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OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. RESULTS: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. CONCLUSION: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.
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Artificial Intelligence , Melanoma , Humans , Melanoma/pathology , Melanoma/mortality , Female , Male , Prognosis , Middle Aged , Risk Factors , Aged , Retrospective Studies , Neoplasm Metastasis , SEER Program , Adult , Algorithms , Skin Neoplasms/pathology , Skin Neoplasms/mortality , Skin Neoplasms/therapy , Logistic ModelsABSTRACT
Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning methods on the Surveillance, Epidemiology, and End Results (SEER) database to forecast the risk probability of distant metastasis. Therefore, the information on UM patients from the SEER database (2000-2020) was split into a 7:3 ratio training set and an internal test set based on distant metastasis presence. Univariate and multivariate logistic regression analyses assessed distant metastasis risk factors. Six machine learning methods constructed a predictive model post-feature variable selection. The model evaluation identified the multilayer perceptron (MLP) as optimal. Shapley additive explanations (SHAP) interpreted the chosen model. A web-based calculator personalized risk probabilities for UM patients. The results show that nine feature variables contributed to the machine learning model. The MLP model demonstrated superior predictive accuracy (Precision = 0.788; ROC AUC = 0.876; PR AUC = 0.788). Grade recode, age, primary site, time from diagnosis to treatment initiation, and total number of malignant tumors were identified as distant metastasis risk factors. Diagnostic method, laterality, rural-urban continuum code, and radiation recode emerged as protective factors. The developed web calculator utilizes the MLP model for personalized risk assessments. In conclusion, the MLP machine learning model emerges as the optimal tool for predicting distant metastasis in UM patients. This model facilitates personalized risk assessments, empowering early and tailored treatment strategies.
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PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .
Subject(s)
Algorithms , Hospitals , Humans , Cohort Studies , Length of Stay , Machine LearningABSTRACT
Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca2+. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.
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Adenocarcinoma , Eye Neoplasms , Stomach Neoplasms , Humans , Reproducibility of Results , Retrospective Studies , Algorithms , Machine LearningABSTRACT
BACKGROUND: Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML). METHODS: We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non-ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10-fold cross-validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM. RESULTS: Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA. CONCLUSION: We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.
Subject(s)
Eye Neoplasms , Liver Neoplasms , Humans , Quality of Life , Retrospective Studies , Machine Learning , Risk Factors , Liver Neoplasms/diagnosisABSTRACT
Acute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and used to develop machine learning (ML) models. The ML model performance was assessed by area under the receiver operating characteristic curve (AUC) and radar plot. Shapley Additive explanations (SHAP) values were used to interpret the importance of all features included in the predictive model. A total of 677 AIS patients were included in the present study. Poor prognosis was observed in 209 patients (30.9%). Six variables, including neuron specific enolase (NSE), homocysteine (HCY), S-100ß, dysphagia, C-reactive protein (CRP), and anticoagulation were included to establish ML models. Six different ML algorithms were tested, and Random Forest model was selected as the final predictive model with the greatest AUC of 0.908. Moreover, according to SHAP results, NSE impacted the predictive model the most, followed by HCY, S-100ß, dysphagia, CRP and anticoagulation. Based on the RF model, an online tool was constructed to predict the prognosis of AIS patients and assist clinicians in optimizing patient treatment. The present study revealed that NSE, HCY, CRP, S-100ß, anticoagulation, and dysphagia were important factors for poor prognosis in AIS patients. ML algorithms were used to develop predictive models for predicting the prognosis of AIS patients, with the RF model presenting the optimal performance.
