Ultrasound molecular imaging for differentiation of benign and malignant tumors in patients

Construction of a Tumor-Targeting Nanobubble with Multiple Scattering Interfaces and its Enhancement of Ultrasound Imaging

Introduction

Recently, nanobubbles (NBs) have gained significant traction in the field of tumor diagnosis and treatment owing to their distinctive advantages. However, the application of NBs is limited due to their restricted size and singular reflection section, resulting in low ultrasonic reflection.

Methods

We synthesized a nano-scale ultrasound contrast agent (IR783-SiO2NPs@NB) by encapsulating SiO2 nanoparticles in an IR783-labeled lipid shell using an improved film hydration method. We characterized its physicochemical properties, examined its microscopic morphology, evaluated its stability and cytotoxicity, and assessed its contrast-enhanced ultrasound imaging capability both in vitro and in vivo.

Results

The results show that IR783-SiO2NPs@NB had a "donut-type" composite microstructure, exhibited uniform particle size distribution (637.2 ± 86.4 nm), demonstrated excellent stability (30 min), high biocompatibility, remarkable tumor specific binding efficiency (99.78%), and an exceptional contrast-enhanced ultrasound imaging capability.

Conclusion

Our newly developed multiple scattering NBs with tumor targeting capacity have excellent contrast-enhanced imaging capability, and they show relatively long contrast enhancement duration in solid tumors, thus providing a new approach to the structural design of NBs.

Ultrasound Molecular Imaging and Its Applications in Cancer Diagnosis and Therapy

Ultrasound imaging is regarded as a highly sensitive imaging modality used in routine clinical examinations. Over the last several decades, ultrasound contrast agents have been widely applied in ultrasound molecular cancer imaging to improve the detection, characterization, and quantification of tumors. To date, a few new potential preclinical and clinical applications regarding ultrasound molecular cancer imaging are being investigated. This review presents an overview of the various kinds of ultrasound contrast agents employed in ultrasound molecular imaging and advanced imaging techniques using these contrast agents. Additionally, we discuss the recent enormous development of ultrasound contrast agents in the relevant preclinical and clinical applications, highlight the recent challenges which need to be overcome to accelerate the clinical translation, and discuss the future perspective of ultrasound molecular cancer imaging using various contrast agents. As a highly promising and valuable tumor-specific imaging technique, it is believed that ultrasound molecular imaging will pave an accurate and efficient way for cancer diagnosis.

Adjunctive Diagnostic Methods for Skin Cancer Detection: A Review of Electrical Impedance-Based Techniques

Skin cancer is among the fastest-growing cancers with an excellent prognosis, if detected early. However, the current method of diagnosis by visual inspection has several disadvantages such as overlapping tumor characteristics, subjectivity, low sensitivity, and specificity. Hence, several adjunctive diagnostic techniques such as thermal imaging, optical imaging, ultrasonography, tape stripping methods, and electrical impedance imaging are employed along with visual inspection to improve the diagnosis. Electrical impedance-based skin cancer detection depends upon the variations in electrical impedance characteristics of the transformed cells. The information provided by this technique is fundamentally different from other adjunctive techniques and thus has good prospects. Depending on the stage, type, and location of skin cancer, various impedance-based devices have been developed. These devices when used as an adjunct to visual methods have increased the sensitivity and specificity of skin cancer detection up to 100% and 87%, respectively, thus demonstrating their potential to minimize unnecessary biopsies. In this review, the authors track the advancements and progress made in this technique for the detection of skin cancer, focusing mainly on the advantages and limitations in the clinical setting. © 2022 Bioelectromagnetics Society.

Preoperative prediction of pathological grading of hepatocellular carcinoma using machine learning-based ultrasomics: A multicenter study

PURPOSE

The present study investigated the value of ultrasomics signatures in the preoperative prediction of the pathological grading of hepatocellular carcinoma (HCC) via machine learning.

