Diagnostic Prediction Model Research Articles

Development of a novel diagnostic model to monitor the progression of metabolic dysfunction-associated steatotic liver disease to hepatocellular carcinoma in females.

The onset of metabolic dysfunction-associated steatotic liver disease-associated hepatocellular carcinoma (MASLD-HCC) is insidious and exhibits sex-specific variations. Effective methods for monitoring MASLD-HCC progression in females have not yet been developed. Transcriptomic data of female liver tissue samples were obtained from multiple public databases. Differentially expressed genes (DEGs) in MASLD-HCC were identified using differential expression and robust rank aggregation analyses. Diagnostic prediction models for MASLD (DP.MASLD) and HCC (DP.HCC) were developed and validated using elastic net analysis, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Bioinformatics was used to assess the pathogenesis of MASLD-HCC. Seven overlapping DEGs were identified in female patients with MASLD and HCC: AKR1B10, CLEC1B, CYP2C19, FREM2, MT1H, NRG1, and THBS1). The area under the ROC curve (AUC) values for the training and validation groups of the DP.MASLD model were 0.864 and 0.782, 0.932 and 1.000, and 0.920 and 0.969 when differentiating between the steatosis and normal liver, steatohepatitis and steatosis, and steatohepatitis and normal liver groups, respectively. The AUCs for DP.HCC were 0.980 and 0.997 in the training and validation groups, respectively. The oncogenesis of female MASLD-HCC is associated with molecular pathways, including cytochrome P450-associated drug metabolism, tyrosine metabolism, fatty acid degradation, focal adhesion, extracellular matrix receptor interactions, and protein digestion and absorption. A novel and effective method to quantitatively assess the risk of MASLD-HCC progression in female patients was developed, and this method will aid in the generation of precise diagnostic, preventive, and therapeutic strategies.

Radical prostatectomy without prostate biopsy based on a noninvasive diagnostic strategy: a prospective single-center study.

Prostate biopsy is the most common approach for diagnosing prostate cancer (PCa); however, it has inherent limitations, such as the invasive procedure, postoperative complications, and false negative results. We aimed to provide a noninvasive diagnostic strategy for patients with highly suspected PCa and to evaluate the feasibility of performing biopsy-spared radical prostatectomy. This prospective study included a total of 57 patients between November 10, 2022, and December 1, 2023. All 57 patients underwent radical prostatectomy without prior prostate biopsy based on a noninvasive diagnostic strategy consisting of a diagnostic prediction model [comprised of the prostate imaging-reporting and data system (PI-RADS) score and prostate-specific antigen density (PSAD)] and the 18F-prostate-specific membrane antigen (PSMA)-1007 positron emission tomography (PET)/computed tomography (CT) examination. The primary endpoint was the positive predictive value (PPV) of clinically significant PCa [the International Society of Urological Pathology (ISUP) grade ≥2, Gleason score ≥3 + 4]. The secondary endpoints were a PPV of any-grade PCa (ISUP grade ≥ 1, Gleason score ≥3 + 3) and high-grade PCa (ISUP grade ≥3, Gleason score ≥4 + 3), and the false positive rate of the diagnostic strategy. Of the 371 screened patients with clinically suspected PCa, 57 patients fulfilled the criteria and consented to participate in this study. The median PSAD level was 0.56 (0.42-0.82) ng/mL2; 13 (22.8%) patients were identified as having a PI-RADS score of 4, and 44 (77.2%) patients with a PI-RADS score of 5. The median SUVmax of 18F-PSMA-1007 PET/CT was 21.6 (15.8-33.0). For the 57 enrolled patients who received radical prostatectomy directly, the PPV of clinically significant PCa was 98.2% [56/57, 95% confidence interval (CI): 90.6-100%]. Only 1.8% (1/57, 95% CI: 0.0-9.4%) of patients were diagnosed with clinically insignificant PCa (ISUP grade = 1, Gleason score = 3 + 3). The PPV of any-grade PCa and high-grade PCa were 100% and 73.7% (42/57, 95% CI: 60.3-84.5%), respectively. No one had a false positive result. We proposed a noninvasive diagnostic strategy consisting sequentially of a diagnostic prediction model and the 18F-PSMA-1007 PET/CT examination for diagnosing PCa. Despite some limitations, our initial findings suggest the potential feasibility of radical prostatectomy without prior prostate biopsy.

