Comparative Diagnostic Accuracy of Contrast-Enhanced Ultrasound and Shear Wave Elastography in Differentiating Benign and Malignant Lesions: A Network Meta-Analysis

Diagnostic value of contrast-enhanced ultrasound and shear-wave elastography for small breast nodules

Background

The study aims to evaluate the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) and shear-wave elastography (SWE) in detecting small malignant breast nodules in an effort to inform further refinements of the Breast Imaging Reporting and Data System (BI-RADS) classification system.

Methods

This study retrospectively analyzed patients with breast nodules who underwent conventional ultrasound, CEUS, and SWE at Gongli Hospital from November 2015 to December 2019. The inclusion criteria were nodules ≤ 2 cm in diameter with pathological outcomes determined by biopsy, no prior treatments, and solid or predominantly solid nodules. The exclusion criteria included pregnancy or lactation and low-quality images. Imaging features were detailed and classified per BI-RADS. Diagnostic accuracy was assessed using receiver operating characteristic curves.

Results

The study included 302 patients with 305 breast nodules, 113 of which were malignant. The diagnostic accuracy was significantly improved by combining the BI-RADS classification with CEUS and SWE. The combined approach yielded a sensitivity of 88.5%, specificity of 87.0%, positive predictive value of 80.0%, negative predictive value of 92.8%, and accuracy of 87.5% with an area under the curve of 0.877. Notably, 55.8% of BI-RADS 4A nodules were downgraded to BI-RADS 3 and confirmed as benign after pathological examination, suggesting the potential to avoid unnecessary biopsies.

Conclusion

The integrated use of the BI-RADS classification, CEUS, and SWE enhances the accuracy of differentiating benign and malignant small breast nodule, potentially reducing the need for unnecessary biopsies.

Comparative analysis of conventional ultrasound and shear wave elastography features in primary breast diffuse large B-cell lymphoma

BACKGROUND

Primary breast diffuse large B-cell lymphoma (PB-DLBCL) is a rare subtype of non-Hodgkin lymphoma that accounts for < 3% of extranodal lymphomas and 1% of breast tumors. Its diagnosis and management are challenging because of its rarity, heterogeneity, and aggressive behavior. Conventional ultrasound (US) is the first-line imaging modality for breast lesions; however, it has limited specificity and accuracy for PB-DLBCL. Shear wave elastography (SWE) is a novel US technique that measures tissue stiffness and may reflect the histological characteristics and biological behavior of breast lesions.

AIM

To compare the conventional US and SWE features of PB-DLBCL and evaluate their diagnostic performance and prognostic value.

METHODS

We retrospectively reviewed the clinical data and US images of 32 patients with pathologically confirmed PB-DLBCL who underwent conventional US and SWE before treatment. We analyzed conventional US features (shape, margin, orientation, echo, posterior acoustic features, calcification, and vascularity) and SWE features (mean elasticity value, standard deviation, minimum elasticity value, maximum elasticity value, and lesion-to-fat ratio) of the PB-DLBCL lesions. Using receiver operating characteristic curve analysis, we determined the optimal cutoff values and diagnostic performance of conventional US and SWE features. We also performed a survival analysis to assess the prognostic value of conventional US and SWE features.

RESULTS

The results showed that the PB-DLBCL lesions were mostly irregular in shape (84.4%), microlobulated or spiculated in margins (75%), parallel in orientation (65.6%), hypoechoic in echo (87.5%), and had posterior acoustic enhancement (65.6%). Calcification was rare (6.3%) and vascularity was variable (31.3% avascular, 37.5% hypovascular, and 31.3% hypervascular). The mean elasticity value of PB-DLBCL lesions was significantly higher than that of benign breast lesions (113.4 ± 46.9 kPa vs 27.8 ± 16.4 kPa, P < 0.001). The optimal cutoff value of the mean elasticity for distinguishing PB-DLBCL from benign breast lesions was 54.5 kPa, with a sensitivity of 93.8%, specificity of 92.9%, positive predictive value of 93.8%, negative predictive value of 92.9%, and accuracy of 93.3%. The mean elasticity value was also significantly correlated with Ki-67 expression level (r = 0.612, P < 0.001), which is a marker of tumor proliferation and aggressiveness. Survival analysis showed that patients with higher mean elasticity values (> 54.5 kPa) had worse overall survival (OS) and progression-free survival (PFS) than those with lower mean elasticity values (< 54.5 kPa) (P = 0.038 for OS and P = 0.027 for PFS).

