Predicting Prognosis of Phyllodes Tumors Using a Mammography- and Magnetic Resonance Imaging-Based Radiomics Model: A Preliminary Study.

PURPOSE: To investigate whether a radiomics model based on mammography (MG) and magnetic resonance imaging (MRI) can be used to predict disease-free survival (DFS) after phyllodes tumor (PT) surgery. METHOD: About 131 PT patients who underwent MG and MRI before surgery between January 2010 and December 2020 were retrospectively enrolled, including 15 patients with recurrence and metastasis and 116 without recurrence. 884 and 3138 radiomic features were extracted from MG and MR images, respectively. Then, multiple radiomics models were established to predict the recurrence risk of the patients by applying a support vector machine classifier. The area under the ROC curve (AUC) was calculated to evaluate model performance. After dividing the patients into high- and low-risk groups based on the predicted radiomics scores, survival analysis was conducted to compare differences between the groups. RESULTS: In total, 3 MG-related and 5 MRI-related radiomic models were established; the prediction performance of the T1WI feature fusion model was the best, with an AUC value of 0.93. After combining the features of MG and MRI, the AUC increased to 0.95. Furthermore, the MG, MRI and all-image radiomic models had statistically significant differences in survival between the high- and low-risk groups (P < .001). All-image radiomics model showed higher survival performance than the MG and MRI radiomics models alone. CONCLUSIONS: Radiomics features based on preoperative MG and MR images can predict DFS after PT surgery, and the prediction score of the image radiomics model can be used as a potential indicator of recurrence risk.

Mitochondrial dynamics, quality control, and mtDNA in alcohol-associated liver disease and liver cancer.

Mitochondria are intracellular organelles responsible for energy production, glucose and lipid metabolism, cell death, cell proliferation, and innate immune response. Mitochondria are highly dynamic organelles that constantly undergo fission, fusion, and intracellular trafficking, as well as degradation and biogenesis. Mitochondrial dysfunction has been implicated in a variety of chronic liver diseases including alcohol-associated liver disease, metabolic dysfunction-associated steatohepatitis, and HCC. In this review, we provide a detailed overview of mitochondrial dynamics, mitophagy, and mitochondrial DNA-mediated innate immune response, and how dysregulation of these mitochondrial processes affects the pathogenesis of alcohol-associated liver disease and HCC. Mitochondrial dynamics and mitochondrial DNA-mediated innate immune response may thereby represent an attractive therapeutic target for ameliorating alcohol-associated liver disease and alcohol-associated HCC.

TMEM120B strengthens breast cancer cell stemness and accelerates chemotherapy resistance via ß1-integrin/FAK-TAZ-mTOR signaling axis by binding to MYH9.