The Protein Corona on Nanoparticles for Tumor Targeting in Prostate Cancer—A Review of the Literature and Experimental Trial Protocol

The National Cancer Institute (NCI) recognizes the potential of technologies based on the use of nanoparticles (NPs) in revolutionizing clinical approaches to the diagnosis and prognosis of cancer. Recent research suggests that once NPs come into contact with the biological fluid of cancer patients, they are covered by proteins, forming a “protein corona” composed of hundreds of plasma proteins. The concept of a personalized, disease-specific protein corona, demonstrating substantial differences in NP corona profiles between patients with and without cancer, has been introduced. We developed the design of an experimental prospective single-center study with patients allocated in a 1:1:1 ratio of one of three arms: untreated patients with benign prostatic hyperplasia (BPH), untreated patients with non-metastatic prostate cancer (PCa), and metastatic prostate cancer patients starting systemic therapies with new androgen-targeted agents or taxanes. The protocol aims to develop and implement sensitive nanotools with two distinct objectives: First, to design NPs capable of selectively binding and detecting biomarkers in order to build a predictive diagnostic model to effectively discriminate between patient sera affected by BPH and PCa. Secondly, within the population with PCa, in the case of initial advanced metastatic diagnosis, the objective is to find biomarkers capable of predicting the response to systemic treatments to improve the precision and efficiency of monitoring treatment outcomes. For protein and metabolite corona experiments, we developed a cross-reactive sensor array platform with cancer detection capacity made of three liposomal formulations with different surface charges. For proteomic-NP studies, proteins were identified and quantified using nano-high-performance LC (nanoHPLC) coupled with MS/MS (nanoHPLC−MS/MS). Metabolites were instead analyzed using an untargeted metabolomic approach. Compared with previous review articles, the novelty of this review is represented by the analysis of the possible clinical applications of protein corona NPs focused on PCa and the presentation of a new clinical protocol in the metastatic phase of PCa.

Arrhythmogenic Cardiomyopathy: Towards Genotype Based Diagnoses and Management.

Arrhythmogenic cardiomyopathy (ACM) is a genetically heterogeneous inherited cardiomyopathy with an estimated prevalence of 1:5000-10 000 that predisposes patients to life-threatening ventricular arrhythmias (VA) and sudden cardiac death (SCD). ACM diagnostic criteria and risk prediction models, particularly for arrhythmogenic right ventricular cardiomyopathy (ARVC), the most common form of ACM, are typically genotype-agnostic, but numerous studies have established clinically meaningful genotype-phenotype associations. Early signs of ACM onset differ by genotype indicating the need for genotype-specific diagnostic criteria and family screening paradigms. Likewise, risk factors for SCD vary by genetic subtype, indicating that genotype-specific guidelines for management are also warranted. Of particular importance, genotype-specific therapeutic approaches are being developed. Results from a randomized controlled trial for flecainide use in ARVC patients are currently pending. Research in a plakophilin-2-deficient mouse model suggests this antiarrhythmic drug may be particularly useful for patients with likely pathogenic or pathogenic (LP/P) PKP2 variants. Additionally, the first gene therapy clinical trials in ARVC patients harboring LP/P PKP2 variants are currently underway. This review aims to provide clinicians caring for ACM patients with an up-to-date overview of the current literature in genotype-specific natural history of disease and management of ACM patients and describe scientific advances that have led to upcoming clinical trials.

Development and validation of the prediction model based on autophagy-associated genes in bronchopulmonary dysplasia

Background Bronchopulmonary dysplasia (BPD) is the most common chronic respiratory disease among preterm infants. Owing to the limitations in current diagnostic methods, developing a predictive model for BPD is crucial. Methods Using 243 autophagy-associated genes and dataset GSE32472, differential expression of autophagy-associated genes was identified at postnatal days 5, 14, and 28 between BPD patients and controls. LASSO and multivariate logistic regression analyses were performed to screen for diagnostic prediction genes. Receiver Operating Characteristic, Harrell’s concordance index, and decision curve analysis (DCA) were used to evaluate the diagnostic prediction model in GSE32472 and GSE220135. A BPD mouse model was constructed and qRT-PCR and Western blot were used to verify gene expression in lung tissue. Results Based on p < 0.05, we constructed a diagnostic prediction model for BPD using WIPI1, TOMM70A, BAG3, and PRKCQ. For the training database, the model’s C-index and Area under Curve were both 0.941, and a high applicability value was demonstrated by the DCA curve. These outcomes were also confirmed in the validation cohort GSE220135, demonstrating the superior diagnostic prediction capability of our approach. In addition, significant variations in immune cell infiltration were observed between BPD patients and controls. According to the results of qRT-PCR, BPD model mice had significantly lower expression levels of WIPI1, TOMM70A, BAG3, and PRKCQ than controls. Conclusions We constructed and validated a diagnostic prediction model for BPD based on WIPI1, TOMM70A, BAG3, and PRKCQ. These four genes may influence BPD development by regulating immune responses and immune cells.

