Evaluation of Tissue Stiffness Around Lesions by Sound Touch Shear Wave Elastography in Breast Malignancy Diagnosis

Predicting hepatocellular carcinoma: A new non-invasive model based on shear wave elastography

BACKGROUND

Integrating conventional ultrasound features with 2D shear wave elastography (2D-SWE) can potentially enhance preoperative hepatocellular carcinoma (HCC) predictions.

AIM

To develop a 2D-SWE-based predictive model for preoperative identification of HCC.

METHODS

A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital. The patients were divided into the modeling group (n = 720) and the control group (n = 164). The study included conventional ultrasound, 2D-SWE, and preoperative laboratory tests. Multiple logistic regression was used to identify independent predictive factors for malignant liver lesions, which were then depicted as nomograms.

RESULTS

In the modeling group analysis, maximal elasticity (Emax) of tumors and their peripheries, platelet count, cirrhosis, and blood flow were independent risk indicators for malignancies. These factors yielded an area under the curve of 0.77 (95% confidence interval: 0.73-0.81) with 84% sensitivity and 61% specificity. The model demonstrated good calibration in both the construction and validation cohorts, as shown by the calibration graph and Hosmer-Lemeshow test (P = 0.683 and P = 0.658, respectively). Additionally, the mean elasticity (Emean) of the tumor periphery was identified as a risk factor for microvascular invasion (MVI) in malignant liver tumors (P = 0.003). Patients receiving antiviral treatment differed significantly in platelet count (P = 0.002), Emax of tumors (P = 0.033), Emean of tumors (P = 0.042), Emax at tumor periphery (P < 0.001), and Emean at tumor periphery (P = 0.003).

CONCLUSION

2D-SWE's hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions, correlating significantly with MVI and antiviral treatment efficacy.

Associated factors leading to misdiagnosis of a combined diagnostic model of different types of strain imaging and conventional ultrasound in evaluation of breast lesions: Selection strategy for using different types of strain imaging in evaluation of breast lesions

OBJECTIVE

To evaluate the effectiveness of a decision tree that integrates conventional ultrasound (CUS) with two different strain imaging (SI) techniques for diagnosing breast lesions, and to analyze the factors contributing to false negative (FN) and false positive (FP) in the decision tree's outcomes.

MATERIALS AND METHODS

Imaging and clinical data of 796 cases in the training set and 351 cases in the validation set were prospectively collected. A decision tree model that combines two types of SI and CUS was constructed, and its diagnostic performance was analyzed. Univariate analysis and multivariate analysis were applied to identify independent risk factors associated with FP and FN results of the decision tree model.

RESULTS

Size, shape, margin, vascularity, the types of internal calcifications, EI score and VTI pattern were found to be significantly independently associated with the diagnosis of benign and malignant breast lesions. Therefore, size, shape, margin, vascularity, EI score and VTI pattern were used to construct decision tree models. The Tree (EI+VTI) model had the highest AUC. Both in the training and validation groups, the AUC of Tree (EI+VTI) was significantly higher compared with that of EI, VTI, and BI-RADS (all, P < 0.05). Orientation, posterior acoustic features and the types of internal calcifications were significantly positively associated with misdiagnosis results of Tree (EI+VTI) in evaluation of breast lesions (all P < 0.05).

CONCLUSION

The diagnostic model based on a decision tree that integrates two distinct types of SI with CUS enhances the diagnostic accuracy of each method when used individually. This integration lowers the misdiagnosis rate, potentially assisting radiologists in more effective lesion assessments. When applying the decision tree model, attention should be paid to the orientation, posterior acoustic features, and the types of internal calcifications of the lesions.

Non-invasive Measurement of the Viscoelasticity of the Optic Nerve and Sclera for Assessing Papilledema: A Pilot Clinical Study

OBJECTIVE

The purpose of this study was to evaluate our novel ultrasound vibro-elastography (UVE) technique for assessing patients with papilledema by non-invasively measuring shear wave speed (SWS), elasticity and viscosity properties of the optic nerve and sclera.