CONCLUSION

Conventional US and SWE provide useful information for diagnosing and forecasting PB-DLBCL. SWE excels in distinguishing PB-DLBCL from benign breast lesions, reflects tumor proliferation and aggressiveness, and improves disease management.

Comparative analysis of ultrasonic elastosonography and contrast-enhanced ultrasonography in the diagnosis of benign and malignant intraocular tumors

PURPOSE

To compare the diagnostic value of ultrasonic elastosonography (UE) and contrast-enhanced ultrasonography (CEUS) for benign and malignant intraocular tumors.

METHODS

This retrospective study enrolled patients with intraocular tumors at Beijing Tongren Hospital, Capital Medical University (August 2016 to January 2020). The strain rate ratio (strain rate of tumor tissue divided by strain rate of surrounding normal tissue) was measured by UE. CEUS was performed using SonoVue® contrast agent. The performance of each method at differentiating benign from malignant intraocular tumors was evaluated by receiver operating characteristic curve analysis.

RESULTS

The analysis included 147 eyes in 145 patients (45.6 ± 13.4 years-old; 66 males): 117 patients (119 eyes) with malignant tumors and 28 patients (28 eyes) with benign tumors. At an optimal cutoff of 22.67 for the strain rate ratio, UE distinguished benign from malignant tumors with a sensitivity of 86.6% and a specificity of 96.4%. CEUS showed that 117 eyes with malignant tumors had a fast-in, fast-out time-intensity curve, and only two eyes with malignant tumors had a fast-in, slow-out curve, while all 28 eyes with benign tumors had a fast-in, slow-out curve. CEUS differentiated benign from malignant tumors with a sensitivity of 98.3% and a specificity of 100%. The diagnostic results differed significantly between the two methods (P = 0.004, McNemar test). The diagnostic performances of the two tests were moderately consistent (κ = 0.657, P < 0.001).

CONCLUSION

Both CEUS and UE have good diagnostic value in the differentiation of benign intraocular tumors from malignant intraocular tumors.

Contrast-enhanced Ultrasound Combined With Elastography for the Evaluation of Muscle-invasive Bladder Cancer in Rats

OBJECTIVES

By comparing with the control group, we evaluated the usefulness of contrast-enhanced ultrasound (CEUS) combined with elastography for the assessment of muscle invasion by bladder cancer (MIBC) in a Sprague-Dawley (SD) rat model.

METHODS

In the experimental group, 40 SD rats developed in situ bladder cancer (BLCA) in response to N-methyl-N-nitrosourea treatment, whereas 40 SD rats were included in the control group for comparison. We compared PI, Emean , microvessel density (MVD), and collagen fiber content (CFC) between the two groups. In the experimental group, Bland-Altman test was used to assess the relationships between various parameters. The largest Youden value was used as the cut-off point, and binomial logistic regression analysis was performed to analyze the PI and Emean . Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic power of parameters, individually and in combination.

RESULTS

The PI, Emean , MVD, and CFC were significantly lower in the control group than in the experimental group (P < .05). The PI, Emean , MVD, and CFC were significantly higher for MIBC than for non-muscle-invasive bladder cancer (P < .05). There were significant correlations between PI and MVD, and between Emean and CFC. The diagnostic efficiency analysis showed PI had the highest sensitivity, CFC had the highest specificity, and PI + Emean had the highest diagnostic efficacy.

CONCLUSION

CEUS and elastography can distinguish lesions from normal tissue. PI, MVD, Emean , and CFC were useful for the detection of BLCA myometrial invasion. The comprehensive utilization of PI and Emean improved diagnostic accuracy and have clinical application.

Ultrasonic Elastography-guided Pleural Biopsy for the Diagnosis of Pleural Effusion: A Multicenter Prospective Study of Diagnostic Test Performance

