Thermal ablation versus liver resection for hepatocellular carcinoma in patients with cirrhosis: a systematic review and meta-analysis of propensity-score matched studies.

The outcomes of cirrhotic patients with hepatocellular carcinoma (HCC) after thermal ablation (TA) versus liver resection (LR) are debated. We aimed to compare the overall survival (OS), disease-free survival (DFS), and operative outcomes after TA and LR for HCC in patients with cirrhosis. Until November 15, 2022, we searched PubMed, Embase, and Cochrane databases by using Medical Subject Heading terms and other terms, and used the Newcastle-Ottawa literature evaluation scale to assess the quality of selected studies. OS, DFS, and operative outcomes were extracted and analyzed. The meta-analysis showed that 5 propensity-score matched (PSM) studies including 933 patients (463 TA vs. 470 LR) were included. After analysis, TA and LR had similar results at 1-year OS (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.01-2.78; P = 0.05) and 3-year OS (OR 0.76; 95% CI 0.56-1.04; P = 0.08), whereas LR increased 5-years OS (OR 0.37; 95% CI 0.18-0.74; P = 0.005). In addition to the DFS, the 1-year DFS was significantly higher in patients with LR. However, there were no obvious differences in 3-year and 5-year DFS when comparing TA and LR. The length of operative time and hospital stay were longer in the LR group. Besides, the LR group had significantly higher rate of perioperative blood transfusions and major complications. Our research proved that LR took advantage of OS and DFS for HCC patients with cirrhosis. Additional well-designed randomized controlled trials are needed.

A risk stratification system developed to predict contralateral incidental malignant foci in early papillary thyroid carcinoma preoperatively.

BACKGROUND: In clinical practice, contralateral incidental malignant foci (CIMFs) can be found in some early (cT1N0M0) papillary thyroid carcinomas (PTCs) on postoperative pathological examination. To screen out the patients with high risk of CIMF preoperatively would help in determining the extent of thyroid surgery. METHODS: From October 2016 to February 2021, 332 patients diagnosed with early (cT1N0M0) PTC who underwent total thyroidectomy were included and randomly allocated into a training dataset (n = 233) and a test dataset (n = 99). Demographic and clinicopathological features were recorded and analyzed using logistic regression analysis. A coefficient-based nomogram was developed and validated. RESULTS: Logistic regression analyses revealed that the predictive model including BRAF V600E mutation, multifocality and margin of the contralateral nodule achieved the best diagnostic performance. The nomogram showed good discrimination, with AUCs of 0.795 (95 % CI, 0.736-0.853) for the training set and 0.726 (95 % CI, 0.609-0.843) for the test set. The calibration curve of the nomogram presented good agreement. CONCLUSION: The risk stratification system can be used to quantify the probability of CIMF and may assist in helping the patients choose total thyroidectomy or thyroid lobectomy with early (cT1N0M0) PTC.

Rho GTPase-activating protein 1 promotes hepatocellular carcinoma progression via modulation by CircPIP5K1A/MiR-101-3p.

AIM: There has been an increased focus on regulating cell function with Rho family GTPases, including proliferation, migration/invasion, polarity, and adhesion. Due to the challenges involved in targeting Rho family GTPases directly, it may be more effective to target their regulators, such as Rho GTPase-activating protein 1 (ARHGAP1). This present research was performed to define the clinical significance of ARHGAP1 expression, as well as its regulatory mechanisms in hepatocellular carcinoma. METHODS: ARHGAP1 and miR-101-3p expression of liver cancer patients, and their relevance with clinicopathological characteristics and prognosis were analyzed by the Cancer Genome Atlas sequencing data, and verified using samples of hepatocellular carcinoma patients. The interactions between miR-101-3p and ARHGAP1 or circPIP5K1A were validated by bioinformatic analyses, as well as confirmed by quantitative reverse transcription polymerase chain reaction, western blotting, and dual-luciferase reporter analysis. Plate clonality assays, cell adhesion and migration experiments, and proliferation experiments were used for assessing the participation of the circPIP5K1A/miR-101-3p/ARHGAP1 pathway in cell proliferation and motility. RESULTS: Elevated ARHGAP1 and reduced miR-101-3p expression are related to poorer survival. MiR-101-3p targets ARHGAP1 to suppress hepatocellular carcinoma cell colony formation and invasion, whereas miR-101-3p inhibitor reverses liver cancer proliferation and metastasis suppression caused by ARHGAP1 knockdown. In addition, circPIP5K1A, which is mainly distributed in the cytosol, showed carcinogenic effects by sponging miR-101-3p, thus regulating ARHGAP1 expression. CONCLUSIONS: ARHGAP1 serves as an oncogenic gene in liver cancer, and the expression thereof is regulated by circPIP5K1A through sponging miR-101-3p.

Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis.

(1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87-91), the specificity was 90% (95% CI: 87-92), and the AUC was 0.95 (95% CI: 0.93-0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field.

Nanoparticles (NPs)-mediated Siglec15 silencing and macrophage repolarization for enhanced cancer immunotherapy.

T cell infiltration and proliferation in tumor tissues are the main factors that significantly affect the therapeutic outcomes of cancer immunotherapy. Emerging evidence has shown that interferon-gamma (IFNγ) could enhance CXCL9 secretion from macrophages to recruit T cells, but Siglec15 expressed on TAMs can attenuate T cell proliferation. Therefore, targeted regulation of macrophage function could be a promising strategy to enhance cancer immunotherapy via concurrently promoting the infiltration and proliferation of T cells in tumor tissues. We herein developed reduction-responsive nanoparticles (NPs) made with poly (disulfide amide) (PDSA) and lipid-poly (ethylene glycol) (lipid-PEG) for systemic delivery of Siglec15 siRNA (siSiglec15) and IFNγ for enhanced cancer immunotherapy. After intravenous administration, these cargo-loaded could highly accumulate in the tumor tissues and be efficiently internalized by tumor-associated macrophages (TAMs). With the highly concentrated glutathione (GSH) in the cytoplasm to destroy the nanostructure, the loaded IFNγ and siSiglec15 could be rapidly released, which could respectively repolarize macrophage phenotype to enhance CXCL9 secretion for T cell infiltration and silence Siglec15 expression to promote T cell proliferation, leading to significant inhibition of hepatocellular carcinoma (HCC) growth when combining with the immune checkpoint inhibitor. The strategy developed herein could be used as an effective tool to enhance cancer immunotherapy.

External Validation of Ultrasound Radiomics for Small (≤ 4 cm) Renal Mass Differentiation: A Comparison with Radiologists.

BACKGROUND: Renal cell carcinoma, especially in small renal masses (≤ 4 cm) (SRM), has increased. Pathological analysis revealed a high proportion of benign masses, highlighting the urgent need for precise SRM differentiation. OBJECTIVES: This research aimed to independently validate the performance of machine learning-based ultrasound (US) radiomics analysis in differentiating benign from malignant SRM, and to compare its performance with that of radiologists. METHODS: A total of 499 patients from two hospitals were retrospectively included in this study and divided into two cohorts. US images were used to extract radiomics features. To obtain the most robust features, inter-observer correlation coefficient, Spearman correlation coefficient, and least absolute shrinkage and selection operator methods were applied for feature selection. Three models were developed in the training data using the stochastic gradient boosting algorithm, including a clinical model, a radiomics model, and a combined model that integrated clinical factors and radiomics features. The performance of these models was evaluated in the independent external validation data, including discrimination, calibration, and clinical usefulness, and compared with pooled radiologists' assessments. RESULTS: The AUCs of the clinical, radiomics, and combined models were 0.844, 0.942, and 0.954, respectively. The radiomics and combined models significantly outperformed the clinical model (all p < 0.05), while no significant difference was observed between them (p = 0.32). The radiomics and combined models showed good discrimination and calibration. Decision curve analysis exhibited that the combined model had clinical usefulness. Compared with the pooled radiologists' assessment (AUC, 0.799), the combined model showed superior classification results (p < 0.01) and higher specificity (p < 0.01) with similar sensitivity (p = 0.62). CONCLUSION: The combined model incorporating clinical factors and radiomics features accurately distinguished benign from malignant SRM.

Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System in Hepatocellular Carcinoma ≤5 cm: Biological Characteristics and Patient Outcomes.

Introduction: The present study aimed to evaluate the influence of biological characteristics of hepatocellular carcinoma (HCC) on the Liver Imaging Reporting and Data System (LI-RADS) v2017 category of contrast-enhanced ultrasound (CEUS) in patients with high risk and compare the outcomes among different categories after radical resection. Methods: Between June 2017 and December 2020, standardized CEUS data of liver nodules were prospectively collected from multiple centers across China. We conducted a retrospective analysis of the prospectively collected data on HCCs measuring no more than 5 cm, as diagnosed by pathology. LI-RADS categories were assigned after thorough evaluation of CEUS features. Then, CEUS LI-RADS categories and major features were compared in different differentiation, Ki-67, and microvascular invasion (MVI) statuses. Differences in recurrence-free survival (RFS) among different LI-RADS categories were further analyzed. Results: A total of 293 HCC nodules in 293 patients were included. This study revealed significant differences in the CEUS LI-RADS category of HCCs among differentiation (p < 0.001) and levels of Ki-67 (p = 0.01) and that poor differentiation (32.7% in LR-M, 12% in LR-5, and 6.2% in LR-4) (p < 0.001) and high level of Ki-67 (median value 30%) were more frequently classified into the LR-M category, whereas well differentiation (37.5% in LR-4, 15.1% in LR-5, and 11.5% in LR-M) and low levels of Ki-67 (median value 11%) were more frequently classified into the LR-4 category. No significant differences were found between MVI and CEUS LI-RADS categories (p > 0.05). With a median follow-up of 23 months, HCCs assigned to different CEUS LI-RADS classes showed no significant differences in RFS after resection. Conclusions: Biological characteristics of HCC, including differentiation and level of Ki-67 expression, could influence major features of CEUS and impact the CEUS LI-RADS category. HCCs in different CEUS LI-RADS categories showed no significant differences in RFS after resection.

ZEB1-mediated biogenesis of circNIPBL sustains the metastasis of bladder cancer via Wnt/ß-catenin pathway.

BACKGROUND: Circular RNAs (circRNAs) circularized by back-splicing of pre-mRNA are widely expressed and affected the proliferation, invasion and metastasis of bladder cancer (BCa). However, the mechanism underlying circRNA biogenesis in mediating the distant metastasis of BCa still unexplored. METHODS: RNA sequencing data between BCa and normal adjacent tissues was applied to identify the differentially expressed circRNAs. The functions of circNIPBL in BCa were investigated via a series of biochemical experiments. The Clinical significance of circNIPBL was examined in a cohort of larger BCa tissues. RESULTS: In the present study, we identified a novel circRNA (hsa_circ_0001472), circNIPBL, which was significantly upregulated and had great influence on the poor prognosis of patients with BCa. Functionally, circNIPBL promotes BCa metastasis in vitro and in vivo. Mechanistically, circNIPBL upregulate the expression of Wnt5a and activated the Wnt/ß-catenin signaling pathway via directly sponged miR-16-2-3p, leading to the upregulation of ZEB1, which triggers the EMT of BCa. Moreover, we revealed that ZEB1 interacted with the flanking introns of exons 2-9 on NIPBL pre-mRNA to trigger circNIPBL biogenesis, thus forming a positive feedback loop. Importantly, circNIPBL overexpression significantly facilitated the distant metastasis of BCa in the orthotopic bladder cancer model, while silencing ZEB1 remarkably blocked the effects of metastasis induced by circNIPBL overexpression. CONCLUSIONS: Our study highlights that circNIPBL-induced Wnt signaling pathway activation triggers ZEB1-mediated circNIPBL biogenesis, which forms a positive feedback loop via the circNIPBL/miR-16-2-3p/Wnt5a/ZEB1 axis, supporting circNIPBL as a novel therapeutic target and potential biomarker for BCa patients.

Multifunctional Redox-Responsive Nanoplatform with Dual Activation of Macrophages and T Cells for Antitumor Immunotherapy.

