Superb Microvascular Imaging Technology Can Improve the Diagnostic Efficiency of the BI-RADS System

Comparative Evaluation of Superb Microvascular Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Differentiating Benign and Malignant Breast Masses

OBJECTIVES

Our study aims to compare the diagnostic performance of superb microvascular imaging (SMI) and dynamic contrast-enhanced magnetic resonance imaging (MRI) in differentiating benign from malignant breast masses, using histopathological findings as the reference standard.

METHODS

This prospective study was conducted from April 2022 to March 2024. A total of 112 breast lesions from 110 patients were evaluated using gray-scale ultrasonography, SMI, and dynamic contrast-enhanced MRI. The vascular index (VI) obtained during SMI examination and kinetic curve patterns from MRI were analyzed.

RESULTS

Histopathological analysis revealed 62 benign and 50 malignant lesions. The VI showed a statistically significant difference between benign and malignant lesions, with a mean VI of 5.12 ± 4.66 in benign masses and 10.13 ± 5.48 in malignant masses (P < .001). The ROC analysis demonstrated an AUC of 0.79 for SMI with a VI cut-off value of 4.15, yielding a sensitivity of 92%, specificity of 60%, and accuracy of 74%. A statistically significant correlation was found between VI values and MRI contrast enhancement kinetic curve types (P < .05). MRI demonstrated superior diagnostic performance, with an AUC of 0.89 and sensitivity, specificity, and accuracy of 98, 80.65, and 88.39%, respectively.

CONCLUSIONS

SMI, when used in conjunction with conventional ultrasonography and MRI, provides significant diagnostic value in differentiating benign from malignant breast masses. The study supports the potential integration of SMI into routine breast cancer diagnostic workflows, particularly in settings where MRI is less accessible.

Development and validation of a screening model for benign and malignant breast masses based on S-Detect and microvascular flow imaging

Background

Imaging examination of a breast mass is essential for improving breast cancer detection. Previous screening models of benign and malignant breast masses demonstrated a high level of subjectivity due to the inability to conduct quantitative evaluations. Thus, this study aimed to construct an objective, convenient, and effective nomogram incorporating S-Detect and microvascular flow imaging (MVFI) to predict breast cancer risk.

Methods

Female patients with breast masses detected by conventional ultrasound examinations at the Second Affiliated Hospital of Anhui Medical University between January 2021 and October 2024 were retrospectively analyzed. All patients underwent preoperative assessments with both S-Detect and MVFI. The pathological results served as the gold standard for diagnosis. After screening, a total of 724 breast masses from 712 patients were randomized into the training (506 masses) and validation (218 masses) groups. Univariate analysis assessed patient age, as well as the location, size, vascular index (VI), and S-Detect-based diagnosis of the masses. Risk factors for predicting breast cancer were screened using multivariate analysis. A nomogram prediction model was then constructed. Diagnostic performance, clinical utilization value, and calibration were determined using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve, respectively. Nomogram risk was calculated for each breast mass for risk stratification.

Results

The training group included 208 benign and 298 malignant masses, while the validation group comprised 85 benign and 133 malignant masses. Multivariate analysis demonstrated that mass size [odds ratio (OR) =1.08; P<0.001], age (OR =1.09; P<0.001), VI (OR =1.07; P<0.001), and S-Detect-based diagnosis (OR =28.37; P<0.001) were risk factors for predicting breast cancer. The area under the curve (AUC) for the nomogram model was significantly greater than that for S-Detect in both the training (0.93 vs. 0.82, P<0.001) and validation (0.91 vs. 0.82, P<0.001) groups. The diagnostic sensitivity and specificity of the nomogram were 93.3% and 79.8% in the training group, and 98.5% and 72.9% in the validation group, respectively. The optimal cut-off value for nomogram risk differentiation between the high-risk and low-risk sets was 0.495, with a significantly higher proportion of malignant breast masses in the high-risk set compared to that in the low-risk set (P<0.001).

