Radiating blood flow signal: A new ultrasound feature of thyroid carcinoma.

OBJECTIVE: To summary radiating blood flow signals and evaluate their diagnostic value in differentiating benign and malignant thyroid nodules. MATERIALS AND METHODS: We retrospectively recruited consecutive patients undergoing US at 4 hospitals from 2018 to 2022. In a training dataset, the correlations of US features with malignant thyroid nodules were assessed by multivariate logistic analysis. Multivariate logistic regression models involving the ACR TI-RADS score, radiating blood flow signals and their combination were built and validated internally and externally. The AUC with 95% asymptotic normal confidence interval as well as sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) with 95% exact binomial confidence intervals were calculated. RESULTS: Among 2475 patients (1818 women, age: 42.47 ± 11.57; 657 men, age: 42.16 ± 11.69), there were 3187 nodules (2342 malignant nodules and 845 benign nodules). Radiating blood flow signals were an independent risk factor for diagnosing thyroid carcinoma. In the training set, the AUC of the model using the combination of radiating blood flow signals and the ACR TI-RADS score (0.95 95 % CI: [0.94, 0.97]; P < 0.001) was significantly higher than that of the ACR TI-RADS model (0.91 [0.89, 0.93]). In the two internal validation sets and the external validation set, the AUCs of the combination model were 0.97 [0.96, 0.98], 0.92 [0.88, 0.96], and 0.91 [0.86, 0.95], respectively, and were all significantly higher than that of the ACR TI-RADS score (0.92 [0.90, 0.95], 0.86 [0.81, 0.91], 0.84 [0.79, 0.89]; P < 0.001). CONCLUSION: Radiating blood flow is a new US feature of thyroid carcinomas that can significantly improve the diagnostic performance vs. the ACR TI-RADS score.

The value of the improved percutaneous and intravenous contrast-enhanced ultrasound diagnostic classification in sentinel lymph nodes of breast cancer.

Background: Metastatic burden of sentinel lymph node (SLN) in breast cancer patient is the basis for the decision to choose SLN biopsy or axillary lymph node dissection (ALND). However, the diagnostic performance of the previous percutaneous contrast-enhanced ultrasound (P-CEUS) and intravenous contrast-enhanced ultrasound (IV-CEUS) pattern were not satisfied. This study aimed to establish new classification based on structural characteristics for P-CEUS and IV-CEUS of SLN in breast cancer and evaluate the diagnostic efficacy. Methods: This retrospective study included consecutive breast cancer patients who had not received neoadjuvant therapy in the First Affiliated Hospital of Sun Yat-sen University between June 2019 and December 2021. Conventional ultrasound, P-CEUS and IV-CEUS were performed. The new classification methods for P-CEUS and IV-CEUS of SLN were established based on structural characteristics of SLN. Pathology was considered as the gold standard, the diagnostic efficacy of P-CEUS, IV-CEUS and combined contrast-enhanced ultrasound in SLNs was analyzed. Results: The detection rate of SLN by P-CEUS in 368 patients was 95.42%. The P-CEUS pattern of SLNs was divided into six types. The IV-CEUS sequence was divided into three types. The IV-CEUS mode was divided into four types. Among the 438 SLNs detected by P-CEUS, 105 (23.97%) were malignant and 333 (76.03%) were benign. Among the previously classified P-CEUS, P-CEUS, IV-CEUS and combined contrast-enhanced ultrasound, the latter had the highest diagnostic efficacy (P<0.05), with sensitivity, specificity, positive predictive value, negative predictive value, accuracy and area under curve (AUC) of 81.90% (86/105), 97.30% (324/333), 90.53% (86/95), 94.46% (324/343), 93.61% (410/438) and 0.896 (0.864-0.923), respectively. Conclusions: The new classification of the P-CEUS and IV-CEUS features of SLNs was performed based on structural characteristics of lymph nodes. Compared with the previously classified P-CEUS, the new classification method has higher diagnostic performance. The combination of new classified P-CEUS and IV-CEUS is helpful to further improve the diagnostic performance of SLNs.

Itaconate inhibits corticosterone-induced necroptosis and neuroinflammation via up-regulating menin in HT22 cells.

