Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study.

OBJECTIVES: To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. METHODS: This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). RESULTS: The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784-0.844) in the training cohort, 0.776 (95% CI, 0.727-0.825) in the testing cohort, and 0.702 (95% CI, 0.614-0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810-0.866) in the training cohort, 0.788 (95% CI, 0.741-0.835) in the testing cohort, and 0.722 (95% CI, 0.637-0.811) in the external validation cohort. CONCLUSIONS: Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. CLINICAL RELEVANCE STATEMENT: By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. KEY POINTS: • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.

Distinct MRI characteristics of spinal cord diffuse midline glioma, H3 K27-altered in comparison to spinal cord glioma without H3 K27-alteration and demyelination disorder.

BACKGROUND: An applicable magnetic resonance imaging (MRI) biomarker for diffuse midline glioma (DMG), H3 K27-altered of the spinal cord is important for non-invasive diagnosis. PURPOSE: To evaluate the efficacy of conventional MRI (cMRI) in distinguishing between DMGs, H3 K27-altered, gliomas without H3 K27-alteration, and demyelinating lesions in the spinal cord. MATERIAL AND METHODS: Between January 2017 and February 2023, patients with pathology-confirmed spinal cord gliomas (including ependymomas) with definite H3 K27 status and demyelinating diseases diagnosed by recognized criteria were recruited as the training set for this retrospective study. Morphologic parameter assessment was performed by two neuroradiologists on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted imaging. Variables with high inter- and intra-observer agreement were included in univariable correlation analysis and multivariable logistic regression. The performance of the final model was verified by internal and external testing sets. RESULTS: The training cohort included 21 patients with DMGs (13 men; mean age = 34.57 ± 13.489 years), 21 with wild-type gliomas (10 men; mean age = 46.76 ± 17.017 years), and 20 with demyelinating diseases (5 men; mean age = 49.50 ± 18.872 years). A significant difference was observed in MRI features, including cyst(s), hemorrhage, pial thickening with enhancement, and the maximum anteroposterior diameter of the spinal cord. The prediction model, integrating age, age2, and morphological characteristics, demonstrated good performance in the internal and external testing cohort (accuracy: 0.810 and 0.800, specificity: 0.810 and 0.720, sensitivity: 0.872 and 0.849, respectively). CONCLUSION: Based on cMRI, we developed a model with good performance for differentiating among DMGs, H3 K27-altered, wild-type glioma, and demyelinating lesions in the spinal cord.

Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images.

OBJECTIVES: Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs). METHODS: In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs. RESULTS: The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05). CONCLUSIONS: Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification. CLINICAL RELEVANCE STATEMENT: Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure. KEY POINTS: • The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.

Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study.

OBJECTIVES: Edema is a complication of gamma knife radiosurgery (GKS) in meningioma patients that leads to a variety of consequences. The aim of this study is to construct radiomics-based machine learning models to predict post-GKS edema development. METHODS: In total, 445 meningioma patients who underwent GKS in our institution were enrolled and partitioned into training and internal validation datasets (8:2). A total of 150 cases from multicenter data were included as the external validation dataset. In each case, 1132 radiomics features were extracted from each pre-treatment MRI sequence (contrast-enhanced T1WI, T2WI, and ADC maps). Nine clinical features and eight semantic features were also generated. Nineteen random survival forest (RSF) and nineteen neural network (DeepSurv) models with different combinations of radiomics, clinical, and semantic features were developed with the training dataset, and evaluated with internal and external validation. A nomogram was derived from the model achieving the highest C-index in external validation. RESULTS: All the models were successfully validated on both validation datasets. The RSF model incorporating clinical, semantic, and ADC radiomics features achieved the best performance with a C-index of 0.861 (95% CI: 0.748-0.975) in internal validation, and 0.780 (95% CI: 0.673-0.887) in external validation. It stratifies high-risk and low-risk cases effectively. The nomogram based on the predicted risks provided personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration. CONCLUSION: This RSF model with a nomogram could represent a non-invasive and cost-effective tool to predict post-GKS edema risk, thus facilitating personalized decision-making in meningioma treatment. CLINICAL RELEVANCE STATEMENT: The RSF model with a nomogram built in this study represents a handy, non-invasive, and cost-effective tool for meningioma patients to assist in better counselling on the risks, appropriate individual treatment decisions, and customized follow-up plans. KEY POINTS: • Machine learning models were built to predict post-GKS edema in meningioma. The random survival forest model with clinical, semantic, and ADC radiomics features achieved excellent performance. • The nomogram based on the predicted risks provides personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration and shows the potential to assist in better counselling, appropriate treatment decisions, and customized follow-up plans. • Given the excellent performance and convenient acquisition of the conventional sequence, we envision that this non-invasive and cost-effective tool will facilitate personalized medicine in meningioma treatment.

