasound combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer.

METHODS

We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively.

RESULTS

Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93).

CONCLUSION

Axillary ultrasound combined with Ki-67 and spectral ultrasound parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Objective

Radio Frequency Time Series (RF TS) is a cutting-edge ultrasound approach in tissue typing. The RF TS does not provide dynamic insights into the propagation medium; when the tissue and probe are fixed. We previously proposed the innovative RFTSDP method in which the RF data are recorded while stimulating the tissue. Applying stimulation can unveil the mechanical characteristics of the tissue in RF echo.

Materials and methods

In this study, an apparatus was developed to induce vibrations at different frequencies to the medium. Data were collected from four PVA phantoms simulating the nonlinear behaviors of healthy, fibroadenoma, cyst, and cancerous breast tissues. Raw focused, raw, and beamformed ultrafast data were collected under conditions of no stimulation, constant force, and various vibrational stimulations using the Supersonic Imagine Aixplorer clinical/research ultrasound imaging system. Time domain (TD), spectral, and nonlinear features were extracted from each RF TS. Support Vector Machine (SVM), Random Forest, and Decision Tree algorithms were employed for classification.

Results

The optimal outcome was achieved using the SVM classifier considering 19 features extracted from beamformed ultrafast data recorded while applying vibration at the frequency of 65 Hz. The classification accuracy, specificity, and precision were 98.44 ± 0.20 %, 99.49 ± 0.01 %, and 98.53 ± 0.04 %, respectively. Applying RFTSDP, a notable 24.45 % improvement in accuracy was observed compared to the case of fixed probe assessing the recorded raw focused data.

Conclusions

External vibration at an appropriate frequency, as applied in RFTSDP, incorporates beneficial information about the medium and its dynamic characteristics into the RF TS, which can improve tissue characterization.

Evaluation of Hepatic Fibrosis Using Ultrasound Backscattered Radiofrequency Signals and One-Dimensional Convolutional Neural Networks

The early detection of hepatic fibrosis is of critical importance. Ultrasound backscattered radiofrequency signals from the liver contain abundant information about its microstructure. We proposed a method for characterizing human hepatic fibrosis using one-dimensional convolutional neural networks (CNNs) based on ultrasound backscattered signals. The proposed CNN model was composed of four one-dimensional convolutional layers, four one-dimensional max-pooling layers, and four fully connected layers. Ultrasound radiofrequency signals collected from 230 participants (F0: 23; F1: 46; F2: 51; F3: 49; F4: 61) with a 3-MHz transducer were analyzed. Liver regions of interest (ROIs) that contained most of the liver ultrasound backscattered signals were manually delineated using B-mode images reconstructed from the backscattered signals. ROI signals were normalized and augmented by using a sliding window technique. After data augmentation, the radiofrequency signal segments were divided into training sets, validation sets and test sets at a ratio of 80%:10%:10%. In the test sets, the proposed algorithm produced an area under the receive operating characteristic curve of 0.933 (accuracy: 91.30%; sensitivity: 92.00%; specificity: 90.48%), 0.997 (accuracy: 94.29%; sensitivity: 94.74%; specificity: 93.75%), 0.818 (accuracy: 75.00%; sensitivity: 69.23%; specificity: 81.82%), and 0.934 (accuracy: 91.67%; sensitivity: 88.89%; specificity: 94.44%) for diagnosis liver fibrosis stage ≥F1, ≥F2, ≥F3, and ≥F4, respectively. Experimental results indicated that the proposed deep learning algorithm based on ultrasound backscattered signals yields a satisfying performance when diagnosing hepatic fibrosis stages. The proposed method may be used as a new quantitative ultrasound approach to characterizing hepatic fibrosis.

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

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; slope: -0.32±0.08 vs. -0.26±0.05; intercept: 0.48±0.02 vs. 0.46±0.02; midbandfit: 0.35±0.03 vs. 0.33±0.03; S1: 15.66±1.01 vs. 13.57±1.69; S2: 10.12±0.69 vs. 9.32±1.27; S3: 9.44±1.12 vs. 8.66±1.09; S4: 7.67±1.01 vs. 6.43±0.65), and the differences were statistically significant (P < 0.05). No effective parameters were found to identify cervical squamous cell carcinoma tissues with different levels of differentiation (P > 0.05). Conclusion: Quantitative analysis of RF time series signals based on ultrasound RF flow is expected to become a simple and non-invasive imaging method for cervical cancer diagnosis. However, whether it can be applied to the identification of early small cervical cancer lesions remains to be determined.

Quantitative evaluation of liver fibrosis based on ultrasound radio frequency signals: An animal experimental study

BACKGROUND

Chronic liver disease is an important cause of liver failure and death worldwide, and liver fibrosis is a common pathological process of most chronic liver diseases. There still lacks a useful tool for evaluating liver fibrosis progression precisely and non-invasively. The purpose of this study was to explore the use of ultrasound radio frequency (RF) signals combined with deep learning approach to evaluate the degree of liver fibrosis quantitatively.