Corticosterone (CORT) damages hippocampal neurons as well as induces neuroinflammation. The tricarboxylic acid cycle metabolite itaconate has an anti-inflammatory role. Necroptosis is a form of programmed cell death, also known as inflammatory cell death. Menin is a multifunctional scaffold protein, which deficiency aggravates neuroinflammation. In this study, we explored whether itaconate inhibits CORT-induced neuroinflammation as well as necroptosis and further investigated the mediatory role of Menin in this protective effect of itaconate by using an exposure of CORT to HT22 cells (a hippocampal neuronal cell line). The viability of HT22 cells was examined by the cell counting kit 8 (CCK-8). The morphology of HT22 cells was observed by transmission electron microscope (TEM). The expressions of necroptosis-related proteins (p-RIP1/RIP1, p-RIP3/RIP3, and p-MLKL/MLKL) were evaluated by western blotting. The contents of inflammatory factors were detected by an enzyme-linked immunosorbent assay (ELISA) kit. Our results showed that CORT increases the contents of pro-inflammatory factors (IL-1ß, TNF-α) as well as decreases the contents of anti-inflammatory factors (IL-4, IL-10) in HT22 cells. We also found that CORT increases the expressions of necroptosis-related proteins (p-RIP1/RIP1, p-RIP3/RIP3, and p-MLKL/MLKL) and decreases the cell viability in HT22 cells, indicating that CORT induces necroptosis in HT22 cells. Itaconate improves CORT-induced neuroinflammation and necroptosis. Furthermore, itaconate upregulates the expression of Menin in CORT-exposed HT22 cells. Importantly, silencing Menin abolishes the antagonistic effect of itaconate on CORT-induced necroptosis and neuroinflammation. In brief, these results indicated that itaconate protects HT22 cells against CORT-induced neuroinflammation and necroptosis via upregulating Menin.

Isthmin: A multifunctional secretion protein.

Isthmin is a polypeptide secreted by adipocytes that was first detected in Xenopus gastrula embryos. Recent studies have focused on the biological functions of isthmin in growth and development, angiogenesis, and metabolism. Distinct spatiotemporal expression of isthmin-1 (ISM-1) was observed during growth and development. ISM-1 plays an important role in the occurrence and development of cancer by regulating cell proliferation, migration, angiogenesis, and immune microenvironments. Moreover, ISM-1, as a newly identified insulin-like adipokine, increases adipocyte glucose uptake and inhibits hepatic lipid synthesis. However, the biological function of ISM-1 remains largely unknown. In this review, we highlight the structure and physiological functions of isthmin and explore its application potential, contributing to a better understanding of its function and providing prevention and treatment strategies for various diseases.

Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management.

Importance: To optimize the integration of artificial intelligence (AI) decision aids and reduce workload in thyroid nodule management, it is critical to incorporate personalized AI into the decision-making processes of radiologists with varying levels of expertise. Objective: To develop an optimized integration of AI decision aids for reducing radiologists' workload while maintaining diagnostic performance compared with traditional AI-assisted strategy. Design, Setting, and Participants: In this diagnostic study, a retrospective set of 1754 ultrasonographic images of 1048 patients with 1754 thyroid nodules from July 1, 2018, to July 31, 2019, was used to build an optimized strategy based on how 16 junior and senior radiologists incorporated AI-assisted diagnosis results with different image features. In the prospective set of this diagnostic study, 300 ultrasonographic images of 268 patients with 300 thyroid nodules from May 1 to December 31, 2021, were used to compare the optimized strategy with the traditional all-AI strategy in terms of diagnostic performance and workload reduction. Data analyses were completed in September 2022. Main Outcomes and Measures: The retrospective set of images was used to develop an optimized integration of AI decision aids for junior and senior radiologists based on the selection of AI-assisted significant or nonsignificant features. In the prospective set of images, the diagnostic performance, time-based cost, and assisted diagnosis were compared between the optimized strategy and the traditional all-AI strategy. Results: The retrospective set included 1754 ultrasonographic images from 1048 patients (mean [SD] age, 42.1 [13.2] years; 749 women [71.5%]) with 1754 thyroid nodules (mean [SD] size, 16.4 [10.6] mm); 748 nodules (42.6%) were benign, and 1006 (57.4%) were malignant. The prospective set included 300 ultrasonographic images from 268 patients (mean [SD] age, 41.7 [14.1] years; 194 women [72.4%]) with 300 thyroid nodules (mean [SD] size, 17.2 [6.8] mm); 125 nodules (41.7%) were benign, and 175 (58.3%) were malignant. For junior radiologists, the ultrasonographic features that were not improved by AI assistance included cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, and nodules smaller than 5 mm, whereas for senior radiologists the features that were not improved by AI assistance were cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, very hypoechoic nodules, nodules taller than wide, lobulated or irregular nodules, and extrathyroidal extension. Compared with the traditional all-AI strategy, the optimized strategy was associated with increased mean task completion times for junior radiologists (reader 11, from 15.2 seconds [95% CI, 13.2-17.2 seconds] to 19.4 seconds [95% CI, 15.6-23.3 seconds]; reader 12, from 12.7 seconds [95% CI, 11.4-13.9 seconds] to 15.6 seconds [95% CI, 13.6-17.7 seconds]), but shorter times for senior radiologists (reader 14, from 19.4 seconds [95% CI, 18.1-20.7 seconds] to 16.8 seconds [95% CI, 15.3-18.3 seconds]; reader 16, from 12.5 seconds [95% CI, 12.1-12.9 seconds] to 10.0 seconds [95% CI, 9.5-10.5 seconds]). There was no significant difference in sensitivity (range, 91%-100%) or specificity (range, 94%-98%) between the 2 strategies for readers 11 to 16. Conclusions and Relevance: This diagnostic study suggests that an optimized AI strategy in thyroid nodule management may reduce diagnostic time-based costs without sacrificing diagnostic accuracy for senior radiologists, while the traditional all-AI strategy may still be more beneficial for junior radiologists.