METHODS

A total of 193 patients were collected from three hospitals. The patients from two hospitals (n = 160) were randomly divided into training set (n = 128) and test set (n = 32) at a 8:2 ratio. The patients from a third hospital were used as an independent validation set (n = 33). The ultrasomics features were extracted from the tumor lesions on the ultrasound images. Support vector machine (SVM) was used to construct three preoperative pathological grading models for HCC on each dataset. The performance of the three models was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between high- and low-grade HCC lesions on the training set, test set, and the independent validation set (p < 0.05). On the test set and the validation set, the combined model's performance was the highest, followed by the ultrasomics model and the clinical model successively (p < 0.05). Their AUC (along with 95 %CI) of these models was 0.874(0.709-0.964), 0.789(0.608-0.912), 0.720(0.534-0.863) and 0.849(0.682-0.949), 0.825(0.654-0.935), 0.770(0.591-0.898), respectively.

CONCLUSION

Machine learning-based ultrasomics signatures could be used for noninvasive preoperative prediction of pathological grading of HCC. The combined model displayed a better predictive performance for pathological grading of HCC and had a stronger generalization ability.

Comparison of Sonazoid contrast-enhanced ultrasound and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid MRI for the histological diagnosis of hepatocellular carcinoma

Background

This study aimed to compare the value of Sonazoid contrast-enhanced ultrasound (SCEUS) with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid magnetic resonance imaging (EOB-MRI) for histological grading diagnosis, especially for early hepatocellular carcinoma (eHCC).

Methods

A total of 163 histopathologically confirmed HCC lesions were retrospectively collected, including 71 eHCCs (27 hypervascular, 44 non-hypervascular) and 92 advanced HCCs (adHCC) (73 hypervascular, 19 non-hypervascular). We performed SCEUS to evaluate the lesions' vascularity during the portal phase (PP) and the echogenicity during the post-vascular phase (PVP). EOB-MRI was used to determine the signal intensity between lesions and the surrounding liver parenchyma on unenhanced T1-weighted images (pre-contrast ratio) in the hepatobiliary phase (HBP) (post-contrast ratio).

Results

For the PP and PVP of SCEUS (for all lesions), the pre-and post-contrast ratios of EOB-MRI (for all hypervascular lesions) showed statistical differences in the diagnosis of some (but not all) histological grades. For the diagnosis of eHCC, isoechogenicity in the PVP achieved the best diagnostic efficacy [area under the receiver operating characteristic curve (AUC) =0.892]. Whether used independently or in a combination of any form, all indicators failed to produce a higher diagnostic efficacy than PVP. Post- (≥0.610) and pre-contrast ratios (≥0.981) yielded acceptable diagnostic efficacy, with, respectively, accuracy levels of 69.3% and 75.5% and AUC values of 0.719 and 0.736. For eHCC diagnosis, the post-contrast ratio (≥0.625) and combined diagnosis using pre- (≥0.907) and post-contrast ratios (≥0.609) revealed the highest sensitivity (92.6%) for hypervascular lesions and perfect specificity (100%) for non-hypervascular lesions.

Conclusions

Unenhanced T1-weighted images and the HBP of EOB-MRI [regardless of the vascularity in the arterial phase (AP)], and the PP and PVP of SCEUS showed their value in the histological grading diagnosis of HCC. In particular, isoechogenicity in the PVP may have promising diagnostic utility for eHCC.

A Displacement Estimated Method for Real Time Tissue Ultrasound Elastography

As an important means of medical imaging, elastic imaging is an indispensable part of mobile telemedicine. Ultrasound elastography has become a research hotspot because it can accurately measure soft tissue lesions. Displacement estimation is the most important step in ultrasound elastography. At present, the phase zero search method is an accurate and fast displacement estimation method. However, when the displacement exceeds 1/4 wavelength, it is invalid. The accuracy of block matching method is not high, but it is suitable for large displacement, so it can overcome this shortcoming. It is worth noting that the quality-guided block matching method has good robustness under complex mutation conditions. It can provide prior knowledge to increase the robustness of the phase-zero search under large displacement conditions. So we propose a novel displacement estimation method for real time tissue ultrasound elastography, which combines the quality-guided block matching method and the phase-zero search method. The experimental results show that this method is more accurate, faster and robust than other displacement estimation methods.

Study on automatic detection and classification of breast nodule using deep convolutional neural network system

Backgrounds

Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules.

Methods

Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy.

Results

Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%.

Conclusions

Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.