Conclusions

This novel nomogram model based on quantitative and objective ultrasound and clinical features can quantify the malignancy risk of breast masses, identify high-risk individuals, and provide a reference for further examinations.

Non-contrast ultrasound assessment of blood flow in clinical practice

Since the first clinical use of ultrasound in the 1940s, significant advancements have been made in its applications. Color Doppler imaging and power Doppler imaging are considered the first and second generations of flow ultrasound assessment tools, respectively. Subsequently, the introduction of contrastenhanced ultrasound has significantly improved the assessment of arterial and venous vascular patterns in lesions and vessels. 'Blood flow brightness-mode imaging' or 'B-flow', a non-Doppler ultrasound flow assessment mode introduced more recently, provides even more information for ultrasound users in flow assessment. Microvascular imaging, introduced about a decade ago, is the third generation of Doppler non-contrast ultrasound flow modes, and is growing in popularity. Using a special wall filter, microvascular imaging overcomes the limitations of color Doppler imaging and power Doppler imaging in the detection of slow flowing signals. Advanced dynamic flow is a third-generation non-contrast Doppler flow technology that has so far gained popularity in obstetric ultrasound, commonly used to evaluate fetal umbilical vessels and heart chambers. This review article presents some recent updates on the various non-contrast ultrasound flow modalities available in clinical practice. It focuses on the design principles of individual flow modalities, discussing their strengths, limitations, and clinical applications, along with a review of the relevant literature.

Novel Ultrasonographic Evaluation of Microvascular Blood Flow for Non-Operative Management of Uncomplicated Acute Appendicitis in Children: A Prospective Clinical Study

OBJECTIVES

To determine whether superb microvascular imaging (SMI) provides a more precise delineation between reversible and irreversible stages of uncomplicated acute appendicitis managed non-operatively.

METHODS

This prospective clinical study examined pediatric patients with acute appendicitis initially treated non-operatively and evaluated using power Doppler (PD) and SMI. We determined case severity, monitor appendiceal blood flow (BF), and appendicitis reversibility. Complicated cases were excluded. Severity was classified using B-mode as well as PD, or SMI: Grade I, smooth wall/normal BF; Grade IIa, irregular wall/increased BF; Grade IIb, irregular wall/decreased BF; and Grade III, absence of wall/loss of BF.

RESULTS

This study examined a total of 100 patients with acute appendicitis, after excluding 29 patients. All 10 patients with normal BF on PD (Grade I) showed similar BF on SMI (Grade I). Among 29 patients with increased BF on PD (Grade IIa), corresponding increased BF was noted on SMI (Grade IIa), and all these patients showed full recovery. Of the 55 patients showing decreased BF on PD (Grade IIb), 52 showed increased BF on SMI (Grade IIa). The remaining three patients, identified with an impacted appendicolith, showed decreased BF on SMI (Grade IIb) and experienced treatment failure, subsequently developing abscesses. In all six patients with undetectable BF on PD (Grade III), SMI similarly could not detect appendiceal BF (Grade III), and non-operative management failed for these patients.

CONCLUSIONS

SMI offers an objective and effective means of delineating the threshold between reversible and irreversible stages in uncomplicated acute appendicitis following non-operative management.

Value of Contrast-Enhanced Microflow Imaging in Diagnosis of Breast Masses in Comparison with Contrast-Enhanced Ultrasound

RATIONALE AND OBJECTIVES

To investigate the value of contrast-enhanced microflow imaging (CEUS-MFI) in distinguishing benign and malignant breast masses.

METHODS

A total of 116 breast masses classified as Breast Imaging Reporting and Data System (BI-RADS) category 3-5 by ultrasound (US) were included. Both contrast-enhanced ultrasound (CEUS) and CEUS-MFI were performed before excision or biopsy, with features and diagnostic efficiency analyzed. The US and CEUS BI-RADS 4A masses were also re-assessed by CEUS-MFI.

