Curve Analysis Research Articles

Anthropometric indices in predicting 10-year cardiovascular risk among males and females aged 40–74 years in south and southeast Asia: analysis of 12 WHO STEPS survey data

The relevance of anthropometric indices in predicting cardiovascular disease (CVD) or CVD risk factors is established across different countries, particularly in the high-income countries. However, past studies severely lacked representation from the south and southeast Asian countries. The main aim of this study was to determine the performance of conventional and new anthropometric indices to best predict 10-year cardiovascular disease (CVD) risk in south Asian and southeast Asian populations. The present study examined data from 14,532 participants in three south Asian and 13,846 participants (all aged between 40 and 74 years) in six southeast Asian countries, drawn from twelve cross-sectional studies (WHO STEPwise approaches to NCD risk factor surveillance [STEPS] survey data from 2008 to 2019). A Predictive performance of ten anthropometric indices were examined for predicting 10-year CVD risk ≥ 10% (CVD-R ≥ 10%). The 10-year CVD-R ≥ 10% was calculated by utilising the WHO CVD risk non-laboratory-based charts. Receiver operating characteristic (ROC) curve analysis was used to identify the optimal anthropometric index. Among the ten anthropometric indices, a body shape index (ABSI), body adiposity index (BAI), body roundness index (BRI), hip index (HI), and waist-height ratio (WHtR) performed best in predicting 10-year CVD risk among south Asian males and females. Improved performances were found for ABSI, BRI, conicity index (CI), WHtR, and waist-hip ratio (WHR) for 10-year CVD-R ≥ 10% predictions among southeast Asian males. Contrastingly, among southeast Asian females, ABSI and CI demonstrated optimal performance in predicting 10-year CVD-R ≥ 10%. The performance of anthropometric indices in predicting CVD risk varies across countries. ABSI, BAI, BRI, HI, and WHtR showed better predictions in south Asians, whereas ABSI, BRI, CI, WHtR, and WHR displayed enhanced predictions in southeast Asians. None.

Development of a CT-assessed adiposity nomogram for predicting outcome in localized ccRCC.

This study aimed to develop and validate a computed tomography-based nomogram assessing visceral and subcutaneous adiposity for predicting outcomes in localized clear cell renal cell carcinoma (ccRCC). A cohort of 364 patients with pathologically confirmed ccRCC participated in this retrospective study, with 254 patients assigned to the training set and 110 to the validation set (a 7:3 distribution ratio). The adipose score (AS) was generated using the least absolute shrinkage and selection operator Cox regression. Subsequently, a nomogram was constructed by integrating the clinical independent predictor with the AS to predict disease-free survival (DFS) in localized ccRCC after surgery. The performance of the nomogram was compared with the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade, and Necrosis (SSIGN) score. In both the training and validation cohorts, the nomogram exhibited superior discrimination compared to SSIGN and UISS (C-index: 0.897 vs. 0.781 vs. 0.776 in the training cohort, and 0.752 vs. 0.596 vs. 0.686 in the validation cohort; 5year AUC: 0.907 vs. 0.805 vs. 0.820 in the training cohort, and 0.832 vs. 0.577 vs. 0.726 in the validation cohort). Decision curve analysis (DCA) revealed a superior net benefit across a wider range of threshold probabilities for predicting 5year DFS compared to UISS and SSIGN scores. The developed prognostic nomogram demonstrated high accuracy and overall superior performance compared to existing prognostic models.

Multimodal data-driven, vertical visualization prediction model for early prediction of atherosclerotic cardiovascular disease in patients with new-onset hypertension.