Cell-free DNA methylation biomarker for the diagnosis of papillary thyroid carcinoma.

BACKGROUND: Cell-free DNA (cfDNA) is being explored as biomarker for non-invasive diagnosis of cancer. We aimed to establish a cfDNA-based DNA methylation marker panel to differentially diagnose papillary thyroid carcinoma (PTC) from benign thyroid nodule (BTN). METHODS: 220 PTC- and 188 BTN patients were enrolled. Methylation markers of PTC were identified from patients' tissue and plasma by reduced representation bisulfite sequencing and methylation haplotype analyses. They were combined with PTC markers from literatures and were tested on additional PTC and BTN samples to verify PTC-detecting ability using targeted methylation sequencing. Top markers were developed into ThyMet and were tested in 113 PTC and 88 BTN cases to train and validate a PTC-plasma classifier. Integration of ThyMet and thyroid ultrasonography was explored to improve accuracy. FINDINGS: From 859 potential PTC plasma-discriminating markers that include 81 markers identified by us, the top 98 most PTC plasma-discriminating markers were selected for ThyMet. A 6-marker ThyMet classifier for PTC plasma was trained. In validation it achieved an Area Under the Curve (AUC) of 0.828, similar to thyroid ultrasonography (0.833) but at higher specificity (0.722 and 0.625 for ThyMet and ultrasonography, respectively). A combinatorial classifier by them, ThyMet-US, improved AUC to 0.923 (sensitivity = 0.957, specificity = 0.708). INTERPRETATION: The ThyMet classifier improved the specificity of differentiating PTC from BTN over ultrasonography. The combinatorial ThyMet-US classifier may be effective in preoperative diagnosis of PTC. FUNDING: This work was supported by the grants from National Natural Science Foundation of China (82072956 and 81772850).

Using new criteria to improve the differentiation between HCC and non-HCC malignancies: clinical practice and discussion in CEUS LI-RADS 2017.

PURPOSE: Using contrast-enhanced ultrasound (CEUS) to evaluate the diagnostic performance of liver imaging reporting and data system (LI-RADS) version 2017 and to explore potential ways to improve the efficacy. METHODS: A total of 315 nodules were classified as LR-1 to LR-5, LR-M, and LR-TIV. New criteria were applied by adjusting the early washout onset (< 45 s) and the time of marked washout (within 3 min). Two subgroups of the LR-M nodules were recategorized as LR-5, respectively. The diagnostic performance was evaluated by calculating the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: By adjusting early washout onset to < 45 s, the LR-5 as a standard for diagnosing HCC had an improved sensitivity (74.1% vs. 56.1%, P < 0.001) without significant change in PPV (93.3% vs. 96.1%, P = 0.267), but the specificity was decreased (48.3% vs. 78.5%, P = 0.018). The LR-M as a standard for the diagnosis of non-HCC malignancies had an increase in specificity (89.2% vs. 66.2%, P < 0.001) but a decrease in sensitivity (31.5% vs. 68.4%, P = 0.023). After reclassification according to the time of marked washout, the sensitivity of the LR-5 increased (80% vs. 56.1%, P < 0.001) without a change in PPV (94.9% vs. 96.1%, P = 0.626) and specificity (80% vs. 78.5%, P = 0.879). For reclassified LR-M nodules, the specificity increased (87.5% versus 66.2%, P < 0.001) with a non-significant decrease in sensitivity (47.3% vs. 68.4%, P = 0.189). CONCLUSIONS: The CEUS LI-RADS showed good confidence in diagnosing HCC while tended to misdiagnose HCC as non-HCC malignancies. Adjusting the marked washout time within 3 min would reduce the possibility of this misdiagnosis.

