OBJECTIVE: Diagnosing subcentimeter solid pulmonary nodules (SSPNs) remains challenging in clinical practice. Deep learning may perform better than conventional methods in differentiating benign and malignant pulmonary nodules. This study aimed to develop and validate a model for differentiating malignant and benign SSPNs using CT images.
METHODS: This retrospective study included consecutive patients with SSPNs detected between January 2015 and October 2021 as an internal dataset. Malignancy was confirmed pathologically; benignity was confirmed pathologically or via follow-up evaluations. The SSPNs were segmented manually. A self-supervision pre-training-based fine-grained network was developed for predicting SSPN malignancy. The pre-trained model was established using data from the National Lung Screening Trial, Lung Nodule Analysis 2016, and a database of 5478 pulmonary nodules from the previous study, with subsequent fine-tuning using the internal dataset. The model\'s efficacy was investigated using an external cohort from another center, and its accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined.
RESULTS: Overall, 1276 patients (mean age, 56 ± 10 years; 497 males) with 1389 SSPNs (mean diameter, 7.5 ± 2.0 mm; 625 benign) were enrolled. The internal dataset was specifically enriched for malignancy. The model\'s performance in the internal testing set (316 SSPNs) was: AUC, 0.964 (95% confidence interval (95%CI): 0.942-0.986); accuracy, 0.934; sensitivity, 0.965; and specificity, 0.908. The model\'s performance in the external test set (202 SSPNs) was: AUC, 0.945 (95% CI: 0.910-0.979); accuracy, 0.911; sensitivity, 0.977; and specificity, 0.860.
CONCLUSIONS: This deep learning model was robust and exhibited good performance in predicting the malignancy of SSPNs, which could help optimize patient management.

OBJECTIVE: To evaluate the diagnostic performance of quantitative magnetic resonance (MR) imaging biomarkers in distinguishing between inflammatory pancreatic masses (IPM) and pancreatic cancer (PC).
METHODS: A literature search was conducted using PubMed, Embase, the Cochrane Library, and Web of Science through August 2023. Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to evaluate the risk of bias and applicability of the studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the DerSimonian-Laird method. Univariate meta-regression analysis was used to identify the potential factors of heterogeneity.
RESULTS: Twenty-four studies were included in this meta-analysis. The two main types of IPM, mass-forming pancreatitis (MFP) and autoimmune pancreatitis (AIP), differ in their apparent diffusion coefficient (ADC) values. Compared with PC, the ADC value was higher in MFP but lower in AIP. The pooled sensitivity/specificity of ADC were 0.80/0.85 for distinguishing MFP from PC and 0.82/0.84 for distinguishing AIP from PC. The pooled sensitivity/specificity for the maximal diameter of the upstream main pancreatic duct (dMPD) was 0.86/0.74, with a cutoff of dMPD ≤ 4 mm, and 0.97/0.52, with a cutoff of dMPD ≤ 5 mm. The pooled sensitivity/specificity for perfusion fraction (f) was 0.82/0.68, and 0.82/0.77 for mass stiffness values.
CONCLUSIONS: Quantitative MR imaging biomarkers are useful in distinguishing between IPM and PC. ADC values differ between MFP and AIP, and they should be separated for consideration in future studies.
CONCLUSIONS: Quantitative MR parameters could serve as non-invasive imaging biomarkers for differentiating malignant pancreatic neoplasms from inflammatory masses of the pancreas, and hence help to avoid unnecessary surgery.
CONCLUSIONS: • Several quantitative MR imaging biomarkers performed well in differential diagnosis between inflammatory pancreatic mass and pancreatic cancer. • The ADC value could discern pancreatic cancer from mass-forming pancreatitis or autoimmune pancreatitis, if the two inflammatory mass types are not combined. • The diameter of main pancreatic duct had the highest specificity for differentiating autoimmune pancreatitis from pancreatic cancer.

OBJECTIVE: To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis.
METHODS: From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them.
RESULTS: Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness.
CONCLUSIONS: The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.