Oligodendrocyte Transcription Factor 2 as a Potential Prognostic Biomarker of Glioblastoma: Kaplan-Meier Analysis and the Development of a Binary Predictive Model Based on Visually Accessible Rembrandt Image and Magnetic Resonance Imaging Radiomic Features.

OBJECTIVE: Oligodendrocyte transcription factor 2 (OLIG2) is universally expressed in human glioblastoma (GB). Our study explores whether OLIG2 expression impacts GB patients' overall survival and establishes a machine learning model for OLIG2 level prediction in patients with GB based on clinical, semantic, and magnetic resonance imaging radiomic features. METHODS: Kaplan-Meier analysis was used to determine the optimal cutoff value of the OLIG2 in 168 GB patients. Three hundred thirteen patients enrolled in the OLIG2 prediction model were randomly divided into training and testing sets in a ratio of 7:3. The radiomic, semantic, and clinical features were collected for each patient. Recursive feature elimination (RFE) was used for feature selection. The random forest (RF) model was built and fine-tuned, and the area under the curve was calculated to evaluate the performance. Finally, a new testing set excluding IDH-mutant patients was built and tested in a predictive model using the fifth edition of the central nervous system tumor classification criteria. RESULTS: One hundred nineteen patients were included in the survival analysis. Oligodendrocyte transcription factor 2 was positively associated with GB survival, with an optimal cutoff of 10% ( P = 0.00093). One hundred thirty-four patients were eligible for the OLIG2 prediction model. An RFE-RF model based on 2 semantic and 21 radiomic signatures achieved areas under the curve of 0.854 in the training set, 0.819 in the testing set, and 0.825 in the new testing set. CONCLUSIONS: Glioblastoma patients with ≤10% OLIG2 expression tended to have worse overall survival. An RFE-RF model integrating 23 features can predict the OLIG2 level of GB patients preoperatively, irrespective of the central nervous system classification criteria, further guiding individualized treatment.

Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.

PURPOSE: This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images. METHODS: The retrospective study on T1-weighted and contrast-enhanced images of 523 meningioma patients from 3 centers between 2010 and 2020. A total of 373 cases split 8:2 for training and validation. Three independent test sets were built based on the remaining 150 cases. Six convolutional neural network detection models trained via transfer learning were evaluated using 4 metrics and receiver operating characteristic analysis. Detected images were used for segmentation. Three segmentation models were trained for meningioma segmentation and were evaluated via 4 metrics. In 3 test sets, intraclass consistency values were used to evaluate the consistency of detection and segmentation models with manually annotated results from 3 different levels of radiologists. RESULTS: The average accuracies of the detection model in the 3 test sets were 97.3%, 93.5%, and 96.0%, respectively. The model of segmentation showed mean Dice similarity coefficient values of 0.884, 0.834, and 0.892, respectively. Intraclass consistency values showed that the results of detection and segmentation models were highly consistent with those of intermediate and senior radiologists and lowly consistent with those of junior radiologists. CONCLUSIONS: The proposed deep learning system exhibits advanced performance comparable with intermediate and senior radiologists in meningioma detection and segmentation. This system could potentially significantly improve the efficiency of the detection and segmentation of meningiomas.

Effects of starvation on the pharmacokinetics and optimal dosages of florfenicol and associated serum biochemistry in Asian seabass (Lates calcarifer).