Fast Fourier transform combined with phase leading compensator for respiratory motion compensation system

Background

The reduction of the delaying effect in the respiratory motion compensation system (RMCS) is still impossible to completely correct the respiratory waveform of the human body due to each patient has a unique respiratory rate. In order to further improve the effectiveness of radiation therapy, this study evaluates our previously developed RMCS and uses the fast Fourier transform (FFT) algorithm combined with the phase lead compensator (PLC) to further improve the compensation rate (CR) of different respiratory frequencies and patterns of patients.

Methods

In this study, an algorithm of FFT automatic frequency detection was developed by using LabVIEW software, uisng FFT combined with PLC and RMCS to compensate the system delay time. Respiratory motion compensation experiments were performed using pre-recorded respiratory signals of 25 patients. During the experiment, the respiratory motion simulation system (RMSS) was placed on the RMCS, and the pre-recorded patient breathing signals were sent to the RMCS by using our previously developed ultrasound image tracking algorithm (UITA). The tracking error of the RMCS is obtained by comparing the encoder signals of the RMSS and RMCS. The compensation effect is verified by root mean squared error (RMSE) and system CR.

Results

The experimental results show that the patient's respiratory patterns compensated by the RMCS after using the proposed FFT combined with PLC control method, the RMSE is between 1.50-5.71 and 3.15-8.31 mm in the right-left (RL) and superior-inferior (SI) directions, respectively. CR is between 72.86-93.25% and 62.3-83.81% in RL and SI, respectively.

Conclusions

This study used FFT combined with PLC control method to apply to RMCS, and used UITA for respiratory motion compensation. Under the automatic frequency detection, the best dominant frequency of the human respiratory waveform can be determinated. In radiotherapy, it can be used to compensate the tumor movement caused by respiratory motion and reduce the radiation damage and side effects of normal tissues nearby the tumor.

Diagnostic Value of Imaging Methods in the Histological Four Grading of Hepatocellular Carcinoma

We attempted to establish an ultrasound (US) imaging-diagnostic system for histopathological grades of differentiation of hepatocellular carcinoma (HCC). We conducted a retrospective study of histopathologically confirmed 200 HCCs, classified as early (45 lesions), well- (31 lesions), moderately (68 lesions) or poorly differentiated (diff.) (56 lesions) HCCs. We performed grayscale US to estimate the presence/absence of halo and mosaic signs, Sonazoid contrast-enhanced US (CEUS) to determine vascularity (hypo/iso/hyper) of lesion in arterial and portal phase (PP), and echogenicity of lesion in post-vascular phase (PVP). All findings were of significance for the diagnosis of some (but not all) histological grades (p < 0.001–0.05). Combined findings with a relatively high diagnostic efficacy for early, poorly and moderately diff. HCC were a combination of absence of halo sign and isoechogenicity in PVP of CEUS (accuracy: 93.0%, AUC: 0.908), hypovascularity in PP (accuracy: 78.0%, area under the curve (AUC): 0.750), and a combination of isovascularity in PP and hypoechogenicity in PVP (accuracy: 75.0%, AUC: 0.739), respectively. On the other hand, neither any individual finding nor any combination of findings yielded an AUC of over 0.657 for the diagnosis of well-diff. HCC. Our study provides encouraging data on Sonazoid CEUS in the histological differential diagnosis of HCC, especially in early HCC, and the effectiveness of this imaging method should be further proved by prospective, large sample, multicenter studies.

Combination diagnosis with elastography strain ratio and molecular markers effectively improves the diagnosis rate of small breast cancer and lymph node metastasis

Background

To evaluate the strain ratio (SR) combined with molecular pathological and serum markers for the diagnosis of breast masses.

Methods

SR and 7-point scale elasticity scores were used with real-time tissue elastography and 2-dimensional color-Doppler ultrasound (US) to diagnose breast lesions in 311 hospitalized patients. Immunohistochemical staining and enzyme-linked immunosorbent assays (ELISAs) were used to examine pathological and serum tumor markers and their correlations with SR findings.