Establishment and Validation of Diagnostic and Prognostic Prediction Models for Liver Metastasis in Patients with Rectal Cancer: A SEER-based Study

Background: Rectal cancer is one of the most common gastrointestinal tumors, among which the liver is the most common site of distant metastasis and liver metastasis that leads to poor prognosis. This study aimed to develop and validate a diagnostic nomogram to predict the occurrence of rectal cancer with liver metastasis (RCLM) and a prognostic nomogram to predict cancer-specific survival (CSS) in RCML patients. Methods: Data on patients with rectal cancer diagnosed between 2010 and 2013 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate logistic regression and multivariate logistic regression were used to determine the independent risk factors of RCLM. Univariate Cox proportional hazards regression and multivariate Cox proportional hazards regression were used to identify independent prognostic factors for RCLM. This study then developed two novel nomograms, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: A total of 29367 patients with rectal cancer were included. Among them, 3403 patients (11.59%) had liver metastases at the time of diagnosis. The independent risk factors of RCLM included sex, race, tumor grade, AJCC N, CEA, marital status, tumor size, total number of primary tumors, and histological type. Age, chemotherapy, total number of primary tumors, surgery sites, and histological type were independent prognostic factors of patients with RCLM. The results of ROC curves, calibration curves, and DCA in the development, validation, and testing sets confirmed that the diagnostic nomogram can precisely predict the occurrence of RCLM. The results of ROC curves, calibration curves, DCA, C-indexes, and Kaplan–Meier (K-M) survival curves in the development, validation, and testing sets confirmed that the prognostic nomogram could precisely predict the prognosis of RCLM. Conclusion: The two nomograms are expected to be effective tools for predicting the risk of liver metastasis for patients with rectal cancer and personalized prognosis prediction for patients with RCLM, which may benefit clinical decision-making.

Development and validation of the placenta-QUS model for the detection of placenta-mediated diseases using quantitative ultrasound measurements: An Ex Vivo proof-of-concept study

IntroductionPlacenta-mediated diseases are associated with structural changes in the placenta. Quantitative Ultrasound (QUS) imaging measures the acoustic properties of the tissue, which are correlated to the underlying tissue structure. We aimed to develop and validate a diagnostic prediction model using QUS measurements for pre-eclampsia (PE) and small-for-gestational-age (SGA) fetuses/neonates. MethodsFor this prospective case-control study, placentas were collected from a group of women who delivered via cesarean section at BC Women's Hospital, Vancouver, Canada. Ultrasound data were collected and processed to compute three QUS parameters, namely, attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC), and effective scatterer diameter (ESD) from the placentas. We developed a logistic regression model using QUS parameters as predictors. The primary outcome was the occurrence of SGA and PE. ResultsThe dataset consisted of 47 placentas, of which 25 placentas were complicated by SGA/PE. The final placenta-QUS model included quadratic and interaction terms of ACE, IBC, and ESD parameters. The placenta-QUS model was well-calibrated, with a calibration slope of 0.99 (0.57–1.05) and a calibration intercept of 0.003 (−0.02 − 0.22). The model predicted the SGA/PE complicated pregnancies with an apparent Area Under the Receive Operating Characteristic Curve (AUROC) of 0.89 (95 % CI: 0.78–0.98). The optimism-adjusted AUROC was 0.88 (95 % CI: 0.78–0.98). DiscussionA model for SGA and PE has been developed using QUS measures from the placenta ex vivo. The model showed promising performance in detecting SGA/PE. Future studies will be performed to assess the model performance using QUS measures in utero.

