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Objectives: To establish superb microvascular imaging (SMI) based thyroid imaging reporting and data system (SMI TI-RADS) for risk stratification of malignancy in thyroid nodules. Methods: In total, 471 patients, comprising 643 thyroid nodules, who received conventional ultrasound (US), SMI, and a final diagnosis were extensively analyzed. A qualitative assessment of US features of the nodules was performed followed by univariable and multivariable logistic regression analyses, leading to the construction of the SMI TI-RADS, which was further verified using internal and external validation cohorts. Results: Among the stand-alone US, predictive factors were the shape and margins of the nodules, echogenicity and echogenic foci, vascularity, extrathyroidal extension, ring-SMI patterns, penetrating vascularity, flow-signal enlarged, and vascularity area ratio. SMI TI-RADS depicted an enhanced area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI: 0.92, 0.96; p < 0.001 relative to other stratification systems), a 79% biopsy yield of malignancy (BYM, 189/240 nodules), and a 21% unnecessary biopsy rate (UBR, 51/240 nodules). In the verification cohorts, we demonstrated AUCs, malignancy biopsy yields, and unnecessary biopsy rates of 0.88 (95% CI: 0.83, 0.94), 79% (59/75 nodules), and 21% (16/75 nodules) for the internal cohort, respectively, and 0.91 (95% CI: 0.85, 0.96), 72% (31/43 nodules), and 28% (12/43 nodules) for the external cohort, respectively. Conclusion: SMI TI-RADS was found to be superior in diagnostic sensitivity, specificity, and efficiency than existing TI-RADSs, showing better stratification of the malignancy risk, and thus decreasing the rate of unnecessary needle biopsy. Critical relevance statement: To develop an imaging and data system based on conventional US and SMI features for stratifying the malignancy risk in thyroid nodules. Key Points: SMI features could improve thyroid nodule risk stratification. SMI TI-RADS showed superior diagnostic efficiency and accuracy for biopsy guidance. SMI TI-RADS can provide better guidance for clinical diagnosis and treatment of thyroid nodules. [ABSTRACT FROM AUTHOR]

To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p -value < 0.05) using the Gradient Boosting classifier. Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization. [ABSTRACT FROM AUTHOR]

PURPOSE: To compare the application value of the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) and the American Thyroid Association (ATA) guidelines in the risk stratification of thyroid isthmic nodules. METHODS: A total of 315 patients with thyroid isthmic nodules (315 nodules) confirmed by surgical pathology or fine-needle aspiration biopsy (FNAB) were selected in this retrospective study. The nodules were evaluated and classified according to ACR TI-RADS and the ATA guidelines. Taking pathological results as the reference, receiver operating characteristic (ROC) curves were drawn to evaluate the diagnostic capabilities of the ACR TI-RADS and the ATA guidelines for the risk stratification of thyroid isthmic nodules. The unnecessary biopsy rates and false-negative rates were compared. RESULTS: Multivariate analysis of ultrasonographic features of suspicious malignancies showed that an aspect ratio > 1 was not an independent risk factor for malignant thyroid nodules located in the isthmus (odds ratio: 3.193, 95%confidence interval: 0.882–11.552) (P = 0.077). The area under the ROC curves for diagnosing malignant thyroid nodules located in the isthmus in by the ACR TI-RADS and the ATA guidelines were 0.853 and 0.835, respectively. Under the management recommendations of the ACR TI-RADS and ATA guidelines, the false-negative rates of malignant thyroid nodules were 66.2%(ATA intermediate suspicion), 62.3%(ACR TR 4), 81.8%(ATA high suspicion) and 86.5%(ACR TR 5). CONCLUSION: Both the ACR TI-RADS and the ATA guidelines have high diagnostic capabilities for the risk stratification of thyroid isthmic nodules. For ACR TR 4 and 5 and ATA intermediate- and high-suspicion thyroid isthmic nodules with a maximum diameter < 1 cm, the criteria for puncture should be lowered, and FNAB should be done to clarify their diagnosis. [ABSTRACT FROM AUTHOR]