BACKGROUND: Pulmonary solid pleura-attached nodules (SPANs) are not very commonly detected and thus not well studied and understood. This study aimed to identify the clinical and CT characteristics for differentiating benign and malignant SPANs.
RESULTS: From January 2017 to March 2023, a total of 295 patients with 300 SPANs (128 benign and 172 malignant) were retrospectively enrolled. Between benign and malignant SPANs, there were significant differences in patients\' age, smoking history, clinical symptoms, CT features, nodule-pleura interface, adjacent pleural change, peripheral concomitant lesions, and lymph node enlargement. Multivariate analysis revealed that smoking history (odds ratio [OR], 2.016; 95% confidence interval [CI], 1.037-3.919; p = 0.039), abutting the mediastinal pleura (OR, 3.325; 95% CI, 1.235-8.949; p = 0.017), nodule diameter (> 15.6 mm) (OR, 2.266; 95% CI, 1.161-4.423; p = 0.016), lobulation (OR, 8.922; 95% CI, 4.567-17.431; p < 0.001), narrow basement to pleura (OR, 6.035; 95% CI, 2.847-12.795; p < 0.001), and simultaneous hilar and mediastinal lymph nodule enlargement (OR, 4.971; 95% CI, 1.526-16.198; p = 0.008) were independent predictors of malignant SPANs, and the area under the curve (AUC) of this model was 0.890 (sensitivity, 82.0%, specificity, 77.3%) (p < 0.001).
CONCLUSIONS: In patients with a smoking history, SPANs abutting the mediastinal pleura, having larger size (> 15.6 mm in diameter), lobulation, narrow basement, or simultaneous hilar and mediastinal lymph nodule enlargement are more likely to be malignant.
UNASSIGNED: The benign and malignant SPANs have significant differences in clinical and CT features. Understanding the differences between benign and malignant SPANs is helpful for selecting the high-risk ones and avoiding unnecessary surgical resection.
CONCLUSIONS: • The solid pleura-attached nodules (SPANs) are closely related to the pleura. • Relationship between nodule and pleura and pleural changes are important for differentiating SPANs. • Benign SPANs frequently have broad pleural thickening or embed in thickened pleura. • Smoking history and lesions abutting the mediastinal pleura are indicators of malignant SPANs. • Malignant SPANs usually have larger diameters, lobulation signs, narrow basements, and lymphadenopathy.

A-to-I RNA editing is a prevalent type of RNA modification in animals. The dysregulation of RNA editing has led to multiple human cancers. However, the role of RNA editing has never been studied in osteosarcoma, a complex bone cancer with unknown molecular basis. We retrieved the RNA-sequencing data from 24 primary osteosarcoma patients and 3 healthy controls. We systematically profiled the RNA editomes in these samples and quantitatively identified reliable differential editing sites (DES) between osteosarcoma and normal samples. RNA editing efficiency is dramatically increased in osteosarcoma, presumably due to the significant up-regulation of editing enzymes ADAR1 and ADAR2. Up-regulated DES in osteosarcoma are enriched in 3\'UTRs. Strikingly, such 3\'UTR sites are further enriched in microRNA binding regions of gene EMP2 and other oncogenes, abolishing the microRNA suppression on target genes. Accordingly, the expression of these tumor-promoting genes is elevated in osteosarcoma. There might be an RNA editing-dependent pathway leading to osteosarcoma. We expanded our knowledge on the potential roles of RNA editing in oncogenesis. Based on these molecular features, our work is valuable for future prognosis and diagnosis of osteosarcoma.

Induced regulatory T cell (iTregs) can be generated in vitro. Thus, iTregs-based therapeutics are receiving increased attention for their potential to treat autoimmune diseases and prevent transplant rejection. However, iTregs fail to maintain FoxP3 expression and suppressive activity, which limits their clinical application. Increasing lines of evidence suggest that methyltransferase-like 14 (METTL14), a critical component of the m6A writer complex, regulates the stability and function of the Treg cells. However, beyond meeting the epigenetic modification of Treg cells, whether Mettl14 plays a role in the fate determination of iTregs is unclear. Here, we systemically investigated the potential function of METTL14 in iTregs differentiation and regulatory activity. In our study, iTregs were generated from CD4+ naïve T cells under iTreg-polarizing conditions, we found that the expression of METTL14 was increased in iTregs compared with CD4+naïve T cells. Subsequently, the expression of METTL14 was knocked down by siRNA-METTL14 interference in CD4+ naïve T cells and cultured under iTreg-polarizing conditions. According to the results, Mettl14 deficiency resulted in the disruption of iTregs differentiation evidenced by the limited FoxP3 expression. Meanwhile, inflammatory cytokines such as IFN-γ and IL-17a were upregulated in cultured iTregs. We next determined the functional change in METTL14-deficient iTregs. The results of the colitis development in Rag1-/- mice and CFSE assays revealed that loss of METTL14 significantly compromised the suppressive function of iTregs in vivo and in vitro. We further checked the altered signaling pathway in METTL14-deficient iTregs. We found that reduced METTL14 leads to activation of the mTOR pathway with increased p-mTOR and p-p70S6K, which are known to modulate the suppressive function of iTregs. In conclusion, our study revealed that Mettl14 plays a critical role in the development and suppressive function of iTregs in vitro and could thus serve as a regulatory element for stabilizing iTregs in cell-based therapy.