: Hypertension is an important contributing factor to atherosclerotic cardiovascular disease (ASCVD), and multiple risk factors, many of which are implicated in metabolic disorders, contribute to the cause of hypertension. Despite the promise of multimodal data-driven prediction model, no such prediction model was available to predict the risk of ASCVD in Chinese individuals with new-onset hypertension and no history of ASCVD. : A total of 514 patients were randomly allocated to training and verification cohorts (ratio, 7 : 3). We employed Boruta feature selection and conducted multivariate Cox regression analyses to identify variables associated with ASCVD in these patients, which were subsequently utilized for constructing the predictive model. The performance of prediction model was assessed in terms of discriminatory power (C-index), calibration (calibration curves), and clinical utility [decision curve analysis (DCA)]. : This model was derived from four clinical variables: 24-h SBP coefficient of variation, 24-h DBP coefficient of variation, urea nitrogen and the triglyceride-glucose (TyG) index. Bootstrapping with 500 iterations was conducted to adjust the C-indexes were C-index = 0.731, 95% confidence interval (CI) 0.620-0.794 and C-index: 0.799, 95% CI 0.677-0.892 in the training and verification cohorts, respectively. Calibration plots with 500 bootstrapping iterations exhibited a strong correlation between the predicted and observed occurrences of ASCVD in both the training and verification cohorts. DCA analysis confirmed the clinical utility of this prediction model. The constructed nomogram demonstrated significant additional prognostic utility for ASCVD, as evidenced by improvements in the C-index, net reclassification improvement, integrated discrimination improvement, and DCA compared with the overall ASCVD risk assessment. The developed longitudinal prediction model based on multimodal data can effectively predict ASCVD risk in individuals with an initial diagnosis of hypertension. : The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300074392).

Prediction Models Of The Aphasia Severity After Stroke By Lesion Load Of Cortical Language Areas and White Matter Tracts: An Atlas-based Study.

ObjectiveTo construct relatively objective, atlas-based multivariate models for predicting early aphasia severity after stroke, using structural magnetic resonance imaging. MethodsWe analyzed the clinical and imaging data of 46 patients with post-stroke aphasia. The aphasia severity was identified with a Western Aphasia Battery Aphasia Quotient. The assessments of stroke lesions were indicated by the lesion load of both the cortical language areas (Areas-LL) and four white matter tracts (i.e., the superior longitudinal fasciculus, SLF-LL; the inferior frontal occipital fasciculi, IFOF-LL; the inferior longitudinal, ILF-LL; and the uncinate fasciculi, UF-LL) extracted from human brain atlas. Correlation analyses and multiple linear regression analyses were conducted to evaluate the correlations between demographic, stroke- and lesion-related variables and aphasia severity. The predictive models were then established according to the identified significant variables. Finally, the receiver operating characteristic (ROC) curve was utilized to assess the accuracy of the predictive models. ResultsThe variables including Areas-LL, the SLF-LL, and the IFOF-LL were significantly negatively associated with aphasia severity (p < 0.05). In multiple linear regression analyses, these variables accounted for 59.4% of the variance (p < 0.05). The ROC curve analyses yielded the validated area under the curve (AUC) 0.84 both for Areas-LL and SLF-LL and 0.76 for IFOF-LL, indicating good predictive performance (p < 0.01). Adding the combination of SLF-LL and IFOF-LL to this model increased the explained variance to 62.6% and the AUC to 0.92. ConclusionsThe application of atlas-based multimodal lesion assessment may help predict the aphasia severity after stroke, which needs to be further validated and generalized for the prediction of more outcome measures in populations with various brain injuries.

Association of Collagen Changes in Distal Anastomotic Margin and Anastomotic Stenosis after Neoadjuvant Chemoradiotherapy for Rectal Cancer.

Neoadjuvant chemoradiotherapy (nCRT) for rectal cancer can lead to structural changes in collagen in the tumor microenvironment and increase the risk of postoperative anastomotic stenosis (AS). However, the quantitative relationship between AS and collagen has not been defined. This study is to quantitatively analyze the collagen features in rectal cancer and explore the relationship between the changes of collagen and postoperative anastomotic stenosis after nCRT. This is a retrospective study. A total of 371 patients with rectal cancer were included. Collagen features in the resection margin of rectal cancer anastomosis was extracted by multiphoton imaging. The least absolute shrinkage operator logistic regression was performed to select features related to AS and the collagen score (CS) was constructed. Area under the receiver operating curve (AUROC) and decision curve analysis were performed to evaluate the discrimination and clinical benefit of the nomogram. The probability of AS was 23% in the training cohort and 15.9% in the validation cohort. In the training cohort, the distance between tumor and resection margin, anastomotic leakage and CS were independent risk factors for postoperative AS in univariate and multivariate analyses. A nomogram was constructed based on these results. The prediction nomogram showed good discrimination (AUROC 0.864; 95% CI 0.776 to 0.952) and was validated in the validation cohort (AUROC 0.918; 95% CI 0.851 to 0.985). CS is an independent risk factor for AS in rectal cancer after nCRT. The predictive model based on CS can predict the occurrence of postoperative AS.