Trans-rectal ultrasound-guided 12-core systematic biopsy (SBx) is the standard diagnostic pathway for prostate cancer (PCa) because of a lack of sufficiently accurate imaging. Quantification of 3-D dynamic contrast-enhanced ultrasound (US) might open the way for a targeted procedure in which biopsies are directed at lesions suspicious on imaging. This work describes the expansion of contrast US dispersion imaging algorithms to 3-D and compares its performance against malignant and benign disease. Furthermore, we examined the feasibility of a multi-parametric approach to predict SBx-core outcomes using machine learning. An area under the receiver operating characteristic (ROC) curve of 0.76 and 0.81 was obtained for all PCa and significant PCa, respectively, an improvement over previous US methods. We found that prostatitis, in particular, was a source of false-positive readings. [ABSTRACT FROM AUTHOR]

In order to provide clinical references for the RFA procedure and to study the pivotal factors affecting the recovery time of radiofrequency ablation (RFA) in patients with papillary thyroid microcarcinoma (PTMC), 176 patients with low-risk intrathyroidal PTMC were included in this research. We randomly divided the whole cohort into training and test groups at a ratio of 7:3. The two-sample t-test was used to detect differences between the two groups. Least absolute shrinkage and selection operator (LASSO) regression was used to select the best predictor variables for predicting the status of RFA zone. Multiple test methods were used to ensure the scientific nature and accuracy of the Cox proportional hazards model. We tested the performance for the parameters and revealed the best cut-off value of each variable by the ROC curve and log-rank tests. The results showed patients aged above 49 years old, with RFA energy above 2800 J, the average diameter of the original tumour above 0.6 cm, or the average diameter of ablation zone at 1 month after RFA above 1.1 cm are risk factors for RFA zone delayed healing. [ABSTRACT FROM AUTHOR]

In order to provide clinical references for the RFA procedure and to study the pivotal factors affecting the recovery time of radiofrequency ablation (RFA) in patients with papillary thyroid microcarcinoma (PTMC), 176 patients with low-risk intrathyroidal PTMC were included in this research. We randomly divided the whole cohort into training and test groups at a ratio of 7:3. The two-sample t-test was used to detect differences between the two groups. Least absolute shrinkage and selection operator (LASSO) regression was used to select the best predictor variables for predicting the status of RFA zone. Multiple test methods were used to ensure the scientific nature and accuracy of the Cox proportional hazards model. We tested the performance for the parameters and revealed the best cut-off value of each variable by the ROC curve and log-rank tests. The results showed patients aged above 49 years old, with RFA energy above 2800 J, the average diameter of the original tumour above 0.6 cm, or the average diameter of ablation zone at 1 month after RFA above 1.1 cm are risk factors for RFA zone delayed healing. [ABSTRACT FROM AUTHOR]

The aim of the work described here was to develop a diagnostic model based on contrast-enhanced ultrasound (CEUS) features to improve performance in predicting the probability of malignancy for breast lesions with an enlarged enhancement extent on CEUS. In total, 299 consecutive patients who underwent CEUS examination and had confirmed pathological results were retrospectively enrolled. Among the 299 patients, an enlarged enhancement extent on CEUS was found in 142 patients. In this special cohort, we analyzed the association of malignant pathologic results with perfusion patterns emphatically by reclassifying the patterns. A diagnostic model was developed and presented as a nomogram, assessed with discrimination and calibration. Receiver operating characteristic (ROC) curve analysis revealed that the areas under the curves of the conventional perfusion and modified perfusion patterns were 0.58 and 0.76 (p < 0.001), respectively. A diagnostic model was built and exhibited good discrimination with a C -index of 0.95 (95% confidence interval: 0.91–0.98), which was confirmed to be 0.93 via internal bootstrapping validation. The nomogram based on CEUS features provides radiologists with a quantitative tool to predict the probability of malignancy in this special cohort of breast lesions. [ABSTRACT FROM AUTHOR]