Multicentre online registration of bronchopulmonary dysplasia in very preterm infants in China: protocol for a prospective, open, observational cohort study

IntroductionBronchopulmonary dysplasia (BPD) in very preterm infants (VPIs) has adverse long-term outcomes and affects the quality of survival. There are no registry studies on BPD in VPIs in China. Our...

Construction of ceRNA Network and Disease Diagnosis Model for Keloid Based on Tumor Suppressor ERRFI1.

The aim of this study is to identify the key biomarker of keloid (KD) with significant diagnostic value and to construct the related competing endogenous RNA (ceRNA) network and disease diagnostic model to provide new ideas for the early diagnosis and prevention of KD. Public databases were used to identify the key gene of KD. Enrichment analysis and immune cell infiltration (ICI) analysis revealed its functional and immune characteristics. Then, a ceRNA network was constructed to explore the potential pathways of it. Random forest (RF) analysis was applied to construct a predictive model for the disease diagnosis of KD. Finally, immunohistochemistry (IHC) and RT-qPCR were used to verify the differential expression of key gene. ERRFI1 was identified as a key biomarker in KD and was lowly expressed in KD. The ceRNA network revealed that H0TAIRM1-has-miR-148a-3p-ERRFI1 may be a potential pathway in KD. Finally, a 2-gene diagnostic prediction model (ERRFI1, HSD3B7) was constructed and externally validated and the results suggested that the model had good diagnostic performance. ERRFI1 is a downregulated gene in KD and is expected to be a promising predictive marker and disease diagnostic gene. ICI may play a role in the progression of KD. The ceRNA network may provide new clues to the potential pathogenesis of KD. Finally, the new KD diagnostic model could be an effective tool for assessing the risk of KD development.

Development and validation of a tumor marker-based model for the prediction of lung cancer: an analysis of a multicenter retrospective study in Shanghai, China.

The incidence and mortality rates of cancer are the highest globally. Developing novel methodologies that precisely, safely, and economically differentiate between benign and malignant lung conditions holds immense clinical importance. This research seeks to construct a predictive model utilizing a combination of diverse biomarkers to effectively discriminate between benign and malignant lung diseases. This retrospective study included patients admitted to the two general hospitals in Shanghai from 2014 to 2015. This study was developed using five tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), cytokeratin fragment 21-1 (CA211), squamous cell carcinoma antigen (SCC), and neuron specific enolase (NSE). The entire sample was divided into two groups according to the hospital: 1033 cases were included in the development cohort and 300 cases in the validation cohort. Logistic regression analysis was used for univariate analysis to explore individual correlations between each selected clinical variable and lung cancer diagnostic outcome. Diagnostic prediction models were constructed and validated based on independent prognostic factors identified using multifactorial analysis. A nomogram was created using these tumor markers (age and sex were additionally included) and validated using the concordance index and calibration curves. Clinical prediction models were evaluated using decision curve analysis. Fully adjusted multivariate analysis showed that the risk of lung cancer was 2.38 times higher in men than in women. CEA positivity was associated with an 13.41-fold increased risk in lung cancer. The area under the curve (AUC) values for the development cohort and validation cohort models were 0.907 and 0.954, respectively. In the established nomogram, the AUC for the receiver operating characteristic curve was 0.907 (95% CI, 0.889-0.925). The validation model confirmed the strong discriminative power of the nomogram (AUC = 0.954). The described calibration curves demonstrated good fit predictions and observation probabilities. In addition, decision curve analysis concluded that the newly established nomogram has important implications for clinical decision making. Combined prediction models based on CEA, CA199, CA211, SCC, and NSE biomarkers could significantly the differentiation between benign and malignant lung diseases, thus facilitating better clinical decision making.

