RESUMEN
Objective: Currently, distinct use of clinical data, routine laboratory indicators or the detection of diabetic autoantibodies in the diagnosis and management of diabetes mellitus is limited. Hence, this study was aimed to screen the indicators, and to establish and validate a multifactorial logistic regression model nomogram for the non-invasive differential prediction of type 1 diabetes mellitus. Methods: Clinical data, routine laboratory indicators, and diabetes autoantibody profiles of diabetic patients admitted between September 2018 and December 2022 were retrospectively analyzed. Logistic regression was used to select the independent influencing factors, and a prediction nomogram based on the multiple logistic regression model was constructed using these independent factors. Moreover, the predictive accuracy and clinical application value of the nomogram were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results: A total of 522 diabetic patients were included in this study. These patients were randomized into training and validation sets in a 7:3 ratio. The predictors screened included age, prealbumin (PA), high-density lipoprotein cholesterol (HDL-C), islet cells autoantibodies (ICA), islets antigen 2 autoantibodies (IA-2A), glutamic acid decarboxylase antibody (GADA), and C-peptide levels. Based on these factors, a multivariate model nomogram was constructed, which had an Area Under Curve (AUC) of 0.966 and 0.961 for the training set and validation set, respectively. Subsequently, the calibration curves demonstrated a strong accuracy of the graph; the DCA and CIC results indicated that the graph could be used as a non-invasive valid predictive tool for the differential diagnosis of type 1 diabetes mellitus, clinically. Conclusion: The established prediction model combining patient's age, PA, HDL-C, ICA, IA-2A, GADA, and C-peptide can assist in differential diagnosis of type 1 diabetes mellitus and type 2 diabetes mellitus and provides a basis for the clinical as well as therapeutic management of the disease.
Asunto(s)
Autoanticuerpos , Diabetes Mellitus Tipo 1 , Valor Predictivo de las Pruebas , Humanos , Autoanticuerpos/sangre , Masculino , Femenino , Persona de Mediana Edad , Adulto , Estudios Retrospectivos , Diabetes Mellitus Tipo 1/inmunología , Diabetes Mellitus Tipo 1/sangre , Diabetes Mellitus Tipo 1/diagnóstico , Nomogramas , Glutamato Descarboxilasa/inmunología , Adulto Joven , Diabetes Mellitus Tipo 2/sangre , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/inmunología , Curva ROC , Biomarcadores/sangre , Adolescente , AncianoRESUMEN
To more accurately diagnose and treat patients with different subtypes of thyroid cancer, we constructed a diagnostic model related to the iodine metabolism of THCA subtypes. THCA expression profiles, corresponding clinicopathological information, and single-cell RNA-seq were downloaded from TCGA and GEO databases. Genes related to thyroid differentiation score were obtained by GSVA. Through logistic analyses, the diagnostic model was finally constructed. DCA curve, ROC curve, machine learning, and K-M analysis were used to verify the accuracy of the model. qRT-PCR was used to verify the expression of hub genes in vitro. There were 104 crossover genes between different TDS and THCA subtypes. Finally, 5 genes (ABAT, CHEK1, GPX3, NME5, and PRKCQ) that could independently predict the TDS subpopulation were obtained, and a diagnostic model was constructed. ROC, DCA, and RCS curves exhibited that the model has accurate prediction ability. K-M and subgroup analysis results showed that low model scores were strongly associated with poor PFI in THCA patients. The model score was significantly negatively correlated with T cell follicular helper. In addition, the diagnostic model was significantly negatively correlated with immune scores. Finally, the results of qRT-PCR corresponded with bioinformatics results. This diagnostic model has good diagnostic and prognostic value for THCA patients, and can be used as an independent prognostic indicator for THCA patients.