Diagnostic and screening potential of plasma exosome miR‑99b‑5p and its combination with other miRNAs for colorectal cancer.

Extracellular vesicles (EVs) secreted by tumor cells have been documented to hold viable biomarker potential. Therefore, the present study evaluated the potential clinical value of EV-microRNAs (miRNAs or miRs) in the plasma exosomes of patients with colorectal cancer (CRC) for the early diagnosis and screening of CRC. In total, 95 plasma samples were collected at The Third Affiliated Hospital of Guangzhou Medical University (Guangzhou, China) between 2017 and 2019. Specifically, 68 samples were from patients with CRC and 27 were from healthy control (HC) donors. High-throughput sequencing was used to detect the expression of miRNAs in the isolated plasma EVs, which was subsequently verified by reverse transcription-quantitative PCR. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic potential of single and combined miRNAs for CRC. Bioinformatics analysis was employed to predict the target genes of candidate miRNAs. Compared with those in the HC group, the CRC group expressed higher levels of miR-99b-5p and miR-409-3p, especially during the early stages of CRC. Clinicopathological analysis confirmed the higher expression levels of miR-99b-5p during the early stages, as well as higher expression levels in the colon compared with those in the rectum. ROC curve analysis revealed that the area under the curve (AUC) of miR-99b-5p for the diagnosis of early CRC was 73.5% (P=0.007). The early diagnostic capability of miR-99b-5p combined with miR-409-3p for CRC was evaluated, and the AUC was found to be 74.1% (P=0.006). In addition, the AUC of the combination of miR-99b-5p, miR-409-3p and carcinoembryonic antigen was 81.2% (P<0.001), indicating that this three-parameter combination displayed higher diagnostic power compared with any single miRNA for early CRC screening. The results from the present study suggest that the expression of miR-99b-5p in plasma exosomes is significantly upregulated in CRC, which holds potential for the early diagnosis of this cancer type. Such potential can be enhanced further by combining it with other miRNAs. Therefore, the present study provides a comprehensive but preliminary insight for the viability of miR-99b-5p (alone or combined with other miRNAs) for CRC diagnosis, which requires further exploration in the future.

A combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound in the Kupffer phase for the diagnosis of well-differentiated hepatocellular carcinoma and atypical focal liver lesions: a prospective, multicenter study.

The objective of this study was to develop a combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound (CEUS) during the Kupffer phase and to evaluate its value in differentiating well-differentiated hepatocellular carcinoma (w-HCC) from atypical benign focal liver lesions (FLLs). A total of 116 patients with preoperatively Sonazoid-CEUS confirmed w-HCC or benign FLL were selected from a prospective multiple study on the clinical application of Sonazoid in FLLs conducted from August 2020 to March 2021. According to the randomization principle, the patients were divided into a training cohort and a test cohort in a 7:3 ratio. Seventy-nine patients were used for establishing and training the radiomics model and combined model. In comparison, 37 patients were used for validating and comparing the performance of the models. The diagnostic efficacy of the models for w-HCC and atypical benign FLLs was evaluated using ROCs curves and decision curves. A combined model nomogram was created to assess its value in reducing unnecessary biopsies. Among the patients, there were 55 cases of w-HCC and 61 cases of atypical benign FLLs, including 28 cases of early liver abscess, 16 cases of atypical hepatic hemangioma, 8 cases of hepatocellular dysplastic nodules (DN), and 9 cases of focal nodular hyperplasia (FNH). The radiomics model and combined model we established had AUCs of 0.905 and 0.951, respectively, in the training cohort, and the AUCs of the two models in the test cohort were 0.826 and 0.912, respectively. The combined model outperformed the radiomics feature model significantly. Decision curve analysis demonstrated that the combined model achieved a higher net benefit within a specific threshold probability range (0.25 to 1.00). A nomogram of the combined model was developed. The combined model based on the radiomics features of Sonazoid-CEUS in the Kupffer phase showed satisfactory performance in diagnosing w-HCC and atypical benign FLLs. It can assist clinicians in timely detecting malignant FLLs and reducing unnecessary biopsies for benign diseases.