Predictive Diagnostic Model for Early Osteoporosis Detection Using Deep Learning and Multimodal Imaging Data: A Systematic Review and Meta-Analysis

Osteoporosis is a common condition that weakens bones, making them more prone to fractures. Early detection is crucial for preventing fractures and improving patients’ quality of life. However, traditional methods, such as Dual-Energy X-ray Absorptiometry (DXA), often struggle to accurately predict fracture risk and may overlook minor changes in bone structure. This study focuses on developing a predictive model for early osteoporosis detection using deep learning algorithms combined with various imaging techniques, including MRI, CT, and High-Resolution Peripheral Quantitative Computed Tomography (HR-pQCT). A systematic review and meta-analysis of studies published between 2014 and 2024 were conducted, examining the use of deep learning models applied to multimodal imaging data. The meta-analysis highlighted differences in the accuracy and effectiveness of various models, and their performance was measured in terms of accuracy, sensitivity, and specificity, following PRISMA guidelines. The results showed that deep learning models outperformed traditional methods in early osteoporosis detection. The use of multiple imaging techniques provided a more detailed assessment of bone health, allowing the models to identify complex patterns that are difficult for human interpretation. These models demonstrated high accuracy and significant potential for improving clinical decision-making. By integrating deep learning with multimodal imaging, this approach offers a promising solution for enhancing the early detection of osteoporosis. The models tested in this study proved to be highly effective, yielding more accurate fracture risk predictions and enabling earlier interventions. This could lead to better patient outcomes and reduced healthcare costs.

Machine Learning Diagnostic Model for Early Stage NSTEMI: Using hs-cTnI 1/2h Changes and Multiple Cardiovascular Biomarkers.

This study demonstrates differences in the distribution of multiple cardiovascular biomarkers between non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA) patients. Diagnostic machine learning predictive models measured at the time of admission and 1/2 h post-admission, achieving competitive diagnostic predictive results. This study aims to explore the diagnostic value of changes in high-sensitivity cardiac troponin I (hs-cTnI) levels in patients with suspected NSTEMI. A total of 267 patients presented with chest pain, requiring confirmation of acute coronary syndrome (ACS) subtypes (NSTEMI vs. UA). Hs-cTnI and other cardiac markers, such as creatine kinase-MB (CK-MB) and Myoglobin (Myo), were analyzed. Machine learning techniques were employed to assess the application of hs-cTnI level changes in the clinical diagnosis of NSTEMI. Levels of CK-MB, Myo, hs-cTnI measured at admission, hs-cTnI measured 1-2 h after admission, and NT-proBNP in NSTEMI patients were significantly higher than those in UA patients (p < 0.001). There was a positive correlation between hs-cTnI and CK-MB, as well as Myo (R = 0.72, R = 0.51, R = 0.60). The optimal diagnostic model, Hybiome_1/2h, demonstrated an F1-Score of 0.74, an AUROC of 0.96, and an AP of 0.89. This study confirms the significant value of hs-cTnI as a sensitive marker of myocardial injury in the diagnosis of NSTEMI. Continuous monitoring of hs-cTnI levels enhances the accuracy of distinguishing NSTEMI from UA. The models indicate that the Hybiome hs-cTnI assays perform comparably well to the Beckman assays in predicting NSTEMI. Moreover, incorporating hs-cTnI measurements taken 1-2 h post-admission significantly enhances the model's effectiveness.

Establishing a Risk Model for Diabetic Nephropathy and Addressing the Therapeutic Effect of Combined Epalrestat- Dapagliflozin Regimen.

To explore the construction of a diagnostic prediction model of diabetic nephropathy (DN) in type 2 diabetic patients for prognostic risk prediction and observe the therapeutic effect of Epalrestat combined with Dapagliflozin on DN. The study consisted of two phases, phase I: A retrospective analysis was conducted on the case information and clinical treatment related data of a total of 460 patients who underwent kidney biopsy from June 2018 to June 2021. They were randomly divided into validation queue and training queue. The predictive factors of the diagnostic prediction model were obtained through multivariate logistic regression. An interventional study of 94 patients with DN admitted between January 2022 and August 2023 was conducted, and they were randomized into a control group (n = 47) receiving Dapagliflozin and a research group (n = 47) receiving Epalrestat combined with Dapagliflozin. The glucose metabolism, renal function, and treatment safety of the two groups before and after treatment were compared. In addition, the adverse reactions during the treatment of the two groups were counted. In the phase I of the study, the DN risk model established showed a good performance in the diagnosis and risk assessment of patients with DN and could provide certain reference opinions for future clinical practice. In the phase II of the study, the research group showed better glucose metabolism and renal function than the control group after treatment (P < .05), but no statistical difference was identified between groups in the incidence of adverse reactions (P > .05). Conclusion. Epalrestat combined with Dapagliflozin is significantly effective in the treatment of DN, which can effectively improve glucose metabolism and renal function in DN patients.