Results

SR had a higher diagnostic value compared to the 7-point scale elasticity score, displaying an obvious low-to-high distribution from benign to malignant lesions with an optimal cutoff point at 3.88, which yielded an area under the curve (AUC) of 0.896 with 89.1% sensitivity, 85.6% specificity, and positive and negative predictive values of 91.0% and 82.8%, respectively. The differences of SR values between small (≤1.5 cm), large (>3 cm) (P=0.010), and moderate (>1.5 cm and ≤3 cm) sizes (P=0.038) in distinguishing benign from malignant breast masses were statistically significant, with SR being most specific and sensitive for diagnosing small lesions. Expression of 3 molecular pathological indicators (p75NTR, p63, and CK5/6), and 5 serum mastocarcinoma markers (uPA, PAI-I, CA27-29, CEA, and CA15-3) showed statistical significance (P<0.05) in distinguishing between benign and malignant breast lesions. Furthermore, SR combined with CA15-3 and CK5/6 positivity showed 94.2% sensitivity and 89.2% specificity as combined markers for triple-negative (TN) breast cancer, whereas SR combined with D2-40 and CK19 were good diagnostic markers for breast cancer lymph node metastasis.

Conclusions

SR, together with a molecular and serological marker, may serve as an additional tool for the diagnosis of small breast cancer tumors.

Adiposity and the risk of thyroid nodules with a high-suspicion sonographic pattern: a large cross-sectional epidemiological study

Background

To our knowledge, no previous studies have evaluated the association between obesity and the risk of thyroid nodules with a high-suspicion sonographic pattern defined by the 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancers. In this large cross-sectional epidemiological study, we investigated whether a positive association existed between anthropometric measurements and the risk of thyroid nodules.

Methods

In total, 10,504 individuals from two towns in northern China were enrolled between May to September 2016. The height and weight were measured and the body mass index (BMI), body fat percentage (BFP), and body surface area (BSA) were calculated. A logistic regression model was used to estimate age-, sex-, and study site-adjusted odds ratios (ORs) and confidence intervals (CIs) for each of the anthropometric measurements with respect to the risk of thyroid nodules.

Results

The final analyses included 9,096 participants, among whom 3,614 (39.7%) had thyroid nodules and 365 (4.0%) had thyroid nodules with a high-suspicion sonographic pattern. An increased risk of any thyroid nodules was associated with greater height, weight, BFP, and BSA in both men and women. Compared with the lowest quartile of BFP, the ORs for the highest quartile were 1.33 (95% CI: 1.09-1.62) in women and 1.89 (95% CI: 1.35-2.65) in men. An increased risk of thyroid nodules with a high-suspicion sonographic pattern was associated with greater weight, BMI, BFP, and BSA in both men and women. Compared with the lowest quartile of BFP, the ORs for the highest quartile were 2.19 (95% CI: 1.32-3.64) in women and 3.10 (95% CI: 1.31-7.36) in men.

Conclusions

Our results demonstrated that BFP was the most pronounced anthropometric factor associated with an increased risk of any thyroid nodules and thyroid nodules with a high-suspicion sonographic pattern.

The features of contrast enhanced ultrasound and BRAF V600E in papillary thyroid carcinoma

Background

BRAF V600E mutation was proved to be associated with thyroid cancer. Papillary thyroid carcinoma (PTC) with positive BRAF mutation might have a more aggressive behavior. We investigated the correlation of the contrast-enhanced ultrasound (CEUS) features with BRAF 600VE in PTC.

Methods

The medical records of 1,199 patients with 1,315 nodules who underwent CEUS prior to fine needle aspiration (FNA) from January 2016 to March 2018 were retrospectively reviewed. The features of their enhancement were assessed from eight aspects: degree of enhancement, method of enhancement, homogeneity of enhancement, completeness of enhancement, boundary of the enhanced lesions, shape of the enhanced lesions, size of the enhanced lesions, and wash out period of the enhanced lesions. The patients then examined for the BRAF V600E mutation using specimens obtained from FNA.

Results

BRAF mutations were found in 888 of 1,315 nodules. The CEUS features were significantly different between BRAF-positive and BRAF-negative nodules. The BRAF mutation positive nodules were more often with larger size, hypo-enhancement, centripetal enhancement, inhomogeneous enhancement, complete enhancement, blurred boundary, irregular shape, and with wash out period at preoperative CEUS than those without BRAF mutations (P<0.001). However, no significant correlation was showed in Spearman's rank correlation between the CEUS features and BRAF mutation, except for degree of enhancement, method pattern of enhancement, and completeness of complete enhancement. Multivariate analysis showed that centripetal (OR: 1.465, 95% CI: 1.129-1.903) and no significant enhancement (OR: 0.790, 95% CI: 0.639-0.977) were predictive for the presence of BRAF mutations. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of no significant enhancement and centripetal enhancement of CEUS for predicting BRAF mutation were 68.3%, 40.0%, 91.6%, 11.7%, and 72.4%, 35.1%, 37.8%, 70.0%, respectively.