Background: Granulomatosis with polyangiitis (GPA) is an antineutrophil-cytoplasmic-antibody (ANCA)-associated small-vessel vasculitis characterized by necrotizing granulomatous inflammation. Symptoms of skin involvement can appear in 30-50% of patients with GPA, and may present as the initial presentation. Case Presentation: We describe two patients who presented with multiple deep, large, nonhealing skin ulcers postoperatively with purulent drainage and fever. Both patients were diagnosed with GPA after an extensive evaluation, including histopathology. Infectious, connective tissue disease and malignant etiologies were excluded. Their cANCA and PR3-ANCA levels were positive. Patient 2 was diagnosed early and recovered well after treatment with corticosteroids and rituximab; however, Patient 1 had a poor prognosis due to a long disease course. Conclusions: Diseases with multiple deep, large skin ulcers and fever can be infectious or noninfectious. Atypical manifestations may lead to missed diagnosis and misdiagnosis. GPA may initially present in a localized form before progressing to a generalized disease. The two cases we have highlighted will prompt clinicians to nevertheless call for a low threshold for diagnosis.

UNASSIGNED: The purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma.
UNASSIGNED: 96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.
UNASSIGNED: A total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.
UNASSIGNED: The CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.

UNASSIGNED: It is still a challenge to differentiate space-occupying brain lesions such as tumefactive demyelinating lesions (TDLs), tumefactive primary angiitis of the central nervous system (TPACNS), primary central nervous system lymphoma (PCNSL), and brain gliomas. Convolutional neural networks (CNNs) have been used to analyze complex medical data and have proven transformative for image-based applications. It can quickly acquire diseases\' radiographic features and correct doctors\' diagnostic bias to improve diagnostic efficiency and accuracy. The study aimed to assess the value of CNN-based deep learning model in the differential diagnosis of space-occupying brain diseases on MRI.
UNASSIGNED: We retrospectively analyzed clinical and MRI data from 480 patients with TDLs (n = 116), TPACNS (n = 64), PCNSL (n = 150), and brain gliomas (n = 150). The patients were randomly assigned to training (n = 240), testing (n = 73), calibration (n = 96), and validation (n = 71) groups. And a CNN-implemented deep learning model guided by clinical experts was developed to identify the contrast-enhanced T1-weighted sequence lesions of these four diseases. We utilized accuracy, sensitivity, specificity, and area under the curve (AUC) to evaluate the performance of the CNN model. The model\'s performance was then compared to the neuroradiologists\' diagnosis.
UNASSIGNED: The CNN model had a total accuracy of 87% which was higher than senior neuroradiologists (74%), and the AUC of TDLs, PCNSL, TPACNS and gliomas were 0.92, 0.92, 0.89 and 0.88, respectively.
UNASSIGNED: The CNN model can accurately identify specific radiographic features of TDLs, TPACNS, PCNSL, and gliomas. It has the potential to be an effective auxiliary diagnostic tool in the clinic, assisting inexperienced clinicians in reducing diagnostic bias and improving diagnostic efficiency.

Single-pixel cameras offer improved performance in non-visible imaging compared with modern digital cameras which capture images with an array of detector pixels. However, the quality of the images reconstructed by single-pixel imaging technology fails to match traditional cameras. Since it requires a sequence of measurements to retrieve a single image, the temporal fluctuation of illumination intensity during the measuring will cause inconsistence for consecutive measurements and thus noise in reconstructed images. In this paper, a normalization protocol utilizing the differential measurements in single-pixel imaging is proposed to reduce such inconsistence with no additional hardware required. Numerical and practical experiments are performed to investigate the influences of temporal fluctuation of different degrees on image quality and to demonstrate the feasibility of the proposed normalization protocol. Experimental results show that our normalization protocol can match the performance of the system with the reference arm. The proposed normalization protocol is straightforward with the potential to be easily applied in any temporal-sequence imaging strategy.