RESULTS

The features of CEUS-MFI including both interior and peripheral enlarged, twisted vessels (both P < 0.05), penetrating vessels (P = 0.007), and radial/spiculated vessels (P < 0.001) were more frequently detected in malignant masses, while peripheral annular vessels were mostly observed in benign masses (P < 0.001). Interestingly, a significant difference in the orientation of penetrating vessels between benign and malignant masses was found (P < 0.001), with parallel orientation mostly displayed in benign masses, while vertical or multiple-direction orientation mostly displayed in malignant masses. The microvascular architecture of breast masses was categorized into five patterns: avascular, line-like, tree-like, root hair-like, and crab claw-like pattern. Benign masses mainly displayed tree-like pattern (77.1% vs 10.9%, P < 0.05); malignant masses mainly displayed root hair-like (34.8% vs 5.7%, P < 0.05) and crab claw-like patterns (50.0% vs 1.4%, P < 0.05). The diagnostic efficiency of CEUS-MFI was higher relative to CEUS and US. In addition, CEUS-MFI decreased the biopsy rates of US and CEUS BI-RADS 4A masses without missing malignancies.

CONCLUSION

CEUS-MFI could be a valuable and promising technique in diagnosis of breast masses, and could provide more diagnostic information for radiologists.

Micro-ultrasound based characterization of cerebrovasculature following focal ischemic stroke and upon short-term rehabilitation

Notwithstanding recanalization treatments in the acute stage of stroke, many survivors suffer long-term impairments. Physical rehabilitation is the only widely available strategy for chronic-stage recovery, but its optimization is hindered by limited understanding of its effects on brain structure and function. Using micro-ultrasound, behavioral testing, and electrophysiology, we investigated the impact of skilled reaching rehabilitation on cerebral hemodynamics, motor function, and neuronal activity in a rat model of focal ischemic stroke. A 50 MHz micro-ultrasound transducer and intracortical electrophysiology were utilized to characterize neurovascular changes three weeks following focal ischemia elicited by endothelin-1 injection into the sensorimotor cortex. Sprague-Dawley rats were rehabilitated through tray reaching, and their fine skilled reaching was assessed via the Montoya staircase. Focal ischemia led to a sustained deficit in forelimb reaching; and increased tortuosity of the penetrating vessels in the perilesional cortex; with no lateralization of spontaneous neuronal activity. Rehabilitation improved skilled reaching; decreased cortical vascularity; was associated with elevated peri- vs. contralesional hypercapnia-induced flow homogenization and increased perilesional spontaneous cortical neuronal activity. Our study demonstrated neurovascular plasticity accompanying rehabilitation-elicited functional recovery in the subacute stage following stroke, and multiple micro-ultrasound-based markers of cerebrovascular structure and function modified in recovery from ischemia and upon rehabilitation.

Prediction of complete response after neoadjuvant chemotherapy for invasive breast cancers: The utility of shear wave elastography and superb microvascular imaging in pretreatment breast ultrasound

PURPOSE

To investigate whether multiparametric parameters of pretreatment breast ultrasound (US) and clinicopathologic factors are associated with pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer.

METHODS

Between November 2018 and September 2022, 88 patients who underwent NAC and subsequent surgery were included in this study (median age, 55 years; interquartile range [IQR], 45, 59.3). Multiparametric breast US including grayscale, shear wave elastography (SWE) and superb microvascular imaging (SMI) of pathologically proven invasive breast cancers were retrospectively reviewed. Clinicopathological and multiparametric parameters of breast US, including size, SWEmax, SWEratio and vascular index on SMI (SMIVI) were compared between the groups. Univariate and multivariate logistic regression analyses were performed to determine factors predicting pCR after NAC. AUROC curve analysis was performed to determine the predictors' optimal cut-off values and diagnostic performance.

