Hedgehog pathway and cancer: A new area (Review).

In years of research on classical pathways, the composition, information transmission mechanism, crosstalk with other pathways, and physiological and pathological effects of hedgehog (HH) pathway have been gradually clarified. HH also plays a critical role in tumor formation and development. According to the update of interpretation of tumor phenotypes, the latest relevant studies have been sorted out, to explore the specific mechanism of HH pathway in regulating different tumor phenotypes through gene mutation and signal regulation. The drugs and natural ingredients involved in regulating HH pathway were also reviewed; five approved drugs and drugs under research exert efficacy by blocking HH pathway, and at least 22 natural components have potential to treat tumors by HH pathway. Nevertheless, there is a deficiency of existing studies. The present review confirmed the great potential of HH pathway in future cancer treatment with factual basis.

The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma.

OBJECTIVE: Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma. MATERIALS AND METHODS: 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status. RESULTS: The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively. CONCLUSIONS: CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.

Automatic detection of cognitive impairment in patients with white matter hyperintensity and causal analysis of related factors using artificial intelligence of MRI.

PURPOSE: White matter hyperintensity (WMH) is a common feature of brain aging, often linked with cognitive decline and dementia. This study aimed to employ deep learning and radiomics to develop models for detecting cognitive impairment in WMH patients and to analyze the causal relationships among cognitive impairment and related factors. MATERIALS AND METHODS: A total of 79 WMH patients from hospital 1 were randomly divided into a training set (62 patients) and a testing set (17 patients). Additionally, 29 patients from hospital 2 were included as an independent testing set. All participants underwent formal neuropsychological assessments to determine cognitive status. Automated identification and segmentation of WMH were conducted using VB-net, with extraction of radiomics features from cortex, white matter, and nuclei. Four machine learning classifiers were trained on the training set and validated on the testing set to detect cognitive impairment. Model performances were evaluated and compared. Causal analyses were conducted among cortex, white matter, nuclei alterations, and cognitive impairment. RESULTS: Among the models, the logistic regression (LR) model based on white matter features demonstrated the highest performance, achieving an AUC of 0.819 in the external test dataset. Causal analyses indicated that age, education level, alterations in cortex, white matter, and nuclei were causal factors of cognitive impairment. CONCLUSION: The LR model based on white matter features exhibited high accuracy in detecting cognitive impairment in WMH patients. Furthermore, the possible causal relationships among alterations in cortex, white matter, nuclei, and cognitive impairment were elucidated.

Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.

This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 to May 2022 were retrospectively enrolled from four medical centers. A cfVB-Net network was used to automatically segment PitNET multiparameter MRI. Radiomics features were extracted from the MRI, and the radiomics score (Radscore) of each patient was calculated. To predict histological subtypes, the Gaussian process (GP) machine learning classifier based on radiomics features was performed. Multi-classification (six-class histological subtype) and binary classification (PRL vs. non-PRL) GP model was constructed. Then, a clinical-radiomics nomogram combining clinical factors and Radscores was constructed using the multivariate logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic (ROC) curves. The PitNET auto-segmentation model eventually achieved the mean Dice similarity coefficient of 0.888 in 1206 patients (mean age 49.3 ± SD years, 52% female). In the multi-classification model, the GP of T2WI got the best area under the ROC curve (AUC), with 0.791, 0.801, and 0.711 in the training, validation, and external testing set, respectively. In the binary classification model, the GP of T2WI combined with CE T1WI demonstrated good performance, with AUC of 0.936, 0.882, and 0.791 in training, validation, and external testing sets, respectively. In the clinical-radiomics nomogram, Radscores and Hardy' grade were identified as predictors for PRL expression. Machine learning and radiomics analysis based on multiparameter MRI exhibited high efficiency and clinical application value in predicting the PitNET histological subtypes.

Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram.