Osteocalcin: may be a useful biomarker for early identification of rapidly progressive central precocious puberty in girls.

To assess serum osteocalcin (OC) as a potential biomarker for the early detection of rapidly progressive central precocious puberty (RP-CPP) in girls. Serum OC levels were quantified using enzyme-linked immunosorbent assays (ELISAs). In the retrospective analysis, receiver operating characteristic (ROC) curve analysis was employed to evaluate the ability of OC to identify RP-CPP. A prospective study and screening tests were utilized to assess the potential of OC for use in the early prediction of RP-CPP. Variable selection in the multivariate analysis was conducted using the Bayesian Information Criterion (BIC) and binary logistic regression was employed to construct the diagnostic prediction model. Girls with RP-CPP had significantly higher serum OC levels compared to girls with non-rapidly progressive central precocious puberty (NRP-CPP) (149.04±40.50 vs. 89.10±31.83 ng/mL, P < 0.001). The optimal OC cut-off point for differentiating RP-CPP from NRP-CPP was 107.05 ng/mL, the area under the ROC curve (AUC) was 0.90 (95%CI: 0.851-0.949; P < 0.001), with a sensitivity of 91.1% and specificity of 70.7%. The results of the prospective study indicated that changes in OC precede alterations in estradiol (E2) and bone age (BA). A diagnostic prediction model that includes duration of breast development, BA, OC, high-density lipoprotein cholesterol (HDL-C), and uterine length achieved an AUC of 0.961, with a sensitivity of 94.1% and specificity of 91.5% for the detection of RP-CPP. If OC is excluded from the model, the AUC decreases to 0.894, with sensitivity and specificity declining to 80.5% and 83.1%, respectively. Serum OC levels may serve as a promising biomarker for the early differentiation between RP-CPP and NRP-CPP in girls. The diagnostic prediction model that incorporates duration of breast development, BA, OC, HDL-C, and uterine length effectively identifies girls with RP-CPP.

Significance of pyroptosis-related genes in the diagnosis and classification of diabetic kidney disease

Objective This study aimed to identify the potential biomarkers associated with pyroptosis in diabetic kidney disease (DKD). Methods Three datasets from the Gene Expression Omnibus (GEO) were downloaded and merged into an integrated dataset. Differentially expressed genes (DEGs) were filtered and intersected with pyroptosis-related genes (PRGs). Pyroptosis-related DEGs (PRDEGs) were obtained and analyzed using functional enrichment analysis. Random forest, Least Absolute Shrinkage and Selection Operator, and logistic regression analyses were used to select the features of PRDEGs. These feature genes were used to build a diagnostic prediction model, identify the subtypes of the disease, and analyze their interactions with transcription factors (TFs)/miRNAs/drugs and small molecules. We conducted a comparative analysis of immune cell infiltration at different risk levels of pyroptosis. qRT-PCR was used to validate the expression of the feature genes. Results A total of 25 PRDEGs were obtained. These genes were coenriched in biological processes and pathways, such as the regulation of inflammatory responses. Five key genes (CASP1, CITED2, HTRA1, PTGS2, S100A12) were identified and verified using qRT-PCR. The diagnostic model based on key genes has a good diagnostic prediction ability. Five key genes interacted with TFs and miRNAs in 67 and 80 pairs, respectively, and interacted with 113 types of drugs or molecules. Immune infiltration of samples with different pyroptosis risk levels showed significant differences. Thus, CASP1, CITED2, HTRA1, PTGS2 and S100A12 are potential DKD biomarkers. Conclusion Genes that regulate pyroptosis can be used as predictors of DKD. Early diagnosis of DKD can aid in its effective treatment.

Noninvasive diagnostic modalities and prediction models for detecting pulmonary hypertension associated with interstitial lung disease: a narrative review.

