Use superb microvascular imaging to diagnose and predict metastatic cervical lymph nodes in patients with papillary thyroid carcinoma.

PURPOSE: Papillary thyroid carcinoma (PTC) with metastatic lymph nodes (LNs) is closely associated with disease recurrence. This study accessed the value of superb microvascular imaging (SMI) in the diagnosis and prediction of metastatic cervical LNs in patients with PTC. METHODS: A total of 183 cervical LNs (103 metastatic and 80 reactive) from 116 patients with PTC were analysed. Metastatic cervical LNs were confirmed by pathology or/and cytology; reactive cervical LNs were confirmed by pathology or clinical features. The characteristic of conventional ultrasound (US) was extracted using univariate and multivariate analyses. The diagnostic performance of US and SMI were compared using the area under the receiver operating curve (AUC) with corresponding sensitivity and specificity. A nomogram was developed to predict metastatic LNs in patients with PTC, based on multivariate analyses. RESULTS: L/S < 2, ill-defined border, absence of hilum, isoechoic or hyperechoic, heterogeneous internal echo, peripheral or mixed vascular pattern on color Doppler flow imaging (CDFI) and SMI, and a larger SMI vascular index appeared more frequently in metastatic LNs in the training datasets than in reactive LNs (P < 0.05). The diagnostic sensitivity, specificity and accuracy of SMI vs US are 94.4% and 87.3%, 79.3% and 69.3%, and 87.6% and 79.1%, respectively; SMI combined with US exhibited a higher AUC [0.926 (0.877-0.975)] than US only [0.829 (0.759-0.900)]. L/S < 2, peripheral or mixed vascular type on CDFI, and peripheral or mixed vascular types on SMI were independent predictors of metastatic LNs with PTC. The nomogram based on these three parameters exhibited excellent discrimination, with an AUC of 0.926. CONCLUSION: SMI was superior to US in diagnosing metastatic LNs in PTC. US combined with SMI significantly improved the diagnostic accuracy of metastatic cervical LNs with PTC. SMI is efficacious for differentiating and predicting metastatic cervical LNs.

Clinical and radiomics integrated nomogram for preoperative prediction of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer.

Objectives: The present study aimed to develop a radiomics nomogram based on conventional ultrasound (CUS) to preoperatively distinguish high tumor-infiltrating lymphocytes (TILs) and low TILs in triple-negative breast cancer (TNBC) patients. Methods: In the present study, 145 TNBC patients were retrospectively included. Pathological evaluation of TILs in the hematoxylin and eosin sections was set as the gold standard. The patients were randomly allocated into training dataset and validation dataset with a ratio of 7:3. Clinical features (age and CUS features) and radiomics features were collected. Then, the Rad-score model was constructed after the radiomics feature selection. The clinical features model and clinical features plus Rad-score (Clin+RS) model were built using logistic regression analysis. Furthermore, the performance of the models was evaluated by analyzing the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: Univariate analysis and LASSO regression were employed to identify a subset of 25 radiomics features from a pool of 837 radiomics features, followed by the calculation of Rad-score. The Clin+RS integrated model, which combined posterior echo and Rad-score, demonstrated better predictive performance compared to both the Rad-score model and clinical model, achieving AUC values of 0.848 in the training dataset and 0.847 in the validation dataset. Conclusion: The Clin+RS integrated model, incorporating posterior echo and Rad-score, demonstrated an acceptable preoperative evaluation of the TIL level. The Clin+RS integrated nomogram holds tremendous potential for preoperative individualized prediction of the TIL level in TNBC.

Radiofrequency ablation for papillary thyroid microcarcinoma close to the thyroid capsule versus far from the thyroid capsule: a retrospective study.

INTRODUCTION: The aim of this study was to evaluate the safety and efficacy of ultrasound-guided radiofrequency ablation (RFA) for the management of papillary thyroid microcarcinoma (PTMC) close to the thyroid capsule. MATERIAL AND METHODS: This was a retrospective study of 202 patients with PTMC who underwent RFA close to the thyroid capsule and 80 patients with PTMC who underwent RFA far from the thyroid capsule between June 2015 and December 2022. The follow-up time after RFA, change in size of tumour, location, thyroid function, the rates of PTMC disappearance, and complications were evaluated. RESULTS: A total of 202 patients with PTMC close to the thyroid capsule and 80 patients with PTMC far from the thyroid capsule successfully treated with RFA were studied. The thyroid function including free triiodothyronine (fT3), free thyroxine (fT4), triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH) showed no changes after RFA for one months in both groups. The tumour size was increased at 1, 3, and 6 months after RFA compared with pre-operative RFA in both groups. The tumour size was decreased at 12 and 24 months after RFA compared with pre-operative RFA both in both group. Seventy-nine PTMC close to the thyroid capsule and 30 PTMC far from the thyroid capsule completely disappeared as assessed by ultrasound examination. Eighty-four PTMC patients close to the thyroid capsule and 34 PTMC patients far from the thyroid capsule had minor complications after RFA treatment. The complication rates between the 2 groups were similar. CONCLUSION: Ultrasound-guided RFA seems to be an effective and safe method for patients with PTMC close to the thyroid capsule.