RESULTS

The pCR group (n = 24) showed a significantly smaller tumor size, lower SWEmax, higher Ki-67 index, higher hormone receptor negativity and negative axillary lymph node metastasis compared to the non-pCR group (n = 64). Multivariate regression analysis showed that SWEmax (adjusted odds ratio[aOR] = 0.956, 95 % confidence interval [CI] = 0.919-0.994, P = 0.025) and Ki-67 index (aOR = 1.083, 95 % CI = 1.012-1.159, P = 0.021) were independently associated with pathologically complete response. The optimal cut-off values for predicting pCR were 27.5 % for Ki-67 with an AUC of 0.743 and 134.8 kPa for SWEmax with an AUC of 0.779. A combination model including clinical factors and SWEmax showed the best diagnostic performance with an AUC of 0.876.

CONCLUSION

A higher Ki-67 index and lower SWEmax measured on pretreatment breast US were independently associated with pCR in invasive breast cancer after NAC.

An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images

An automated computer-aided approach might aid radiologists in diagnosing breast cancer at a primary stage. This study proposes a novel decision support system to classify breast tumors into benign and malignant based on clinically important features, using ultrasound images. Nine handcrafted features, which align with the clinical markers used by radiologists, are extracted from the region of interest (ROI) of ultrasound images. To validate that these elected clinical markers have a significant impact on predicting the benign and malignant classes, ten machine learning (ML) models are experimented with resulting in test accuracies in the range of 96 to 99%. In addition, four feature selection techniques are explored where two features are eliminated according to the feature ranking score of each feature selection method. The Random Forest classifier is trained with the resultant four feature sets. Results indicate that even when eliminating only two features, the performance of the model is reduced for each feature selection technique. These experiments validate the efficiency and effectiveness of the clinically important features. To develop the decision support system, a probability density function (PDF) graph is generated for each feature in order to find a threshold range to distinguish benign and malignant tumors. Based on the threshold range of particular features, a decision support system is developed in such a way that if at least eight out of nine features are within the threshold range, the image will be denoted as true predicted. With this algorithm, a test accuracy of 99.38% and an F1 Score of 99.05% is achieved, which means that our decision support system outperforms all the previously trained ML models. Moreover, after calculating individual class-based test accuracies, for the benign class, a test accuracy of 99.31% has been attained where only three benign instances are misclassified out of 437 instances, and for the malignant class, a test accuracy of 99.52% has been attained where only one malignant instance is misclassified out of 210 instances. This system is robust, time-effective, and reliable as the radiologists' criteria are followed and may aid specialists in making a diagnosis.

Breast Imaging Reporting and Data System evaluation of breast lesions improved with virtual touch tissue imaging average grayscale values

BACKGROUND

Early breast cancer diagnosis is of great clinical importance for selecting treatment options, improving prognosis, and enhancing the quality of patients' survival.

OBJECTIVE

We investigated the value of virtual touch tissue imaging average grayscale values (VAGV) helper Breast Imaging Reporting and Data System (BI-RADS) in diagnosing breast malignancy.

METHODS

We retrospectively analyzed 141 breast tumors in 134 patients. All breast lesions were diagnosed pathologically by biopsy or surgical excision. All patients first underwent conventional ultrasound (US) followed by virtual touch tissue imaging (VTI). The measurement of the VAGV of the lesion was performed by Image J software. BI-RADS classification was performed for each lesion according to the US. We performed a two-by-two comparison of the diagnostic values of VAGV, BI-RADS, and BI-RADS+VAGV.

RESULTS

VAGV was lower in malignant tumors than in benign ones (35.82 ± 13.39 versus 73.58 ± 42.69, P< 0.001). The area under the receiver operating characteristic curve (AUC) value, sensitivity, and specificity of VAGV was 0.834, 84.09%, and 69.07%, respectively. Among BI-RADS, VAGV, and BI-RADS+VAGV, BI-RADS+VAGV had the highest AUC (0.926 versus 0.882, P= 0.0066; 0.926 versus 0.834, P= 0.0012). There was perfect agreement between the two radiologists using VAGV (ICC= 0.9796) and substantial agreement using BI-RADS (Kappa= 0.725).