Association between programmed cell death ligand-1 expression in patients with cervical cancer and apparent diffusion coefficient values: a promising tool for patient´s immunotherapy selection.

To investigate the associations between apparent diffusion coefficient (ADC) values extracted from three different region of interest (ROI) position approaches and programmed cell death ligand-1 (PD-L1) expression, and evaluate the performance of the nomogram established based on ADC values and clinicopathological parameters in predicting PD-L1 expression in cervical cancer (CC) patients. Through retrospective recruitment, a training cohort of 683 CC patients was created, and a validation cohort of 332 CC patients was prospectively recruited. ROIs were delineated using three different methods to measure the mean ADC (ADCmean), single-section ADC (ADCss), and the minimum ADC of tumors (ADCmin). Logistic regression was employed to identify independent factors related to PD-L1 expression. A nomogram was drawn based on ADC values combined with clinicopathological features, its discrimination and calibration performances were estimated using the area under the curve (AUC) of receiver operating characteristic and calibration curve. The clinical benefits were evaluated by decision curve analysis. The ADCmin independently correlated with PD-L1 expression. The nomogram constructed with ADCmin and other independent clinicopathological-related factors: FIGO staging, pathological grade, parametrial invasion, and lymph node status demonstrated excellent diagnostic performance (AUC = 0.912 and 0.903, respectively), good calibration capacities, and greater net benefits compared to the clinicopathological model in both the training and validation cohorts. ADCmin independently correlated PD-L1 expression, and the nomogram established with ADCmin and clinicopathological independent prognostic factors had a strong predictive performance for PD-L1 expression, thereby serving as a promising tool for selecting cases eligible for immunotherapy. The minimum ADC can serve as a reliable imaging biomarker related to PD-L1 expression; the established nomogram combines the minimum ADC and clinicopathological factors that can assist clinical immunotherapy decisions.

Development and validation of a predictive model for end-stage renal disease in systemic lupus erythematosus patients.

Systemic lupus erythematosus (SLE) affects many populations. This study aims to develop a predictive model and create a nomogram for assessing the risk of end-stage renal disease (ESRD) in patients diagnosed with SLE. Data from electronic health records of SLE patients treated at the Affiliated Hospital of North Sichuan Medical College between 2013 and 2023 were collected. The dataset underwent thorough cleaning and variable assignment procedures. Subsequently, variables were selected using one-way logistic regression and lasso logistic regression methods, followed by multifactorial logistic regression to construct nomograms. The model's performance was assessed using calibration, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves. Statistical significance was set at P < 0.05. The predictive variables for ESRD development in SLE patients included anti-GP210 antibody presence, urinary occult blood, proteinuria, white blood cell count, complement 4 levels, uric acid, creatinine, total protein, globulin, glomerular filtration rate, pH, specific gravity, very low-density lipoprotein, homocysteine, apolipoprotein B, and absolute counts of cytotoxic T cells. The nomogram exhibited a broad predictive range. The ROC area under the curve (AUC) was 0.886 (0.858-0.913) for the training set and 0.840 (0.783-0.897) for the testing set, indicating good model performance. The model demonstrated both applicability and significant clinical benefits. The developed model presents strong predictive capabilities and considerable clinical utility in estimating the risk of ESRD in patients with SLE.

Low serum creatine kinase level is associated with poor prognosis in patients with hepatocellular carcinoma: A retrospective single‑center study.