Starvation has influence on physiology and pharmacokinetic (PK) characteristics of many drugs in land animals. However, similar PK information in fish is lacking. The current study examined the effects of starvation on fish PK, taking florfenicol (FF) in Asian seabass as an example. FF was orally administered at a single dose of 10 mg/kg into 35-day starved fish reared at 25 and 30°C and the serum FF concentration was analyzed by HPLC-FLD. At 30°C, the absorption and elimination half-lives of the starved fish were increased by 30% (from 0.44 to 0.57 h) and 55% (from 7.2 to 11.18 h), respectively. The volume of distribution, clearance, and area under the curve were changed from 1.25 to 0.71 L/kg, 0.120 to 0.044 L/kg/h, and 88 to 228 h·µg/ml, respectively. Similar starvation-induced PK changes were also observed at 25°C. The serum biochemical parameters, mainly the alanine aminotransferase, aspartate aminotransferase, and glucose levels, were significantly reduced in the starvation group. Overall, FF absorption, distribution, and elimination rates were reduced by starvation, resulting in four to five times lower optimal dosage than the non-starved fish. Drug treatment in starved fish should be treated with caution as overdosing and/or tissue residues could perceivably occur.

miR-124 and VAMP3 Act Antagonistically in Human Neuroblastoma.

Neuroblastoma (NB) is the most common extracranial solid tumor that affects developing nerve cells in the fetus, infants, and children. miR-124 is a microRNA (miRNA) enriched in neuronal tissues, and VAMP3 (vesicle-associated membrane protein 3) has been reported to be an miR-124 target, although the relationship between NB and miR-124 or VAMP3 is unknown. Our current work identified that miR-124 levels are high in NB cases and that elevated miR-124 correlates with worse NB outcomes. Conversely, depressed VAMP3 correlates with worse NB outcomes. To investigate the mechanisms by which miR-124 and VAMP3 regulate NB, we altered miR-124 or VAMP3 expression in human NB cells and observed that increased miR-124 and reduced VAMP3 stimulated cell proliferation and suppressed apoptosis, while increased VAMP3 had the opposite effects. Genome-wide mRNA expression analyses identified gene and pathway changes which might explain the NB cell phenotypes. Together, our studies suggest that miR-124 and VAMP3 could be potential new markers of NB and targets of NB treatments.

The Preparation of Golgi Apparatus-Targeted Polymer Dots Encapsulated with Carbon Nanodots of Bright Near-Infrared Fluorescence for Long-Term Bioimaging.

As a vital organelle in eukaryotic cells, the Golgi apparatus is responsible for processing and transporting proteins in cells. Precisely monitoring the status of the Golgi apparatus with targeted fluorescence imaging technology is of enormous importance but remains a dramatically challenging task. In this study, we demonstrate the construction of the first Golgi apparatus-targeted near-infrared (NIR) fluorescent nanoprobe, termed Golgi-Pdots. As a starting point of our investigation, hydrophobic carbon nanodots (CNDs) with bright NIR fluorescence at 674 nm (fluorescence quantum yield: 12.18%), a narrow emission band of 23 nm, and excellent stability were easily prepared from Magnolia Denudata flowers using an ultrasonic method. Incorporating the CNDs into a polymer matrix modified with Golgi-targeting molecules allowed for the production of the water-soluble Golgi-Pdots, which showed high colloidal stability and similar optical properties compared with pristine CNDs. Further studies revealed that the Golgi-Pdots showed good biocompatibility and Golgi apparatus-targeting capability. Based on these fascinating merits, utilizing Golgi-Pdots for the long-term tracking of the Golgi apparatus inside live cells was immensely successful.

Polymorphism of E6 and E7 Genes in Human Papillomavirus Types 31 and 33 in Northeast China.