Asunto(s)
Yodo , Neoplasias de la Tiroides , Humanos , Neoplasias de la Tiroides/genética , Neoplasias de la Tiroides/diagnóstico , Neoplasias de la Tiroides/patología , Biología Computacional/métodos , Femenino , Masculino , Aprendizaje Automático , Persona de Mediana Edad , Glándula Tiroides/patología , Glándula Tiroides/metabolismo , Curva ROC , Diferenciación Celular , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismoRESUMEN
Pyroptosis-related genes have great potential for prognosis, an accurate prognostic model based on pyroptosis genes has not been seen in Colorectal adenocarcinoma (COAD). Furthermore, understanding the mechanisms of gene expression characteristics and the Tumor Immune Microenvironment associated with the prognosis of COAD is still largely unknown. Constructing a prognostic model based on pyroptosis-related genes, and revealing prognosis-related mechanisms associated with the gene expression characteristics and tumor microenvironment. 59 pyroptosis-related genes were collected. The gene expression data and clinical data of COAD were downloaded from The Cancer Genome Atlas. External validation datasets were downloaded from the Gene Expression Omnibus database. 10 characteristic genes with prognostic values were obtained using univariate and LASSO Cox. 10-gene Riskscore prognostic model was constructed. Both gene set enrichment analysis and network propagation methods were used to find pathways and key genes leading to different prognostic risks. The area under the ROC curves were used to evaluate the performance of the model to distinguish between high-risk and low-risk patients, the results were 0.718, 0.672, and 0.669 for 1-, 3-, and 5-year survival times. A nomogram based on Riskscore and clinical characteristics showed the probability of survival at 1, 3, and 5 years, and the calibration curves showed good agreement between the predicted and actual observations, its C-index is 0.793. The decision curves showed that the net benefit of the nomogram was significantly superior to that of the other single variables. Four key pathways leading to different prognostic risks were obtained. Six key genes with prognostic value, significant expression differences (Pâ <â .05) and significant survival differences (Pâ <â .05) between high/low risk groups were obtained from the gene set of all 4 key pathways. This study constructed a prognostic model for COAD using 10 pyroptosis-related genes with prognostic value. This study also revealed significant differences in specific pathways and the tumor immune microenvironment (TME) between the high-risk group and the low-risk group, highlighted the roles of ALDH5A1 and Wnt signaling in promoting COAD and the suppressive effects of the IL-4/IL-13 pathway and RORC on COAD. The study will be helpful for precision therapy.
Asunto(s)
Neoplasias del Colon , Nomogramas , Piroptosis , Microambiente Tumoral , Humanos , Microambiente Tumoral/genética , Microambiente Tumoral/inmunología , Piroptosis/genética , Pronóstico , Neoplasias del Colon/genética , Neoplasias del Colon/mortalidad , Neoplasias del Colon/inmunología , Medición de Riesgo/métodos , Regulación Neoplásica de la Expresión Génica , Biomarcadores de Tumor/genética , Masculino , Femenino , Curva ROCRESUMEN
The timely and precise diagnosis of appendicitis was deemed essential. This study sought to examine the diagnostic significance of hub genes linked to appendicitis and to delve deeper into the pathophysiology of the condition. Differential gene expression analysis revealed distinct genes in the appendicitis group compared to other abdominal pain group, while weighted gene co-expression network analysis identified appendicitis-associated modules. Further analysis of common genes was conducted using Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis. The diagnostic efficiency of hub genes was explored through the use of nomograms and receiver operator characteristic curves. Additionally, immunoinfiltration analysis was performed to investigate the immune cell infiltration in both groups. The causal relationship between hub genes and appendicitis, as well as gut microbiota and appendicitis, was ultimately examined through Mendelian randomization. By conducting differential expression analysis and weighted gene co-expression network analysis, a total of 757 common genes were identified. Subsequent Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses revealed that these common genes were primarily associated with positive regulation of cell adhesion, focal adhesion, protein serine kinase activity, and amyotrophic lateral sclerosis. Utilizing Cytoscape software, the top 10 genes with the highest degree of interaction were identified as RPS3A, RPSA, RPL5, RPL37A, RPS27L, FLT3LG, ARL6IP1, RPL32, MRPL3, and GSPT1. Evaluation using nomograms and receiver operator characteristic curves demonstrated the diagnostic value of these hub genes. Ultimately, a causal relationship between hub genes and appendicitis was not identified in our study. Nevertheless, our findings indicate that appendicitis is correlated with 9 gut microbiota. This study identified 5 hub genes, specifically HSP90AA1, RPL5, MYC, CD44, and RPS3A, which exhibit diagnostic significance of appendicitis. Furthermore, the elucidation of these hub genes aids in enhancing our comprehension of the molecular pathways implicated in the development of appendicitis.