BACKGROUND: Breast cancer stem cell (CSC) expansion results in tumor progression and chemoresistance; however, the modulation of CSC pluripotency remains unexplored. Transmembrane protein 120B (TMEM120B) is a newly discovered protein expressed in human tissues, especially in malignant tissues; however, its role in CSC expansion has not been studied. This study aimed to determine the role of TMEM120B in transcriptional coactivator with PDZ-binding motif (TAZ)-mediated CSC expansion and chemotherapy resistance. METHODS: Both bioinformatics analysis and immunohistochemistry assays were performed to examine expression patterns of TMEM120B in lung, breast, gastric, colon, and ovarian cancers. Clinicopathological factors and overall survival were also evaluated. Next, colony formation assay, MTT assay, EdU assay, transwell assay, wound healing assay, flow cytometric analysis, sphere formation assay, western blotting analysis, mouse xenograft model analysis, RNA-sequencing assay, immunofluorescence assay, and reverse transcriptase-polymerase chain reaction were performed to investigate the effect of TMEM120B interaction on proliferation, invasion, stemness, chemotherapy sensitivity, and integrin/FAK/TAZ/mTOR activation. Further, liquid chromatography-tandem mass spectrometry analysis, GST pull-down assay, and immunoprecipitation assays were performed to evaluate the interactions between TMEM120B, myosin heavy chain 9 (MYH9), and CUL9. RESULTS: TMEM120B expression was elevated in lung, breast, gastric, colon, and ovarian cancers. TMEM120B expression positively correlated with advanced TNM stage, lymph node metastasis, and poor prognosis. Overexpression of TMEM120B promoted breast cancer cell proliferation, invasion, and stemness by activating TAZ-mTOR signaling. TMEM120B directly bound to the coil-coil domain of MYH9, which accelerated the assembly of focal adhesions (FAs) and facilitated the translocation of TAZ. Furthermore, TMEM120B stabilized MYH9 by preventing its degradation by CUL9 in a ubiquitin-dependent manner. Overexpression of TMEM120B enhanced resistance to docetaxel and doxorubicin. Conversely, overexpression of TMEM120B-∆CCD delayed the formation of FAs, suppressed TAZ-mTOR signaling, and abrogated chemotherapy resistance. TMEM120B expression was elevated in breast cancer patients with poor treatment outcomes (Miller/Payne grades 1-2) than in those with better outcomes (Miller/Payne grades 3-5). CONCLUSIONS: Our study reveals that TMEM120B bound to and stabilized MYH9 by preventing its degradation. This interaction activated the ß1-integrin/FAK-TAZ-mTOR signaling axis, maintaining stemness and accelerating chemotherapy resistance.

Components of the tumor immune microenvironment based on m-IHC correlate with prognosis and subtype of triple-negative breast cancer.

BACKGROUND AND AIM: The spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple-negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m-IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m-IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system. METHODS: The clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD-L1, and Pan-CK markers were stained by m-IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes. RESULTS: The components of the TIME score were established by m-IHC and demonstrated positive prognostic value for TNBC (p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD-L1+ cell% in the SA, CD3 + cell% in the TC, and PD-L1+ /CD8+ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes. CONCLUSION: We demonstrated that the m-IHC-based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.

Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images.

Objective.Epidermal growth factor receptor (EGFR) mutation genotyping plays a pivotal role in targeted therapy for non-small cell lung cancer (NSCLC). We aimed to develop a computed tomography (CT) image-based hybrid deep radiomics model to predict EGFR mutation status in NSCLC and investigate the correlations between deep image and quantitative radiomics features.Approach.First, we retrospectively enrolled 818 patients from our centre and 131 patients from The Cancer Imaging Archive database to establish a training cohort (N= 654), an independent internal validation cohort (N= 164) and an external validation cohort (N= 131). Second, to predict EGFR mutation status, we developed three CT image-based models, namely, a multi-task deep neural network (DNN), a radiomics model and a feature fusion model. Third, we proposed a hybrid loss function to train the DNN model. Finally, to evaluate the model performance, we computed the areas under the receiver operating characteristic curves (AUCs) and decision curve analysis curves of the models.Main results.For the two validation cohorts, the feature fusion model achieved AUC values of 0.86 ± 0.03 and 0.80 ± 0.05, which were significantly higher than those of the single-task DNN and radiomics models (allP< 0.05). There was no significant difference between the feature fusion and the multi-task DNN models (P> 0.8). The binary prediction scores showed excellent prognostic value in predicting disease-free survival (P= 0.02) and overall survival (P< 0.005) for validation cohort 2.Significance.The results demonstrate that (1) the feature fusion and multi-task DNN models achieve significantly higher performance than that of the conventional radiomics and single-task DNN models, (2) the feature fusion model can decode the imaging phenotypes representing NSCLC heterogeneity related to both EGFR mutation and patient NSCLC prognosis, and (3) high correlations exist between some deep image and radiomics features.

ZNF500 abolishes breast cancer proliferation and sensitizes chemotherapy by stabilizing P53 via competing with MDM2.