Subject(s)
Deglutition Disorders , Ischemic Stroke , Humans , Prognosis , Ischemic Stroke/diagnosis , Retrospective Studies , S100 Calcium Binding Protein beta Subunit , C-Reactive Protein , Homocysteine , Machine Learning , Risk Assessment , AnticoagulantsABSTRACT
BACKGROUND: MicrorNA-144 (MiR-144) has been shown to be an attractive prognostic tumor biomarker and play a fundamental role in various cancers, However, the conclusion was inconsistency. The aim of this study was to identify the prognostic role of miR-144 in cancers. METHODS: Relevant studies were searched in PubMed, EMBASE and Web of Science up to April 20, 2022. Hazard ratios (HR), odds ratio (OR) and 95% confidence intervals were pooled from the selected studies. RESULTS: A total of 15 articles involving 1846 participants fulfilled the inclusion criteria. The results revealed that low miR-144 expression was significantly associated with favorable overall survival (HR: 0.68, 95% confidence interval [CI]: 0.53-0.88) in various cancers. Low miR-144 expression had better predictive value in patients with urinary system cancer (HR: 0.48, 95% CI: 0.35-0.64). In addition, low miR-144 expression was associated with tumor diameter (big vs small) (OR: 1.69, 95% CI: 1.08-2.75), tumor stage (III-IV vs I-II) (OR: 2.52, 95% CI: 3.76-8.14) and invasion depth (T3 + T4 vs T2 + T1) (OR: 3.24, 95% CI: 1.72-4.89). CONCLUSION: miR-144 may serve as a prognostic biomarker in cancers.
Subject(s)
MicroRNAs , Neoplasms , Humans , Prognosis , MicroRNAs/genetics , Biomarkers, Tumor/geneticsABSTRACT
Toothache (TA) is a common and severe pain, but its effects on the brain are somewhat unclear. In this study, functional magnetic resonance imaging (fMRI) was used to compare regional homogeneity (ReHo) between TA patients and a normal control group and to explore the brain activity changes during TA, establishing the theoretical basis for the mechanism of neuropathic pain. In total, 20 TA patients and 20 healthy controls (HCs) were recruited and underwent assessment of pain, and then resting-state fMRI (rs-fMRI). The ReHo method was used to analyze the original whole-brain images. Pearson's correlation analysis was used to assess the relationship between mean ReHo values in each brain region and clinical symptoms, and the receiver operating characteristic (ROC) curve was used to conduct correlation analysis on the brain regions studied. The ReHo values of the right lingual gyrus (RLG), right superior occipital gyrus (RSOG), left middle occipital gyrus (LMOG) and right postcentral gyrus (RPG) in the TA group were significantly higher than in HCs. The mean ReHo values in the RLG were positively correlated with the anxiety score (AS) (r = 0.723, p < 0.001), depression score (DS) (r = 0.850, p < 0.001) and visual analogue score (VAS) (r = 0.837, p < 0.001). The mean ReHo values of RSOG were also positively correlated with AS (r = 0.687, p = 0.001), DS (r = 0.661, p = 0.002) and VAS (r = 0.712, p < 0.001). The areas under the ROC curve of specific brain area ReHo values were as follows: RLG, 0.975; RSOG, 0.959; LMOG, 0.975; RPG, 1.000. Various degrees of brain activity changes reflected by ReHo values in different areas of the brain indicate the impact of TA on brain function. These findings may reveal related neural mechanisms underlying TA.
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AIM: To study the characteristics, relative distribution and to compare causes of red eye in ophthalmic clinics in Urumchi and Shanghai, China. METHODS: Data on continuous cases of red-eye patients admitted to the Ophthalmology Center of Xinhua Hospital Affiliated to Shanghai Jiao Tong University and the First Affiliated Hospital of Xinjiang Medical University were collected between November 2018 and September 2019. Demographic data, the incidence of red eye and related disease distribution of all cases were obtained. The independent t-test method was used for age comparison, while the Chi-square test was used to compare classified data information. RESULTS: The information on 335 and 415 patients with red eyes in Shanghai and Urumchi were collected, respectively. The main causes of red eye were conjunctival disease and dry eye. The age of female patients with red eyes was significantly higher than that of males, and the proportion of female patients with dry eyes was also higher. Red-eye-related diseases occurred more frequently in patients over 46 years old than in those under 18, and dry eye was more common with increasing age. The incidence of infectious conjunctivitis in Urumchi was significantly higher than that in Shanghai, and allergic conjunctivitis occurred more frequently in spring, summer, or autumn than in winter (all P<0.05). CONCLUSION: Significant differences exist in the distribution of red-eye-related diseases in Urumchi and Shanghai regions of China, and distribution varies with age and season, the latter being an important feature of allergic conjunctivitis.