Asunto(s)
Apendicitis , Análisis de la Aleatorización Mendeliana , Humanos , Apendicitis/genética , Apendicitis/diagnóstico , Perfilación de la Expresión Génica/métodos , Curva ROC , Redes Reguladoras de Genes , Nomogramas , Microbioma Gastrointestinal/genéticaRESUMEN
PURPOSE: To determine the misclassification rate of the keratoconus percentage (KISA%) index efficacy in eyes with progressive keratoconus. METHODS: This was a retrospective case-control study of consecutive patients with confirmed progressive keratoconus and a contemporaneous normal control group with 1.00 diopters or greater regular astigmatism. Scheimpflug imaging (Pentacam HR) was obtained for all patients. KISA% index and inferior-superior (IS) values were obtained from the Pentacam topometric/keratoconus staging map. Receiver operating characteristic curves were generated to determine the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity values. RESULTS: There were 160 eyes from 160 patients evaluated, including 80 eyes from 80 patients with progressive keratoconus and 80 eyes from 80 control patients. There were 20 eyes (25%) with progressive keratoconus misclassified by the KISA% index, with 16 eyes (20%) of the progressive keratoconus cohort classified as normal (ie, KISA% < 60). There were 4 eyes (5%) with progressive keratoconus that would classify as having "normal topography" using the published criteria for very asymmetric ectasia with normal topography of KISA% less than 60 and IS value less than 1.45. All controls had a KISA% index value of less than 15. The optimal cut-off value to distinguish cohorts was 15.31 (AUROC = 0.972, 93.75% sensitivity). KISA% index values of 60 and 100 achieved low sensitivity (80% and 73.75%, respectively). CONCLUSIONS: The KISA% index misclassified a significant proportion of eyes with progressive keratoconus as normal. Although highly specific for clinical keratoconus, the KISA% index lacks sensitivity, does not effectively discriminate between normal and abnormal topography, and thus should not be used in large data analysis or artificial intelligence-based modeling. [J Refract Surg. 2024;40(9):e614-e624.].
Asunto(s)
Topografía de la Córnea , Progresión de la Enfermedad , Queratocono , Curva ROC , Humanos , Queratocono/clasificación , Queratocono/diagnóstico , Estudios Retrospectivos , Topografía de la Córnea/métodos , Masculino , Femenino , Adulto , Estudios de Casos y Controles , Adulto Joven , Córnea/patología , Córnea/diagnóstico por imagen , Sensibilidad y Especificidad , Agudeza Visual/fisiología , Adolescente , Área Bajo la Curva , Persona de Mediana Edad , Errores DiagnósticosRESUMEN
OBJECTIVE: To construct and validate a nomogram model for predicting sepsis-associated acute kidney injury (SA-AKI) risk in intensive care unit (ICU) patients. METHODS: A retrospective cohort study was conducted. Adult sepsis patients admitted to the department of ICU of the 940th Hospital of Joint Logistic Support Force of PLA from January 2017 to December 2022 were enrolled. Demographic characteristics, clinical data within 24 hours after admission to ICU diagnosis, and clinical outcomes were collected. Patients were divided into training set and validation set according to a 7 : 3 ratio. According to the consensus report of the 28th Acute Disease Quality Initiative Working Group (ADQI 28), the data were analyzed with serum creatinine as the parameter and AKI occurrence 7 days after sepsis diagnosis as the outcome. Lasso regression analysis and univariate and multivariate Logistic regression analysis were performed to construct the nomogram prediction model for SA-AKI. The discrimination and accuracy of the model were evaluated by the Hosmer-Lemeshow test, receiver operator characteristic curve (ROC curve), decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 247 sepsis patients were enrolled, 184 patients developed SA-AKI (74.49%). The number of AKI patients in the training and validation sets were 130 (75.58%) and 54 (72.00%), respectively. After Lasso regression analysis and univariate and multivariate Logistic regression analysis, four independent predictive factors related to the occurrence of SA-AKI were selected, namely procalcitonin (PCT), prothrombin activity (PTA), platelet distribution width (PDW), and uric acid (UA) were significantly associated with the onset of SA-AKI, the odds ratio (OR) and 95% confidence interval (95%CI) was 1.03 (1.01-1.05), 0.97 (0.55-0.99), 2.68 (1.21-5.96), 1.01 (1.00-1.01), all P < 0.05, respectively. A nomogram model was constructed using the above four variables. ROC curve analysis showed that the area under the curve (AUC) was 0.869 (95%CI was 0.870-0.930) in the training set and 0.710 (95%CI was 0.588-0.832) in the validation set. The P-values of the Hosmer-Lemeshow test were 0.384 and 0.294, respectively. In the training set, with an optimal cut-off value of 0.760, a sensitivity of 77.5% and specificity of 88.1% were achieved. Both DCA and CIC plots demonstrated the model's good clinical utility. CONCLUSIONS: A nomogram model based on clinical indicators of sepsis patients admitted to the ICU within 24 hours could be used to predict the risk of SA-AKI, which would be beneficial for early identification and treatment on SA-AKI.