Pulmonary hypertension (PH) is highly prevalent in patients with interstitial lung disease (ILD) and is associated with increased morbidity and mortality. Widely available noninvasive screening tools are warranted to identify patients at risk for PH, especially severe PH, that could be managed at expert centres. This review summarises current evidence on noninvasive diagnostic modalities and prediction models for the timely detection of PH in patients with ILD. It critically evaluates these approaches and discusses future perspectives in the field. A comprehensive literature search was carried out in PubMed and Scopus, identifying 39 articles that fulfilled inclusion criteria. There is currently no single noninvasive test capable of accurately detecting and diagnosing PH in ILD patients. Estimated right ventricular pressure (RVSP) on Doppler echocardiography remains the single most predictive factor of PH, with other indirect echocardiographic markers increasing its diagnostic accuracy. However, RVSP can be difficult to estimate in patients due to suboptimal views from extensive lung disease. The majority of existing composite scores, including variables obtained from chest computed tomography, pulmonary function tests and cardiopulmonary exercise tests, were derived from retrospective studies, whilst lacking validation in external cohorts. Only two available scores, one based on a stepwise echocardiographic approach and the other on functional parameters, predicted the presence of PH with sufficient accuracy and used a validation cohort. Although several methodological limitations prohibit their generalisability, their use may help physicians to detect PH earlier. Further research on the potential of artificial intelligence may guide a more tailored approach, for timely PH diagnosis.

Hemophagocytic Lymphohistiocytosis: Clinical Characteristics and Diagnostic Prediction Model

To investigate the clinical characteristics of patients with hemophagocytic lymphohistiocytosis (HLH) and quantify the diagnostic value of various indexes in patients with elevated soluble interleukin-2 receptor (sCD25), so as to construct a diagnostic prediction model of HLH. The clinical characteristics of 121 patients with elevated sCD25 (≥2 400 U/ml) in the Third Affiliated Hospital of Sun Yat-Sen University were analyzed retrospectively. The patients were divided into HLH group and non-HLH group according to the diagnostic criteria of HLH. The patients with HLH were divided into infection group, tumor group, macrophage activation syndrome (MAS) group and unknown etiology group according to their etiology. The basic data and treatment of the patients were collected for univariate and multivariate logistic analysis to establish a diagnostic prediction model of HLH. Among the 121 enrolled patients with elevated sCD25, 68 were diagnosed as HLH. The proportion of patients using vasopressors, the incidence rate of disseminated intravascular coagulation (DIC), and the HScore in the HLH group were higher than those in the non-HLH group (P < 0.05). Hepatomegaly, splenomegaly, and hemophagocytosis were more common in HLH patients(P < 0.05). Compared with the patients in non-HLH group, patients in HLH group had lower levels of neutrophils, platelets, fibrinogen, IgG, and IgM, while the levels of triglycerides, ferritin (FER), sCD25, serum glutamic oxaloacetic transaminase (SGOT), alkaline phosphatase (ALP), total bilirubin (TBil), lactate dehydrogenase (LDH), and D-dimer were higher (P < 0.05). In subgroup analysis, the level of sCD25 in tumor group was higher than that in infection group. The level of sCD25/ferritin in tumor group was higher than that in infection group and MAS group. Compared with HLH patients in the tumor group, the procalcitonin (PCT) level, proportion of patients using vasopressors, positive rate of hemophagocytosis, and incidence rate of DIC were all higher in the infection group, and the differences were statistically significant (P < 0.05). The results of multivariate analysis showed that fever, splenomegaly, hemophagocytosis, cytopenias, IgM, M.sCD25 [multiple of sCD25 detection value relative to the diagnostic threshold (2400 U/ml)], fibrinogen, and triglycerides were independent predictive factors for HLH (P < 0.05).The diagnostic prediction model H constructed based on temperature, splenomegaly, hemophagocytosis, cytopenias, IgM, M.sCD25, fibrinogen, triglycerides showed good predictive accuracy. The optimal cutoff value of H was 39.45, the sensitivity of the model was 94.12%, the specificity was 83.02%. sCD25, sCD25/FER, PCT, hemophagocytosis, hemodynamic instability and DIC could help to distinguish the underlying etiology of HLH. The prediction model H has high discrimination and calibration, which could be used as a relatively accurate clinical diagnostic tool for HLH.

The Integration of Radiomics and Artificial Intelligence in Modern Medicine.