Conclusions

Our study indicated that preoperative thyroid nodule characteristics on CEUS might serve as a useful tool to BRAF mutation in PTC.

An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions

Background

Deep learning-based computer-aided diagnosis (CAD) is an important method in aiding diagnosis for radiologists. We investigated the accuracy of a deep learning-based CAD in classifying breast lesions with different histological types.

Methods

A total of 448 breast lesions were detected on ultrasound (US) and classified by an experienced radiologist, a resident and deep learning-based CAD respectively. The pathological results of the lesions were chosen as the golden standard. The diagnostic performances of the three raters in different pathological types were analyzed.

Results

For the overall diagnostic performance, deep learning-based CAD presented a significantly higher specificity (76.96%) compared with the two radiologists. The area under ROC of CAD was almost equal with the experienced radiologist (0.81 vs. 0.81), while significantly higher than the resident (0.81 vs. 0.70, P<0.0001). In the benign lesions, deep learning-based CAD had a higher accuracy than both the two radiologists, which correctly classified as benign lesions in 119/135 of fibroadenomas (88.1%), 25/35 of adenosis (71.4%), 14/27 of intraductal papillary tumors (51.9%), 5/10 of inflammation (50%), and 4/8 of sclerosing adenosis (50%). But only the differences between CAD and the two radiologists in fibroadenomas had statistical significance (P=0.0011 and P=0.0313), and the differences between CAD and the resident in adenosis had statistical significance (P=0.012). In the malignant lesions, 151/168 of invasive ductal carcinomas (89.9%), 21/29 of ductal carcinoma in situ (DCIS) (72.4%) and 6/7 of invasive lobular carcinomas (85.7%) were diagnosed as malignancies by deep learning-based CAD, with no significant differences between CAD and the two radiologists.

Conclusions

In the diagnosis of these common types of breast lesions, deep learning-based CAD had a satisfying performance. Deep learning-based CAD had a better performance in the breast benign lesions, especially in fibroadenomas and adenosis. Therefore, deep learning-based CAD is a promising supplemental tool to US to increase the specificity and avoid unnecessary benign biopsies.

A new tool for diagnosing parathyroid lesions: angio plus ultrasound imaging

Background

Our aim was to examine the potential value of angio plus ultrasound imaging in diagnosing parathyroid lesions. Angio plus ultrasound imaging uses a new paradigm in Doppler performance, which allows for a better detection of flow in small vessels while maintaining workflow of conventional color flow imaging characteristics.

Methods

Thirty parathyroid lesions, composed of 26 histopathologically diagnosed adenoma and 4 hyperplasia (two hyperplasia in the same patient), from 29 consecutive patients (6 men and 23 women; median age: 53 years, range: 25-78 years) were evaluated using both color Doppler and angio plus ultrasound imaging. The polar vessel (visible or invisible), number and distribution (peripheral, central, and both) of blood flow signals were compared between the two techniques.

Results

On color Doppler, the polar vessel was visible in 6 (20%) lesions. The median number of blood flow signals was 6 [3-23]. The distribution of peripheral, central and both was shown in 4 (13.3%), 9 (30%), and 15 (50%) lesions respectively, and no blood signals was shown in 2 (6.7%) lesions. On angio plus ultrasound imaging, the polar vessel was visible in 16 (53.3%) lesions. The median number of blood flow signals was 3 [0-18]. The distribution of peripheral, central and both was shown in 1 (3.3%), 4 (13.3%), and 25 (83.3%) lesions respectively. There was significant difference between color Doppler and angio plus ultrasound imaging in the detection of the polar vessel, number and distribution of blood flow signals.

Conclusions

The typical polar vessel and increased vascularity commonly associated with parathyroid lesions may be obtained with angio plus ultrasound imaging. Angio plus ultrasound imaging can improve the detection rate and distribution characteristics of blood flow signals of parathyroid lesions. Further research is needed to clarify the clinical meaning of increased blood flow information, such as the differentiation of parathyroid lesions from other lesions.