METHODS

Shear wave speeds were measured at three frequencies-100, 150 and 200 Hz-on the optic nerve and sclera tissues for assessing patients with papilledema resulting from idiopathic intracranial hypertension (IIH). The method was evaluated in six papilledema patients and six controls on two separate locations for each participant (i.e., optic nerve and posterior sclera). SWSs of the optic nerve and sclera were analyzed by using a 2-D speed map technique within a circular region of interest (ROI) (i.e., the diameter of the ROI was 1.5 mm × 3.0 mm at the optic nerve and sclera, respectively). Elasticity and viscosity were then analyzed using the wave speed dispersion over the three frequencies.

RESULTS

We measured values of SWS at both locations, optic nerve and sclera, of the right eye and left eye at three different frequencies in IIH patients and controls. The SWS (mean ± standard deviation [m/s]) of the right eye was significantly higher at the sclera in IIH patients compared with controls (i.e., patients vs. controls: 5.91 ± 0.54 vs. 3.86 ± 0.56, p < 0.0001 at 100 Hz), but there was no significant difference at the optic nerve (i.e., patients vs. controls: 3.62 ± 0.39 vs. 3.36 ± 0.35, p = 0.1100 at 100Hz). We observed increased elasticity (kPa) in IIH patients, indicating there are significant differences in elasticity between patients and controls at the optic nerve and sclera (i.e., right eye [patients vs. controls]: 14.42 ± 6.59 vs. 6.5 ± 5.71, p = 0.0065 [optic nerve]; 33.04 ± 10.62 vs. 9.16 ± 7.15, p < 0.0001 [sclera]). Viscosity was also (Pa·s) higher in the sclera and optic nerve of the left eye (i.e., left eye [patient vs. control]: 8.89 ± 4.37 vs. 7.27 ± 5.01, p = 0.3790 (optic nerve); 16.05 ± 10.79 vs. 8.49 ± 6.09, p < 0.0194 [sclera]).

CONCLUSION

This research illustrates the feasibility of using our UVE system to evaluate stiffness of different tissues in the eye non-invasively. It suggests that the viscoelasticity of the posterior sclera is higher than that of the optic nerve. We found that the posterior sclera is stiffer than the optic nerve in patients with papilledema resulting from IIH, making UVE a potential non-invasive technique for assessing papilledema.

Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer

Objective

Our aim was to develop dual-modal CNN models based on combining conventional ultrasound (US) images and shear-wave elastography (SWE) of peritumoral region to improve prediction of breast cancer.

Method

We retrospectively collected US images and SWE data of 1271 ACR- BIRADS 4 breast lesions from 1116 female patients (mean age ± standard deviation, 45.40 ± 9.65 years). The lesions were divided into three subgroups based on the maximum diameter (MD): ≤15 mm; >15 mm and ≤25 mm; >25 mm. We recorded lesion stiffness (SWV1) and 5-point average stiffness of the peritumoral tissue (SWV5). The CNN models were built based on the segmentation of different widths of peritumoral tissue (0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm) and internal SWE image of the lesions. All single-parameter CNN models, dual-modal CNN models, and quantitative SWE parameters in the training cohort (971 lesions) and the validation cohort (300 lesions) were assessed by receiver operating characteristic (ROC) curve.

Results

The US + 1.0 mm SWE model achieved the highest area under the ROC curve (AUC) in the subgroup of lesions with MD ≤15 mm in both the training (0.94) and the validation cohorts (0.91). In the subgroups with MD between15 and 25 mm and above 25 mm, the US + 2.0 mm SWE model achieved the highest AUCs in both the training cohort (0.96 and 0.95, respectively) and the validation cohort (0.93 and 0.91, respectively).

Conclusion

The dual-modal CNN models based on the combination of US and peritumoral region SWE images allow accurate prediction of breast cancer.