CONCLUSION

Our study shows that VAGV can accurately diagnose breast cancer. VAGV effectively improves the diagnostic performance of BI-RADS.

Value of micro-flow imaging and high-definition micro-flow imaging in differentiating malignant and benign breast lesions

AIM

To evaluate the value of non-contrast micro-flow imaging (MFI) and high-definition micro-flow imaging (HD-MFI) in differentiating malignant and benign breast lesions.

MATERIALS AND METHODS

One hundred and thirty-three patients with 138 breast lesions (80 benign and 58 malignant lesions) were examined using colour Doppler flow imaging (CDFI), MFI, and HD-MFI before biopsy, with blood flow signals graded into four types (grade 0, 1, 2, and 3) and penetrating vessels evaluated. The micro-vascular patterns of MFI and HD-MFI were evaluated and classified into five patterns: avascular, line-like, tree-like, root hair-like, and crab claw-like pattern. The diagnostic efficiency of micro-vascular patterns was analysed. Moreover, ultrasound Breast Imaging Reporting and Data System (BI-RADS) 4A lesions were also re-assessed according to the micro-vascular patterns of MFI or HD-MFI.

RESULTS

The capability of detecting blood flow and penetrating vessels from high to low was HD-MFI, MFI, and CDFI, respectively (p<0.05). Rich blood flow signals, penetrating vessels, and root hair-like or crab claw-like pattern were more likely in malignant breast lesions, while few blood flow signals, tree-like pattern were mostly in benign lesions (p<0.05). The diagnostic efficiency of HD-MFI and MFI were higher than CDFI (p>0.05). MFI could reduce unnecessary biopsy of 52 US BI-RADS 4A lesions but with two malignancies missed, while 56 ultrasound BI-RADS 4A lesions could be downgraded by HD-MFI with none malignancies missed.

CONCLUSIONS

MFI and HD-MFI can detect more blood flow in breast lesions than CDFI, and could help distinguish benign and malignant breast lesions. HD-MFI could reduce the unnecessary biopsy of US BI-RADS 4A lesions without missed malignancy.

The potential role of combined shear wave elastography and superb microvascular imaging for early prediction the pathological response to neoadjuvant chemotherapy in breast cancer

Objectives

The potential role of shear wave elastography (SWE) and superb microvascular imaging (SMI) for early assessment of treatment response to neoadjuvant chemotherapy (NAC) in breast cancer remains unexplored. This study aimed to identify potential factors associated with the pathological response to NAC using these advanced ultrasound techniques.

Methods

Between August 2021 and October 2022, 68 patients with breast cancer undergoing NAC were recruited. Patients underwent conventional ultrasonography, SMI, and SWE examinations at baseline and post-2nd cycle of NAC. Maximum tumor diameter (Dmax), maximum elastic value (Emax), peak systolic velocity (PSV), and resistance index (RI) at baseline and the rate of change of these parameters post-2nd cycle were recorded. After chemotherapy, all patients underwent surgery. Using the Miller-Payne's grade, patients were categorized into response (grades 3, 4, or 5) and non-response (grades 1 or 2) group. Parameters were compared using t-tests at baseline and post-2nd cycle. Binary logistic regression analysis was used to identify variables and their odds ratios (ORs) related to responses and a prediction model was established. ROC curves were drawn to analyze the efficacy of each parameter and their combined model for early NAC response prediction.

Results

Among the 68 patients, 15(22.06%) were categorized into the non-response group, whereas 53(77.94%) were categorized into the response group. At baseline, no significant differences were observed between the two groups (p>0.05). Post-2nd cycle of NAC, rates of change of Emax, PSV and RI (ΔEmax, ΔPSV and ΔRI) were higher in responders than non-responders (p<0.05). Binary logistic regression analysis revealed that ΔEmax (OR 0.797 95% CI, 0.683-0.929), ΔPSV (OR 0.926, 95%CI, 0.860-0.998), and ΔRI (OR 0.841, 95%CI, 0.736-0.960) were independently associated with the pathological response of breast cancer after NAC. The combined prediction model exhibited higher accuracy in the early evaluation of the response to NAC (AUC 0.945, 95%CI, 0.873-1.000).