Objective: To evaluate the value of a nomogram combined MRI Diffusion Weighted Imaging (DWI) and clinical features to predict the treatment response of Neoadjuvant Chemotherapy (NAC) in patients with osteosarcoma. Methods: A retrospective analysis was conducted on 209 osteosarcoma patients admitted into two bone cancer treatment centers (133 males, 76females; mean age 16.31 ± 11.42 years) from January 2016 to January 2022. Patients were classified as pathological good responders (pGRs) if postoperative histopathological examination revealed ≥90% tumor necrosis, and non-pGRs if <90%. Their clinical features were subjected to univariate and multivariate analysis, and features with statistically significance were utilized to construct a clinical signature using machine learning algorithms. Apparent diffusion coefficient (ADC) values pre-NAC (ADC 0) and post two chemotherapy cycles (ADC 1) were recorded. Regions of interest (ROIs) were delineated from pre-treatment DWI images (b=1000 s/mm²) for radiomic features extraction. Variance thresholding, SelectKBest, and LASSO regression were used to select features with strong relevance, and three machine learning models (Logistic Regression, RandomForest and XGBoost) were used to construct radiomics signatures for predicting treatment response. Finally, the clinical and radiomics signatures were integrated to establish a comprehensive nomogram model. Predictive performance was assessed using ROC curve analysis, with model clinical utility appraised through AUC and decision curve analysis (DCA). Results: Of the 209patients, 51 (24.4%) were pGRs, while 158 (75.6%) were non-pGRs. No significant ADC1 difference was observed between groups (P>0.05), but pGRs had a higher ADC 0 (P<0.01). ROC analysis indicated an AUC of 0.681 (95% CI: 0.482-0.862) for ADC 0 at the threshold of ≥1.37×10-3 mm²/s, achieving 74.7% sensitivity and 75.7% specificity. The clinical and radiomics models reached AUCs of 0.669 (95% CI: 0.401-0.826) and 0.768 (95% CI: 0.681-0.922) respectively in the test set. The combined nomogram displayed superior discrimination with an AUC of 0.848 (95% CI: 0.668-0.951) and 75.8% accuracy. The DCA suggested the clinical utility of the nomogram. Conclusion: The nomogram based on combined radiomics and clinical features outperformed standalone clinical or radiomics model, offering enhanced accuracy in evaluating NAC response in osteosarcoma. It held significant promise for clinical applications.

Integrated Bioinformatics and Experimental Analysis of Long Noncoding RNA Associated-ceRNA as Prognostic Biomarkers in Advanced Stomach Adenocarcinoma.

BACKGROUND: Advanced stomach adenocarcinoma (ASTAD) is a highly malignant and prognostically poor stage of gastric cancer. Recently, long noncoding RNA (lncRNA) was found to play a crucial role, including as competing endogenous RNA (ceRNA) in cancer. However, studies on large-scale sample in ASTAD are still lacking, thus we constructed the ceRNA network of ASTAD to explore its molecular mechanism. METHODS: We compared the expression of mRNAs, lncRNAs and miRNAs between ASTAD and normal tissues utilizing RNA-Seq and miRNA-seq Data from The Cancer Genome Atlas (TCGA). GO and KEGG enrichment analysis were executed for annotating the functions of differentially expressed mRNAs. Subsequently, we investigated the expression correlations between the differentially expressed lncRNAs and their respective mRNAs by constructing a ceRNA network. Kaplan-Meier survival analysis was used to assess the relationship between high/low risk scores based on this network with patient prognosis in TCGA training cohort and GSE15459 validation cohort. In vitro functional assays were employed to verify the cancer-promoting effects of key lncRNAs in the ceRNA network and their possible mechanisms. RESULTS: In ASTAD tissues, a total of 176 lncRNAs, 124 miRNAs, and 2205 mRNAs were identified as differentially expressed. Our constructed ceRNA network consisted 6 differentially expressed lncRNAs (PVT1, MAGI2-AS3, KCNQ1OT1, LINC02086, AC125807.2 and LINC02535), 25 miRNAs and 130 mRNAs, and the risk score derived from these lincRNAs could predict ASTAD patient outcomes. Key lncRNA LINC02086 was experimentally verified to enhance proliferation and migration of gastric cancer cells by competitively binding to miR-93a-5p with MMP3. CONCLUSION: Our comprehensive ceRNA network for ASTAD provides valuable insights into its molecular mechanisms, and LINC02086 may be used as an innovative target for clinical treatment.

Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging.

Purpose: Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs). Method: This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models. Result: Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion: The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.

Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI.