Zinc finger protein 500 (ZNF500) has an unknown expression pattern and biological function in human tissues. Our study revealed that the ZNF500 mRNA and protein levels were higher in breast cancer tissues than those in their normal counterparts. However, ZNF500 expression was negatively correlated with advanced TNM stage (p = 0.018), positive lymph node metastasis (p = 0.014), and a poor prognosis (p < 0.001). ZNF500 overexpression abolished in vivo and in vitro breast cancer cell proliferation by activating the p53-p21-E2F4 signaling axis and directly interacting with p53 via its C2H2 domain. This may prevent ubiquitination of p53 in a manner that is competitive to MDM2, thus stabilizing p53. When ZNF500-∆C2H2 was overexpressed, the suppressed proliferation of breast cancer cells was neutralized in vitro and in vivo. In human breast cancer tissues, ZNF500 expression was positively correlated with p53 (p = 0.022) and E2F4 (p = 0.004) expression. ZNF500 expression was significantly lower in patients with Miller/Payne Grade 1-2 than in those with Miller/Payne Grade 3-5 (p = 0.012). ZNF500 suppresses breast cancer cell proliferation and sensitizes cells to chemotherapy.

Concurrent Dual-Contrast Enhancement Using Fe3O4 Nanoparticles to Achieve a CEST Signal Controllability.

Traditional T2 magnetic resonance imaging (MRI) contrast agents have defects inherent to negative contrast agents, while chemical exchange saturation transfer (CEST) contrast agents can quantify substances at trace concentrations. After reaching a certain concentration, iron-based contrast agents can "shut down" CEST signals. The application range of T2 contrast agents can be widened through a combination of CEST and T2 contrast agents, which has promising application prospects. The purpose of this study is to develop a T2 MRI negative contrast agent with a controllable size and to explore the feasibility of dual contrast enhancement by combining T2 with CEST contrast agents. The study was carried out in vitro with HCT-116 human colon cancer cells. A GE SIGNA Pioneer 3.0 T medical MRI scanner was used to acquire CEST images with different saturation radio-frequency powers (1.25/2.5/3.75/5 µT) by 2D spin echo-echo planar imaging (SE-EPI). Magnetic resonance image compilation (MAGiC) was acquired by a multidynamic multiecho 2D fast spin-echo sequence. The feasibility of this dual-contrast enhancement method was assessed by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, dynamic light scattering, ζ potential analysis, inductively coupled plasma, X-ray photoelectron spectroscopy, X-ray powder diffraction, vibrating-sample magnetometry, MRI, and a Cell Counting Kit-8 assay. The association between the transverse relaxation rate r2 and the pH of the iron-based contrast agents was analyzed by linear fitting, and the linear relationship between the CEST effect in different B1 fields and pH was analyzed by the ratio method. Fe3O4 nanoparticles (NPs) with a mean particle size of 82.6 ± 22.4 nm were prepared by a classical process, and their surface was successfully modified with -OH active functional groups. They exhibited self-aggregation in an acidic environment. The CEST effect was enhanced as the B1 field increased, and an in vitro pH map was successfully plotted using the ratio method. Fe3O4 NPs could stably serve as reference agents at different pH values. At a concentration of 30 µg/mL, Fe3O4 NPs "shut down" the CEST signals, but when the concentration of Fe3O4 NPs was less than 10 µg/mL, the two contrast agents coexisted. The prepared Fe3O4 NPs had almost no toxicity, and when their concentration rose to 200 µg/mL at pH 6.5 or 7.4, they did not reach the half-maximum inhibitory concentration (IC50). Fe3O4 magnetic NPs with a controllable size and no toxicity were successfully synthesized. By combining Fe3O4 NPs with a CEST contrast agent, the two contrast agents could be imaged simultaneously; at higher concentrations, the iron-based contrast agent "shut down" the CEST signal. An in vitro pH map was successfully plotted by the ratio method. CEST signal inhibition can be used to realize the pH mapping of solid tumors and the identification of tumor active components, thus providing a new imaging method for tumor efficacy evaluation.

Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.

DHT-Net: Dynamic Hierarchical Transformer Network for Liver and Tumor Segmentation.

Automatic segmentation of liver tumors is crucial to assist radiologists in clinical diagnosis. While various deep learningbased algorithms have been proposed, such as U-Net and its variants, the inability to explicitly model long-range dependencies in CNN limits the extraction of complex tumor features. Some researchers have applied Transformer-based 3D networks to analyze medical images. However, the previous methods focus on modeling the local information (eg. edge) or global information (eg. morphology) with fixed network weights. To learn and extract complex tumor features of varied tumor size, location, and morphology for more accurate segmentation, we propose a Dynamic Hierarchical Transformer Network, named DHT-Net. The DHT-Net mainly contains a Dynamic Hierarchical Transformer (DHTrans) structure and an Edge Aggregation Block (EAB). The DHTrans first automatically senses the tumor location by Dynamic Adaptive Convolution, which employs hierarchical operations with the different receptive field sizes to learn the features of various tumors, thus enhancing the semantic representation ability of tumor features. Then, to adequately capture the irregular morphological features in the tumor region, DHTrans aggregates global and local texture information in a complementary manner. In addition, we introduce the EAB to extract detailed edge features in the shallow fine-grained details of the network, which provides sharp boundaries of liver and tumor regions. We evaluate DHT-Net on two challenging public datasets, LiTS and 3DIRCADb. The proposed method has shown superior liver and tumor segmentation performance compared to several state-of-the-art 2D, 3D, and 2.5D hybrid models.

The efficacy of nalmefene on anesthetic recovery of patients: a study protocol for a multicenter randomized controlled trial.

INTRODUCTION: So far, the recovery quality after general anesthesia is still unsatisfied. Nalmefene is a drug to treat opioid overdose and reverse opioid actions. We aim to investigate the efficacy of nalmefene on optimizing the recovery quality of patients after general anesthesia. METHODS: It is a prospective, placebo-controlled, two-arm parallel groups, multicentre, double-blind, randomized (PPPMDR) clinical trial. The participants (n = 520) will be randomly assigned into two groups. Each patient will receive either: a single dose of nalmefene 0.25 µg/kg in the intervention group, or the same volume of 0.9% NaCl solution in the control group at the end of the surgery. The primary outcome will be the time interval between the end of anaesthesia and recovery endpoints achieved (Aldrete recovery score ≥ 9) in post-anesthesia care unit (PACU). The other variables are the time interval from the end of operation to extubation; Richmond Agitation Sedation Scale (RASS) score at extubation; the time at Montreal Cognitive Assessment Scale (MoCA) orientation score ≥ 5; visual analog scale (VAS) score and adverse effects including postoperative nausea and vomiting (PONV), and pruritus in PACU and 24 h postoperatively. ANALYSIS: This trial aims to study whether small dose of nalmefene can shorten the time from the end of surgery to Aldrete score ≥ 9 and improve opioid-induced side effects.This trial focuses on providing the reliable clinical evidence for satisfactory quality of recovery. ETHICS AND DISSEMINATION: This clinical trial has been approved and supported by the ethics committee of the Renji Hospital, Shanghai Jiaotong University, School of Medicine (KY2020-150); Shanghai Tongren Hospital (2021-030-01);The First Affiliated Hospital of Guangxi Medical University (2021-032); and The First Affiliated Hospital of Zhengzhou University(2021-KY-0495-003). Analysis of the study results will be submitted to a peer-reviewed journal for publication. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04713358, Registered on September 23, 2021.

Licorice protects against ulcerative colitis via the Nrf2/PINK1-mediated mitochondrial autophagy.