Conclusion

SWE and SMI techniques enable early identification of tumor characteristics associated with the pathological response to NAC and may be potentially indicative of an effective response. These factors may eventually be used for the early assessment of NAC treatment for clinical management.

[Methodology and clinical use of superb microvascular imaging in assessing micro-circulation changes of fingertips in systemic sclerosis]

OBJECTIVE

To explore the feasibility of superb microvascular imaging (SMI) in evaluating microcirculation damage of the finger of systemic sclerosis (SSc), and determining the optimal scanning method by assessing the effect of scanning position (finger pulp or nail bed), plane (transverse or sagittal) and Doppler gain on the results.

METHODS

In the study, 32 SSc patients and 32 non-SSc volunteers admitted to Peking University Third Hospital from February to October 2022 were included. The SMI image under different gain set (40 dB or 35 dB) of the third fingertip (sagittal scans or transverse scan of nail bed or pulp) of both hands were collected while vascular index (VI) was measured.

RESULTS

Non-SSc volunteer presented abundant SMI signal distributed in the third fingertip. Arteriole of nail bed was observed on the dorsal side of the distal phalanx under SMI and gave off multiple vertical branches towards the nail. The arteriole of finger pulp ran parallel to the skin and gave off vertical branches towards the skin distributing subcutaneously as a network. In SSc group, the SMI signal in nail bed and finger pulp was reduced. The arteriole of nail bed and finger pulp was discontinuous and presented as sporadic dots and short rod-like color signal under SMI. The vascular index of the SSc patients was significantly lower than that of the non-SSc controls (P < 0.001). Among different positions and sections, the area under the receiver operating characteristic curve (AUC) of the sagittal plane of nail bed was the highest. Under low gain, the AUC of sagittal plane of nail bed was 0.871, the cut-off value was 5.4%, the sensitivity was 90.6%, and the specificity was 74.2%. Under high gain, the AUC was 0.893, the cut-off value was 14.0%, the sensitivity was 75.0%, and the specificity was 93.6%. Multivariate analysis showed that there was statistical significance on the diagnostic impact of the sagittal plane of nail bed (P < 0.005 for high gain condition; P < 0.05 for low gain condition).

CONCLUSION

SMI can be used to evaluate the abnormal changes of vascular in patients with SSc. Using the sagittal scan of nail bed with high gain can evaluate the vascular loss of the fingertip in SSc patient accurately and specifically.

Superb Microvascular Imaging for the Differentiation of Benign and Malignant Breast Lesions: A System Review and Meta-Analysis

OBJECTIVE

To evaluate the diagnostic performance of SMI in the diagnosis of benign and malignant breast lesions.

METHODS

A systematic search of PubMed, EMBASE, Cochrane, OVID, SCI, and SCOPUS was performed to find relevant studies which applied SMI to differentiate benign and malignant breast lesions. All the studies were published before October 10, 2022. Only studies published in English were collected. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. Summary receiver operating characteristic (SROC) modeling was also performed to the diagnostic performance of SMI in the diagnosis of benign and malignant breast lesions. Subgroup analyses and meta-regression were performed to find out the heterogeneity.

RESULTS

Twenty studies which include a total of 2873 lesions (1748 benign and 1125 malignant) in 2740 patients were evaluated in this meta-analysis. The summary sensitivity and specificity were 0.82 (95% confidence interval [CI]: 0.76-0.86), 0.70 (95% CI: 0.64-0.76) for SMI vascular degree, 0.77 (95% CI: 0.67-0.84), 0.79 (95% CI: 0.75-0.83) for SMI vascular distribution, 0.78 (95% CI: 0.70-0.84), 0.75 (95% CI: 0.69-0.80) for SMI vascular morphology, 0.81 (95% CI: 0.72-0.87), 0.80 (95% CI: 0.75-0.85) SMI penetration vessel. For SMI overall vascular features, the summary sensitivity and summary specificity were 0.74 (95% CI: 0.61-0.84) and 0.80 (95% CI: 0.76-0.84). The result of subgroup analysis and meta-analysis showed malignant rate and country might be the cause of heterogeneity of diagnostic accuracy of vascular grade and morphology.