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Breast cancer (BC) was the most common malignant tumor in women, and breast infiltrating ductal carcinoma (IDC) accounted for about 80% of all BC cases. BC patients who had bone metastases (BM) were more likely to have poor prognosis and bad quality of life, and earlier attention to patients at a high risk of BM was important. This study aimed to develop a predictive model based on machine learning to predict risk of BM in patients with IDC. Six different machine learning algorithms, including Logistic regression (LR), Naive Bayes classifiers (NBC), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme gradient boosting (XGB), were used to build prediction models. The XGB model offered the best predictive performance among these 6 models in internal and external validation sets (AUC: 0.888, accuracy: 0.803, sensitivity: 0.801, and specificity: 0.837). Finally, an XGB model-based web predictor was developed to predict risk of BM in IDC patients, which may help physicians make personalized clinical decisions and treatment plans for IDC patients.
Subject(s)
Breast Neoplasms , Carcinoma, Ductal , Bayes Theorem , Female , Humans , Machine Learning , Quality of LifeABSTRACT
Objective: We used the amplitude of low-frequency fluctuation (ALFF) method to investigate spontaneous brain activity in patients with optic neuritis (ON) in specific frequency bands. Data and Methods: A sample of 21 patients with ON (13 female and eight male) and 21 healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) scans in the resting state. We analyzed the ALFF values at different frequencies (slow-4 band: 0.027-0.073 Hz; slow-5 band: 0.01-0.027 Hz) in ON patients and HCs. Results: In the slow-4 frequency range, compared with HCs, ON patients had apparently lower ALFF in the insula and the whack precuneus. In the slow-5 frequency range, ON patients showed significantly increased ALFF in the left parietal inferior and the left postcentral. Conclusion: Our results suggest that ON may be involved in abnormal brain function and can provide a basis for clinical research.
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So far, intense pulsed light (IPL) has been widely used in the treatment of meibomian gland dysfunction (MGD), but there was still a lack of research on its specific mechanism. Determining whether there was a correlation between liposome changes and remission of clinical signs in patients with MGD treated with IPL was of great significance in the clinical evaluation of efficacy in patients with MGD. Our study enrolled the 10 healthy subjects and 26 adult patients, who were diagnosed with MGD and had not received any alternative treatments for at least 3 months. Each patient received a series of three treatments at 3-week intervals. The meibum was collected before the first treatment (T0) and the third treatment (T2). The significant changes in ocular surface parameters before and after IPL treatment were analyzed. The results showed that IPL significantly improved the symptoms of MGD, including ocular surface disease index (OSDI), tear breakup time (TBUT), redness of conjunctival (CR), corneal fluorescein staining (CF), the meibomian gland expressibility (MGE), and meibum quality (all p < 0.05). Lipidomics analysis of the meibum characterized the changes in lipid profiles induced by IPL. A total of 323 lipid species compounds were identified in the spectrum. A total of 41 lipid species were significantly different in patients with MGD (T0) vs. healthy controls. Following IPL treatment (T2), 24 lipid species were significantly different compared with T0: TG (10 lipid species), LPC (6 lipid species), OAHFA (4 lipid species), Cer (2 lipid species), SM (1 lipid species), and PE (1 lipid specie). Among these lipids, 4 of the lipids was a high correlation with TBUT, 5 was TH, 6 was CR, and 11 was meibum quality. In a ward, IPL treatment can achieve the therapeutic effect by changing the alternations of tear film lipids in patients with MGD. The changes in lipid expression profiles are potential indexes to evaluate the therapeutic effectiveness of IPL treatment or other treatments on MGD.