PURPOSE: Study of the effects and mechanisms of licorice in the treatment of ulcerative colitis (UC) from the perspective of mitochondrial autophagy. METHODS: BALB/C mice were induced with 3% dextran sodium sulfate to build an animal model of UC. After 7 days of modeling, different doses of licorice were administered for 7 days. Hematoxylin and eosin staining is used to detect pathological changes in the colon. Mitochondrial membrane potentials and reactive oxygen species (ROS) contents were detected by flow cytometry, and autophagy of mitochondria was observed by transmission electron microscopy. Determination of inflammatory cytokines by enzyme-linked immunosorbent assay. The oxidizing factors are detected by the kits. Western blot analysis was used to detect expressions for nuclear factor called erythropoietin (Nrf2), pten-induced protein kinase 1 (PINK1), Parkin, HO-1, P62, and LC3. RESULTS: Licorice improved the pathological condition of UC mice, increasing the mitochondrial membrane potential and decreasing the ROS content. Promotes the emergence of autophagosomes and autophagosomes. The contents of interleukin (IL)-1ß, IL-6, IL-17, and tumor necrosis factor-alpha were downregulated, the contents of superoxide dismutase and glutathione peroxidase were upregulated and the contents of malondialdehyde were downregulated. In addition, licorice promotes the expression of Nrf2, PINK1, Parkin, HO-1, P62, and LC3. CONCLUSION: Licorice was shown to reduce levels of inflammatory factors and oxidative stress in mice with UC, possibly by promoting mitochondrial autophagy through the activation of the Nrf2/PINK1 pathway.

Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors.

BACKGROUND: Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). PURPOSE: To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. STUDY TYPE: Retrospective. POPULATION: A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). ASSESSMENT: A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. STATISTICAL TESTS: Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. RESULTS: In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. DATA CONCLUSION: Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Cystic Neoplasms of the Pancreas: Differential Diagnosis and Radiology Correlation.

Although the probability of pancreatic cystic neoplasms (PCNs) being detected is raising year by year, their differential diagnosis and individualized treatment are still a challenge in clinical work. PCNs are tumors containing cystic components with different biological behaviors, and their clinical manifestations, epidemiology, imaging features, and malignant risks are different. Some are benign [e.g., serous cystic neoplasms (SCNs)], with a barely possible that turning into malignant, while others display a low or higher malignant risk [e.g., solid pseudopapillary neoplasms (SPNs), intraductal papillary mucinous neoplasms (IPMNs), and mucinous cystic neoplasms (MCNs)]. PCN management should concentrate on preventing the progression of malignant tumors while preventing complications caused by unnecessary surgical intervention. Clinically, various advanced imaging equipment are usually combined to obtain a more reliable preoperative diagnosis. The challenge for clinicians and radiologists is how to accurately diagnose PCNs before surgery so that corresponding surgical methods and follow-up strategies can be developed or not, as appropriate. The objective of this review is to sum up the clinical features, imaging findings and management of the most common PCNs according to the classic literature and latest guidelines.

Hepatocytic p62 suppresses ductular reaction and tumorigenesis in mouse livers with mTORC1 activation and defective autophagy.