The effects of the renin inhibitor aliskiren on neuronal toxic damage.

The neurotoxicity of amyloid-ß (Aß) and its deposition in neurons plays a critical role in the occurrence and development of Alzheimer's disease (AD). Several preclinical experiments have found that the renin inhibitor aliskiren has a wide range of physiological effects, including hindering the progression of atherosclerosis and anti-inflammatory. This study is aimed to explore the effect of aliskiren on neuronal toxic damage and the underlying mechanism. This study established an in vitro nerve injury model through Aß 1-42 induction; the effects of aliskiren on the viability, inflammatory damage and apoptosis of SH-SY5Y cells were examined. For the sake of explore the underlying mechanism; SwissTargetPrediction website and molecular docking were utilized to predict the target of aliskiren. Then the impacts of the target protein overexpression were determined to verify its mediation. The results of the current study demonstrate that aliskiren has no effect on the viability of SH-SY5Y cells while Aß1-42accumulation could significantly downregulate cell viability. In addition, aliskiren could alleviate neuronal inflammatory damage and apoptosis arise from Aß 1-42accumulation. Following confirming the high expression level of the predicted target PDE4B in damaged cells, it was found that PDE4B overexpression can reverse the influence of aliskiren on cell viability, inflammatory damage and apoptosis. In conclusion, aliskiren upregulates cell viability, reduces inflammatory damage and apoptosis induced by Aß accumulation in AD via inhibiting PDE4B. These findings have expanded the scope of future application of aliskiren and provided a theoretical basis.

Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study.

BACKGROUND: Strategies for integrating artificial intelligence (AI) into thyroid nodule management require additional development and testing. We developed a deep-learning AI model (ThyNet) to differentiate between malignant tumours and benign thyroid nodules and aimed to investigate how ThyNet could help radiologists improve diagnostic performance and avoid unnecessary fine needle aspiration. METHODS: ThyNet was developed and trained on 18 049 images of 8339 patients (training set) from two hospitals (the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, and Sun Yat-sen University Cancer Center, Guangzhou, China) and tested on 4305 images of 2775 patients (total test set) from seven hospitals (the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China; the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; the Guangzhou Army General Hospital, Guangzhou, China; the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; the First Affiliated Hospital of Sun Yat-sen University; Sun Yat-sen University Cancer Center; and the First Affiliated Hospital of Guangxi Medical University, Nanning, China) in three stages. All nodules in the training and total test set were pathologically confirmed. The diagnostic performance of ThyNet was first compared with 12 radiologists (test set A); a ThyNet-assisted strategy, in which ThyNet assisted diagnoses made by radiologists, was developed to improve diagnostic performance of radiologists using images (test set B); the ThyNet assisted strategy was then tested in a real-world clinical setting (using images and videos; test set C). In a simulated scenario, the number of unnecessary fine needle aspirations avoided by ThyNet-assisted strategy was calculated. FINDINGS: The area under the receiver operating characteristic curve (AUROC) for accurate diagnosis of ThyNet (0·922 [95% CI 0·910-0·934]) was significantly higher than that of the radiologists (0·839 [0·834-0·844]; p<0·0001). Furthermore, ThyNet-assisted strategy improved the pooled AUROC of the radiologists from 0·837 (0·832-0·842) when diagnosing without ThyNet to 0·875 (0·871-0·880; p<0·0001) with ThyNet for reviewing images, and from 0·862 (0·851-0·872) to 0·873 (0·863-0·883; p<0·0001) in the clinical test, which used images and videos. In the simulated scenario, the number of fine needle aspirations decreased from 61·9% to 35·2% using the ThyNet-assisted strategy, while missed malignancy decreased from 18·9% to 17·0%. INTERPRETATION: The ThyNet-assisted strategy can significantly improve the diagnostic performance of radiologists and help reduce unnecessary fine needle aspirations for thyroid nodules. FUNDING: National Natural Science Foundation of China and Guangzhou Science and Technology Project.

Diagnostic Performance of Quantitative and Qualitative Elastography for Axillary Lymph Node Metastasis in Breast Cancer: A Systematic Review and Meta-Analysis.