Rationale: The diagnostic yield of traditional ultrasound-guided pleural biopsy remains unsatisfactory, particularly when the pleural thickness is ⩽5 mm and/or no pleural nodules are detected. Pleural ultrasound elastography (UE) has a better diagnostic yield than traditional ultrasound for malignant pleural effusion (MPE). However, studies on UE-guided pleural biopsies are lacking. Objectives: To evaluate the feasibility and safety of UE-guided pleural biopsy. Methods: In this multicenter prospective single-arm trial, patients with pleural effusion whose pleural thickness was ⩽5 mm with no pleural nodules were enrolled between July 2019 and August 2021. The diagnostic yield of UE-guided pleural biopsy for pleural effusion and its sensitivity for detecting MPE were evaluated. Results: Ninety-eight patients (mean age, 62.4 ± 13.2 yr; 65 men) were prospectively enrolled. The diagnostic yield of UE-guided pleural biopsy for making any diagnosis was 92.9% (91/98), and its sensitivity for MPE was 88.7% (55/62). In addition, its sensitivity for pleural tuberculosis was 69.6% (16/23). The rate of postoperative chest pain was acceptable, and there was no pneumothorax. Conclusions: UE-guided pleural biopsy is a novel technique for diagnosing MPE with good diagnostic yield and sensitivity. Clinical trial registered with https://www.chictr.org.cn (ChiCTR2000033572).

Effect of caspase-1 (CASP1) combined with multimodal ultrasound features on the prognosis of breast cancer patients

Background

Breast cancer (BRCA) is the malignant tumor with the highest incidence rate among women in the world, and its mortality rate ranks second. The purpose of our study is to explore the correlation between caspase-1 (CASP1) and the prognosis of BRCA patients and the potential mechanism of action, and to analyze the clinical value of CASP1 combined with multimodal ultrasound features in early screening and prognosis of BRCA.

Methods

We analyzed The Cancer Genome Atlas (TCGA) database to confirm that CASP1 was expressed in BRCA patients and determine whether its expression was correlated with patient prognosis. The relationship between CASP1 expression and survival was measured by the clinicopathological parameters. Multivariate analysis was performed using Cox regression, and a nomogram was developed using these results for quality assurance purposes. The correlations between CASP1 and immune cells were investigated using the Tumor Immune Estimation Resource (TIMER) and TCGA databases. Next, we performed gene set enrichment analysis (GSEA) to determine the potential mechanism of action. Finally, to analyze the effect of CASP1 combined with multimodal ultrasonography characteristics on the prognosis of BRCA patients was studied by analyzing the clinical data of patients.

Results

CASP1 expression was lower in BRCA tumor tissues than in the surrounding tissues. Patients with high CASP1 expression had better overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) than those with low CASP1 expression. GSEA suggested that CASP1 may affect the cell cycle, immune environment, inflammation, apoptosis, the HIPPOMERLIN pathway, Natural killer (NK) cell regulation of cytotoxicity, p53 expression, the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway, the mitogen-activated protein kinase (MAPK) pathway, extracellular matrix, etc., thereby influencing the biological events in BRCA. Among conventional ultrasound features and contrast-enhanced ultrasound (CEUS) features, mass margin status and blood flow grade were associated with the expression of CASP1. Meanwhile, patients with poor ultrasound features tended to have low CASP1 expression.

Conclusions

CASP1 may be a novel predictive marker for BRCA patients. CASP1 combined with multimodal ultrasound features has good clinical value in the early screening and prognostic prediction of BRCA.

Contrast-Enhanced Ultrasonography (CEUS) in Imaging of the Reproductive System in Dogs: A Literature Review

The use of contrast-enhanced ultrasound (CEUS) has been widely reported for reproductive imaging in humans and animals. This review aims to analyze the utility of CEUS in characterizing canine reproductive physiology and pathologies. In September 2022, a search for articles about CEUS in canine testicles, prostate, uterus, placenta, and mammary glands was conducted on PubMed and Scopus from 1990 to 2022, showing 36 total results. CEUS differentiated testicular abnormalities and neoplastic lesions, but it could not characterize tumors. In prostatic diseases, CEUS in dogs was widely studied in animal models for prostatic cancer treatment. In veterinary medicine, this diagnostic tool could distinguish prostatic adenocarcinomas. In ovaries, CEUS differentiated the follicular phases. In CEH-pyometra syndrome, it showed a different enhancement between endometrium and cysts, and highlighted angiogenesis. CEUS was shown to be safe in pregnant dogs and was able to assess normal and abnormal fetal-maternal blood flow and placental dysfunction. In normal mammary glands, CEUS showed vascularization only in diestrus, with differences between mammary glands. CEUS was not specific for neoplastic versus non-neoplastic masses and for benign tumors, except for complex carcinomas and neoplastic vascularization. Works on CEUS showed its usefulness in a wide spectrum of pathologies of this non-invasive, reliable diagnostic procedure.