High expression of programmed death ligand 1 (PD-L1) and strong immune evasion ability of the tumor microenvironment (TME) are maintained through mutual regulation between different immune and stromal cells, which causes obstructions for cancer immunotherapy, especially immunosuppressive M2-like phenotype tumor-associated macrophages (TAMs). Repolarization of TAMs to the M1-like phenotype could secrete proinflammatory cytokines and reverse the immunosuppressive state of the TME. However, we found that reactive oxygen species (ROS) generated by repolarized TAMs could be a double-edged sword: ROS cause a stronger suppressive effect on CD8 T cells through an increased proportion of apoptotic regulatory T (Treg) cells. Thus, simply repolarizing TAMs while ignoring the suppressed function of T cells is insufficient for generating adequate antitumor immunity. Accordingly, we engineered multifunctional redox-responsive nanoplatform NPs (M+C+siPD-L1) with Toll-like receptor agonist (M), catalase (C), and siPD-L1 encased for coregulation of both TAMs and T cells to maximize cancer immunotherapy. Our results demonstrated that NPs (M+C+siPD-L1) showed superior biocompatibility and intratumor accumulation. For in vitro experiments, NPs (M+C+siPD-L1) simultaneously repolarized TAMs to the M1-like phenotype, hydrolyzed extra ROS, knocked down the expression of PD-L1 on tumor cells, and rescued the function of CD8 T cells suppressed by Treg cells. In both orthotopic Hepa1-6 and 4T1 tumor-bearing mouse models, NPs (M+C+siPD-L1) could effectively evoke active systemic antitumor immunity and inhibit tumor growth. The combination of repolarizing TAMs, hydrolyzing extra ROS, and knocking down the expression of PD-L1 proves to be a synergistic approach in cancer immunotherapy.

Qualitative and quantitative features of deep endometriosis in contrast-enhanced ultrasound: An initial experience and literature review.

PURPOSE: This research aims to present the findings of contrast-enhanced ultrasound (CEUS) in a series of patients with proven deep endometriosis (DE) and provide an updated literature review. MATERIALS AND METHODS: Between January 2018 and October 2022, seven patients with DE lesions had their imaging and medical records retrospectively reviewed. Clinical data, recorded images of a standardized conventional B-mode ultrasound, and Sonovue® CEUS were interpreted by two blinded, independent, experienced radiologists in consensus. The enhanced characteristics of the DE lesion on CEUS were also assessed using VueBox® software quantitatively. RESULTS: DE lesion appeared as irregular hypoechoic or heterogeneous on conventional ultrasound with dotted blood flow signal on color Doppler. Six of seven DE lesions showed heterogeneous hypo-enhancement in arterial phases. All the lesions showed a heterogeneous washout rapidly that began in the late arterial phase. In quantified analysis, the mean relative peak enhancement compared with adjacent tissue was 0.47±0.25. CONCLUSION: Our findings and literature review suggested that CEUS could be a feasible and promising non-invasive modality for diagnosing DE.

Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study.