Asunto(s)
Lesión Renal Aguda , Unidades de Cuidados Intensivos , Nomogramas , Curva ROC , Sepsis , Humanos , Lesión Renal Aguda/diagnóstico , Lesión Renal Aguda/etiología , Sepsis/diagnóstico , Sepsis/complicaciones , Estudios Retrospectivos , Factores de Riesgo , Modelos Logísticos , Femenino , Masculino , Creatinina/sangre , Persona de Mediana Edad , Estudios de CohortesRESUMEN
OBJECTIVE: We aimed to investigate the serum concentration of the spexin, which has been shown to have an anorexic effect in animal models, in pregnant women with hyperemesis gravidarum (HG). METHODS: This case-control study was conducted with 80 pregnant women who applied to the Umraniye Training and Research Hospital Gynecology and Obstetrics Clinic between April 2022 and September 2022. The HG group consisted of 40 pregnant women who were diagnosed with HG in the first 14 weeks of pregnancy, and the control group consisted of 40 healthy pregnant women matched with the HG group in terms of age, BMI, and gestational week. RESULTS: Both groups were similar in terms of demographic characteristics and gestational age at blood sampling for spexin (p > 0.05). While maternal serum spexin concentration was 342.4 pg/ml in the HG group, it was 272.8 pg/ml in the control group (p = 0.003). ROC analysis was performed to determine the value of maternal serum spexin concentration in terms of predicting HG. AUC analysis of maternal serum spexin for HG estimation was 0.693 (p = 0.003, 95% CI =0.577 - 0.809). The optimal cutoff value for maternal serum spexin concentration was determined as 305.90 pg/ml with 65% sensitivity and 65% specificity. CONCLUSIONS: High serum spexin concentration is thought to play a role in the etiopathogenesis of HG, and this should be supported by demonstrating changes in serum spexin concentrations in pregnant women with HG whose symptoms alleviated and weight regain started after treatment.
Asunto(s)
Hiperemesis Gravídica , Hormonas Peptídicas , Humanos , Femenino , Embarazo , Hiperemesis Gravídica/sangre , Hiperemesis Gravídica/diagnóstico , Adulto , Estudios de Casos y Controles , Hormonas Peptídicas/sangre , Biomarcadores/sangre , Curva ROC , Adulto JovenRESUMEN
INTRODUCTION: Obstetrics research has predominantly focused on the management and identification of factors associated with labor dystocia. Despite these efforts, clinicians currently lack the necessary tools to effectively predict a woman's risk of experiencing labor dystocia. Therefore, the objective of this study was to create a predictive model for labor dystocia. MATERIAL AND METHODS: The study population included nulliparous women with a single baby in the cephalic presentation in spontaneous labor at term. With a cohort-based registry design utilizing data from the Copenhagen Pregnancy Cohort and the Danish Medical Birth Registry, we included women who had given birth from 2014 to 2020 at Copenhagen University Hospital-Rigshospitalet, Denmark. Logistic regression analysis, augmented by a super learner algorithm, was employed to construct the prediction model with candidate predictors pre-selected based on clinical reasoning and existing evidence. These predictors included maternal age, pre-pregnancy body mass index, height, gestational age, physical activity, self-reported medical condition, WHO-5 score, and fertility treatment. Model performance was evaluated using the area under the receiver operating characteristics curve (AUC) for discriminative capacity and Brier score for model calibration. RESULTS: A total of 12,445 women involving 5,525 events of labor dystocia (44%) were included. All candidate predictors were retained in the final model, which demonstrated discriminative ability with an AUC of 62.3% (95% CI:60.7-64.0) and Brier score of 0.24. CONCLUSIONS: Our model represents an initial advancement in the prediction of labor dystocia utilizing readily available information obtainable upon admission in active labor. As a next step further model development and external testing across other populations is warranted. With time a well-performing model may be a step towards facilitating risk stratification and the development of a user-friendly online tool for clinicians.
Asunto(s)
Índice de Masa Corporal , Distocia , Edad Materna , Paridad , Humanos , Femenino , Embarazo , Distocia/epidemiología , Adulto , Factores de Riesgo , Dinamarca/epidemiología , Curva ROC , Inicio del Trabajo de Parto , Sistema de Registros , Edad GestacionalRESUMEN
Accurate prediction of coronary artery disease (CAD) is crucial for enabling early clinical diagnosis and tailoring personalized treatment options. This study attempts to construct a machine learning (ML) model for predicting CAD risk and further elucidate the complex nonlinear interactions between the disease and its risk factors. Employing the Z-Alizadeh Sani dataset, which includes records of 303 patients, univariate analysis and the Boruta algorithm were applied for feature selection, and nine different ML techniques were subsequently deployed to produce predictive models. To elucidate the intricate pathogenesis of CAD, this study harnessed the analytical capabilities of Shapley values, alongside the use of generalized additive models for curve fitting, to probe into the nonlinear interactions between the disease and its associated risk factors. Furthermore, we implemented a piecewise linear regression model to precisely pinpoint inflection points within these complex nonlinear dynamics. The findings of this investigation reveal that logistic regression (LR) stands out as the preeminent predictive model, demonstrating remarkable efficacy, it achieved an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.981 (95% CI: 0.952-1), and an Area Under the Precision-Recall Curve (AUPRC) of 0.993. The utilization of the 14 most pivotal features in constructing a dynamic nomogram. Analysis of the Shapley smoothing curves uncovered distinctive "S"-shaped and "C"-shaped relationships linking age and triglycerides to CAD, respectively. In summary, machine learning models could provide valuable insights for the early diagnosis of CAD. The SHAP method may provide a personalized risk assessment of the relationship between CAD and its risk factors.