Shear-wave elastography combined with contrast-enhanced ultrasound algorithm for noninvasive characterization of focal liver lesions

PURPOSE

To establish shear-wave elastography (SWE) combined with contrast-enhanced ultrasound (CEUS) algorithm (SCCA) and improve the diagnostic performance in differentiating focal liver lesions (FLLs).

MATERIAL AND METHODS

We retrospectively selected patients with FLLs between January 2018 and December 2019 at the First Affiliated Hospital of Sun Yat-sen University. Histopathology was used as a standard criterion except for hemangiomas and focal nodular hyperplasia. CEUS with SonoVue (Bracco Imaging) and SCCA combining CEUS and maximum value of elastography with < 20 kPa and > 90 kPa thresholds were used for the diagnosis of FLLs. The diagnostic performance of CEUS and SCCA was calculated and compared.

RESULTS

A total of 171 FLLs were included, with 124 malignant FLLs and 47 benign FLLs. The area under curve (AUC), sensitivity, and specificity in detecting malignant FLLs were 0.83, 91.94%, and 74.47% for CEUS, respectively, and 0.89, 91.94%, and 85.11% for SCCA, respectively. The AUC of SCCA was significantly higher than that of CEUS (P = 0.019). Decision curves indicated that SCCA provided greater clinical benefits. The SCCA provided significantly improved prediction of clinical outcomes, with a net reclassification improvement index of 10.64% (P = 0.018) and integrated discrimination improvement of 0.106 (P = 0.019). For subgroup analysis, we divided the FLLs into a chronic-liver-disease group (n = 88 FLLs) and a normal-liver group (n = 83 FLLs) according to the liver background. In the chronic-liver-disease group, there were no differences between the CEUS-based and SCCA diagnoses. In the normal-liver group, the AUC of SCCA and CEUS in the characterization of FLLs were 0.89 and 0.83, respectively (P = 0.018).

CONCLUSION

SCCA is a feasible tool for differentiating FLLs in patients with normal liver backgrounds. Further investigations are necessary to validate the universality of this algorithm.

Study on the Diagnostic Value of Contrast-Enhanced Ultrasound and Magnetic Resonance Imaging in Prostate Cancer

Objective

The aim is to study the different roles of single and joint application of magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) in prostate malignant tumors.

Methods

72 patients with prostate masses who underwent CEUS and MRI examination in our hospital from October 2021 and March 2022 were enrolled in this research. The differentially diagnostic roles of CEUS, MRI, and CEUS combined MRI for prostate cancer was assessed on basis of pathological findings as the reference standard. The specificity and sensitivity of the joint application for prostate malignant tumors with various prostate-specific antigen (PSA) levels were also evaluated.

Results

The sensitivity of CEUS, MRI, and the joint application for prostate cancer were 72.1%, 74.4%, and 90.7%, respectively. Compared with single application, the sensitivity of CEUS combined with MRI was significantly higher. The specificity of MRI, CEUS, and the combination of the two for prostate cancer were 82.8%, 79.3%, and 89.7%, respectively, and the statistical differences for specificity were not found. The area under ROC curve (AUC) of CEUS combined with MRI in prostate malignant tumor diagnosis was obviously more than that of CEUS and MRI (P < 0.05). CEUS combined with MRI has relative high sensitivity in these patients with different levels of PSA.

Conclusions

Contrast-enhanced ultrasound combined with MRI can significantly improve the sensitivity and specificity of prostate cancer diagnosis so that patients can be better diagnosed in advance.

3D Harmonic and Subharmonic Imaging for Characterizing Breast Lesions: A Multi-Center Clinical Trial

OBJECTIVE

Breast cancer is the most frequent type of cancer among women. This multi-center study assessed the ability of 3D contrast-enhanced ultrasound to characterize suspicious breast lesions using clinical assessments and quantitative parameters.

METHODS

Women with suspicious breast lesions scheduled for biopsy were enrolled in this prospective, study. Following 2D grayscale ultrasound and power Doppler imaging (PDI), a contrast agent (Definity; Lantheus) was administrated. Contrast-enhanced 3D harmonic imaging (HI; transmitting/receiving at 5.0/10.0 MHz), as well as 3D subharmonic imaging (SHI; transmitting/receiving at 5.8/2.9 MHz), were performed using a modified Logiq 9 scanner (GE Healthcare). Five radiologists independently scored the imaging modes (including standard-of-care imaging) using a 7-point BIRADS scale as well as lesion vascularity and diagnostic confidence. Parametric volumes were constructed from time-intensity curves for vascular heterogeneity, perfusion, and area under the curve. Diagnostic accuracy was determined relative to pathology using receiver operating characteristic (ROC) and reverse, step-wise logistical regression analyses. The κ-statistic was calculated for inter-reader agreement.