Background: Studies have shown inconsistent results regarding the diagnostic performance of ultrasound elastography for axillary lymph node metastasis (ALNM) in breast cancer. This meta-analysis aimed to estimate the diagnostic performance of ultrasound elastography (divided into quantitative and qualitative elastography) for ALNM in patients with breast cancer. Methods: The PubMed and Embase databases were searched for eligible studies exploring the diagnostic performance of ultrasound elastography for ALNM in patients with breast cancer. The included studies were divided into quantitative and qualitative elastography groups to perform separate meta-analyses. The diagnostic performance was investigated with pooled sensitivity and specificity and diagnostic odds ratio (DOR) using a bivariate mixed-effects regression model. A summary receiver operating characteristic curve was constructed, and the area under the curve (AUC) was calculated. Results: Seven and 11 studies were included in the quantitative and qualitative elastography meta-analyses, respectively. The pooled sensitivity and specificity, DOR, and AUC with their corresponding 95% confidence intervals were 0.82 (0.75, 0.87), 0.88 (0.78, 0.93), 33 (13, 83), and 0.89 (0.86, 0.91), respectively, for quantitative elastography and 0.81 (0.69, 0.89), 0.92 (0.79, 0.97), 46 (12, 181), and 0.92 (0.89, 0.94), respectively, for qualitative elastography. No significant publication bias existed. Fagan plots demonstrated good clinical utility. However, substantial heterogeneity existed among studies. Study design, measurement, and reference standard served as potential sources of heterogeneity for quantitative studies, which were measurement and reference standard for qualitative studies. Conclusions: Both quantitative and qualitative elastography seem to be feasible, non-invasive diagnostic tools for ALNM in breast cancer. Nevertheless, the results must be interpreted carefully, paying attention to heterogeneity issues, especially for quantitative elastography studies.

Ultrasound combined with biochemical parameters can be used to differentiate parathyroid carcinoma from benign tumors in patients with primary hyperparathyroidism.

BACKGROUND: Primary hyperparathyroidism (PHPT) results from excessive secretion of parathyroid hormone from parathyroid tumors. Differentiating parathyroid tumors can be challenging before operation. OBJECTIVES: To differentiate parathyroid carcinoma from benign tumors in patients with PHPT by the application of ultrasound and biochemical parameters. METHODS: This study is a retrospective study. The study enrolled 17 patients with parathyroid carcinoma (PC) and 57 patients with parathyroid adenoma (PA), confirmed by postoperative pathology, between September 2010 and July 2017. This study retrospectively compared the ultrasonic features of the tumors included echotexture, maximum lesion diameter, shape, margin, blood flow inside the mass, intralesional calcifications, cysts in the mass, and biochemical parameters included serum calcium, phosphorus, parathyroid hormone (PTH), alkaline phosphatase (ALP) levels, gender distribution and age of patients between patients with PC and those with PA. RESULTS: In the US images, the two groups showed significant differences in heterogeneity, the appearance of a taller-than-wide shape, irregular or lobulated margins, and intralesional calcifications (p < #x003C;< #x200A;0.05). However, no significant difference was found in echogenicity, maximum lesion diameter, blood flow, and cystic components of the mass (p > #x003E;> #x200A;0.05). The mean PTH levels were significantly different between the two groups (p < #x003C;< #x200A;0.05). The PC and PA patients did not differ significantly in terms of mean serum calcium, mean serum phosphorus, and mean ALP levels (p > #x003E;> #x200A;0.05). There were significant differences to distinguish PC from PA in calcifications in mass or/and taller-than-wide shape combine with PTH > #x003E;> #x200A;1000 pg/mL (p < #x003C;< #x200A;0.05). Significant difference existed in the age between the two groups (p < #x003C;< #x200A;0.001). No significant difference existed in the gender distribution between the two groups (p > #x003E;> #x200A;0.05). CONCLUSION: Ultrasound features especially intralesional calcifications and taller-than-wide shape combine with an extremely high serum PTH (>1000 pg/mL) are helpful in differentiating between benign and parathyroid tumors in patients with PHPT.

Malignancy risk stratification and FNA recommendations for thyroid nodules: A comparison of ACR TI-RADS, AACE/ACE/AME and ATA guidelines.

