The diagnostic performance of shear wave velocity ratio for the differential diagnosis of benign and malignant breast lesions: Compared with VTQ, and mammography

The synergized diagnostic value of VTQ with chemokine CXCL13 in lung tumors

Virtual Touch Tissue Quantification (VTQ) offers several advantages in the diagnosis of various lung diseases. Chemokine expression levels, such as CXCL13, play a vital role in the occurrence and development of tumors and aid in the diagnosis process. The purpose of this study was to evaluate the combined value of VTQ and changes in CXCL13 expression levels for the diagnosis of lung tumors. A total of 60 patients with thoracic nodules and pleural effusion were included, with 30 of them having malignant pleural effusion (based on pathology) and the remaining 30 having benign thoracic nodules and pleural effusion. The relative expression level of CXCL13 was measured in the collected pleural effusions using Enzyme-Linked Immunosorbent Assay (ELISA). The relationship between CXCL13 expression levels and various clinical features was analyzed. A Receiver Operating Characteristic (ROC) curve analysis was conducted on the VTQ results and relative expression levels of CXCL13, and the areas under the curve, critical values, sensitivity, and specificity were calculated. Multivariate analysis incorporating multiple indicators was performed to determine the accuracy of lung tumor diagnosis. The results showed that the expression levels of CXCL13 and VTQ were significantly higher in the lung cancer group compared to the control group (P < 0.05). In the Non-Small Cell Lung Cancer (NSCLC) group, CXCL13 expression levels increased with later TNM staging and poorer tumor differentiation. The expression level of CXCL13 in adenocarcinoma was higher than that in squamous cell carcinoma. The ROC curve analysis revealed that CXCL13 had an area under the curve (AUC) of 0.74 (0.61, 0.86) with an optimal cut-off value of 777.82 pg/ml for diagnosing lung tumors. The ROC curve analysis of VTQ showed an AUC of 0.67 (0.53, 0.82) with a sensitivity of 60.0% and a specificity of 83.3%, and an optimal diagnostic cut-off of 3.33 m/s. The combination of CXCL13 and VTQ for diagnosing thoracic tumors had an AUC of 0.842 (0.74, 0.94), which was significantly higher than either factor alone. The results of the study demonstrate the strong potential of combining VTQ results with chemokine CXCL13 expression levels for lung tumor diagnosis. Additionally, the findings suggest that elevated relative expression of CXCL13 in cases of malignant pleural effusion caused by non-small cell lung cancer may indicate a poor prognosis. This provides promising potential for using CXCL13 as a screening tool and prognostic indicator for patients with advanced lung cancer complicated by malignant pleural effusion.

An Optimized Radiomics Model Based on Automated Breast Volume Scan Images to Identify Breast Lesions: Comparison of Machine Learning Methods: Comparison of Machine Learning Methods

OBJECTIVES

To develop and test an optimized radiomics model based on multi-planar automated breast volume scan (ABVS) images to identify malignant and benign breast lesions.

METHODS

Patients (n = 200) with breast lesions who underwent ABVS examinations were included. For each patient, 208 radiomics features were extracted from the ABVS images, including axial plane and coronal plane. Recursive feature elimination, random forest, and chi-square test were used to select features. A support vector machine, logistic regression, and extreme gradient boosting were utilized as classifiers to differentiate malignant and benign breast lesions. The area under the curve, sensitivity, specificity, accuracy, and precision was used to evaluate the performance of the radiomics models. Generalization of the radiomics models was verified through 5-fold cross-validation.

RESULTS

For a single plane or a combination of planes, a combination of recursive feature elimination, and support vector machine yielded the best performance when identifying breast lesions. The machine learning models based on a combination of planes performed better than those based on a single plane. Regarding the axial plane and coronal plane, the machine learning model using a combination of recursive feature elimination and support vector machine yielded the optimal identification performance: average area under the curve (0.857 ± 0.058, 95% confidence interval, 0.763-0.957); the average values of sensitivity, specificity, accuracy, and precision were 87.9, 68.2, 80.7, and 82.9%, respectively.

CONCLUSIONS

The optimized radiomics model based on ABVS images can provide valuable information for identifying benign and malignant breast lesions preoperatively and guide the accurate clinical treatment. Further external validation is required.