Previous studies have reported low serum creatine kinase (s-CK) levels as a poor prognostic factor in various cancers. However, there have been no reports on its significance in hepatocellular carcinoma. The present study aimed to evaluate the association of the preoperative s-CK levels with clinicopathologic features and their prognostic impact on survival in patients with hepatocellular carcinoma. This retrospective study included 163 patients with hepatocellular carcinoma (127 male and 36 female patients; median age, 69 years) who underwent radical liver resection between January 2004 and December 2021. A cutoff preoperative s-CK level of 91 U/l determined by receiver operating characteristic curve analysis was used to evaluate the significance of s-CK in predicting overall and recurrence-free survival. In addition, the prognostic impact of s-CK was evaluated using univariate and multivariate analysis. s-CK level was not associated with clinicopathologic factors. Overall survival and recurrence-free survival of the low s-CK group were significantly worse compared with the high s-CK group (P=0.043 and P=0.029, respectively). By multivariate analysis, low s-CK was an independent risk factor for poor overall survival and recurrence-free survival (P=0.019 and P=0.014, respectively). This trend was the same for male patients, but no significant difference was observed for female patients. Low preoperative s-CK level might be a poor prognostic biomarker in patients with hepatocellular carcinoma.

Clinical values of preoperative red blood cell distribution width and platelet parameters in patients with papillary thyroid carcinoma.

The prevalence of thyroid carcinoma is increasing, and papillary thyroid carcinoma (PTC) is the most frequent subtype. More and more attention is being concentrated on the association between inflammation indicators and malignant tumors. The aim of the present study was to analyze whether the preoperative red blood cell distribution width (RDW) and platelet parameters, including mean platelet volume (MPV) and platelet distribution width (PDW), can be applied to distinguish between patients with PTC or papillary thyroid microcarcinoma (PTMC) and healthy controls, and to explore the associations with clinicopathological characteristics. The study retrospectively compared the RDW, MPV and PDW values of 780 patients with PTC or PTMC against a healthy control group. Receiver operating characteristic (ROC) curves were conducted to determine diagnostic accuracy. Furthermore, the clinicopathological features of the patients with PTC or PTMC were compared between higher and lower platelet parameter groups based on the RDW, MPV and PDW values. Significantly higher preoperative RDW, MPV and PDW values were found in patients with PTC or PTMC compared with those of the healthy group. ROC curve analysis showed that the area under the curve (AUC) plus 95% confidence interval (95% CI) values of RDW, MPV and PDW were 0.808 (0.780-0.835), 0.771 (0.743-0.799) and 0.711 (0.681-0.742), respectively. When RDW and MPV were combined together, the AUC (95% CI) value was enhanced to 0.858 (0.835-0.881) for the patients with PTC. For the patients with PTMC, RDW, MPV and PDW had AUC (95% CI) values of 0.812 (0.783-0.840), 0.779 (0.749-0.808) and 0.718 (0.685-0.751), respectively. When RDW and MPV were combined together, the AUC (95% CI) value was enhanced to 0.858 (0.835-0.881). A higher RDW was significantly associated with being female, deeper tumor infiltration, and normal FT3 and FT4 levels. A higher PDW was significantly associated with elevated thyrotropin receptor antibody levels. In conclusion, as convenient and available inflammation indicators, RDW, PDW and MPV have diagnostic ability and can distinguish between patients with PTC or PTMC and healthy controls. In addition, the combined application of RDW and MPV can improve the diagnostic power. The values of RDW and MPV were associated with clinicopathological characteristics. To the best of our knowledge, this is the first study to prove the usefulness of preoperative RDW combined with MPV in diagnosing patients with PTC or PTMC.

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BC-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

Hemodynamic profiles in idiopathic intracranial hypertension: Doppler ultrasonography as a diagnostic tool for pulsatile tinnitus