Important parameters should be paid attention in PTMC radiofrequency ablation.

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.

Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis.

Background: Conventional ultrasound (CUS) is the first choice for discrimination benign and malignant lymphadenectasis in supraclavicular lymph nodes (SCLNs), which is important for the further treatment. Radiomics provide more comprehensive and richer information than radiographic images, which are imperceptible to human eyes. Objective: This study aimed to explore the clinical value of CUS-based radiomics analysis in preoperative differentiation of malignant from benign lymphadenectasis in CUS suspected SCLNs. Methods: The characteristics of CUS images of 189 SCLNs were retrospectively analyzed, including 139 pathologically confirmed benign SCLNs and 50 malignant SCLNs. The data were randomly divided (7:3) into a training set (n=131) and a validation set (n=58). A total of 744 radiomics features were extracted from CUS images, radiomics score (Rad-score) built were using least absolute shrinkage and selection operator (LASSO) logistic regression. Rad-score model, CUS model, radiomics-CUS (Rad-score + CUS) model, clinic-radiomics (Clin + Rad-score) model, and combined CUS-clinic-radiomics (Clin + CUS + Rad-score) model were built using logistic regression. Diagnostic accuracy was assessed by receiver operating characteristic (ROC) curve analysis. Results: A total of 20 radiomics features were selected from 744 radiomics features and calculated to construct Rad-score. The AUCs of Rad-score model, CUS model, Clin + Rad-score model, Rad-score + CUS model, and Clin + CUS + Rad-score model were 0.80, 0.72, 0.85, 0.83, 0.86 in the training set and 0.77, 0.80, 0.82, 0.81, 0.85 in the validation set. There was no statistical significance among the AUC of all models in the training and validation set. The calibration curve also indicated the good predictive performance of the proposed nomogram. Conclusions: The Rad-score model, derived from supraclavicular ultrasound images, showed good predictive effect in differentiating benign from malignant lesions in patients with suspected supraclavicular lymphadenectasis.

Ultrasound-based radiomics nomogram combined with clinical features for the prediction of central lymph node metastasis in papillary thyroid carcinoma patients with Hashimoto's thyroiditis.

Background: Hashimoto thyroiditis (HT) is the most common autoimmune thyroid disease and is considered an independent risk factor for papillary thyroid carcinoma (PTC), with a higher incidence of PTC in patients with HT. Objective: To build an integrated nomogram using clinical information and ultrasound-based radiomics features in patients with papillary thyroid carcinoma (PTC) with Hashimoto thyroiditis (HT) to predict central lymph node metastasis (CLNM). Methods: In total, 235 patients with PTC with HT were enrolled in this study, including 101 with CLNM and 134 without CLNM. They were divided randomly into training and validation datasets with a 7:3 ratio for developing and evaluating clinical features plus conventional ultrasound features (Clin-CUS) model and clinical features plus radiomics scores (Clin-RS) model, respectively. In the Clin-RS model, the Pyradiomics package (V1.3.0) was used to extract radiomics variables, and LASSO regression was used to select features and construct radiomics scores (RS). The Clin-CUS and Clin-RS nomogram models were built using logistic regression analysis. Results: Twenty-seven CLNM-associated radiomics features were selected using univariate analysis and LASSO regression from 1488 radiomics features and were calculated to construct the RS. The integrated model (Clin-RS) had better diagnostic performance than the Clin-CUS model for differentiating CLNM in the training dataset (AUC: 0.845 vs. 0.778) and the validation dataset (AUC: 0.808 vs. 0.751), respectively. Conclusion: Our findings suggest that applying an ultrasound-based radiomics approach can effectively predict CLNM in patients with PTC with HT. By incorporating clinical information and RS, the Clin-RS model can achieve a high diagnostic performance in diagnosing CLNM in patients with PTC with HT.

Neck swelling 19 days after thyroidectomy: A case report of a rare chyle leak patient.

The occurrence of chyle leak after neck dissection for thyroid carcinoma is uncommon, but it usually occurs within 1-10 days after surgery. We encountered a 30-year-old patient with chyle leak 19 days after the operation for thyroid carcinoma. The initial symptom was swelling of the neck, which was quickly diagnosed as chyle leak by puncture. This report describes the patient's presentation and discusses the conservative management of this complication. We highlight that more attention should be given to chyle leak in high-risk patients, and stricter dietary management for these patients after surgery may avoid this complication. In addition, timely diagnosis and rapid implementation of various conservative treatment measures play an important role in patient recovery.

Auto-Segmentation Ultrasound-Based Radiomics Technology to Stratify Patient With Diabetic Kidney Disease: A Multi-Center Retrospective Study.