OBJECTIVE: To compare diagnostic performance and malignancy risk stratification among guidelines set forth by the American Thyroid Association (ATA) in 2015, the American Association of Clinical Endocrinologists (AACE), the American College of Endocrinology (ACE) and the Association Medici Endocrinologi (AME) in 2016, and the American College of Radiology (ACR) in 2017. METHODS: The retrospective study was approved by the hospital ethics committee, and the informed consent requirement was waived. From October 2015 to March 2016, a total of 230 patients with 230 consecutive thyroid nodules were enrolled in this study. Each nodule was classified by one junior and one senior radiologist separately according to ACR TI-RADS, AACE/ACE/AME and ATA guidelines. The malignancy diagnostic performance and the number of FNA recommendations were pairwise compared among three guidelines using chi-square tests. RESULTS: Of the 230 thyroid nodules, 137 were malignant, and 93 were benign. However, 19.6% of the nodules (45 of 230) did not match any pattern using the ATA guidelines but with a high risk of malignancy (68.9%). The ACR TI-RADS derived the highest diagnostic performance, from both junior radiologist (AUC 0.815) and senior radiologist (AUC 0.864). The ACR guidelines also showed the greatest level of sensitivity (junior: 86.1%, senior: 94.9%), compared with AACE/ACE/AME and ATA guidelines. The number of thyroid nodules recommended to fine-needle aspiration (FNA) was the lowest (37.8%, 40.4%) by ACR TI-RADS, and meanwhile, the malignant detection rate within these nodules was highest (64.4%, 68.8%). CONCLUSIONS: The ACR guidelines present a higher level of diagnostic indicators and may offer a meaningful reduction in FNA recommendations with a higher malignancy detection rate.

Value of multimodality imaging in the diagnosis of breast lesions with calcification: A retrospective study.

PURPOSE: To assess the value of conventional ultrasound (US), contrast-enhanced ultrasound (CEUS) and mammography in the diagnosis of breast lesions with calcifications. METHODS: A total of 87 breast lesions with calcification were subjected to US, CEUS and mammography and divided into 3 groups: Group A (all cases), Group A1 (31 cases who underwent US and CEUS first followed by mammography), and Group A2 (56 cases who underwent mammography first followed by US and CEUS). A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficacy of different methods in different groups. RESULTS: In Group A, the area under the ROC curve (AUROC) of CEUS were 0.937, which were significantly higher than that of mammography (p < 0.05). In Group A1, the AUROC of CEUS were 0.842, which were not significantly different from that of US and mammography (p > 0.05). In Group A2, the AUROC of CEUS were 0.987, which were significantly higher than that of mammography and US (p < 0.05). CONCLUSION: Based on the mammography results, the combination of US and CEUS might improve the diagnostic efficacy in breast lesions with calcification.

Does contrast-enhanced ultrasound (CEUS) play a better role in diagnosis of breast lesions with calcification? A comparison with MRI.

OBJECTIVE: To compare the efficacy of contrast-enhanced ultrasound enabled reclassification of Breast Imaging Reporting and Data System (CEUS-BI-RADS) with MRI in the diagnosis of breast lesions with calcification. METHODS: A total of 52 breast lesions with calcification from 51 patients were detected by ultrasound as hyperechoic foci and categorized as BI-RADS 3-5. The 51 patients further underwent CEUS scan and MRI. The ultrasound-BI-RADS combined with CEUS 5-point score system redefined the classification of BI-RADS which was called CEUS-BI-RADS. The diagnostic efficacy of three methods was assessed by receiver operating characteristic (ROC) curve analysis. Histopathological assessment used as the gold-standard. RESULTS: The sensitivities of Ultrasound-BI-RADS, MRI classification of BI-RADS (MRI-BI-RADS) and CEUS-BI-RADS were 85%, 90% and 95% without significant difference among the three modalities (p > 0.05). The diagnostic specificities of ultrasound-BI-RADS, MRI-BI-RADS and CEUS-BI-RADS were 78.1%, 78.1% and 96.8%, respectively (p < 0.05); and the accuracy were 80.7%, 82.6% and 96.1% for ultrasound-BI-RADS, MRI-BI-RADS and CEUS-BI-RADS, respectively (p < 0.05). The area under ROC (AUROC) in differentiation of breast lesions with calcification was 0.945 for CEUS-BI-RADS, 0.907 for MRI-BI-RADS and 0.853 for ultrasound-BI-RADS, with no significant difference among the three modalities (p > 0.05). CONCLUSION: The CEUS-BI-RADS has a better diagnostic efficiency than MRI-BI-RADS in the differentiation of the breast lesions with calcification. ADVANCES IN KNOWLEDGE: •CEUS is a better method in differentiation of breast lesions with calcification.•CEUS-BI-RADS increases the efficiency of diagnosis compared to MRI.

Exosome-transmitted circular RNA hsa_circ_0051443 suppresses hepatocellular carcinoma progression.