Persistent infection with human papillomavirus (HPV) types 31 and 33 is an important causative factor for cervical cancer. The E6/E7 genes are key oncogenes involved in the immortalization and transformation of human epithelial cells. Genetic polymorphism may lead to differences in the virus' carcinogenic potential, the immune reaction of the host, and the potencies of vaccines. Few studies on HPV31/33 E6/E7 genetic polymorphism have been carried out. To study the genetic polymorphism of HPV31 and HPV33 E6/E7 genes in northeast China, these genes (HPV31 E6/E7, n = 151; HPV33 E6/E7, n = 136) were sequenced and compared to reference sequences (J04353.1, M12732.1) using BioEdit. Phylogenetic trees were constructed by the neighbor-joining method using MegaX. The diversity of the secondary structure was estimated using the PSIPred server. The positively selected sites were analyzed using PAML4.9. The major histocompatibility complex (MHC) class I and MHCII epitopes were predicted using the ProPred-I server and ProPredserver. B-cell epitopes were predicted using the ABCpred server. In the 151 HPV31E6 sequences, 25 (25/450) single-nucleotide mutations were found, 14 of which were synonymous mutations and 11 were nonsynonymous. In the 151 HPV31E7 sequences, 8 (8/297) nucleotide mutations were found, 3 of which were synonymous mutations and 5 were nonsynonymous. In the 136 HPV33E6 sequences, 17 (17/450) nucleotide mutations were observed, 7 of which were synonymous mutations and 10 were nonsynonymous. C14T/G (T5I/S) was a triallelic mutation. Finally, in the 136 HPV33E7 sequences, 9 (9/294) nucleotide mutations were observed, 3 of which were synonymous mutations and 6 were nonsynonymous. C134T/A (A45V/E) and C278G/A (T93S/N) were triallelic mutations. Lineage A was the most common lineage in both HPV31 and HPV33. In all of the sequences, we only identified one positively selected site, HPV33 E6 (K93N). Most nonsynonymous mutations were localized at sites belonging to MHC and/or B-cell predicted epitopes. Data obtained in this study should contribute to the development and application of detection probes, targeted drugs, and vaccines.

A Comparative and Summative Study of Radiomics-based Overall Survival Prediction in Glioblastoma Patients.

PURPOSE: This study aimed to assess different machine learning models based on radiomic features, Visually Accessible Rembrandt Images features and clinical characteristics in overall survival prediction of glioblastoma and to identify the reproducible features. MATERIALS AND METHODS: Patients with preoperative magnetic resonance scans were allocated into 3 data sets. The Least Absolute Shrinkage and Selection Operator was used for feature selection. The prediction models were built by random survival forest (RSF) and Cox regression. C-index and integrated Brier scores were calculated to compare model performances. RESULTS: Patients with cortical involvement had shorter survival times in the training set (P = 0.006). Random survival forest showed higher C-index than Cox, and the RSF model based on the radiomic features was the best one (testing set: C-index = 0.935 ± 0.023). Ten reproducible radiomic features were summarized. CONCLUSIONS: The RSF model based on radiomic features had promising potential in predicting overall survival of glioblastoma. Ten reproducible features were identified.

Performances of clinical characteristics and radiological findings in identifying COVID-19 from suspected cases.

BACKGROUND: To identify effective factors and establish a model to distinguish COVID-19 patients from suspected cases. METHODS: The clinical characteristics, laboratory results and initial chest CT findings of suspected COVID-19 patients in 3 institutions were retrospectively reviewed. Univariate and multivariate logistic regression were performed to identify significant features. A nomogram was constructed, with calibration validated internally and externally. RESULTS: 239 patients from 2 institutions were enrolled in the primary cohort including 157 COVID-19 and 82 non-COVID-19 patients. 11 features were selected by LASSO selection, and 8 features were found significant using multivariate logistic regression analysis. We found that the COVID-19 group are more likely to have fever (OR 4.22), contact history (OR 284.73), lower WBC count (OR 0.63), left lower lobe involvement (OR 9.42), multifocal lesions (OR 8.98), pleural thickening (OR 5.59), peripheral distribution (OR 0.09), and less mediastinal lymphadenopathy (OR 0.037). The nomogram developed accordingly for clinical practice showed satisfactory internal and external validation. CONCLUSIONS: In conclusion, fever, contact history, decreased WBC count, left lower lobe involvement, pleural thickening, multifocal lesions, peripheral distribution, and absence of mediastinal lymphadenopathy are able to distinguish COVID-19 patients from other suspected patients. The corresponding nomogram is a useful tool in clinical practice.

Enhanced antibacterial behavior of a novel Cu-bearing high-entropy alloy.