BACKGROUND: Ultrasonography is the most widely used technique to diagnose echinococcosis; however, challenges in using this technique and the demand on medical resources, especially in low-income or remote areas, can delay diagnosis. We aimed to develop a deep convolutional neural network (DCNN) model based on ultrasonography to identify echinococcosis and its types, especially alveolar echinococcosis. METHODS: This retrospective, large-scale, multicentre study used ultrasound images from patients assessed at 84 hospitals in China, obtained between Jan 1, 2002, and Dec 31, 2021. Patients with a diagnosis of cystic echinococcosis, alveolar echinococcosis, or seven other types of focal liver lesions were included. We tested ResNet-50, ResNext-50, and VGG-16 as the backbone network architecture for a classification DCNN model and input the perinodular information from the ultrasound images. We trained and validated the DCNN model to diagnose and classify echinococcosis using still greyscale ultrasound images of focal liver lesions in four stages: differentiating between echinococcosis and other focal liver lesions (stage one); differentiating cystic echinococcosis, alveolar echinococcosis, and other focal liver lesions (stage two); differentiating cystic echinococcosis, alveolar echinococcosis, benign other focal liver lesions, and malignant focal liver lesions (stage three); and differentiating between active and transitional cystic echinococcosis and inactive cystic echinococcosis (stage four). We then tested the algorithm on internal, external, and prospective test datasets. The performance of DCNN was also compared with that of 12 radiologists recruited between Jan 15, 2022, and Jan 28, 2022, from Qinghai, Xinjiang, Anhui, Henan, Xizang, and Beijing, China, with different levels of diagnostic experience for echinococcosis and other focal liver lesions in a subset of ultrasound data that were randomly chosen from the prospective test dataset. The study is registered at ClinicalTrials.gov (NCT03871140). FINDINGS: The study took place between Jan 1, 2002, and Dec 31, 2021. In total, to train and test the DCNN model, we used 9631 liver ultrasound images from 6784 patients (2819 [41·7%] female patients and 3943 [58·3%] male patients) from 87 Chinese hospitals. The DCNN model was trained with 6328 images, internally validated with 984 images, and tested with 2319 images. The ResNet-50 network architecture outperformed VGG-16 and ResNext-50 and was generalisable, with areas under the receiver operating characteristic curve (AUCs) of 0·982 (95% CI 0·960-0·994), 0·984 (0·972-0·992), and 0·913 (0·886-0·935) in distinguishing echinococcosis from other focal liver lesions; 0·986 (0·966-0·996), 0·962 (0·946-0·975), and 0·900 (0·872-0·924) in distinguishing alveolar echinococcosis from cystic echinococcosis and other focal liver lesions; and 0·974 (0·818-1·000), 0·956 (0·875-0·991), and 0·944 (0·844-0·988) in distinguishing active and transitional cystic echinococcosis from inactive echinococcosis in the three test datasets. Specifically, in patients with the hepatitis B or hepatitis C virus, the model could distinguish alveolar echinococcosis from hepatocellular carcinoma with an AUC of 0·892 (0·812-0·946). In identifying echinococcosis, the model showed significantly better performance compared with senior radiologists from a high-endemicity area (AUC 0·942 [0·904-0·967] vs 0·844 [0·820-0·866]; p=0·027) and improved the diagnostic ability of junior, attending, and senior radiologists before and after assistance with AI with comparison of AUCs of 0·743 (0·714-0·770) versus 0·850 (0·826-0·871); p<0·0001, 0·808 (0·782-0·832) versus 0·886 (0·864-0·905); p<0·0001, and 0·844 (0·820-0·866) versus 0·870 (0·847-0·890); p=0·092, respectively. INTERPRETATION: The DCNN model was shown to be accurate and robust, and could improve the ultrasound diagnostic ability of radiologists for echinococcosis and its types for highly endemic and remote regions. FUNDING: National Natural Science Foundation of China and National Key Research & Development Program of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

HMGB1 released from dead tumor cells after insufficient radiofrequency ablation promotes progression of HCC residual tumor via ERK1/2 pathway.

BACKGROUND: Radiofrequency ablation (RFA) is a first-line treatment for early-stage hepatocellular carcinoma (HCC). However, the recurrence after RFA remains an urgent challenge. Current studies have shown that residual tumor after RFA is an important cause of recurrence. OBJECTIVE: We hypothesized that the products of dead tumor cells after RFA have direct effects on the development of residual tumors. Further, we investigated the underlying mechanisms. METHODS: The proliferation and invasion ability of HepG2 and Huh7 cells were assessed using CCK-8, colony formation, EdU, transwell invasion and migration assay. Immunofluorescence and western blotting were used to show HMGB1 released from dead tumor cells. The levels of MMP2, MMP9, CyclinE1 and pERK1/2 were determined using western blotting. Finally, in vivo validation was performed in BALB/c nude mice xenograft tumor models. RESULTS: The products of dead tumor cells after thermal treatment can promote the proliferation and invasion of residual HCC cells. Dead tumor cells could release high-mobility group box 1 (HMGB1) after thermal treatment. Similar to the products of dead tumor cells, the recombinant protein of HMGB1 can promote the proliferation and invasion of residual HCC cells. Moreover, HMGB1 could bind to receptor of advanced glycation end-products. Then, it activated the ERK1/2 pathway and significantly upregulated the expressions of MMP2, MMP9, and CyclinE1. CONCLUSION: Our study reveals that HMGB1 released by dead tumor cells after thermal treatment can promote the proliferation and invasion of residual HCC cells. Hence, the HMGB1/RAGE/ERK1/2 pathway is a potential target for improving the prognosis of HCC after radiofrequency ablation.

Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.

OBJECTIVES: To establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Reporting and Data System (BI-RADS) categories simultaneously. METHODS: This multicenter study prospectively collected a dataset of ultrasound images for 5012 patients at thirty-two hospitals from December 2018 to December 2020. A deep learning (DL) model was developed to conduct binary categorization (benign and malignant) and BI-RADS categories (2, 3, 4a, 4b, 4c, and 5) simultaneously. The training set of 4212 patients and the internal test set of 416 patients were from thirty hospitals. The remaining two hospitals with 384 patients were used as an external test set. Three experienced radiologists performed a reader study on 324 patients randomly selected from the test sets. We compared the performance of the DL model with that of three radiologists and the consensus of the three radiologists. RESULTS: In the external test set, the DL model achieved areas under the receiver operating characteristic curve (AUCs) of 0.980 and 0.945 for the binary categorization and six-way categorizations, respectively. In the reader study set, the DL BI-RADS categories achieved a similar AUC (0.901 vs. 0.933, p = 0.0632), sensitivity (90.98% vs. 95.90%, p = 0.1094), and accuracy (83.33% vs. 79.01%, p = 0.0541), but higher specificity (78.71% vs. 68.81%, p = 0.0012) than those of the consensus of the three radiologists. CONCLUSIONS: The DL model performed well in distinguishing benign from malignant breast lesions and yielded outcomes similar to experienced radiologists. This indicates the potential applicability of the DL model in clinical diagnosis. KEY POINTS: • The DL model can achieve binary categorization for benign and malignant breast lesions and six-way BI-RADS categorizations for categories 2, 3, 4a, 4b, 4c, and 5, simultaneously. • The DL model showed acceptable agreement with radiologists for the classification of breast lesions. • The DL model performed well in distinguishing benign from malignant breast lesions and had promise in helping reduce unnecessary biopsies of BI-RADS 4a lesions.

Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma based on B-Mode US and CEUS.

OBJECTIVES: To develop a preoperative prediction model to identify macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) and evaluate the model's diagnostic performance in differentiating MTM-HCC from HCC. METHODS: We conducted a mono-center retrospective study in a grade A tertiary hospital in China. Consecutive patients with suspected HCC from February 2019 to December 2020 were eligible for inclusion. All consenting patients underwent CEUS examination and were histologically diagnosed. Based on the clinical and US features between the two groups, we developed a binary logistic regression model and a nomogram for predicting MTM-HCC. RESULTS: A total of 161 patients (median age, 57 years; interquartile range, 48-64 years; 129 men) were included in the analysis. Twenty-seven of the HCCs (16.8%) were of the MTM subtype. Binary logistic regression analysis indicated that PVP hypoenhancement (OR = 15.497; 95% CI: 1.369, 175.451; p = 0.027), AFP > 454.6 ng/mL (OR = 8.658; 95% CI: 3.030, 24.741; p < 0.001), ALB ≤ 29.9 g/L (OR = 3.937; 95% CI: 1.017, 15.234; p = 0.047), halo sign (OR = 3.868; 95% CI: 1.314, 11.391; p = 0.016), and intratumoral artery (OR = 2.928; 95% CI: 1.039, 8.255; p = 0.042) were predictors for MTM subtype. Combining any two criteria showed a high sensitivity (100.0%); combining all five criteria showed a high specificity (99.2%); and the AUC value of the logistic regression model was 0.88 (95% CI: 0.81, 0.92). CONCLUSIONS: BMUS and CEUS could be used for identifying patients suspected of having MTM-HCC. Combining clinical information, BMUS, and CEUS features could achieve a noninvasive diagnosis of MTM-HCC. KEY POINTS: • Contrast-enhanced ultrasound examination helps clinicians to identify MTM-HCCs preoperatively. • PVP hypoenhancement, high AFP levels, low ALB levels, halo signs, and intratumoral arteries could be used to predict MTM-HCCs. • A logistic regression model and nomogram were built to noninvasively diagnose MTM-HCCs with an AUC value of 0.88 (95% CI: 0.81, 0.92).