With profound effects on patient care, the role of artificial intelligence (AI) in radiomics has become a disruptive force in contemporary medicine. Radiomics, the quantitative feature extraction and analysis from medical images, offers useful imaging biomarkers that can reveal important information about the nature of diseases, how well patients respond to treatment and patient outcomes. The use of AI techniques in radiomics, such as machine learning and deep learning, has made it possible to create sophisticated computer-aided diagnostic systems, predictive models, and decision support tools. The many uses of AI in radiomics are examined in this review, encompassing its involvement of quantitative feature extraction from medical images, the machine learning, deep learning and computer-aided diagnostic (CAD) systems approaches in radiomics, and the effect of radiomics and AI on improving workflow automation and efficiency, optimize clinical trials and patient stratification. This review also covers the predictive modeling improvement by machine learning in radiomics, the multimodal integration and enhanced deep learning architectures, and the regulatory and clinical adoption considerations for radiomics-based CAD. Particular emphasis is given to the enormous potential for enhancing diagnosis precision, treatment personalization, and overall patient outcomes.

Differentiating clinical characteristics of perianal inflammatory bowel disease from perianal hidradenitis suppurativa

AbstractBackgroundPerianal draining tunnels in hidradenitis suppurativa (HS) and perianal fistulizing inflammatory bowel disease (IBD) present diagnostic and management dilemmas.MethodsWe conducted a retrospective chart review of patients with perianal disease evaluated at Mayo Clinic from January 1, 1998, through July 31, 2021. Patients' demographic and clinical data were extracted, and 28 clinical features were collected. After experimenting with several machine learning techniques, random forests were used to select the 15 most important clinical features to construct the diagnostic prediction model to distinguish perianal HS from fistulizing perianal IBD.ResultsA total of 263 patients were included (98 with HS, 100 with IBD, and 65 with both IBD and HS). Patients with HS had a higher mean body mass index, a higher smoking rate, and more commonly showed cutaneous manifestations of tunnels and comedones, while fistulas, abscesses, induration, anal tags, ulcers, and anal fissures were more common in patients with IBD. In addition to having lesions in the perianal area, patients with IBD often had lesions in the buttocks and perineum, while those with HS had additional lesions in the axillae and groin. Among the statistically significant features, the 15 most important were identified by random forest: fistula, tunnel, digestive symptom, knife‐cut ulcer, perineum, body mass index, age, axilla, abscess, tags, smoking, groin, genital cutaneous edema, erythema, and bilateral/unilateral.ConclusionsThe results of this study may help differentiate perianal lesions, especially perineal HS and fistulizing perineal IBD, and provide promise for a better therapeutic outcome.

Establishment and external validation of an early warning model of diabetic peripheral neuropathy based on random forest and logistic regression

ObjectiveThe primary objective of this study was to investigate the risk factors for diabetic peripheral neuropathy (DPN) and to establish an early diagnostic prediction model for its onset, based on clinical data and biochemical indices.MethodsRetrospective data were collected from 1,446 diabetic patients at the First Affiliated Hospital of Anhui University of Chinese Medicine and were split into training and internal validation sets in a 7:3 ratio. Additionally, 360 diabetic patients from the Second Affiliated Hospital were used as an external validation cohort. Feature selection was conducted within the training set, where univariate logistic regression identified variables with a p-value < 0.05, followed by backward elimination to construct the logistic regression model. Concurrently, the random forest algorithm was applied to the training set to identify the top 10 most important features, with hyperparameter optimization performed via grid search combined with cross-validation. Model performance was evaluated using ROC curves, decision curve analysis, and calibration curves. Model fit was assessed using the Hosmer-Lemeshow test, followed by Brier Score evaluation for the random forest model. Ten-fold cross-validation was employed for further validation, and SHAP analysis was conducted to enhance model interpretability.ResultsA nomogram model was developed using logistic regression with key features: limb numbness, limb pain, diabetic retinopathy, diabetic kidney disease, urinary protein, diastolic blood pressure, white blood cell count, HbA1c, and high-density lipoprotein cholesterol. The model achieved AUCs of 0.91, 0.88, and 0.88 for the training, validation, and test sets, respectively, with a mean AUC of 0.902 across 10-fold cross-validation. Hosmer-Lemeshow test results showed p-values of 0.595, 0.418, and 0.126 for the training, validation, and test sets, respectively. The random forest model demonstrated AUCs of 0.95, 0.88, and 0.88 for the training, validation, and test sets, respectively, with a mean AUC of 0.886 across 10-fold cross-validation. The Brier score indicates a good calibration level, with values of 0.104, 0.143, and 0.142 for the training, validation, and test sets, respectively.ConclusionThe developed nomogram exhibits promise as an effective tool for the diagnosis of diabetic peripheral neuropathy in clinical settings.