Background: An increasing proportion of patients with diabetic kidney disease (DKD) has been observed among incident hemodialysis patients in large cities, which is consistent with the continuous growth of diabetes in the past 20 years. Purpose: In this multicenter retrospective study, we developed a deep learning (DL)-based automatic segmentation and radiomics technology to stratify patients with DKD and evaluate the possibility of clinical application across centers. Materials and Methods: The research participants were enrolled retrospectively and separated into three parts: training, validation, and independent test datasets for further analysis. DeepLabV3+ network, PyRadiomics package, and least absolute shrinkage and selection operator were used for segmentation, extraction of radiomics variables, and regression, respectively. Results: A total of 499 patients from three centers were enrolled in this study including 246 patients with type II diabetes mellitus (T2DM) and 253 patients with DKD. The mean intersection-over-union (Miou) and mean pixel accuracy (mPA) of automatic segmentation of the data from the three medical centers were 0.812 ± 0.003, 0.781 ± 0.009, 0.805 ± 0.020 and 0.890 ± 0.004, 0.870 ± 0.002, 0.893 ± 0.007, respectively. The variables from the renal parenchyma and sinus provided different information for the diagnosis and follow-up of DKD. The area under the curve (AUC) of the radiomics model for differentiating between DKD and T2DM patients was 0.674 ± 0.074 and for differentiating between the high and low stages of DKD was 0.803 ± 0.037. Conclusion: In this study, we developed a DL-based automatic segmentation, radiomics technology to stratify patients with DKD. The DL technology was proposed to achieve fast and accurate anatomical-level segmentation in the kidney, and an ultrasound-based radiomics model can achieve high diagnostic performance in the diagnosis and follow-up of patients with DKD.

Cavitation assisted endoplasmic reticulum targeted sonodynamic droplets to enhanced anti-PD-L1 immunotherapy in pancreatic cancer.

BACKGROUND: Sonodynamic therapy (SDT) induces immunogenic cell death (ICD) in tumors and promises to play an assistive role in immunotherapy in pancreatic cancer. However, the short half-life and limited diffusion distance of reactive oxygen species (ROS) impair ICD induction, especially in tumors with relatively poor blood perfusion and dense stroma. RESULTS: To address this problem, we fabricated cavitation-assisted endoplasmic reticulum (ER) targeted sonodynamic nanodroplets (PMPS NDs, 329 nm). The good sonodynamic effect and precise endoplasmic reticulum target effect was verified. After intravenous injection, the cRGD peptide modified nanodroplets initially aggregated around the tumor vascular endothelial cells. Stimulated by ultrasound, the liquid-to-gas bubbles began to oscillate and cavitate. This acoustic droplet evaporation strategy facilitated transport of the nanoparticle across the vessel, with deep penetration. This loosened the tumor stroma and facilitated accumulation and penetration of loaded sonosensitizer after 6 h. The modified sonosensitizer can selectively accumulate in the ER to generate a large amount of ROS in situ, inducing potent ER stress, amplified ICD and dendritic cell maturation in vitro and in vivo. Furthermore, the elevated antitumor effect of SDT plus anti-PD-L1 immunotherapy was verified using an orthotopic tumor model. CONCLUSIONS: This study reports a cavitation assisted ER targeted sonodynamic therapy that can enhance the effect of anti-PD-L1 immunotherapy effectively in orthotopic and distant pancreatic cancer.

Ultrasound-Enhanced Reactive Oxygen Species Responsive Charge-Reversal Polymeric Nanocarriers for Efficient Pancreatic Cancer Gene Delivery.

Inefficient intracellular gene release and transfection limit nonviral gene delivery applications in cancer therapy. Reactive oxygen species (ROS) responsive nonviral gene delivery is the most widely explored strategy for such applications, yet the development of fast and safe ROS responsive nanocarriers proves to be a challenge because of the intracellular chemical equilibrium of high ROS and glutathione levels. Here, we report an ultrasound-enhanced ROS responsive charge-reversal polymeric nanocarrier (BTIL) for fast and efficient pancreatic cancer gene delivery. The BTIL is composed of B-PDEAEA/DNA polyplex-based cores and IR780-loaded liposome coatings. The IR780 is able to produce an excess of ROS under low intensity ultrasound irradiation, thus disequilibrating the chemical equilibrium of ROS and glutathione, and promoting the ROS-responsive positive-to-negative charge-reversal of the B-PDEAEA polymer. This charge conversion results in fast polyplex dissociation and intracellular gene release, inducing efficient gene transfection and cancer cell apoptosis. Moreover, following the intravenous administration, BTIL maintains a stable and long circulation in the bloodstream, achieves orthotopic pancreatic ductal adenocarcinoma distribution, and exhibits potent antitumor activity with negligible side effects. Our results reveal the proposed strategy to be both promising and universal for the development of fast and safe ROS responsive nonviral gene delivery in cancer therapy.

US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients.

The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p>0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6% vs. 73.9%, p=0.007; Sensitivity: 78.6% vs. 92.9%, p=0.006) for evaluating OMs in the test set. The US-DLM+Clin model is valuable in differentiating HCC from OM in the setting of cirrhosis.