Two-dimensional shear wave elastography with two different systems for the diagnosis of breast lesions

BACKGROUND: Two-dimensional (2D) - shear wave elastography (SWE) has made promising advances in the diagnostic of breast lesions. However, few studies have assessed whether the diagnostic effectiveness of different platforms employing 2D-SWE is equal or different. OBJECTIVE: To compare the diagnostic effectiveness of 2D-SWE techniques from two different systems in differentiating malignant breast lesions from benign ones. METHODS: A total of 84 breast lesions were retrospectively analyzed by experienced radiologists using 2D-SWE on two ultrasound systems, i.e. system-1 (LOGIQ E9 system, GE Healthcare, Wauwatosa, WI, USA), and system-2 (Aixplorer US system, SuperSonic Imagine, Aix-en-Provence, France). Qualitative and quantitative parameters including color sign, the maximum elasticity modulus values (E-max), the mean elasticity modulus values (E-mean) and standard deviation (E-sd) of elasticity modulus values in two 2D-SWE systems were analyzed. The diagnostic performance between system-1 and system-2 were evaluated in terms of the areas under the receiver operating characteristic curves (AUROCs). RESULTS: Among the 84 lesions in this study, 66 (78.6%) were benign and 18 (21.4%) were malignant. E-max in system-1 showed the best diagnostic performance with a cut-off value of 174.5 kPa with the associated sensitivity and specificity of 100.0% and 80.3% respectively. Meanwhile, E-sd in system-2 displayed the best diagnostic performance with a cut-off value of 12.7 kPa, with the associated sensitivity and specificity of 94.4% and 80.3% respectively. The diagnostic performance of the two 2D-SWE systems was not statistically different according to ROC analysis of E-max, E-mean, and E-sd. CONCLUSION: For identifying breast lesions, system-1 and system-2 appear to be similar in diagnostic performance. However, different cut-off values for different parameters might be selected to obtain the best diagnostic performance for the two 2D-SWE systems.

Shear-Wave-Elastography in Neurofibromatosis Type I

Ultrasound shear wave elastography (SWE) is an increasingly used imaging modality that expands clinical ultrasound by measuring the elasticity of various tissues, such as the altered elasticity of tumors. Peripheral nerve tumors are rare, have been well-characterized by B-mode-ultrasound, but have not yet been investigated with SWE. Given the lack of studies, a first step would be to investigate homogeneous peripheral nerve tumors (PNTs), histologically neurofibromas or schwannomas, which can occur in multiple in neurofibromatosis type 1 and 2 (NF1 and 2), respectively. Hence, we measured shear wave velocity (SWV) in 30 PNTs of 11 patients with NF1 within the median nerve. The SWV in PNTs ranged between 2.8 ± 0.8 m/s and correlated with their width and approximate volume but not with their length or height. Furthermore, we determined the extent to which PNTs alter the SWV of the median nerve for three positions of the wrist joint: neutral (zero-degree), individual maximal flexion and maximal extension. Here, SWV was decreased in NF1 patients compared to age- and sex-matched controls (p = 0.029) during maximal wrist extension. We speculate that the presence of PNTs may have a biomechanical impact on peripheral nerves which has not been demonstrated yet.

Comparison of skin sensitivity following breast reconstruction with three different techniques: Autologous fat grafting, DIEP flap and expander/implant1

BACKGROUND

Autologous fat grafting (AFG) has been established over the past two decades as an additive technique during and after breast reconstruction. Complete reconstruction of the breast mound with AFG alone represents an exceptional technique that has been published mostly in case reports or in studies with limited cases.The purpose of this study is to investigate the influence of three different techniques for breast reconstruction on the recovery of skin sensitivity at the reconstructed breast.

METHODS

The study included 30 patients after mastectomy following breast cancer. Three groups were examined: A) breast reconstruction by autologous fat grafting (AFG), B) breast reconstruction by deep inferior epigastric artery perforator flap (DIEP) and C) breast reconstruction by expander/implant (TE).Biometric data were compared; sensitivity tests were performed using Semmes-Weinstein monofilaments.The non-operated, healthy contralateral breasts of the patients were used as a reference.

RESULTS

While the traditional reconstruction techniques by microsurgical anastomosed perforator flap or expander/implant showed a strongly decreased or completely missing sensitivity of the skin, the tests after reconstruction by AFG represented high values of sensory recovery, which came close to the reference group of non-operated breasts.

CONCLUSION

To our knowledge, this is the first study to compare skin sensitivity after AFG-based reconstruction to established techniques for breast reconstruction. We could demonstrate in a limited group of patients, that breast reconstruction by autologous fat grafting can achieve higher values of skin sensitivity compared to traditional techniques.