Contact infection of bacteria and viruses has been a critical threat to human health. The worldwide outbreak of COVID-19 put forward urgent requirements for the research and development of the self-antibacterial materials, especially the antibacterial alloys. Based on the concept of high-entropy alloys, the present work designed and prepared a novel Co0.4FeCr0.9Cu0.3 antibacterial high-entropy alloy with superior antibacterial properties without intricate or rigorous annealing processes, which outperform the antibacterial stainless steels. The antibacterial tests presented a 99.97% antibacterial rate against Escherichia coli and a 99.96% antibacterial rate against Staphylococcus aureus after 24 h. In contrast, the classic antibacterial copper-bearing stainless steel only performed the 71.50% and 80.84% antibacterial rate, respectively. The results of the reactive oxygen species analysis indicated that the copper ion release and the immediate contact with copper-rich phase had a synergistic effect in enhancing antibacterial properties. Moreover, this alloy exhibited excellent corrosion resistance when compared with the classic antibacterial stainless steels, and the compression test indicated the yield strength of the alloy was 1015 MPa. These findings generate fresh insights into guiding the designs of structure-function-integrated antibacterial alloys.

Association between preoperative serum albumin and prognosis in patients with adrenocortical carcinoma after primary resection: a retrospective study.

BACKGROUND: Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy with a poor prognosis. Given the limited treatment options, prognostic assessment of ACC is increasingly crucial. In this study, we aim to assess the correlation between preoperative serum albumin and prognosis in patients with ACC after primary resection. METHODS: We retrospectively collected and reviewed medical information about 71 ACC patients who underwent primary resection. Survival analysis was performed by Kaplan-Meier analysis with log-rank test or Breslow test. Receiver operating characteristic (ROC) curve and Jordan index was generated to explore optimal cut-off value of albumin. Univariate and multivariate analysis was conducted using Cox's hazards model. Statistical significance was defined as P < 0.05. RESULTS: Among included patients, 33 patients (46.5%) relapsed at the end of follow-up, while 39 patients (54.9%) died. The median overall survival (OS) of included patients was 17 (range 1-104) months, and median recurrence-free survival (RFS) was 10 (range 0-104) months. In univariate analysis, the albumin was significantly associated with OS (HR:0.491, 95% CI: 0.260-0.930, P = 0.029) and RFS (HR: 0.383, 95% CI: 0.192-0.766, P = 0.007). In multivariate analysis, serum albumin as an independent prognostic factor of OS was confirmed (HR: 0.351, 95% CI: 0.126-0.982, P = 0.046). CONCLUSIONS: Preoperative albumin might be a significant prognostic factor for ACC patients after primary resection. This result may be useful for risk stratification and management of this rare malignancy.

Benefit of Postoperative Radiotherapy for Patients With Nonmetastatic Adrenocortical Carcinoma: A Population-Based Analysis.

BACKGROUND: Adrenocortical carcinoma (ACC) is an aggressive cancer with high recurrence rates and poor prognosis, even after radical surgery. The survival benefit of adjuvant radiotherapy (RT) in patients with ACC has not been well explored. The aim of this study was to evaluate the effect of adjuvant RT on the survival outcome of patients with ACC. PATIENTS AND METHODS: All patients with nonmetastatic ACC who underwent complete resection were identified from the SEER database (2004-2016). Overall survival (OS) was estimated using the Kaplan-Meier method. Multivariable Cox regression analysis was performed to identify prognostic factors associated with survival. RESULTS: Of 365 patients with nonmetastatic ACC, 55 (15.1%) received adjuvant RT and the remainder underwent surgery alone. Patient characteristics were similar between the 2 groups, but those with a higher disease stage were more likely to receive adjuvant RT. Overall, patients receiving RT seemed to have better survival compared with the non-RT group (3-year OS rate, 63.1% vs 52.8%; P<.062). After adjustment for confounding factors, adjuvant RT was indeed associated with a 48% decreased risk of death (hazard ratio, 0.52; 95% CI, 0.29-0.91; P=.023) for all patients. In addition, adjuvant RT may confer a survival benefit only in patients with a high risk of recurrence (3-year OS rate, 55.1% vs 40.0%; P=.048) rather than in those with low/moderate-risk ACC (P=.559). CONCLUSIONS: Our findings suggest that adjuvant RT may be associated with improved survival in patients with nonmetastatic ACC who underwent radical surgery, especially those with high risk of recurrence.