Extracellular communication in the tumor microenvironment is critical. Results of qRT-PCR show that circ-0051443 is significantly lower in the plasma exosomes and tissues from patients with hepatocellular carcinoma (HCC) than healthy controls. Compared with the producer cells, circ-0051443 is mainly packaged into exosomes. A receiver operating characteristic curve (ROC) shows that the patients with HCC can be distinguished from the controls by exosomal circ-0051443. The role of exosomal circ-0051443 in HCC was determined by animal and cell analyses. Circ-0051443 is transmitted from normal cells to HCC cells via exosomes and suppresses the malignant biological behaviors by promoting cell apoptosis and arresting the cell cycle. Exosomal circ-0051443 decreases the weight and volume of the xenograft tumors in nude mice via BAK1 upregulation in these tumors. BAK1 expression is mediated by exosomal circ-0051443 through competitive bound to miR-331-3p. Therefore, exosomal circ-0051443 can serve as a predictor and potential therapeutic target for HCC.

Ultrasomics for Early Evaluation of the Tumor Response to MicroRNA-122 in a Nude Mouse Hepatocellular Carcinoma Model.

OBJECTIVES: To explore the value of ultrasomics in temporal monitoring of tumor changes in response to gene therapy in hepatocellular carcinoma compared with methods according to the Response Evaluation Criteria in Solid Tumors (RECIST) and modified RECIST (mRECIST). METHODS: Hepatocellular carcinoma-bearing mice were injected intratumorally with microRNA-122 (miR-122) mimics and an miR-122 negative control in the treatment and control groups, respectively. The injections were performed every 3 days for 5 times (on days 0, 3, 6, 9, and 12). Before each injection and at the experiment ending, 2-dimensional ultrasound imaging was performed for tumor size measurement with RECIST and computing a quantitative imaging analysis with ultrasomics. To analyze the tumor perfusion by mRECIST, perfusion parameters were analyzed offline based on dynamic contrast-enhanced ultrasound image videos using SonoLiver software (TomTec, Unterschleissheim, Germany) on day 13. Tumor miR-122 expression was then analyzed by real-time reverse transcription-polymerase chain reaction experiments. RESULTS: Tumors in mice treated with miR-122 mimics demonstrated a mean ± SD 763- ± 60-fold increase in miR-122 levels compared with tumors in the control group. With RECIST, a significant therapeutic response evaluated by tumor size changes was detected after day 9 (days 9, 12, and 13; P < .001). With mRECIST, no parameters showed significant differences (P > .05). Significant different features of the 2-dimensional ultrasound images between the groups were detected by the ultrasomics analysis, and the model could be successfully built. The ultrasomics score values between the groups were statistically significant after day 6 (days 6, 9, 12, and 13; P < .05). CONCLUSIONS: Ultrasomics revealed significant changes after the second injection of miR-122, showing the potential as an important imaging biomarker for gene therapy.

Comparison between M-score and LR-M in the reporting system of contrast-enhanced ultrasound LI-RADS.

OBJECTIVE: To develop a contrast-enhanced ultrasound (CEUS) M-score and compare it with LR-M in CEUS Liver Imaging Reporting and Data System (LI-RADS). METHODS: We retrospectively enrolled 105 consecutive high-risk patients with hepatocellular carcinoma (HCC) and 105 with intrahepatic cholangiocarcinoma (ICC). The subjects were selected by propensity score matching between November 2003 and December 2017. A CEUS M-score for predicting ICC was constructed based on specific CEUS features by the least absolute shrinkage and selection operator regularised regression. M-score was used to develop a modified CEUS LI-RADS. The diagnostic performance of the modified CEUS LI-RADS using M-score for diagnosing HCC and ICC was compared with American College of Radiology (ACR) CEUS LI-RADS using LR-M. RESULTS: The most useful features for ICC were as follows: poorly circumscribed (69.52%), rim enhancement (63.81%), early washout (92.38%), intratumoural vein (56.19%), obscure boundary of intratumoural non-enhanced area (57.14%), and marked washout (59.05%, all p < 0.001). For predicting ICC, the M-score had a higher specificity (88.57% vs. 63.81%) with lower sensitivity (89.52% vs. 95.24%) compared with LR-M. For diagnosing HCC, the sensitivity of modified LI-RADS (80.95%) was much higher than that of ACR LI-RADS (57.14%), but the specificity was lower (90.48% vs. 96.19%). The area under the curve (AUC) of modified LI-RADS (0.857) was much higher than that of ACR LI-RADS (0.767, p = 0.0001). The modified positive predictive value (PPV) of ACR LI-RADS and modified LI-RADS were 99.42% and 98.99%, respectively. CONCLUSIONS: The modified LI-RADS with M-score had higher sensitivity for diagnosing HCC and higher specificity for diagnosing ICC than ACR LI-RADS. KEY POINTS: • For predicting ICC, the M-score had a higher specificity (88.57% vs. 63.81%) with lower sensitivity (89.52% vs. 95.24%) compared with LR-M. • A CEUS M-score for predicting ICC consisted of more detailed CEUS features (poorly circumscribed, rim enhancement, early washout, intratumoural vein, obscure boundary of intratumoural non-enhanced area, and marked washout) was constructed. • For diagnosing HCC, the sensitivity of modified LI-RADS (80.95%) was much higher than that of ACR LI-RADS (57.14%), but the specificity was lower (90.48% vs. 96.19%). The modified positive predictive value (PPV) of ACR LI-RADS and modified LI-RADS were 99.42% and 98.99%, respectively.