Asunto(s)
Enfermedad de la Arteria Coronaria , Aprendizaje Automático , Humanos , Enfermedad de la Arteria Coronaria/diagnóstico , Enfermedad de la Arteria Coronaria/epidemiología , Femenino , Masculino , Persona de Mediana Edad , Factores de Riesgo , Curva ROC , Anciano , Modelos Logísticos , Algoritmos , Nomogramas , Medición de Riesgo/métodosRESUMEN
Camera image-based deep learning (DL) techniques have achieved promising results in dental caries screening. To apply the intraoral camera image-based DL technique for dental caries detection and assess its diagnostic performance, we employed the ensemble technique in the image classification task. 2,682 intraoral camera images were used as the dataset for image classification according to dental caries presence and caries-lesion localization using DL models such as ResNet-50, Inception-v3, Inception-ResNet-v2, and Faster R-convolutional neural network according to diagnostic study design. 534 participants whose mean age [SD] was 47.67 [±13.94] years were enrolled. The dataset was divided into training (56.0%), validation (14.0%), and test subset (30.0%) annotated by one experienced dentist as a reference standard about dental caries detection and lesion location. The confusion matrix, area under the receiver operating characteristic curve (AUROC), and average precision (AP) were evaluated for performance analysis. In the end-to-end dental caries image classification, the ensemble DL models had consistently improved performance, in which as the best results, the ensemble model of Inception-ResNet-v2 achieved 0.94 of AUROC and 0.97 of AP. On the other hand, the explainable model achieved 0.91 of AUROC and 0.96 of AP after the ensemble application. For dental caries classification using intraoral camera images, the application of ensemble techniques exhibited consistently improved performance regardless of the DL models. Furthermore, the trial to create an explainable DL model based on carious lesion detection yielded favorable results.
Asunto(s)
Aprendizaje Profundo , Caries Dental , Humanos , Caries Dental/diagnóstico por imagen , Caries Dental/diagnóstico , Caries Dental/patología , Femenino , Persona de Mediana Edad , Adulto , Masculino , Redes Neurales de la Computación , Procesamiento de Imagen Asistido por Computador/métodos , Curva ROCRESUMEN
This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78-0.92), 0.76 (95% CI 0.68-0.84), and 0.89 (95% CI 0.84-0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86-1.00), 0.79 (95% CI 0.68-0.91), and 0.96 (95% CI 0.90-1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72-0.96), 0.75 (95% CI 0.62-0.87), and 0.86 (95% CI 0.75-0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.
Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas , Inmunoterapia , Neoplasias Pulmonares , Nomogramas , Tomografía Computarizada por Rayos X , Humanos , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Carcinoma de Pulmón de Células no Pequeñas/tratamiento farmacológico , Carcinoma de Pulmón de Células no Pequeñas/terapia , Carcinoma de Pulmón de Células no Pequeñas/patología , Femenino , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/tratamiento farmacológico , Neoplasias Pulmonares/terapia , Neoplasias Pulmonares/patología , Masculino , Persona de Mediana Edad , Anciano , Tomografía Computarizada por Rayos X/métodos , Inmunoterapia/métodos , Curva ROC , Resultado del Tratamiento , Estudios Prospectivos , RadiómicaRESUMEN
The aim of this study was to investigate the measurement of the incident angle of the main blood vessel, and the benefits of its integral with ultrasound malignant features of breast nodules for the assessment of breast malignancy based on BI-RADS. The incident angles of main blood vessels of 185 breast nodules in 185 patients who underwent breast nodule surgical excision or biopsy were quantitatively measured using color Doppler ultrasound from October 2022 to October 2023 in a tertiary hospital, and related data were collected and analyzed. Based on histopathology as the gold standard, the breast nodules were classified into benign and malignant groups. The incident angle values of both groups were compared, Receiver Operating Characteristic (ROC) curves were plotted, and the optimal cutoff value for distinguishing between benign and malignant breast nodules was determined. The malignancy risk of the breast nodules was assessed using the incident angle of the breast main vessel, BI-RADS classification, and a combination of ultrasound malignant features with the incident angle. The areas under the ROC curves (AUC) for each method were calculated and compared. The average incident angle of the main vessel of the breast nodule for the benign and malignant breast nodule groups was (41.47 ± 14.27)° and (22.65 ± 