The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study.

OBJECTIVES: Based on the current clinical routine, we aimed to develop a novel deep learning model to distinguish coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia and validate it with a real-world dataset (RWD). METHODS: A total of 563 chest CT scans of 380 patients (227/380 were diagnosed with COVID-19 pneumonia) from 5 hospitals were collected to train our deep learning (DL) model. Lung regions were extracted by U-net, then transformed and fed to pre-trained ResNet-50-based IDANNet (Identification and Analysis of New covid-19 Net) to produce a diagnostic probability. Fivefold cross-validation was employed to validate the application of our model. Another 318 scans of 316 patients (243/316 were diagnosed with COVID-19 pneumonia) from 2 other hospitals were enrolled prospectively as the RWDs to testify our DL model's performance and compared it with that from 3 experienced radiologists. RESULTS: A three-dimensional DL model was successfully established. The diagnostic threshold to differentiate COVID-19 and non-COVID-19 pneumonia was 0.685 with an AUC of 0.906 (95% CI: 0.886-0.913) in the internal validation group. In the RWD cohort, our model achieved an AUC of 0.868 (95% CI: 0.851-0.876) with the sensitivity of 0.811 and the specificity of 0.822, non-inferior to the performance of 3 experienced radiologists, suggesting promising clinical practical usage. CONCLUSIONS: The established DL model was able to achieve accurate identification of COVID-19 pneumonia from other suspected ones in the real-world situation, which could become a reliable tool in clinical routine. KEY POINTS: • In an internal validation set, our DL model achieved the best performance to differentiate COVID-19 from non-COVID-19 pneumonia with a sensitivity of 0.836, a specificity of 0.800, and an AUC of 0.906 (95% CI: 0.886-0.913) when the threshold was set at 0.685. • In the prospective RWD cohort, our DL diagnostic model achieved a sensitivity of 0.811, a specificity of 0.822, and AUC of 0.868 (95% CI: 0.851-0.876), non-inferior to the performance of 3 experienced radiologists. • The attention heatmaps were fully generated by the model without additional manual annotation and the attention regions were highly aligned with the ROIs acquired by human radiologists for diagnosis.

Adrenal Surgery for Synchronously Metastatic Adrenocortical Carcinoma: A Population-Based Analysis.

BACKGROUND: Metastatic adrenocortical carcinoma (ACC) is an aggressive cancer with poor prognosis, with limited treatment options. The survival benefit of adrenal surgery in patients with synchronous metastatic disease has not been well explored. METHODS: Patients with ACC with synchronous metastases were identified from the Surveillance, Epidemiology, and End Results database (2010-2016). The effect of adrenal surgery on different patterns of distant metastases was assessed. The overall survival was estimated by the Kaplan-Meier method. Multivariable Cox regression analysis was performed to identify prognostic factors associated with survival outcome. RESULTS: A total of 202 patients with synchronous metastatic ACC were identified from the SEER database, 76 (37.6%) patients underwent adrenal surgery. Compared to nonsurgical patients, patients who underwent adrenal surgery had a better survival (median overall survival: 4 vs. 13 months, P < 0.001). In sub-analyses, except for patients with liver metastases (P = 0.670), adrenalectomy could consistently confer a significant survival benefit in patients with lung metastases (P = 0.003), bone metastases (P = 0.020), and multiple metastases (P = 0.002). Cox regression analysis revealed that in addition to adrenalectomy [hazard ratio (HR) = 0.64, 95% confidence interval (CI) 0.45-0.92; P = 0.017], metastasectomy (HR = 0.48, 95% CI 0.26-0.86; P = 0.013), and chemotherapy (HR = 0.59, 95% CI 0.42-0.82; P = 0.002) were also associated with improved survival. CONCLUSIONS: Our findings support the view that adrenal surgery may be associated with improved survival in patients with synchronous metastatic ACC (except for patients with liver metastases), and the metastatic sites have significant prognostic implications on survival outcomes with adrenal surgery.