RATIONALE AND OBJECTIVES: Ki67 proliferation index is associated with more aggressive tumor behavior and recurrence of pituitary adenomas (PAs). Recently, radiomics and deep learning have been introduced into the study of pituitary tumors. The present study aimed to investigate the feasibility of predicting the Ki67 proliferation index of PAs using the deep segmentation network and radiomics analysis based on multiparameter MRI. MATERIALS AND METHODS: First, the cfVB-Net autosegmentation model was trained; subsequently, its performance was evaluated in terms of the dice similarity coefficient (DSC). In the present study, 1214 patients were classified into the high Ki67 expression group (HG) and the low Ki67 expression group (LG). Analyses of three classification models based on radiomics features were performed to distinguish HG from LG. Clinical factors, imaging features, and Radscores were collectively used to create a nomogram in order to effectively predict Ki67 expression. RESULTS: The cfVB-Net segmentation model demonstrated good performance (DSC: 0.723-0.930). Overall, 18, 15, and 11 optimal features in contrast-enhanced (CE) T1WI, T1WI, and T2WI were obtained for differentiating between HG and LG, respectively. Notably, the best results were presented in the bagging decision tree when CE T1WI and T1WI were combined (area under the receiver operating characteristic curve: training set, 0.927; validation set, 0.831; and independent testing set, 0.825). In the nomogram, age, Hardy' grade, and Radscores were identified as risk predictors of high Ki67 expression. CONCLUSION: The deep segmentation network and radiomics analysis based on multiparameter MRI exhibited good performance and clinical application value in predicting the expression of Ki67 in PAs.

Review on chronic metabolic diseases surrounding bile acids and gut microbiota: What we have explored so far.

Bile acid, the final product of cholesterol breakdown, functions as a complex regulator and signaling factor in human metabolism. Chronic metabolic diseases pose significant medical challenges. Growing research underscores bile acids' capacity to enhance metabolism via diverse pathways, regulating disorders and offering treatment potential. Numerous bile-acid-triggered pathways have become treatment targets. This review outlines bile acid synthesis, its role as a signal in chronic metabolic diseases, and highlights its interaction with gut microbiota in different metabolic conditions. Exploring host-bacteria-bile acid links emerges as a valuable future research direction with clinical implications.

Biosynthesis of 4-Acyl-5-aminoimidazole Alkaloids Featuring a New Friedel-Crafts Acyltransferase.

Friedel-Crafts acylation (FCA) is a highly beneficial approach in organic chemistry for creating the important C-C bonds that are necessary for building intricate frameworks between aromatic substrates and an acyl group. However, there are few reports about enzyme catalyzed FCA reactions. In this study, 4-acyl-5-aminoimidazole alkaloids (AAIAs), streptimidazoles A-C (1-3), and the enantiopure (+)-nocarimidazole C (4) as well as their ribosides, streptimidazolesides A-D (5-8), were identified from the fermentation broth of Streptomyces sp. OUCMDZ-944 or heterologous S. coelicolor M1154 mutant. The biosynthetic gene cluster (smz) was identified, and the biosynthetic pathway of AAIAs was elucidated for the first time. In vivo and in vitro studies proved the catalytic activity of the four essential genes smzB, -C, -E, and -F for AAIAs biosynthesis and clarified the biosynthetic process of the alkaloids. The ligase SmzE activates fatty acyl groups and connects them to the acyl carrier protein (ACP) holo-SmzF. Then, the acyl group is transferred onto the key residue Cys49 of SmzB, a new Friedel-Crafts acyltransferase (FCase). Subsequently, the FCA reaction between the acyl groups and 5-aminoimidazole ribonucleotide (AIR) occurs to generate the key intermediate AAIA-nucleotides catalyzed by SmzB. Finally, the hydrolase SmzC catalyzes the N-glycosidic bond cleavage of the intermediates to form AAIAs. Structural simulation, molecular modeling, and mutational analysis of SmzB showed that Tyr26, Cys49, and Tyr93 are the key catalytic residues in the C-C bond formation of the acyl chain of AAIAs, providing mechanistic insights into the enzymatic FCA reaction.

Image Generation and Recognition for Railway Surface Defect Detection.

Railway defects can result in substantial economic and human losses. Among all defects, surface defects are the most common and prominent type, and various optical-based non-destructive testing (NDT) methods have been employed to detect them. In NDT, reliable and accurate interpretation of test data is vital for effective defect detection. Among the many sources of errors, human errors are the most unpredictable and frequent. Artificial intelligence (AI) has the potential to address this challenge; however, the lack of sufficient railway images with diverse types of defects is the major obstacle to training the AI models through supervised learning. To overcome this obstacle, this research proposes the RailGAN model, which enhances the basic CycleGAN model by introducing a pre-sampling stage for railway tracks. Two pre-sampling techniques are tested for the RailGAN model: image-filtration, and U-Net. By applying both techniques to 20 real-time railway images, it is demonstrated that U-Net produces more consistent results in image segmentation across all images and is less affected by the pixel intensity values of the railway track. Comparison of the RailGAN model with U-Net and the original CycleGAN model on real-time railway images reveals that the original CycleGAN model generates defects in the irrelevant background, while the RailGAN model produces synthetic defect patterns exclusively on the railway surface. The artificial images generated by the RailGAN model closely resemble real cracks on railway tracks and are suitable for training neural-network-based defect identification algorithms. The effectiveness of the RailGAN model can be evaluated by training a defect identification algorithm with the generated dataset and applying it to real defect images. The proposed RailGAN model has the potential to improve the accuracy of NDT for railway defects, which can ultimately lead to increased safety and reduced economic losses. The method is currently performed offline, but further study is planned to achieve real-time defect detection in the future.