11.09)°, respectively, with a statistically significant difference (t = 10.027, P < 0.001). There was a significant negative correlation between the breast nodule vessel incident angle and histopathological malignancy (r = - 0.593, P < 0.001). The ROC curve and Youden index suggested that the optimal cutoff value for distinguishing between benign and malignant breast nodules using the vascular incident angle was 26.9°, with a sensitivity of 76.34%, specificity of 84.78%, positive predictive value of 83.53%, negative predictive value of 78.00%, and AUC of 0.853. The diagnostic performance of BI-RADS classification based on ultrasound malignant features of the breast nodules alone in assessing the malignancy risk of breast nodules showed a sensitivity of 78.50%, specificity of 92.39%, positive predictive value of 91.25%, negative predictive value of 79.95%, and AUC of 0.905. The integral of ultrasound malignant features and vascular incident angle for BI-RADS based assessment for breast nodule malignancy risk demonstrated a sensitivity of 90.32%, specificity of 89.13%, positive predictive value of 89.36%, negative predictive value of 90.11%, and AUC of 0.940. The differences in negative predictive value and AUC between ultrasound malignant features BI-RADS classification alone and the combination of ultrasound malignant features BI-RADS classification with the incident angle of the main vessel of the breast nodule were all statistically significant (x2 = 3.243, P = 0.042; Z = 2.955, P = 0.003). Measuring the incident angle of the main blood vessel of breast nodules and combining this measurement with ultrasound malignant features for BI-RADS classification can enhance the effectiveness of malignancy risk assessment of breast nodules, increase the negative predictive value, and potentially reduce unnecessary biopsies.
Asunto(s)
Neoplasias de la Mama , Mama , Curva ROC , Humanos , Femenino , Neoplasias de la Mama/diagnóstico por imagen , Neoplasias de la Mama/patología , Persona de Mediana Edad , Adulto , Mama/diagnóstico por imagen , Mama/patología , Mama/irrigación sanguínea , Anciano , Ultrasonografía Mamaria/métodos , Ultrasonografía Doppler en Color/métodos , Diagnóstico DiferencialRESUMEN
Despite widespread cervical cancer (CC) screening programs, low participation has led to high morbidity and mortality rates, especially in developing countries. Because early-stage CC often has no symptoms, a non-invasive and convenient diagnostic method is needed to improve disease detection. In this study, we developed a new approach for differentiating both CC and cervical intraepithelial neoplasia (CIN)2/3, a precancerous lesion, from healthy individuals by exploring CC fatty acid metabolic reprogramming. Analysis of public datasets suggested that various fatty acid metabolizing enzymes were expressed at higher levels in CC tissues than in normal tissues. Correspondingly, 11 free fatty acids (FFAs) showed significantly different serum levels in CC patient samples compared with healthy donor samples. Nine of these 11 FFAs also displayed significant alterations in CIN2/3 patients. We then generated diagnostic models using combinations of these FFAs, with the optimal model including stearic and dihomo-γ-linolenic acids. Receiver operating characteristic curve analyses suggested that this diagnostic model could detect CC and CIN2/3 more accurately than using serum squamous cell carcinoma antigen level. In addition, the diagnostic model using FFAs was able to detect patients regardless of clinical stage or histological type. Overall, the serum FFA diagnostic model developed in this study could be a powerful new tool for the non-invasive early detection of CC and CIN2/3.
Asunto(s)
Ácidos Esteáricos , Displasia del Cuello del Útero , Neoplasias del Cuello Uterino , Humanos , Femenino , Displasia del Cuello del Útero/diagnóstico , Displasia del Cuello del Útero/sangre , Neoplasias del Cuello Uterino/sangre , Neoplasias del Cuello Uterino/diagnóstico , Ácidos Esteáricos/sangre , Adulto , Ácido 8,11,14-Eicosatrienoico/sangre , Persona de Mediana Edad , Biomarcadores de Tumor/sangre , Curva ROCRESUMEN
In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients.
Asunto(s)
Unidades de Cuidados Intensivos , Aprendizaje Automático , Respiración Artificial , Desconexión del Ventilador , Humanos , Desconexión del Ventilador/métodos , Masculino , Femenino , Persona de Mediana Edad , Respiración Artificial/métodos , Anciano , Medición de Riesgo/métodos , Curva ROC , Factores de RiesgoRESUMEN
OBJECTIVE: Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm. METHODS: A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed. RESULTS: Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%. CONCLUSION: The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.