CONCLUSION

SMI vascular features have high sensitivity and specificity in the differentiation of benign and malignant lesions. Future international multicenter studies in various regions with large sample size are required to confirm these findings.

Intraoperative shear-wave elastography and superb microvascular imaging contribute to the glioma grading

BACKGROUND

To explore the diagnostic value and feasibility of shear wave elastography and superb microvascular imaging in the grading diagnosis of glioma intraoperatively.

MATERIALS AND METHODS

Forty-nine patients with glioma were included in this study. B-mode ultrasonography, Young's modulus in shear-wave elastography (SWE) and vascular architecture in superb microvascular imaging(SMI) of tumor tissue and peritumoral tissue were analyzed. Receiver operating characteristic(ROC) curve analysis was used to evaluate the diagnostic effect of SWE. Logistic regression model was used to calculate the prediction probability of HGG diagnosis.

RESULTS

Compared with LGG, HGG was often characterized by peritumoral edema in B mode (P < 0.05). There was a significant difference in Young's modulus between HGG and LGG; the diagnostic threshold of HGG and LGG was 13.05 kPa, the sensitivity was 78.3%, and the specificity was 76.9%. The vascular architectures of the tumor tissue and peritumoral tissues of HGG and LGG were significantly different (P < 0.05). The vascular architectures of peritumoral tissue in HGG often characterized by distorted blood flow signals surrounding the tumor (14/26,53.8%); in the tumor tissue, HGG often presents as dilated and bent vessels(19/26,73.1%). The elasticity value of SWE and the tumor vascular architectures of SMI were correlated with the diagnosis of HGG.

CONCLUSION

Intraoperative ultrasound (ioUS), especially SWE, and SMI are beneficial for the differentiation of HGG and LGG and may help optimize clinical surgical procedures.

A Novel Nomogram Based on Imaging Biomarkers of Shear Wave Elastography, Angio Planewave Ultrasensitive Imaging, and Conventional Ultrasound for Preoperative Prediction of Malignancy in Patients with Breast Lesions

Several studies have demonstrated the difficulties in distinguishing malignant lesions of the breast from benign lesions owing to overlapping morphological features on ultrasound. Consequently, we aimed to develop a nomogram based on shear wave elastography (SWE), Angio Planewave Ultrasensitive imaging (Angio PLUS (AP)), and conventional ultrasound imaging biomarkers to predict malignancy in patients with breast lesions. This prospective study included 117 female patients with suspicious lesions of the breast. Features of lesions were extracted from SWE, AP, and conventional ultrasound images. The least absolute shrinkage and selection operator (Lasso) algorithms were used to select breast cancer-related imaging biomarkers, and a nomogram was developed based on six of the 16 imaging biomarkers. This model exhibited good discrimination (area under the receiver operating characteristic curve (AUC): 0.969; 95% confidence interval (CI): 0.928, 0.989) between malignant and benign breast lesions. Moreover, the nomogram also showed demonstrated good calibration and clinical usefulness. In conclusion, our nomogram can be a potentially useful tool for individually-tailored diagnosis of breast tumors in clinical practice.

Diagnostic performance of the thyroid imaging reporting and data system improved by color-coded acoustic radiation force pulse imaging

OBJECTIVE

To explore the value of color-coded virtual touch tissue imaging (CCV) using acoustic radiation force pulse technology (ARFI) in diagnosing malignant thyroid nodules.