RESULTS

Data were successfully acquired in 219 cases and biopsies indicated 164 (75%) benign and 55 (25%) malignant lesions. SHI depicted more anastomoses and vascularity than HI (P < .021), but there were no differences by pathology (P > .27). Ultrasound achieved accuracies of 82 to 85%, which was significantly better than standard-of-care imaging (72%; P < .03). SHI increased diagnostic confidence by 3 to 6% (P < .05), but inter-reader agreements were medium to low (κ < 0.52). The best regression model achieved 97% accuracy by combining clinical reads and parametric SHI.

CONCLUSIONS

Combining quantitative 3D SHI parameters and clinical assessments improves the characterization of suspicious breast lesions.

CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases

Simple Summary

The objective of the study was to assess the radiomic features obtained by computed tomography (CT) examination as prognostic biomarkers in patients with colorectal liver metastases, in order to predict histopathological outcomes following liver resection. We obtained good performance considering the single significant textural metric in the identification of the front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type, and in the detection of recurrences.

Abstract

Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study included a training set and an external validation set. The internal training set included 49 patients with a median age of 60 years and 119 liver colorectal metastases. The validation cohort consisted of 28 patients with single liver colorectal metastasis and a median age of 61 years. Radiomic features were extracted using PyRadiomics on CT portal phase. Nonparametric Kruskal–Wallis tests, intraclass correlation, receiver operating characteristic (ROC) analyses, linear regression modeling, and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The median value of intraclass correlation coefficients for the features was 0.92 (range 0.87–0.96). The best performance in discriminating expansive versus infiltrative front of tumor growth was wavelet_HHL_glcm_Imc2, with an accuracy of 79%, a sensitivity of 84%, and a specificity of 67%. The best performance in discriminating expansive versus tumor budding was wavelet_LLL_firstorder_Mean, with an accuracy of 86%, a sensitivity of 91%, and a specificity of 65%. The best performance in differentiating the mucinous type of tumor was original_firstorder_RobustMeanAbsoluteDeviation, with an accuracy of 88%, a sensitivity of 42%, and a specificity of 100%. The best performance in identifying tumor recurrence was the wavelet_HLH_glcm_Idmn, with an accuracy of 85%, a sensitivity of 81%, and a specificity of 88%. The best linear regression model was obtained with the identification of recurrence considering the linear combination of the 16 significant textural metrics (accuracy of 97%, sensitivity of 94%, and specificity of 98%). The best performance for each outcome was reached using KNN as a classifier with an accuracy greater than 86% in the training and validation sets for each classification problem; the best results were obtained with the identification of tumor front growth considering the seven significant textural features (accuracy of 97%, sensitivity of 90%, and specificity of 100%). Conclusions: This study confirmed the capacity of radiomics data to identify several prognostic features that may affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Multimodality MRI-based radiomics for aggressiveness prediction in papillary thyroid cancer

Objective

To investigate the ability of a multimodality MRI-based radiomics model in predicting the aggressiveness of papillary thyroid carcinoma (PTC).

Methods

This study included consecutive patients who underwent neck magnetic resonance (MR) scans and subsequent thyroidectomy during the study period. The pathological diagnosis of thyroidectomy specimens was the gold standard to determine the aggressiveness. Thyroid nodules were manually segmented on three modal MR images, and then radiomics features were extracted. A machine learning model was established to evaluate the prediction of PTC aggressiveness.

Results

The study cohort included 107 patients with PTC confirmed by pathology (cross-validation cohort: n = 71; test cohort: n = 36). A total of 1584 features were extracted from contrast-enhanced T1-weighted (CE-T1 WI), T2-weighted (T2 WI) and diffusion weighted (DWI) images of each patient. Sparse representation method is used for radiation feature selection and classification model establishment. The accuracy of the independent test set that using only one modality, like CE-T1WI, T2WI or DWI was not particularly satisfactory. In contrast, the result of these three modalities combined achieved 0.917.