Asunto(s)
Neoplasias Pulmonares , Nódulo Pulmonar Solitario , Tomografía Computarizada por Rayos X , Humanos , Femenino , Masculino , Neoplasias Pulmonares/diagnóstico por imagen , Persona de Mediana Edad , Estudios Retrospectivos , Nódulo Pulmonar Solitario/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Anciano , Curva ROC , Nódulos Pulmonares Múltiples/diagnóstico por imagen , Nódulos Pulmonares Múltiples/patología , Hallazgos Incidentales , Sensibilidad y Especificidad , Algoritmos , Adulto , Área Bajo la Curva , RadiómicaRESUMEN
BACKGROUND: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction. METHODS: We explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage and selection operator logistic regression) in myopia risk prediction. We apply these models to the Shanghai Jinshan Myopia Cohort Study and also conduct a simulation study to investigate the impact of missing mechanisms, the degree of ME, and the importance of predictors on model performance. Model performance is evaluated using the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS: Our findings indicate that in scenarios with missing data and ME, using MI-ME in combination with logistic regression yields the best prediction results. In scenarios without ME, employing MI-MAR to handle missing data outperforms SI regardless of the missing mechanisms. When ME has a greater impact on prediction than missing data, the relative advantage of MI-MAR diminishes, and MI-ME becomes more superior. Furthermore, our results demonstrate that statistical models exhibit better prediction performance than machine-learning models. CONCLUSION: MI-ME emerges as a reliable method for handling missing data and ME in important predictors for early-onset myopia risk prediction.
Asunto(s)
Aprendizaje Automático , Miopía , Humanos , Miopía/diagnóstico , Miopía/epidemiología , Femenino , Niño , Masculino , Modelos Logísticos , Modelos Estadísticos , Medición de Riesgo/métodos , Medición de Riesgo/estadística & datos numéricos , Factores de Riesgo , Curva ROC , Teorema de Bayes , China/epidemiología , Estudios de Cohortes , Edad de InicioRESUMEN
OBJECTIVE: The purpose of this study was to investigate the diagnostic value of circ_0013958 in acute myocardial infarction (AMI) patients and its influence on the prognosis of AMI patients. METHODS: The GSE160717 dataset was downloaded from the NCBI database and differentially expressed genes were analyzed between the control group and the AMI group. The up-regulated genes included circ_0013958. The expression of circ_0013958 in both groups was further verified by RT-qPCR. The Receiver Operating Characteristic curve was used to evaluate the diagnostic value of circ_0013958 in AMI. Pearson correlation analysis was used to examine the correlation between circ_0013958 levles and biochemical indicators. Binary logistic regression was used to analyze the risk factors affecting the occurrence of AMI. Prognostic analysis was performed using COX regression analysis and the Kaplan-Meier Curve. RESULTS: Compared to the control group, the level of circ_0013958 in AMI patients increased. Circ_0013958 can effectively distinguish AMI patients from non-AMI patients. Circ_0013958 levels were positively correlated with cTnI, LDH, CRP and TC levels. The elevated level of circ_0013958 was an independent risk factor for the occurrence of AMI. Higher circ_0013958 levels were also associated with the occurrence of major adverse cardiac events (MACEs) in AMI patients. Additionally, elevated circ_0013958 levels reduced the survival probability of AMI patients. CONCLUSION: Circ_0013958 levels were up-regulated in AMI patients. It can be used as a diagnosis biomarker for AMI. The level of circ_0013958 was correlated with the disease severity and was an independent risk factor for the occurrence of AMI. Elevated circ_0013958 levels were associated with poor prognosis in AMI patients.
Asunto(s)
Infarto del Miocardio , ARN Circular , Humanos , Infarto del Miocardio/genética , Pronóstico , Masculino , Femenino , ARN Circular/genética , Persona de Mediana Edad , Curva ROC , Anciano , Factores de Riesgo , Biomarcadores/sangre , Biomarcadores/metabolismoRESUMEN
BACKGROUND: Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sentinel lymph nodes (SLNs). However, ALND can lead to significant postoperative complications without always providing additional clinical benefits. This study aims to develop machine-learning (ML) models to predict non-sentinel lymph node (non-SLN) metastasis in Chinese BC patients with three or more positive SLNs, potentially allowing the omission of ALND. METHODS: Data from 2217 BC patients who underwent SLN biopsy at Shantou University Medical College were analyzed, with 634 having positive SLNs. Patients were categorized into those with ≤ 2 positive SLNs and those with ≥ 3 positive SLNs. We applied nine ML algorithms to predict non-SLN metastasis. Model performance was evaluated using ROC curves, precision-recall curves, and calibration curves. Decision Curve Analysis (DCA) assessed the clinical utility of the models. RESULTS: The RF model showed superior predictive performance, achieving an AUC of 0.987 in the training set and 0.828 in the validation set. Key predictive features included size of positive SLNs, tumor size, number of SLNs, and ER status. In external validation, the RF model achieved an AUC of 0.870, demonstrating robust predictive capabilities. CONCLUSION: The developed RF model accurately predicts non-SLN metastasis in BC patients with ≥ 3 positive SLNs, suggesting that ALND might be avoided in selected patients by applying additional axillary radiotherapy. This approach could reduce the incidence of postoperative complications and improve patient quality of life. Further validation in prospective clinical trials is warranted.