BACKGROUND & AIMS: Either activation of mTORC1 due to loss of Tsc1 (tuberous sclerosis complex 1) or defective hepatic autophagy due to loss of Atg5 leads to spontaneous liver tumorigenesis in mice. The purpose of this study was to investigate the mechanisms by which autophagy contributes to the hepatic metabolic changes and tumorigenesis mediated by mTORC1 activation. METHODS: Atg5 Flox/Flox (Atg5F/F) and Tsc1F/F mice were crossed with albumin-Cre mice to generate liver-specific Atg5 knockout (L-Atg5 KO), L-Tsc1 KO and L-Atg5/Tsc1 double KO (DKO) mice. These mice were crossed with p62/Sqstm1F/F (p62) and whole body Nrf2 KO mice to generate L-Atg5/Tsc1/p62 and L-Atg5/Tsc1-Nrf2 triple KO mice. These mice were housed for various periods up to 12 months, and blood and liver tissues were harvested for biochemical and histological analysis RESULTS: Deletion of Atg5 in L-Tsc1 KO mice inhibited liver tumorigenesis but increased mortality and was accompanied by drastically enhanced hepatic ductular reaction (DR), hepatocyte degeneration and metabolic reprogramming. Deletion of p62 reversed DR, hepatocyte degeneration and metabolic reprogramming as well as the mortality of L-Atg5/Tsc1 DKO mice, but unexpectedly promoted liver tumorigenesis via activation of a group of oncogenic signaling pathways. Nrf2 ablation markedly improved DR with increased hepatocyte population and improved metabolic reprogramming and survival of the L-Atg5/Tsc1 DKO mice without tumor formation. Decreased p62 and increased mTOR activity were also observed in a subset of human hepatocellular carcinomas. CONCLUSIONS: These results reveal previously undescribed functions of hepatic p62 in suppressing tumorigenesis and regulating liver cell repopulation and metabolic reprogramming resulting from persistent mTORC1 activation and defective autophagy. LAY SUMMARY: Metabolic liver disease and viral hepatitis are common chronic liver diseases and risk factors of hepatocellular carcinoma, which are often associated with impaired hepatic autophagy and increased mTOR activation. Using multiple genetically engineered mouse models of defective hepatic autophagy and persistent mTOR activation, we dissected the complex mechanisms behind this observation. Our results uncovered an unexpected novel tumor suppressor function of p62/Sqstm1, which regulated liver cell repopulation, ductular reaction and metabolic reprogramming in liver tumorigenesis.

Predicting the pathological grade of breast phyllodes tumors: a nomogram based on clinical and magnetic resonance imaging features.

OBJECTIVE: To explore the potential factors related to the pathological grade of breast phyllodes tumors (PTs) and to establish a nomogram to improve their differentiation ability. METHODS: Patients with PTs diagnosed by post-operative pathology who underwent pretreatment magnetic resonance imaging (MRI) from January 2015 to June 2020 were retrospectively reviewed. Traditional clinical features and MRI features evaluated according to the fifth BI-RADS were analyzed by statistical methods and introduced to a stepwise multivariate logistic regression analysis to develop a prediction model. Then, a nomogram was developed to graphically predict the probability of non-benign (borderline/malignant) PTs. RESULTS: Finally, 61 benign, 73 borderline and 48 malignant PTs were identified in 182 patients. Family history of tumor, diameter, lobulation, cystic component, signal on fat saturated T2 weighted imaging (FS T2WI), BI-RADS category and time-signal intensity curve (TIC) patterns were found to be significantly different between benign and non-benign PTs. The nomogram was finally developed based on five risk factors: family history of tumor, lobulation, cystic component, signal on FS T2WI and internal enhancement. The AUC of the nomogram was 0.795 (95% CI: 0.639, 0.835). CONCLUSION: Family history of tumor, lobulation, cystic components, signals on FS T2WI and internal enhancement are independent predictors of non-benign PTs. The prediction nomogram developed based on these features can be used as a supplemental tool to pre-operatively differentiate PTs grades. ADVANCES IN KNOWLEDGE: More sample size and characteristics were used to explore the factors related to the pathological grade of PTs and establish a predictive nomogram for the first time.

Imaging characteristics of a rare case of monostotic fibrous dysplasia of the sacrum: A case report.