Conclusion

Our study shows that multimodality MR image based on radiomics model can accurately distinguish aggressiveness in PTC from non-aggressiveness PTC before operation. This method may be helpful to inform the treatment strategy and prognosis of patients with aggressiveness PTC.

Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study

BACKGROUND

Ultrasound is commonly used in breast cancer screening but lacks quantification ability and diagnostic power due to its low specificity, which can lead to overdiagnosis and unnecessary biopsies. This study evaluated the diagnostic efficacy and clinical utility of adding shear-wave elastography (SWE) to the screening of the Breast Imaging Reporting and Data System (BI-RADS) category 4 breast cancer.

METHODS

A machine learning-based diagnostic model was constructed using data retrospectively collected from 3 independent cohorts with features selected using lasso regression and support vector machine-recursive feature elimination algorithms. Propensity score matching (PSM) was used to preclude confounding baseline characteristics between malignant and benign lesions. A decision curve analysis (DCA) was used to evaluate the clinical benefit of the diagnostic model in identifying high-risk tumor patients for intervention while simultaneously avoiding overtreatment of low-risk patients with integrative evaluation using a net benefit value and treatment reduction rate.

RESULTS

In our training center, a total of 122 patients were enrolled, and 577 breast tumors were collected. The comparison between malignant and benign lesions revealed significant differences in patient age, tumor size, resistance index (RI), and elasticity values. The maximum elasticity value (Emax) was identified as an independent diagnostic feature and was included in the diagnostic model. The combination of Emax with BI-RADS category 4 demonstrated a significantly better diagnostic efficacy than the BI-RADS category alone [BI-RADS+Emax: AUC =0.908, 95% confidence interval (CI): 0.842-0.974; BI-RADS: AUC =0.862, 95% CI: 0.784-0.94; P=0.024] and significantly increased the clinical benefit for patients and policy makers by effectively reducing overdiagnosis and biopsy rates. In the BI-RADS category 4A subgroup, adding Emax to breast cancer screening benefited patients and showed a greater absolute benefit than did the BI-RADS category alone when used for patients with a higher probability of cancer (>0.403), demonstrating a 50% overtreatment reduction.

CONCLUSIONS

Adding Emax to BI-RADS category 4 breast cancer screening using SWE significantly reduced overdiagnosis and biopsy rates compared with the BI-RADS category alone, especially for BI-RADS 4A patients.

Evaluation of thyroid nodules by shear wave elastography: a review of current knowledge

PURPOSE

Shear wave elastography (SWE), as a tool for diagnosing thyroid malignancy, has gathered considerable attention during the past decade. Diverging results exist regarding the diagnostic performance of thyroid SWE.

METHODS

A comprehensive literature review of thyroid SWE was conducted using the terms "Thyroid" and "shear wave elastography" in PubMed.

RESULTS

The majority of studies found SWE promising for differentiating malignant and benign thyroid nodules on a group level, whereas results are less convincing on the individual level due to huge overlap in elasticity indices. Further, there is lack of consensus on the optimum outcome reflecting nodule elasticity and the cut-off point predicting thyroid malignancy. While heterogeneity between studies hinders a clinically meaningful meta-analysis, the results are discussed in a clinical perspective with regard to applicability in clinical practice as well as methodological advantages and pitfalls of this technology.

CONCLUSION

Technological as well as biological hindrances seem to exist for SWE to be clinically reliable in assessing benign and malignant thyroid nodules. Structural heterogeneity of thyroid nodules in combination with operator-dependent factors such as pre-compression and selection of scanning plane are likely explanations for these findings. Standardization and consensus on the SWE acquisition process applied in future studies are needed for SWE to be considered a clinically reliable diagnostic tool for detection of thyroid cancer.

Contrast-Enhanced Ultrasound in the Differential Diagnosis and Risk Stratification of ACR TI-RADS Category 4 and 5 Thyroid Nodules With Non-Hypovascular

Objective

This study aims to investigate the value of contrast-enhanced ultrasound (CEUS) in the differential diagnosis and risk stratification of ACR TI-RADS category 4 and 5 thyroid nodules with non-hypovascular.

Methods

From January 2016 to December 2019 in our hospital, 217 ACR TI-RADS category 4 and 5 nodules with non-hypovascular in 210 consecutive patients were included for a derivation cohort. With surgery and/or fine-needle aspiration (FNA) as a reference, conventional ultrasound (US) features and CEUS features were analyzed. Multivariate logistic regression analysis was used to screen the independent risk factors and establish a risk predictive model. Between January 2020 and March 2021, a second cohort of 100 consecutive patients with 101 nodules were included for an external validation cohort. The model was converted into a simplified risk score and was validated in the validation cohort. The area under the receiver operating characteristic curves (AUC) were used to assess the models’ diagnostic performance.