METHODS

Images including 189 thyroid nodules were collected as training samples and a binary logistic regression analysis was used to calculate regression coefficients for Thyroid Imaging Reporting and Data System (TI-RADS) and CCV. An integrated prediction model (TI-RADS+CCV) was then developed based on the regression coefficients. Another testing dataset involving 40 thyroid nodules was used to validate and compare the diagnostic performance of TI-RADS, CCV, and the integrated predictive models using the receiver operating characteristic (ROC) curves.

RESULTS

Both TI-RADS and CCV are independent predictors. The diagnostic performance advantage of CCV is insignificant compared to TI-RADS (P = 0.61). However, the diagnostic performance of the integrated prediction model is significantly higher than that of TI-RADS or CCV (all P < 0.05). Applying to the validation image dateset, the integrated predictive model yields an area under the curve (AUC) of 0.880.

CONCLUSIONS

Developing a new predictive model that integrates the regression coefficients calculated from TI-RADS and CCV enables to achieve the superior performance of thyroid nodule diagnosis to that of using TI-RADS or CCV alone.

Microvascular assessment of fascio-cutaneous flaps by ultrasound: A large animal study

Objectives: Blood perfusion quality of a flap is the main prognostic factor for success. Microvascular evaluation remains mostly inaccessible. We aimed to evaluate the microflow imaging mode, MV-Flow, in assessing flap microvascularization in a pig model of the fascio-cutaneous flap. Methods: On five pigs, bilateral saphenous fascio-cutaneous flaps were procured on the superficial femoral vessels. A conventional ultrasound evaluation in pulsed Doppler and color Doppler was conducted on the ten flaps allowing for the calculation of the saphenous artery flow rate. The MV-Flow mode was then applied: for qualitative analysis, with identification of saphenous artery collaterals; then quantitative, with repeated measurements of the Vascularity Index (VI), percentage of pixels where flow is detected relative to the total ultrasound view area. The measurements were then repeated after increasing arterial flow by clamping the distal femoral artery. Results: The MV-Flow mode allowed a better follow-up of the saphenous artery's collaterals and detected microflows not seen with the color Doppler. The VI was correlated to the saphenous artery flow rate (Spearman rho of 0.64; p = 0.002) and allowed to monitor the flap perfusion variations. Conclusion: Ultrasound imaging of microvascularization by MV-Flow mode and its quantification by VI provides valuable information in evaluating the microvascularization of flaps.

Diagnostic Value of Superb Microvascular Imaging in Differentiating Benign and Malignant Breast Tumors: A Systematic Review and Meta-Analysis

PURPOSE

We performed a systematic review and meta-analysis of studies that investigated the diagnostic performance of Superb Microvascular Imaging (SMI) in differentiating between benign and malignant breast tumors.

METHODS

Studies published between January 2010 and March 2022 were retrieved by online literature search conducted in PubMed, Embase, Cochrane Library, Web of Science, China Biology Medicine Disc, China National Knowledge Infrastructure, Wanfang, and Vip databases. Pooled sensitivity, specificity, and diagnostic odd ratios were calculated using Stata software 15.0. Heterogeneity among the included studies was assessed using I2 statistic and Q test. Meta-regression and subgroup analyses were conducted to investigate potential sources of heterogeneity. Influence analysis was conducted to determine the robustness of the pooled conclusions. Deeks' funnel plot asymmetry test was performed to assess publication bias. A summary receiver operating characteristic curve (SROC) was constructed.

RESULTS

Twenty-three studies involving 2749 breast lesions were included in our meta-analysis. The pooled sensitivity and specificity were 0.80 (95% confidence interval [CI], 0.77-0.84, inconsistency index [I2] = 28.32%) and 0.84 (95% CI, 0.79-0.88, I2 = 89.36%), respectively. The pooled diagnostic odds ratio was 19.95 (95% CI, 14.84-26.82). The area under the SROC (AUC) was 0.85 (95% CI, 0.81-0.87).

CONCLUSION

SMI has a relatively high sensitivity, specificity, and accuracy for differentiating between benign and malignant breast lesions. It represents a promising supplementary technique for the diagnosis of breast neoplasms.