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Functional connectivity of the primary visual cortex was explored with resting functional magnetic resonance imaging among adults with strabismus and amblyopia and healthy controls. We used the two-sample test and receiver operating characteristic curves to investigate the differences in mean functional connectivity values between the groups with strabismus and amblyopia and healthy controls. Compared with healthy controls, functional connectivity values in the left Brodmann areas 17, including bilateral lingual/angular gyri, were reduced in groups with strabismus and amblyopia. Moreover, functional connectivity values in the right Brodmann area 17, including left cuneus, right inferior occipital gyrus, and left inferior parietal lobule, were reduced in adults with strabismus and amblyopia. Our findings indicate that functional connectivity abnormalities exist between the primary visual cortex and other regions. This may be the basis of the pathological mechanism of visual dysfunction and stereovision disorders in adults with strabismus and amblyopia.
Subject(s)
Amblyopia/physiopathology , Connectome , Primary Visual Cortex/physiopathology , Strabismus/physiopathology , Adult , Amblyopia/diagnostic imaging , Female , Humans , Magnetic Resonance Imaging , Male , Primary Visual Cortex/diagnostic imaging , Strabismus/diagnostic imaging , Young AdultABSTRACT
Objective: To explore the risk factors for abnormal blinking in children and the role of the tear-film lipid layer thickness (LLT) as a function of duration of video display terminal (VDT) use in children. Methods: Children attending the Optometry Clinic of Xinhua Hospital affiliated with Shanghai Jiao Tong University were recruited for the study between June 2019 and June 2020. Time spent viewing a VDT (VDTt) over the previous 6 months was recorded. Incomplete blinking (IB) and blinking rate were measured over a 10 s period using the Lipiview® interferometer (Tear Science, Morrisville, NC, USA), and participants were allocated into groups with normal blinking (NBG, blink rate < 20 blinks/min) and abnormal blinking (ABG, blink rate ≥ 20 blinks/min). T-test, chi-square test and Mann-Whitney U-test were used to compare the differences in tear film (TF) stability indexes and meibomian gland function indexes between the two groups. Binary logistic analysis was used to analyze the risk factors for abnormal blinking and protective factors related to children's use of VDT, and receiver operating characteristic (ROC) curve analysis was also conducted. Results: A total of 167 children were included, with no statistically significant differences in age or sex between the two groups. According to the t-test, VDTt was significantly higher in ABG than NBG, while TF stability indices including tear break up time, LLT and the height of the tear meniscus, were significantly higher in NBG than ABG (P < 0.001). The results also showed better meibomian gland function in NBG than ABG (P < 0.05). Binary logistic analysis showed that VDTt is an important risk factor for abnormal blinking, and the average of LLT (AVG) was found to be an important protective factor for children using a VDT for long periods, with a cut-off value of 1.5 h and 57.5 nm, respectively. ROC curve analysis showed that the area under the curve value of VDTt and AVG was 0.833 and 0.969, respectively (P < 0.001). Conclusion: In children, VDTt is an important risk factor for abnormal blinking, and the AVG is an important protective factor for children using VDT for long periods.
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Background: In recent years, deep learning has been widely used in a variety of ophthalmic diseases. As a common ophthalmic disease, meibomian gland dysfunction (MGD) has a unique phenotype in in-vivo laser confocal microscope imaging (VLCMI). The purpose of our study was to investigate a deep learning algorithm to differentiate and classify obstructive MGD (OMGD), atrophic MGD (AMGD) and normal groups. Methods: In this study, a multi-layer deep convolution neural network (CNN) was trained using VLCMI from OMGD, AMGD and healthy subjects as verified by medical experts. The automatic differential diagnosis of OMGD, AMGD and healthy people was tested by comparing its image-based identification of each group with the medical expert diagnosis. The CNN was trained and validated with 4,985 and 1,663 VLCMI images, respectively. By using established enhancement techniques, 1,663 untrained VLCMI images were tested. Results: In this study, we included 2,766 healthy control VLCMIs, 2,744 from OMGD and 2,801 from AMGD. Of the three models, differential diagnostic accuracy of the DenseNet169 CNN was highest at over 97%. The sensitivity and specificity of the DenseNet169 model for OMGD were 88.8 and 95.4%, respectively; and for AMGD 89.4 and 98.4%, respectively. Conclusion: This study described a deep learning algorithm to automatically check and classify VLCMI images of MGD. By optimizing the algorithm, the classifier model displayed excellent accuracy. With further development, this model may become an effective tool for the differential diagnosis of MGD.