Results

Micro-calcification, irregular margin, earlier wash-out, centripetal enhancement, and absence of ring enhancement were independent risk factors and strongly discriminated malignancy in the derivation cohort (AUC = 0.921, 95% CI 0.876–0.953) and the validation cohort (0.900, 0.824–0.951). There was no significant difference (P = 0.3282) between the conventional US and CEUS in differentiating malignant non-hypovascular thyroid nodules, but a combination of them (the predictive model) had better performance than the single method (all P <0.05), with a sensitivity of 87.0%, specificity of 86.2%, and accuracy of 86.6% in the derivation cohort. The risk score based on the independent risk factors divided non-hypovascular thyroid nodules into low-suspicious (0–3 points; malignancy risk <50%) and high-suspicious (4–7 points; malignancy risk ≥ 50%), the latter with nodule ≥10mm was recommended for FNA. The risk score showed a good ability of risk stratification in the validation cohort. Comparing ACR TI-RADS in screening suitable non-hypovascular nodules for FNA, the risk score could avoid 30.8% benign nodules for FNA.

Conclusions

CEUS is helpful in combination with conventional US in differentiating ACR TI-RADS category 4 and 5 nodules with non-hypovascular. The risk score in this study has the potential to improve the diagnosis and risk stratification of non-hypovascular thyroid nodules.

Shear wave elastography-based ultrasomics: differentiating malignant from benign focal liver lesions

PURPOSE

Ultrasomics is a radiomics technique that extracts high-throughput quantitative data from ultrasound imaging. The aim of this study was to differentiate malignant from benign focal liver lesions (FLLs) using two-dimensional shear wave elastography (2D-SWE)-based ultrasomics.

METHODS

A total of 175 FLLs in 169 patients were prospectively analyzed. The study population was divided into a training cohort (n = 122) and a validation cohort (n = 53). The maxima, minima, mean, and standard deviation of 2D-SWE measurements were expressed in kilopascals (E_max, E_min, E_mean, and E_SD). The ultrasonics technique was used to extract the features from the 2D-SWE images. Support vector machine was used to establish two prediction models: the ultrasomics score (ultrasomics features only) and the combined score (SWE measurements and ultrasomics features). The diagnostic performance of the models in differentiating FLLs was analyzed.

RESULTS

A total of 1044 features were extracted and 15 features were selected. The AUC for the combined score, ultrasomics score, E_max, E_mean, E_min and E_SD were 0.94, 0.91, 0.92, 0.89, 0.67, and 0.89, respectively. The combined score had the best diagnostic performance. The sensitivity, specificity, PPV, NPV, +LR, LR of the combined score were 92.59%, 87.50%, 94.59%, 82.50%, 7.35%, and 0.09%, respectively. The decision curve analysis results showed that when the threshold probability was > 29%, the combined score showed improved benefits for patients compared to using the ultrasomics score and 2D-SWE measurements.

CONCLUSION

The results of this study demonstrated that the combined score had good diagnostic accuracy in differentiating malignant from benign FLLs.

Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification

Objective

Shear-wave elastography (SWE) can improve the diagnostic specificity of the B-model ultrasonography (US) in breast cancer. However, whether deep learning-based radiomics signatures based on the B-mode US (B-US-RS) or SWE (SWE-RS) could further improve the diagnostic performance remains to be investigated. We aimed to develop the B-US-RS and SWE-RS and determine their performances in classifying breast masses.

Materials and Methods

This retrospective study included 291 women (mean age ± standard deviation, 40.9 ± 12.3 years) from two centers who had US-visible solid breast masses and underwent biopsy and/or surgical resection between June 2015 and July 2017. B-mode US and SWE images of the 198 masses in 198 patients (training cohort) from center 1 were segmented, respectively, to construct B-US-RS and SWE-RS using the least absolute shrinkage and selection operator regression and tested in an independent validation cohort of 65 masses in 65 patients from center 1 and in an external validation cohort of 28 masses in 28 patients from center 2. The performances of B-US-RS and SWE-RS were assessed using receiver operating characteristic (ROC) analysis and compared with that of radiologist assessment [Breast Imaging Reporting and Data System (BI-RADS)] and quantitative SWE parameters [maximum elasticity (E _max), mean elasticity (E _mean), elasticity ratio (E _ratio), and elastic modulus standard deviation (E _SD)] by using the McNemar test.