Purpose: This study aims to evaluate the role of ophthalmic Doppler ultrasonography (USG) in differentiating IIH patients with and without bilateral PT by analyzing hemodynamic changes in the orbital vessels. Materials and Methods: A prospective, single-center, case-control study was conducted at Harran University from November 2023 to June 2024. The study included 20 IIH patients. Participants underwent clinical assessments, ophthalmic evaluations, brain imaging, lumbar puncture, and transorbital sonography. IIH patients were categorized into PT (n=8) and non-PT (n=12) groups. Doppler USG measured end-diastolic velocity (EDV), peak systolic velocity (PSV), time-averaged velocity (TAV), resistive index (RI), pulsatility index (PI), and systolic to the diastolic ratio (S/D) of the central retinal artery. Results: Significant differences were observed in left eye EDV and TAV, with lower values in the PT group. Although not statistically significant, right-eye EDV and TAV also trended lower in the PT group. Additionally, both eyes' PSV values showed a trend toward statistical significance, being lower in the PT group. ROC curve analysis identified optimal thresholds for left eye EDV (5.35; sensitivity 75%, specificity 83.33%) and TAV (9; sensitivity 50%, specificity 91.67%). Conclusion: Orbital Doppler USG reveals distinct hemodynamic profiles in IIH patients with PT, suggesting EDV and TAV as potential biomarkers. Larger cohort studies are needed to validate and clarify IIH-PT mechanisms.

AI-powered genomic mutation signature for predicting immune checkpoint inhibitor therapy outcomes in gastroesophageal cancer: a multi-cohort analysis

BackgroundImmune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy.MethodsIn this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib’s efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments.ResultsWe developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line.ConclusionThe GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.

Advancing microvascular invasion diagnosis: a multi-center investigation of novel MRI-based models for precise detection and classification in early-stage small hepatocellular carcinoma (≤ 3cm).

This study aimed to develop two preoperative magnetic resonance imaging (MRI) based models for detecting and classifying microvascular invasion (MVI) in early-stage small hepatocellular carcinoma (sHCC) patients. MVI is graded as M0 (no invasion), M1 (invasion of five or fewer vessels located within 1cm of the tumor's peritumoral region), and M2 (invasion of more than five vessels or those located more than 1cm from the tumor's surface). This study enrolled 395 early-stage sHCC (≤ 3cm) patients from three centers who underwent preoperative gadopentetate-enhanced MRI. From the first two centers, 310 patients were randomly divided into training (n = 217) and validation (n = 93) cohorts in a 7:3 ratio to develop the first model for predicting MVI presence. Among these, 153 patients with identified MVI were further divided into training (n = 112) and validation (n = 41) cohorts, using the same ratio, to construct the second model for MVI classification. An independent test cohort of 85 patients from the third center to validate both models. Univariate and multivariate logistic regression analyses identified independent predictors of MVI and its classification in the training cohorts. Based on these predictors, two nomograms were developed and assessed for their discriminative ability, calibration, and clinical usefulness. Besides, considering the two models are supposed applied in a serial fashion in the real clinical setting, we evaluate the performance of the two models together on the test cohorts by applying them simultaneously. Kaplan-Meier survival curve analysis was employed to assess the correlation between predicted MVI status and early recurrence, similar to the association observed with actual MVI status and early recurrence. The MVI detection nomogram, with platelet count (PLT), activated partial thromboplastin time (APTT), rim arterial phase hyperenhancement (Rim APHE) and arterial peritumoral enhancement, achieved area under the curve (AUC) of 0.827, 0.761 and 0.798 in the training, validation, and test cohorts, respectively. The MVI classification nomogram, integrating Total protein (TP), Shape, Arterial peritumoral enhancement and enhancement pattern, achieved AUC of 0.824, 0.772, and 0.807 across the three cohorts. When the two models were applied on the test cohorts in a serial fashion, they both demonstrated good performance, which means the two models had good clinical applicability. Calibration and decision curve analysis (DCA) results affirmed the model's reliability and clinical utility. Notably, early recurrence was more prevalent in the MVI grade 2 (M2) group compared to the MVI-absent and M1 groups, regardless of the actual or predicted MVI status. The nomograms exhibited excellent predictive performance for detecting and classifying MVI in patients with early-stage sHCC, particularly identifying high-risk M2 patients preoperatively.