Differentiation of intrahepatic cholangiocarcinoma from hepatocellular carcinoma in high-risk patients: A predictive model using contrast-enhanced ultrasound.

AIM: To develop a contrast-enhanced ultrasound (CEUS) predictive model for distinguishing intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) in high-risk patients. METHODS: This retrospective study consisted of 88 consecutive high-risk patients with ICC and 88 high-risk patients with HCC selected by propensity score matching between May 2004 and July 2016. Patients were assigned to two groups, namely, a training set and validation set, at a 1:1 ratio. A CEUS score for diagnosing ICC was generated based on significant CEUS features. Then, a nomogram based on the CEUS score was developed, integrating the clinical data. The performance of the nomogram was then validated and compared with that of the LR-M of the CEUS Liver Imaging Reporting and Data System (LI-RADS). RESULTS: The most useful CEUS features for ICC were as follows: rim enhancement (64.5%), early washout (91.9%), intratumoral vein (58.1%), obscure boundary of intratumoral non-enhanced area (64.5%), and marked washout (61.3%, all P < 0.05). In the validation set, the area under the curve (AUC) of the CEUS score (AUC = 0.953) for differentiation between ICC and HCC was improved compared to the LI-RADS (AUC = 0.742) (P < 0.001). When clinical data were added, the CEUS score nomogram was superior to the LI-RADS nomogram (AUC: 0.973 vs 0.916, P = 0.036, Net Reclassification Improvement: 0.077, Integrated Discrimination Index: 0.152). Subgroup analysis demonstrated that the CEUS score model was notably improved compared to the LI-RADS in tumors smaller than 5.0 cm (P < 0.05) but not improved in tumors smaller than 3.0 cm (P > 0.05). CONCLUSION: The CEUS predictive model for differentiation between ICC and HCC in high-risk patients had improved discrimination and clinical usefulness compared to the CEUS LI-RADS.

Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics.

AIMS: To establish multiparametric radiomics of rectal tumor for the preoperative prediction of lymph node (LN) metastasis. MATERIALS AND METHODS: This prospective study consisted of 115 consecutive patients with rectal carcinoma between April 2015 and April 2017. The multiparametric radiomics scores were extracted from the endorectal ultrasound (ERUS), computed tomography (CT) and shear-wave elastography (SWE) features of the rectal tumor, LN, and peripheral tissues. The three radiomics scores were generated. Further validation as an independent predictor was performed using multivariate logistic regression together with clinical data, and a nomogram was subsequently developed. The predictive performance of the multiparametric radiomics nomogram was compared with that of conventional radiomics. KEY FINDINGS: All three scores (ERUS, CT, and SWE) were significantly higher in patients with LN metastasis than in patients with negative LN metastasis (all P < 0.05) in both training and validation set. Multivariate analysis indicated that CT and SWE scores were independent risk variables (odds ratio, OR = 6.764 and 5.482, respectively). In validation cohort, the multiparametric radiomics nomogram showed the highest predictive accuracy for LN metastasis, with a concordance index (C-index) of 0.857 compared with the conventional radiomics nomogram (C-index, 0.703, P = 0.100), resulting in a significantly improved net reclassification index (NRI) (P < 0.05) and integrated discriminatory improvement (IDI) (P = 0.002). Decision curve analysis showed that the multiparametric radiomics nomogram had a higher overall net benefit. SIGNIFICANCE: Multiparametric radiomics of rectal cancer, which captures blood supply and stiffness phenotypes, is a useful tool for predicting LN metastasis preoperatively and has marked discrimination accuracy compared to conventional radiomics.