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OBJECTIVE: To explore the risk factors for abnormal blinking in children and compare these between boys and girls. METHODS: Children attending the Children's Optometry Clinic between June 2019 and June 2020 were recruited for the study. The time they had spent viewing video displays (VDTt) over the past 6 months was recorded. Incomplete blinking (IB) and blinking rate were measured and all participants were allocated to groups based on their blink rate (<20 times/min = normal blinking group, NBG; ≥20 times/min = abnormal blinking group, ABG). Tear film (TF) stability was also evaluated. The corresponding statistical methods are used to analyze the data. RESULTS: A total of 87 boys and 80 girls were enrolled in the study. No significant difference in age was found between the 2 groups. There was a significant difference in TF stability between the two groups (P<0.05). According to binary logistic analysis, VDTt and ocular protection index (OPI) are important risk factors for abnormal blinking, with cut-off values of 1.75 hours and 1.014 respectively in boys; and 1.25 hours and 1.770 respectively in girls. The average of lipid layer thickness was an important protective factor for children using VDT for long periods, with a cut-off value of 58.5 nm in boys and 53.5nm in girls. CONCLUSION: Risk factors for abnormal blinking in both boys and girls include VDTt and OPI.
Subject(s)
Blinking , Computer Terminals , Child , Female , Humans , Male , Risk Factors , Sex Factors , TearsABSTRACT
OBJECTIVE: To investigate the changes of amplitude of low-frequency fluctuation (ALFF) in brain regions of patients with hypertensive retinopathy by using resting-state functional magnetic resonance imaging (rs-fMRI) and change in the relationship of ALFF value with potential emotional and psychological changes. METHODS: Thirty-one patients with hypertensive retinopathy (HR) (16 men and 15 women) and 31 healthy controls (HCs; 16 men and 15 women) matched for age, sex, and weight were enrolled in the research. The changes in mean ALFF values could reflect brain activity between HR patients and HCs. We used the independent samples t-test to evaluate different demographic and general information between the two groups. Two-sample t-test was used to detect differences of mean ALFF values in the brain region between the two groups using the same software. RESULTS: The ALFF values in the brain areas of HR and HCs were different. HR patients had lower ALFF value in the left medial superior frontal gyrus and left middle frontal gyrus than the HCs. The higher ALFF values were found in the cerebellum (left inferior and right superior lobes, vermis) and left inferior temporal gyrus of the HR patients than the controls. CONCLUSION: Our findings showed fluctuations in ALFF values in the HR patients' brain regions. ALFF values reflect over or reduced activity in brain regions. Abnormal ALFF values in these brain areas can predict early HR development, preventing the malignant transformation of hypertensive microangiopathy.
Subject(s)
Brain/diagnostic imaging , Hypertensive Retinopathy , Brain Mapping/methods , Case-Control Studies , Female , Humans , Magnetic Resonance Imaging , Male , Middle Aged , ROC Curve , RestABSTRACT
This study investigated functional alterations in the cerebral network of patients with hypertensive retinopathy (HR) by resting-state functional magnetic resonance imaging (rs-fMRI) and degree centrality (DC) methods. 31 patients with HR along with 31 healthy controls (HC) closely matched in gender and age were enrolled for the research. All participants were examined by rs-fMRI, and the DC method was applied to evaluate alterations in spontaneous cerebral activity between the 2 groups. We used the independent samples t test to evaluate demographic and general information differences between HR patients and HCs. The 2-sample t test was used to compare the DC values of different cerebral regions between the 2 groups. The accuracy of differential diagnostic HR was analyzed by receiver operating characteristic (ROC) curve method for rs-fMRI DC values changes. Pearson's correlation coefficient was applied to determine the correlation between differences in DC in specific cerebral areas and clinical manifestation. Results showed that DC values were higher in the left cerebellum posterior lobe (LCPL), left medial occipital gyrus (LMOG), and bilateral precuneus (BP) of HR patients compared to HCs. Mean DC values were lower in the right medial frontal gyrus/bilateral anterior cingulate cortex of HR patients. Anxiety and depression scores were positively correlated with DC values of LMOG and LCPL, respectively. Bilateral best-corrected visual acuity in HR patients was negatively correlated with the DC value of BP. Hence, changes in DC in specific cerebral areas of patients with HR reflect functional alterations that provide insight into the pathophysiologic mechanisms of HR.