Results

The single best-performing quantitative SWE parameter, E _max, had a higher specificity than BI-RADS assessment in the training and independent validation cohorts (P < 0.001 for both). The areas under the ROC curves (AUCs) of B-US-RS and SWE-RS both were 0.99 (95% CI = 0.99-1.00) in the training cohort, 1.00 (95% CI = 1.00-1.00) in the independent validation cohort, and 1.00 (95% CI = 1.00-1.00) in the external validation cohort. The specificities of B-US-RS and SWE-RS were higher than that of E _max in the training (P < 0.001 for both) and independent validation cohorts (P = 0.02 for both).

Conclusion

The B-US-RS and SWE-RS outperformed the quantitative SWE parameters and BI-RADS assessment for classifying breast masses. The integration of the deep learning-based radiomics approach would help improve the classification ability of B-mode US and SWE for breast masses.

Characterizing Breast Lesions Using Quantitative Parametric 3D Subharmonic Imaging: A Multicenter Study

RATIONALE AND OBJECTIVES

Breast cancer is the leading type of cancer among women. Visualization and characterization of breast lesions based on vascularity kinetics was evaluated using three-dimensional (3D) contrast-enhanced ultrasound imaging in a clinical study.

MATERIALS AND METHODS

Breast lesions (n = 219) were imaged using power Doppler imaging (PDI), 3D contrast-enhanced harmonic imaging (HI), and 3D contrast-enhanced subharmonic imaging (SHI) with a modified Logiq 9 ultrasound scanner using a 4D10L transducer. Quantitative metrics of vascularity derived from 3D parametric volumes (based on contrast perfusion; PER and area under the curve; AUC) were generated by off-line processing of contrast wash-in and wash-out. Diagnostic accuracy of these quantitative vascular parameters was assessed with biopsy results as the reference standard.

RESULTS

Vascularity was observed with PDI in 93 lesions (69 benign and 24 malignant), 3D HI in 8 lesions (5 benign and 3 malignant), and 3D SHI in 83 lesions (58 benign and 25 malignant). Diagnostic accuracy for vascular heterogeneity, PER, and AUC ranged from 0.52 to 0.75, while the best logistical regression model (vascular heterogeneity ratio, central PER, and central AUC) reached 0.90.

CONCLUSION

3D SHI successfully detects contrast agent flow in breast lesions and characterization of these lesions based on quantitative measures of vascular heterogeneity and 3D parametric volumes is promising.

Quantitative shear wave elastography in primary invasive breast cancers, based on collagen-S100A4 pathology, indicates axillary lymph node metastasis

Background

The purpose of this study was to evaluate the value of quantitative shear wave elastography (SWE) in indicating the axillary lymph node metastasis (LNM) of invasive breast cancers (IBCs) and to investigate if S100A4 plays a key role in promoting metastasis and increasing stiffness in IBC.

Methods

The differences in SWE of 223 IBC patients were compared between the LNM+ and LNM- groups and the optimal cutoff values of SWE for diagnosing LNM were calculated. We searched the gene expression omnibus (GEO) to determine whether S100A4 was more highly expressed in IBCs that were LNM+ than in those that were LNM-. Sirius red and immunohistochemical staining were used to examine the collagen deposition and S100A4 expression of included tissue samples, and correlations of SWE and S100A4 expression with collagen deposition were analyzed.

Results

The optimal cutoff values for Emax (the maximum stiff value), Emean (the mean stiff value), and EmeanR (the ratio of Emean between mass and parenchyma) for diagnosing axillary LNM were 111.05 kPa, 79.80 kPa, and 6.89, respectively. GSE9893 exhibited more increased S100A4 expression in IBCs that were LNM+ than in those that were LNM-. Collagen volume fraction (CVF) and the average optical density of S100A4 (AOD_S100A4) in the LNM+ group were significantly higher than those in the LNM- group. Emax, Emean, EmeanR, and AOD_S100A4 were all positively correlated with CVF.

Conclusions

SWE in primary IBC could be useful for indicating axillary LNM. S100A4 may be a factor that regulates cancer-associated collagen deposition and metastasis; however, prospective molecular biological studies are needed.