uRP: An integrated research platform for one-stop analysis of medical images.

Introduction: Medical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible. Methods: We present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable. Results and Discussion: The uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.

Prediction of Ki-67 expression in gastrointestinal stromal tumors using radiomics of plain and multiphase contrast-enhanced CT.

OBJECTIVE: To study the value of radiomics models based on plain and multiphase contrast-enhanced CT to predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). METHODS: A total of 215 patients with GISTs were retrospectively analyzed, including 150 patients in one hospital as the training set and 65 patients in another hospital as the external verification set. The tumor at the largest level of CT images was delineated as the region of interest (ROI). The maximum diameter of the ROI was defined as the tumor size. A total of 851 radiomics features were extracted from each ROI by 3D Slicer Radiomics. After dimensionality reduction, three machine learning classification algorithms including logistic regression (LR), random forest (RF), and support vector machine (SVM) were used for Ki-67 expression prediction. Using a multivariable logistic model, a nomogram was established to predict the expression of Ki-67 individually. RESULTS: Delong tests showed that the SVM models had the highest accuracy in the arterial phase (Z value 0.217-1.139) and venous phase (Z value 0.022-1.396). For the plain phase, LR and SVM models had the highest accuracy (Z value 0.874-1.824, 1.139-1.763). For the delayed phase, LR models had the highest accuracy (Z value 0.056-1.824). For the combined phase, RF models had the highest accuracy (Z value 0.232-1.978). There was no significant difference among the above models for KI-67 expression prediction (Z value 0.022-1.978). A nomogram was developed with a C-index of 0.913 (95% CI, 0.878 to 0.956). CONCLUSIONS: Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. CLINICAL RELEVANCE STATEMENT: CT radiomics could accurately predict the expression of Ki-67 in GIST, which has a great clinical value in reflecting the proliferative activity of tumor cells and helping determine whether a patient is suitable for adjuvant therapy with imatinib. KEY POINTS: • Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. • For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. • A radiomics nomogram was developed to allow personalized preoperative evaluation with high accuracy.

The Importance of Marital Status in the Morbidity and Prognosis of Lung Metastasis in Newly Diagnosed Ovarian Cancer.

Objective: The study aimed to evaluate the risk factors for the morbidity and prognosis of lung metastases (LM) in patients with newly diagnosed ovarian cancer (OC), and further explore the important role of marital status. Materials and methods: Based on the Surveillance, Epidemiology, and End Results (SEER) dataset, OC patients from 2010 and 2019 were retrospectively analyzed. Logistic regression analysis and Kaplan-Meier method were applied to evaluate the vital factors of incidence and survival outcome in LM population. Cox regression analysis was performed to identify risk factors for the prognosis of OC patients with LM. The predictive potential was showed by two established nomograms and examined by the concordance index (C-index), calibration curves, the area under the curve (AUC), decision curve analyses (DCAs) and clinical impact curves (CICs). Results: There are 25,202 eligible OC patients were enrolled in the study, the morbidity of LM at 5.61%. Multivariable logistic regression models illustrated that chemotherapy (P<0.01), surgical treatment of bilateral or more areas (P<0.01), T stage (P<0.01), N1 stage (P<0.01), bone metastasis (P<0.01), brain metastasis (P<0.01) and liver metastasis (P<0.01) were all significantly connected with LM in OC. Multivariable Cox regression analyses illustrated that unmarried, radiotherapy, elder people and positive cancer antigen 125 (CA-125) were significantly associated with shorter survival time, while chemotherapy made contributions to improve survival. Our study found that marital relationships promoted LM and was associated with the better prognosis, while unmarried patients had the opposite results. With the further development of our research, the cross-action of social, economic and psychological factors together determined the great impact of marital status on the morbidity and prognosis of OC patients combined with LM. Finally, the stability of the models was proved by internal verification. Conclusion: The population-based cohort study provides references for guiding clinical screening and individualized treatment of OC patients with LM. Under the influence of society and economy, marital status is closely related to the morbidity and prognosis of OC, which can be an important direction to explore the risk of OC lung metastasis in the future.