Gail model and fifth edition of ultrasound BI-RADS help predict axillary lymph node metastasis in breast cancer-A multicenter prospective study.

RATIONALE AND OBJECTIVES: We aim to assess the performance of the Gail model and the fifth edition of ultrasound BI-RADS (Breast Imaging Reporting and Data System) in breast cancer for predicting axillary lymph node metastasis (ALNM). MATERIALS AND METHODS: We prospectively studied 958 female patients with breast cancer between 2018 and 2019 from 35 hospitals in China. Based on B-mode, color Doppler, and elastography, radiologists classified the degree of suspicion based on the fifth edition of BI-RADS. Individual breast cancer risk was assessed with the Gail model. The association between the US BI-RADS category and the Gail model in terms of ALNM was analyzed. RESULTS: We found that US BI-RADS category was significantly and independently associated with ALNM (P < 0.001). The sensitivity, specificity, and accuracy of BI-RADS category 5 for predicting ALNM were 63.6%, 71.6%, and 68.6%, respectively. Combining the Gail model with the BI-RADS category showed a significantly higher sensitivity than using the BI-RADS category alone (67.8% vs. 63.6%, P < 0.001). The diagnostic accuracy of the BI-RADS category combined with the Gail model was better than that of the Gail model alone (area under the curve: 0.71 vs. 0.50, P < 0.001). CONCLUSION: Based on the conventional ultrasound and elastography, the fifth edition of ultrasound BI-RADS category could be used to predict the ALNM of breast cancer. ALNM was likely to occur in patients with BI-RADS category 5. The Gail model could improve the diagnostic sensitivity of the US BI-RADS category for predicting ALNM in breast cancer.

Fully Automatic Dual-Probe Lung Ultrasound Scanning Robot for Screening Triage.

Two-dimensional lung ultrasound (LUS) has widely emerged as a rapid and noninvasive imaging tool for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, image differences will be magnified due to changes in ultrasound (US) imaging experience, such as US probe attitude control and force control, which will directly affect the diagnosis results. In addition, the risk of virus transmission between sonographer and patients is increased due to frequent physical contact. In this study, a fully automatic dual-probe US scanning robot for the acquisition of LUS images is proposed and developed. Furthermore, the trajectory was optimized based on the velocity look-ahead strategy, the stability of contact force of the system and the scanning efficiency were improved by 24.13% and 29.46%, respectively. Also, the control ability of the contact force of robotic automatic scanning was 34.14 times higher than that of traditional manual scanning, which significantly improves the smoothness of scanning. Importantly, there was no significant difference in image quality obtained by robotic automatic scanning and manual scanning. Furthermore, the scanning time for a single person is less than 4 min, which greatly improves the efficiency of screening triage of group COVID-19 diagnosis and suspected patients and reduces the risk of virus exposure and spread.

ACR TI-RADS classification combined with number of nodules, halo features optimizes diagnosis and prediction of follicular thyroid cancer.