Asunto(s)
Neoplasias de la Mama , Metástasis Linfática , Aprendizaje Automático , Biopsia del Ganglio Linfático Centinela , Ganglio Linfático Centinela , Humanos , Neoplasias de la Mama/patología , Neoplasias de la Mama/cirugía , Femenino , Metástasis Linfática/patología , Persona de Mediana Edad , Ganglio Linfático Centinela/patología , Ganglio Linfático Centinela/cirugía , Biopsia del Ganglio Linfático Centinela/métodos , Adulto , Anciano , Escisión del Ganglio Linfático , China/epidemiología , Axila , Algoritmos , Estudios Retrospectivos , Ganglios Linfáticos/patología , Ganglios Linfáticos/cirugía , Curva ROC , Pueblos del Este de AsiaRESUMEN
Purpose: This study uses deep neural network-generated rim-to-disc area ratio (RADAR) measurements and the disc damage likelihood scale (DDLS) to measure the rate of optic disc rim loss in a large cohort of glaucoma patients. Methods: A deep neural network was used to calculate RADAR and DDLS for each optic disc photograph (ODP). Patient demographics, diagnosis, intraocular pressure (IOP), and mean deviation (MD) from perimetry were analyzed as risk factors for faster progression of RADAR. Receiver operating characteristic (ROC) curves were used to compare RADAR and DDLS in their utility to distinguish glaucoma from glaucoma suspect (GS) and for detecting glaucoma progression. Results: A total of 13,679 ODPs with evidence of glaucomatous optic nerve damage from 4106 eyes of 2407 patients with glaucoma or GS were included. Of these eyes, 3264 (79.5%) had a diagnosis of glaucoma, and 842 (20.5%) eyes were GS. Mean ± SD baseline RADAR of GS and glaucoma were 0.67 ± 0.13 and 0.57 ± 0.18, respectively (P < 0.001). Older age, greater IOP fluctuation, baseline MD, right eye, and diagnosis of secondary open-angle glaucoma were associated with slope of RADAR. The mean baseline DDLS of GS and glaucoma were 3.78 and 4.39, respectively. Both RADAR and DDLS showed a less steep slope in advanced glaucoma. In glaucoma, the change of RADAR and DDLS correlated with the corresponding change in MD. RADAR and DDLS had a similar ability to discriminate glaucoma from GS and detect disease progression. Area under the ROC curve of RADAR and DDLS was 0.658 and 0.648. Conclusions: Automated calculation of RADAR and DDLS with a neural network can be used to evaluate the extent and long-term rate of optic disc rim loss and is further evidence of long-term nerve fiber loss in treated patients with glaucoma. Translational Relevance: Our study provides a large clinic-based experience for RADAR and DDLS measurements in GS and glaucoma with a neural network.
Asunto(s)
Progresión de la Enfermedad , Glaucoma , Presión Intraocular , Redes Neurales de la Computación , Disco Óptico , Curva ROC , Humanos , Disco Óptico/patología , Disco Óptico/diagnóstico por imagen , Masculino , Femenino , Persona de Mediana Edad , Presión Intraocular/fisiología , Glaucoma/diagnóstico , Glaucoma/fisiopatología , Anciano , Fotograbar , Enfermedades del Nervio Óptico/diagnóstico , Campos Visuales/fisiología , Pruebas del Campo Visual/métodos , Adulto , Estudios RetrospectivosRESUMEN
Assessing fertilized human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Existing models used for embryo quality assessment and ploidy detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we develop and compare various embryo ploidy status prediction models across distinct embryo development stages. We present BELA, a state-of-the-art ploidy prediction model that surpasses previous image- and video-based models without necessitating input from embryologists. BELA uses multitask learning to predict quality scores that are thereafter used to predict ploidy status. By achieving an area under the receiver operating characteristic curve of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists' manual scores. While not a replacement for preimplantation genetic testing for aneuploidy, BELA exemplifies how such models can streamline the embryo evaluation process.