BACKGROUND: Fibrous dysplasia (FD) is a common benign intramedullary fibro-osseous lesion. Involvement of the spine is rare, with the literature including only case reports, and cases of monostotic FD (MFD) in the sacrum are extremely rare. A correct preoperative diagnosis of spinal MFD is important for clinicians to select proper treatment. CASE SUMMARY: We retrospectively assessed a case report of MFD in the sacrum. This patient was examined by computed tomography (CT) and magnetic resonance imaging (MRI), and the diagnosis was confirmed by pathology. A review of the literature was performed to analyze the imaging characteristics and differential diagnoses of spinal MFD. For our patient, the CT scan showed the lesion to be expansile, with ground glass opacity and a sclerotic rim. On MRI, the lesion showed iso-low signal intensity on T1WI and iso-high signal intensity on T2WI. A low signal rim was found on T1WI and T2WI. Our patient was treated by posterior focal excision, decompression, bone grafting, fusion and pedicle screw fixation. A satisfactory result was achieved, with pain disappearance. No complications had occurred at the 1-year follow up. CONCLUSION: MFD is an expansile osteolytic change. Ground glass opacity and a sclerotic margin are obvious characteristics. The lesion often involves the vertebral body and posterior element. Knowledge of these imaging characteristics of spinal FD could be helpful for diagnosis and prevent unnecessary procedures.

Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features.

PURPOSE: To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification. PATIENTS AND METHODS: Consecutive mammograms from January 2013 to December 2018 were retrospectively reviewed. Traditional clinical features were recorded, and mammographic features were estimated according to the 5th BI-RADS. Included calcifications were randomly divided into the training and validation cohorts. A nomogram was developed to graphically predict the risk of malignancy (risk) based on stepwise multivariate logistic regression analysis. The discrimination and calibration performance of the model were assessed in both the training and validation cohorts. RESULTS: Finally, 1018 amorphous calcifications with final pathological results in 907 women were identified with a malignancy rate of 28.4% (95% CI: 25.7%, 31.3%). The malignancy rates of subgroups divided by the distribution of calcifications, quantity of calcifications, age, menopausal status and family history of cancer were significantly different. There were 712 cases and 306 cases in the training and validation cohorts. The prediction nomogram was finally developed based on four risk factors, including age and distribution, maximum diameter and quantity of calcifications. The AUC of the nomogram was 0.799 (95% CI: 0.761, 0.836) in the training cohort and 0.795 (95% CI: 0.738, 0.852) in the validation cohort. CONCLUSION: On mammography, the distribution, maximum diameter and quantity of calcifications are independent predictors of malignant amorphous calcifications and can be easily obtained in the clinic. The nomogram developed in this study for individualized malignancy risk stratification of amorphous calcifications shows good discrimination performance.

Ferroptosis-Inducing Nanomedicine for Cancer Therapy.

Ferroptosis, a new iron- and reactive oxygen species-dependent form of regulated cell death, has attracted much attention in the therapy of various types of tumors. With the development of nanomaterials, more and more evidence shows the potential of ferroptosis combined with nanomaterials for cancer therapy. Recently, there has been much effort to develop ferroptosis-inducing nanomedicine, specially combined with the conventional or emerging therapy. Therefore, it is necessary to outline the previous work on ferroptosis-inducing nanomedicine and clarify directions for improvement and application to cancer therapy in the future. In this review, we will comprehensively focus on the strategies of cancer therapy based on ferroptosis-inducing nanomedicine currently, elaborate on the design ideas of synthesis, analyze the advantages and limitations, and finally look forward to the future perspective on the emerging field.

Role and Mechanisms of Mitophagy in Liver Diseases.

The mitochondrion is an organelle that plays a vital role in the regulation of hepatic cellular redox, lipid metabolism, and cell death. Mitochondrial dysfunction is associated with both acute and chronic liver diseases with emerging evidence indicating that mitophagy, a selective form of autophagy for damaged/excessive mitochondria, plays a key role in the liver's physiology and pathophysiology. This review will focus on mitochondrial dynamics, mitophagy regulation, and their roles in various liver diseases (alcoholic liver disease, non-alcoholic fatty liver disease, drug-induced liver injury, hepatic ischemia-reperfusion injury, viral hepatitis, and cancer) with the hope that a better understanding of the molecular events and signaling pathways in mitophagy regulation will help identify promising targets for the future treatment of liver diseases.