Comparative effectiveness and safety of intravesical instillation treatment of interstitial cystitis/bladder pain syndrome: a systematic review and network meta-analysis of randomized controlled trials.

INTRODUCTION AND HYPOTHESIS: A large variety of agents are available for intravesical instillation treatment of interstitial cystitis/bladder pain syndrome (IC/BPS). The purpose of the study was to compare the efficacy and safety of those agents. METHODS: PubMed, the Cochrane Library, and Embase were searched from database inception to February 2020 for randomized controlled trials. The language of publication was limited in English. Population, intervention, comparison, outcome, and study design was used to assess the eligible studies for inclusion and the Cochrane Collaboration's risk of bias tool was used to assess the methodological quality of the studies included. The primary outcome was O'Leary-Sant Interstitial Cystitis Problem Index (ICPI) and O'Leary-Sant Interstitial Cystitis Symptom Index (ICSI) improvement. RESULTS: Eleven randomized controlled trials covering 8 agents with 902 patients were enrolled. According to the results of the ICPI and ICSI, 0.1 µM resiniferatoxin was more effective than other therapies. Combination therapy of hyaluronic acid and chondroitin sulphate ranked second in ICSI, third in ICPI, and first in the visual analog scale (VAS). Among regimens included for complication comparison, chondroitin sulphate was safer than other agents, with a probability of 78.5%. CONCLUSIONS: Resiniferatoxin (0.1 µM) is more effective at ICPI and ICSI improvement than other agents. More well-designed randomized controlled trials with a large sample size directly comparing the efficacy and safety of those agents are in need in the future to confirm our findings.

piRBase: a comprehensive database of piRNA sequences.

PIWI-interacting RNAs are a class of small RNAs that is most abundantly expressed in animal germline. Substantial research is going on to reveal the functions of piRNAs in the epigenetic and post-transcriptional regulation of transposons and genes. To collect and annotate these data, we developed piRBase, a database assisting piRNA functional study. Since its launch in 2014, piRBase has integrated 264 data sets from 21 organisms, and the number of collected piRNAs has reached 173 million. The latest piRBase release (v2.0, 2018) was more focused on the comprehensive annotation of piRNA sequences, as well as the increasing number of piRNAs. In addition, piRBase release v2.0 also contained the potential information of piRNA targets and disease related piRNA. All datasets in piRBase is free to access, and available for browse, search and bulk downloads at http://www.regulatoryrna.org/database/piRNA/.

Primary tumour resection for synchronously metastatic phaeochromocytoma and paraganglioma: A population-based study.

CONTEXT: Until recently, there are few effective treatment options for patients with synchronous metastatic phaeochromocytoma (PHEO) and paraganglioma (PGL). Surgical resection may improve the survival outcomes of these patients. OBJECTIVE: To assess the role of surgical resection of the primary tumour in patients with synchronous metastatic PHEO and PGL. DESIGN: Retrospective analysis of patients with synchronous metastatic PHEO/PGL using the Surveillance, Epidemiology, and End Results database (1988-2016). PATIENTS: Patients with synchronous metastatic PHEO/PGL who underwent primary tumour resection. MEASUREMENTS: Overall survival and Cox regression analyses. RESULTS: A total of 99 patients with metastatic PHEO and 127 metastatic PGL patients were identified from the SEER database. Compared to metastatic PHEO, metastatic PGL patients had a better overall survival (5-year survival rate: 33.3% vs. 49.0%, p = .001). In metastatic PHEO patients, 53 (53.5%) patients underwent surgery for primary site. Surgically treated patients had an improved survival compared to non-surgery patients (5-year survival rate: 50.9% vs. 29.6%, p = .017). Among metastatic PGL patients, primary tumour resection was performed in 74 (58.3%) patients and had no significant effect on the survival of metastatic PGL. In sub-analyses, surgery only conferred a survival benefit in patients with primary tumours originated from aortic/carotid bodies, rather than other sites or abdominal tumours. CONCLUSION: Our findings suggest that primary tumour resection is associated with improved survival in patients with synchronous metastatic PHEO and those with PGL diseases located in aortic/carotid bodies. In addition, PHEO and PGL should be treated as two distinct clinical entities.