Prediction of non-sentinel lymph node metastases in T1–2 sentinel lymph node-positive breast cancer patients undergoing mastectomy following neoadjuvant therapy

BackgroundAxillary lymph node dissection (ALND) is the standard axillary management for breast cancer patients with positive sentinel lymph node biopsy (SLNB) after neoadjuvant therapy. Nevertheless, when that happens, the frequency of additional positive nodes is not properly evaluated. We aim to develop a prediction model to assess the frequency of additional nodal disease after a positive sentinel lymph node following neoadjuvant therapy.MethodsWe retrospectively analyzed the ultrasound and clinicopathological characteristics of breast cancer patients with 1–3 positive sentinel lymph nodes (SLN) undergoing mastectomy after neoadjuvant therapy (NAT) at our institution, and performed univariate and multivariate logistic analyses to confirm the factors affecting non-SLN metastasis. These factors were included to establish a nomogram, and the area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were utilized to assess the validity of this model.ResultsA total of 126 breast cancer patients were ultimately included in our study, 38 (53.5%) patients were diagnosed with non-SLN metastases of all 71 patients in training set. The results of multifactorial logistic analysis suggested that lymph node metastasis ratio (LNR), short axis of lymph node and progesterone receptor (PR) were strongly associated with non-SLN metastasis. We established a nomogram using the above three variables as predictors, which yielded an area under the curve of 0.795, and validated with a favorable AUC of 0.876.ConclusionThe nomogram we constructed can accurately predict the likelihood of non-SLN metastasis in our patients with 1–3 positive SLN after NAT, which may help guide decision making regarding axillary management.

Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.

The study aims to investigate the potential of training efficient deep learning models by using 2.5D (2.5-Dimension) masks of sICH. Furthermore, it intends to evaluate and compare the predictive performance of a joint model incorporating four types of features with standalone 2.5D deep learning, radiomics, radiology, and clinical models for early expansion in sICH. A total of 254 sICH patients were enrolled retrospectively and divided into two groups according to whether the hematoma was enlarged or not. The 2.5D mask of sICH is constructed with the maximum axial, coronal and sagittal planes of the hematoma, which is used to train the deep learning model and extract deep learning features. Predictive models were built on clinic, radiology, radiomics and deep learning features separately and four type features jointly. The diagnostic performance of each model was measured using the receiver operating characteristic curve (AUC), Accuracy, Recall, F1 and decision curve analysis (DCA). The AUCs of the clinic model, radiology model, radiomics model, deep learning model, joint model, and nomogram model on the train set (training and Cross-validation) were 0.639, 0.682, 0.859, 0.807, 0.939, and 0.942, respectively, while the AUCs on the test set (external validation) were 0.680, 0.758, 0.802, 0.857, 0.929, and 0.926. Decision curve analysis showed that the joint model was superior to the other models and demonstrated good consistency between the predicted probability of early hematoma expansion and the actual occurrence probability. Our study demonstrates that the joint model is a more efficient and robust prediction model, as verified by multicenter data. This finding highlights the potential clinical utility of a multifactorial prediction model that integrates various data sources for prognostication in patients with intracerebral hemorrhage. The Critical Relevance Statement: Combining 2.5D deep learning features with clinic features, radiology markers, and radiomics signatures to establish a joint model enabling physicians to conduct better-individualized assessments the risk of early expansion of sICH.

Identification of key hub genes in knee osteoarthritis through integrated bioinformatics analysis.