Subject(s)
Brain/pathology , Hypertensive Retinopathy/physiopathology , Magnetic Resonance Imaging , Nerve Net/pathology , Adult , Brain/physiopathology , Female , Humans , Hypertensive Retinopathy/pathology , Magnetic Resonance Imaging/methods , Male , Middle Aged , Nerve Net/physiopathology , Prefrontal Cortex/metabolism , Prefrontal Cortex/pathology , ROC CurveABSTRACT
BACKGROUND: Neovascular glaucoma (NVG) is a secondary refractory disease with a poor prognosis, and there are few advanced studies on its pathogenesis and treatment. In this research, the fractional amplitude of low-frequency fluctuation (fALFF) technology was used in resting-state functional magnetic resonance imaging (rsfMRI) to investigate intrinsic neuron activity in the patient's brain with NVG. METHODS: Sixteen patients with NVG (eight males and eight females) and 16 healthy controls (HCs) of similar age and sex were included. All patients and controls received rsfMRI scans, and the differences between the two groups in fALFF values were compared by independent sample t-test. Receiver operating characteristic (ROC) curves were used to compare fALFF values in the brain regions of NVG patients and HCs and assess accuracy. Finally, Pearson linear correlation analysis assessed the correlation between fALFF signals in brain regions and the clinical evaluation indicators of patients with NVG. RESULTS: In patients with NVG, fALFF signal values in the right Rolandic operculum, left anterior cingulate and paracingulate gyri, and right caudate were significantly decreased. In contrast, fALFF signal values in the left precuneus were significantly higher than those recorded in the HCs. Analysis of the ROC curve for each brain region showed that the area under the ROC curve of NVG patients was large (close to 1), and the accuracy was good. In the NVG group, the hospital anxiety and depression scale (r=-0.952, P<0.001) and left best-corrected visual acuity (r=-0.802, P<0.001) had a negative linear correlation with the fALFF signal value of the right Rolandic operculum. The hospital anxiety and depression scale had a negative linear correlation with the fALFF signal value of the right caudate (r=-0.948, P<0.001). CONCLUSIONS: NVG patients showed dysfunction in several brain regions. These findings may assist in revealing the underlying neural mechanism of brain activity associated with NVG.
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BACKGROUND: In rare instances, primary liver cancer can be associated with intraocular metastasis (IOM). AIM: To investigate the correlation between a diverse range of clinical characteristics and IOM in diabetic patients with primary liver cancer, and to determine potential risk factors in predicting IOM. METHODS: We recruited a total of 722 diabetic patients with primary liver cancer. The differences between the IOM and non-intraocular metastasis (NIOM) groups in these patients were assessed using the chi-squared test and Student's t-test. Binary logistic regression analysis was subsequently used to determine risk factors. Finally, the diagnostic value of IOM in this cohort with primary liver cancer was analyzed by receiver operating characteristic (ROC) curve analysis. RESULTS: In all, 13 patients had IOM. There were no remarkable intergroup differences with respect to age, sex, histopathological sub-types, or blood biochemical parameters. However, the IOM group had significantly higher alpha-fetoprotein (AFP) and cancer antigen 125 (CA125) values than the NIOM group. Binary logistic regression identified AFP and CA125 to be significant risk factors for IOM in diabetic patients with primary liver cancer. ROC curve analysis showed that the area under the curve values for AFP and CA125 were 0.727 and 0.796, with the cut-off values of 994.20 ng/mL and 120.23 U/mL, respectively. The sensitivity and speciï¬city for AFP were 92.3% and 59.9%, while those for CA125 were 84.6% and 70.1%, respectively. CONCLUSION: Elevated AFP and CA125 represent significant risk factors for IOM in diabetic patients with primary liver cancer.