OBJECTIVES: To investigate the application value of The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) category combined with other ultrasound features of nodules in distinguishing follicular thyroid carcinoma (FTC) from thyroid follicular adenoma (FTA). METHODS: We collected and retrospectively analyzed clinical and ultrasound data for 118 and 459 patients with FTCs and FTAs, respectively, at our hospital. Next, we used ACR TI-RADS classification combined with other ultrasound features of nodules to distinguish FTC from FTA. Multivariate Logistic regression was used to screen independent risk factors for FTC, which were subsequently used to construct a nomogram for predicting FTC. RESULTS: ACR TI-RADS categories 4 and 5, unilateral multiple nodules, and halo thickness≥2 mm were independent risk factors for FTC. ACR TI-RADS category combined with number of nodules, halo features of the nodule was a significantly better prediction model for FTC diagnosis (AUC = 0.869) than that of ACR TI-RADS classification alone (AUC = 0.756). CONCLUTIONS: Clinicians need to pay attention to the halo of nodules when distinguishing FTA from FTC. Notably, ACR TI-RADS combined with other nodule ultrasound features has superior predictive performance in diagnosis of FTC compared to ACR TI-RADS classification alone, thus can provide an important reference value for preoperative diagnosis of FTC.

Utility of Sonazoid-Enhanced Ultrasound for the Macroscopic Classification of Hepatocellular Carcinoma: A Meta-analysis.

We assessed the diagnostic value of Sonazoid-enhanced ultrasound (SEUS) in determining the macroscopic classification of hepatocellular carcinoma (HCC) because of its strong relevance to the poor prognosis of the non-simple nodular (non-SN) type. The PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for studies investigating patients who underwent surgery for HCC after undergoing SEUS pre-operatively. Five studies involving a total of 334 patients met the inclusion criteria. The summary sensitivity and specificity were 0.74 (95% confidence interval [CI]: 0.63-0.83) and 0.92 (95% CI: 0.82-0.97), respectively. The positive and negative likelihood ratios of SEUS for determining the macroscopic classification of HCC in Kupffer phase were 9.21 (95% CI: 4.02-21.13) and 0.28 (95% CI: 0.19-0.41), respectively. The diagnostic odds ratio of SEUS for determining the macroscopic classification of HCC was 34.2 (95% CI: 11.64-100.51), and the area under the summary receiver operating characteristic curve was 0.87 (95% CI: 0.84-0.90). Subgroup analysis suggested that small HCCs (≤30 mm) and studies including fewer than 70 patients may be associated with a higher diagnostic odds ratio than the corresponding subsets. SEUS had moderate diagnostic value for determining the macroscopic classification of HCC in the Kupffer phase.

Innovative signature establishment using lymphangiogenesis-related lncRNA pairs to predict prognosis of hepatocellular carcinoma.

Aims: Hepatocellular carcinoma (HCC) remains a major tumoral burden globally, and its heterogeneity encumbers prognostic prediction. The lymphangiogenesis-related long non-coding RNAs (lrlncRNAs) reported to be implicated in immune response regulation show potential importance in predicting the prognostic and therapeutic outcome. Hence, this study aims to establish a lrlncRNA pairs-based signature not requiring specific expression levels of transcripts, which displays promising clinical practicality and satisfactory predictive capability. Main methods: Transcriptomic and clinical information of the Liver Hepatocellular Carcinoma (LIHC) project retrieved from the TCGA portal were used to find differently expressed lrlncRNA (DElrlncRNA) via analysis performed between lymphangiogenesis-related genes (lr-genes) and lncRNAs(lrlncRNA), and to ultimately construct the signature based on lrlncRNA pairs screened out via Lasso and Cox regression analyses. Akaike information criterion (AIC) values were computed to find the cut-off point optimum for high-risk and low-risk group allocation. The signature then underwent trials in terms of its predictive value for survival, clinicopathological features, immune cells infiltration in tumoral microenvironment, selected checkpoint biomarkers and chemosensitivity. Key findings: A novel lymphangiogenesis-related lncRNA pair signature was established using nine lrlncRNA pairs identified and significantly related to overall survival, clinicopathological features, immune cells infiltration and susceptibility to chemotherapy. Moreover, the signature efficacy was verified in acknowledged clinicopathological subgroups and partially validated by qRT-PCR assay in various human HCC cell lines. Significance: The novel lrlncRNA-pairs based signature was shown to effectively and independently estimate HCC prognosis and help screen patients suitable for anti-tumor immunotherapy and chemotherapy.

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study.

BACKGROUND: Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model's ability to assist the radiologists. METHODS: A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model's ability to assist the radiologists using two different methods. RESULTS: The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities. CONCLUSIONS: The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.