Knee osteoarthritis (KOA) is a common chronic joint disease globally. Synovial inflammation plays a pivotal role in its pathogenesis, preceding cartilage damage. Identifying biomarkers in osteoarthritic synovial tissues holds promise for early diagnosis and targeted interventions. Gene expression profiles were obtained from the Gene Expression Omnibus database. Subsequent analyses included differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) on the combined datasets. We performed functional enrichment analysis on the overlapping genes between DEGs and module genes and constructed a protein-protein interaction network. Using Cytoscape software, we identified hub genes related to the disease and conducted gene set enrichment analysis on these hub genes. The CIBERSORT algorithm was employed to evaluate the correlation between hub genes and the abundance of immune cells within tissues. Finally, Mendelian randomization analysis was utilized to assess the potential of these hub genes as biomarkers. We identified 46 differentially expressed genes (DEGs), comprising 20 upregulated and 26 downregulated genes. Using WGCNA, we constructed a gene co-expression network and selected the most relevant modules, resulting in 24 intersecting genes with the DEGs. KEGG enrichment analysis of the intersecting genes identified the IL-17 signaling pathway, associated with inflammation, as the most significant pathway. Cytoscape software was utilized to rank the candidate genes, with JUN, ATF3, FOSB, NR4A2, and IL6 emerging as the top five based on the Degree algorithm. A nomogram model incorporating these five genes, supported by ROC curve analysis, validated their diagnostic efficacy. Immune infiltration and correlation analysis revealed that macrophages were significantly associated with JUN (p < 0.01), FOSB (p < 0.01), and NR4A2 (p < 0.05). Additionally, T follicular helper cells showed significant associations with ATF3 (p < 0.05), FOSB (p < 0.05), and JUN (p < 0.05). Mendelian randomization analysis provided strong evidence linking JUN (IVW: OR = 0.910, p = 0.005) and IL6 (IVW: OR = 1.024, p = 0.026) with KOA. Through the utilization of various bioinformatics analysis methods, we have pinpointed key hub genes relevant to knee osteoarthritis. These findings hold promise for advancing pre-symptomatic diagnostic strategies and enhancing our understanding of the biological underpinnings behind knee osteoarthritis susceptibility genes.

Fat Mass Index Predicts the Effect of Weight Loss and Quality of Life Early After Laparoscopic Sleeve Gastrectomy.

Fat mass index (FMI) is a body composition indicator that reflects body fat content. Laparoscopic sleeve gastrectomy (LSG) is widely performed in patients with obesity. This study aimed to evaluate the value of the FMI in predicting weight loss effect and quality of life early after LSG. From January 2014 to July 2022, the clinical data and computed tomography (CT) images of patients who underwent LSG at a tertiary referral teaching hospital were analyzed. Body composition indicators were calculated using the SliceOmatic software. Achieving initial body mass index within 6months postoperatively was defined as early eligible weight loss (EEWL). The relationship between body composition and EEWL was analyzed. A total of 243 patients were included. Receiver operating characteristic (ROC) curve analysis showed that the predictive value of the FMI for EEWL in patients after LSG was higher than that of other indicators (all P < 0.05; area under the curve = 0.813). The best FMI cut-off point was 13.662. Accordingly, the patients were divided into the high-FMI group and low-FMI group. The %EWL and BMI of patients in the low-FMI group at 1, 3, 6, 9, 12, and 24months after surgery were better than those in the high-FMI group (all P < 0.001). Patients in the low-FMI group had higher BAROS (Bariatric Analysis and Reporting Outcome System) scores than those in the high-FMI group (P < 0.001). Compared with other body composition indicators, FMI can effectively predict the early effect of weight loss and quality of life after LSG.

Value of portal venous gas and a nomogram for predicting severe neonatal necrotizing enterocolitis.

Whether portal venous gas (PVG) is a sign of severe neonatal necrotizing enterocolitis (NEC) and predicts poor prognosis remains uncertain. Patients from two centres were randomly assigned to a training set or a validation set. A nomogram model for predicting severe NEC was developed on the basis of the independent risk factors selected by least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis. The model was evaluated based on the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). A total of 585 patients met the study criteria, and propensity score matching resulted in 141 matched pairs for further analysis. Patients with PVG had a greater risk of surgical intervention or death compared with patients without PVG. A prediction model for severe NEC was established based on PVG, invasive mechanical ventilation (IMV), serum platelet count (PLT) and pH <7.35 at the onset of NEC. The model had a moderate predictive value with an AUC > 0.8. The calibration curve and DCA suggested that the nomogram model had good performance for clinical application. A prediction nomogram model based on PVG and other risk factors can help physicians identify severe NEC early and develop reasonable treatment plans. PVG is an important and common imaging manifestation of NEC. Controversy exists regarding whether PVG is an indication for surgical intervention and predicts poor prognosis. Our study suggested that patients with PVG had a greater risk of surgical intervention or death compared with patients without PVG. PVG, IMV, PLT and pH <7.35 at the onset of NEC are independent risk factors for severe NEC. A prediction nomogram model based on PVG and other risk factors may help physicians identify severe NEC early and develop reasonable treatment plans.