METHODS

In this study, by extracting the output of deep learning models as a prediction value, a quantitative liver fibrosis prediction method was achieved based on the bidirectional long short-term memory (Bi-LSTM) network to analyze radio frequency (RF) signals. The dataset consisted of 160 sets of ultrasound RF signals of rat livers, including five fibrosis stages 0-4, upon pathological diagnosis. In total, 150 sets of RF signals were used to train four deep learning classification models, the output of which contained quantitative information. In each training stage of the four models, a large number of signal segments were extracted from the 150 sets and divided randomly into training and validation sets in a ratio of 80:20. Ten sets of RF data using the gold standard of quantitative fibrosis parameter (q-FP) of liver tissues were left for independent testing. To validate the proposed method, correlation analysis was carried out between q-FP and the quantitative prediction results based on the independent test data.

RESULTS

The accuracy of the four deep learning networks using the training and validation data was above 0.83 and 0.80, and the corresponding areas under the receiver operating characteristic curves were higher than 0.95 and 0.93, respectively. For the quantitative analysis in the independent test set, the determination coefficient, R², of the linear regression analysis between the quantitative prediction results and q-FP was above 0.93. liver fibrosis is a common pathological process of most chronic liver diseases.

CONCLUSIONS

This study indicates that a prediction system based on ultrasound RF signals and a deep learning approach is promising for realizing quantitative and visualized diagnosis of liver fibrosis, which would be of great value in monitoring liver fibrosis non-invasively.

Early differentiating between the chemotherapy responders and nonresponders: preliminary results with ultrasonic spectrum analysis of the RF time series in preclinical breast cancer models

Background

This study was aimed to assess whether ultrasonic spectrum analysis of radiofrequency (RF) time series using a clinical ultrasound system allows for early differentiating between the chemotherapy responders and nonresponders in human breast cancer xenografts that imitate clinical responding and nonresponding tumors.

Methods

Clinically responding (n = 20; MCF-7) and nonresponding (n = 20; MBA-MD-231) breast cancer xenografts were established in 40 nude mice. Ten mice from each group received either chemotherapy (adriamycin, 4 mg/kg) or saline as controls. Each tumor was imaged longitudinally with a clinical ultrasound scanner at baseline (day 0) and subsequently on days 2, 4, 6, 8 and 12 following treatment, and the corresponding RF time-series data were collected. Changes in six RF time-series parameters (slope, intercept, S1, S2, S3 and S4) were compared with the measurement of the tumor cell density, and their differential performances of the treatment response were analyzed.

Results

Adriamycin significantly inhibited tumor growth and decreased the cancer cell density in responders (P < 0.001) but not in nonresponders (P > 0.05). Fold changes of slope were significantly increased in responders two days after adriamycin treatment (P = 0.002), but not in nonresponders (P > 0.05). Early changes in slope on day 2 could differentiate the treatment response in 100% of both responders (95% CI, 62.9–100.0%) and nonresponders (95% CI, 88.4–100%).

Conclusions

Ultrasonic RF time series allowed for the monitoring of the tumor response to chemotherapy and could further serve as biomarkers for early differentiating between the treatment responders and nonresponders.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0248-y) contains supplementary material, which is available to authorized users.

Monitoring breast cancer response to neoadjuvant chemotherapy with ultrasound signal statistics and integrated backscatter

Background

Neoadjuvant chemotherapy (NAC) is used in patients with breast cancer to reduce tumor focus, metastatic risk, and patient mortality. Monitoring NAC effects is necessary to capture resistant patients and stop or change treatment. The existing methods for evaluating NAC results have some limitations. The aim of this study was to assess the tumor response at an early stage, after the first doses of the NAC, based on the variability of the backscattered ultrasound energy, and backscatter statistics. The backscatter statistics has not previously been used to monitor NAC effects.

Methods

The B-mode ultrasound images and raw radio frequency data from breast tumors were obtained using an ultrasound scanner before chemotherapy and 1 week after each NAC cycle. The study included twenty-four malignant breast cancers diagnosed in sixteen patients and qualified for neoadjuvant treatment before surgery. The shape parameter of the homodyned K distribution and integrated backscatter, along with the tumor size in the longest dimension, were determined based on ultrasound data and used as markers for NAC response. Cancer tumors were assigned to responding and non-responding groups, according to histopathological evaluation, which was a reference in assessing the utility of markers. Statistical analysis was performed to rate the ability of markers to predict the final NAC response based on data obtained after subsequent therapeutic doses.

Results

Statistically significant differences (p<0.05) between groups were obtained after 2, 3, 4, and 5 doses of NAC for quantitative ultrasound markers and after 5 doses for the assessment based on maximum tumor dimension. Statistical analysis showed that, after the second and third NAC courses the classification based on integrated backscatter marker was characterized by an AUC of 0.69 and 0.82, respectively. The introduction of the second quantitative marker describing the statistical properties of scattering increased the corresponding AUC values to 0.82 and 0.91.

Conclusions

Quantitative ultrasound information can characterize the tumor's pathological response better and at an earlier stage of therapy than the assessment of the reduction of its dimensions. The introduction of statistical parameters of ultrasonic backscatter to monitor the effects of chemotherapy can increase the effectiveness of monitoring and contribute to a better personalization of NAC therapy.