Although conventional ultrasound (CUS) allows for clear detection of parotid gland lesions (PGLs), it fails to accurately provide benign-malignant differentiation due to overlapping morphological features. Radiomics is capable of processing large-quantity volume of data hidden in CUS image undiscovered by naked eyes. The aim was to explore the potential of CUS-based radiomics score (Rad-score) in distinguishing benign (BPGLs) and malignant PGLs (MPGLs). A consecutive of 281 PGLs (197 in training set and 84 in test set) with definite pathological confirmation was retrospectively enrolled. 1465 radiomics features were extracted from CUS images and Rad-score was constructed by using Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different nomogram models, including clinic-radiomics (Clin + Rad-score), CUS-clinic (CUS + Clin) and combined CUS-clinic-radiomics (CUS + Clin + Rad-score), were built using logistic regression. The diagnostic performance of different models were calculated and compared by area under receiver operating curve (AUC) and corresponding sensitivity and specificity. Finally, 26 radiomics features were independent signatures for predicting MPGLs, with MPGLs having higher Rad-scores in both cohorts (both P < 0.05). In the test population, CUS + Clin + Rad-score obtained an excellent diagnostic result, with significantly higher AUC value (AUC = 0.91) when compared to that of CUS + Clin (AUC = 0.84) and Clin + Rad-score (AUC = 0.74), respectively (both P < 0.05). In addition, the sensitivity of this combined model was higher than that of single Rad-score model (100.00% vs. 71.43%, P = 0.031) without compromising the specificity value (82.86% vs. 88.57%, P = 0.334). The calibration curve and decision curve analysis also indicated the clinical effectiveness of the proposed combined nomogram. The combined CUS-clinic-radiomics model may help improve the discrimination of BPGLs from MPGLs. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To clarify the clinical role of contrast enhanced ultrasound (CEUS) in predicting BI-RADS 4 breast disease. METHODS: A total of 92 sites of BI-RADS 4 breast diseases were reassessed by routine ultrasound and CEUS. The main indicators of routine ultrasound, CEUS and the combination of the two modalities were picked up by binary logistic regression analysis, scoring 0 for benign and 1 for malignant characteristics with pathology as referential standard to generate corresponding score systems of them. Finally, the receiver operating characteristic curves (ROC) were applied to compare the diagnostic efficacy of the three score systems. RESULTS: As pathological results showing, there were 43 malignant and 49 benign sites in total. Binary logistic regression analysis indicated that the margin, micro-calcification, and hyperechoic halo were main indicators for routine ultrasound score system. The scope expansion and pathological vessels in CEUS were main indicators for CEUS score system. The microcalcification, hyperechoic halo, the scope expansion and pathological vessels in CEUS were main indicators for the combination score system of the two modalities. The sensitivity, specificity and the area under ROC curve (AUC) of routine US, CEUS and the combination of the two modalities score systems were 74.4%, 91.8%, 0.885 and 88.4%, 87.8%, 0.901 and 86.0%, 95.9%, 0.937, respectively. CONCLUSION: In summary, CEUS is helpful for us to identify malignant disease and plays a supplementary role in predicting BI-RADS 4 breast disease in some extent. The scope expansion and pathological vessels in CEUS may be the most useful indicators. [ABSTRACT FROM AUTHOR]

Background: Elastography is a promising way to evaluate tissue differences regarding stiffness, and the stiffness of the malignant breast lesions increased at the lesion margin. However, there is a lack of data on the value of the shear wave elastography (SWE) parameters of the surrounding tissue (shell) of different diameter on the diagnosis of benign and malignant breast lesions. Therefore, the purpose of our study was to evaluate the diagnostic performance of shell elasticity in the diagnosis of benign and malignant breast lesions using SWE.Methods: Between September 2016 and June 2017, women with breast lesions underwent both conventional ultrasound (US) and SWE. Elastic values of the lesions peripheral tissue were determined according to the shell size, which was automatically drawn along the edge of the lesion using the following software guidelines: (1): 1 mm; (2): 2 mm; and (3): 3 mm. Quantitative elastographic features of the inner lesions and shell, including the elasticity mean (Emean), elasticity maximum (Emax), and elasticity minimum (Emin), were calculated using an online-available software. The receiver operating characteristic curves (ROCs) of the elastographic features was analyzed to assess the diagnostic performance, and the area under curve (AUC) of each elastographic feature was obtained. Logistic regression analysis was used to predict significant factors of malignancy, permitting the design of predictive models.Results: This prospective study included 63 breast lesions of 63 women. Of the 63 lesions, 33 were malignant and 30 were benign. The diagnostic performance of Emax-3shell was the highest (AUC = 0.76) with a sensitivity of 60.6% and a specificity of 83.3%. According to stepwise logistic regression analysis, the Emax-3shell and the Emin-3shell were significant predictors of malignancy (p < 0.05). The AUC of the predictive equation was 0.86.Conclusions: SWE features, particularly the combination of Emax-3shell and Emin-3shell can improve the diagnosis of breast lesions. [ABSTRACT FROM AUTHOR]