Comprehensive bioinformatics and experimental analysis of SH3PXD2B reveals its carcinogenic effect in gastric carcinoma.

AIMS: We aim to explore the possibility and mechanism of SH3PXD2B as a reliable biomarker for gastric cancer (GC). MAIN METHODS: We used public databases to analyze the molecular characteristics and disease associations of SH3PXD2B, and KM database for prognostic analysis. The TCGA gastric cancer dataset was used for single gene correlation, differential expression, functional enrichment and immunoinfiltration analysis. SH3PXD2B protein interaction network was constructed by the STRING database. And the GSCALite database was used to explore sensitive drugs and perform SH3PXD2B molecular docking. The impact of SH3PXD2B silencing and over-expression by lentivirus transduction on the proliferation and invasion of human GC HGC-27 and NUGC-3 cells was determined. KEY FINDINGS: The high expression of SH3PXD2B in gastric cancer was related to the poor prognosis of patients. It may affect the progression of gastric cancer by forming a regulatory network with FBN1, ADAM15 and other molecules, and the mechanism may involve regulating the infiltration of Treg, TAM and other immunosuppressive cells. The cytofunctional experiments verified that it significantly promoted the proliferation and migration of gastric cancer cells. In addition, we found that some drugs were sensitive to the expression of SH3PXD2B such as sotrastaurin, BHG712 and sirolimus, and they had strong molecular combination of SH3PXD2B, which may provide guidance for the treatment of gastric cancer. SIGNIFICANCE: Our study strongly suggests that SH3PXD2B is a carcinogenic molecule that can be used as a biomarker for GC detection, prognosis, treatment design, and follow-up.

The predictive role of impulsivity and perceived social support in psychiatric symptoms of women with methamphetamine use disorder.

Background: Currently, few studies have examined the mental states of Women methamphetamine patients, and the influence of impulsivity and perceived social support on substance misuse-induced mental disorders is unclear. We want to examine the mental state of women with methamphetamine use disorder and compare it to the Chinese norm value of healthy women. Investigate the connection between impulsivity, perceived social support and mental state of women with methamphetamine use disorder. Method: Two hundred thirty women subjects with a history of methamphetamine usage were recruited. The Chinese version of the SCL-90-R, (SCL-90) was used to evaluate psychological health problems, while the Multidimensional Scale of Perceived Social Support (MSPSS) and Barratt Impulsiveness Seale-11 (BIS-11) were utilized to evaluate perceived social support and impulsivity, respectively. The t-test, Pearson correlation analysis, multivariable linear regression, stepwise regression models, moderating effect analysis were used to analyze the statistics. Results: There was a noticeable difference between the Chinese norm and all participants' SCL-90 ratings, especially for Somatization (t = 24.34, p < 0.001), Anxiety (t = 22.23, p < 0.001), Phobic anxiety (t = 26.47, p < 0.001), and Psychoticism (t = 24.27, p < 0.001). In addition, perceived social support levels and impulsivity levels are independently predictive of SCL-90 scores. Lastly, the impact of Impulsivity on SCL-90 can be modulated by perceived social support. Conclusion: According to this study, women with methamphetamine use disorder have worse mental health conditions compared to healthy subjects. Furthermore, certain psychological symptoms associated with methamphetamine use in women can be aggravated by impulsivity, while perceived social support acts as a protective factor for methamphetamine-related psychiatric symptoms. Specifically, perceived social support weakens the impact of impulsivity on psychiatric symptoms in women with methamphetamine use disorder.

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images.

BACKGROUND: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear. PURPOSE: To distinguish primary site of BM and identify the best DL models. STUDY TYPE: Retrospective. POPULATION: A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included. FIELD STRENGTH/SEQUENCE: A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE). ASSESSMENT: Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps. STATISTICAL TESTS: The area under the receiver operating characteristics curve (AUC) assess each classification performance. RESULTS: 3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions. DATA CONCLUSION: DL models may help to distinguish the origins of BM based on MRI data. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI.

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs). Methods: 155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models. Results: Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion: The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.