Similar Datasets
Project description:Meningiomas are common intracranial tumors with complex behavior that can be difficult to predict. Historically, morphology has been used to predict tumor aggressiveness and risk of recurrence but has limitations as a prognostic tool. Recent work has shown the value of DNA methylation, transcriptomic, and copy number data for identifying groups of tumors which have distinct biological signatures and predicting recurrence risk. Here, we describe development, clinical validation, and implementation of a methylation classifier based on k-means clustering for prognostic stratification of meningiomas. Previously published work has validated the concept of meningioma prognostic stratification through DNA methylation data, but our system is unique in that it is the first clinically validated classifier which identifies risk groups based exclusively on DNA methylation signatures. This work has the potential to improve diagnostic workup, recurrence risk prediction, and clinical management of meningiomas.
2026-01-12 | GSE292325 | GEO
Project description:Meningiomas are common intracranial tumors with complex behavior that can be difficult to predict. Historically, morphology has been used to predict tumor aggressiveness and risk of recurrence but has limitations as a prognostic tool. Recent work has shown the value of DNA methylation, transcriptomic, and copy number data for identifying groups of tumors which have distinct biological signatures and predicting recurrence risk. Here, we describe development, clinical validation, and implementation of a methylation classifier based on k-means clustering for prognostic stratification of meningiomas. Previously published work has validated the concept of meningioma prognostic stratification through DNA methylation data, but our system is unique in that it is the first clinically validated classifier which identifies risk groups based exclusively on DNA methylation signatures. This work has the potential to improve diagnostic workup, recurrence risk prediction, and clinical management of meningiomas.
2026-01-12 | GSE292326 | GEO
Project description:Development, validation, and clinical utility of a novel methylation classifier for recurrence risk prediction in meningiomas
| PRJNA1237935 | ENA
Project description:Development, validation, and clinical utility of a novel methylation classifier for recurrence risk prediction in meningiomas [dataset 1]
| PRJNA1237938 | ENA
Project description:Development, validation, and clinical utility of a novel methylation classifier for recurrence risk prediction in meningiomas [dataset 2]
| PRJNA1237939 | ENA
Project description:A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade I) PSPF meningiomas treated in our institution between January 2007 to January 2019 were included. All included patients had complete preoperative magnetic resonance imaging (MRI) and more than one year MRI follow-up after surgery. Preoperative contrast-enhanced T1WI, T2WI, T1WI, and T2 fluid-attenuated inversion recovery (FLAIR) were analyzed retrospectively. The most significant 12 clinical features (extracted by LightGBM) and 73 texture features (extracted by SVM) were combined in random forest to predict P/R, and personalized radiomic scores were calculated. Thirteen patients (13/57, 22.8%) had P/R after surgery. The radiomic score was a high-risk factor for P/R with hazard ratio of 15.73 (p < 0.05) in multivariate hazards analysis. In receiver operating characteristic (ROC) analysis, an AUC of 0.91 with cut-off value of 0.269 was observed in radiomic scores for predicting P/R. Subtotal resection, low apparent diffusion coefficient (ADC) values, and high radiomic scores were associated with shorter progression-free survival (p < 0.05). Among different data input, machine learning using combined clinical and texture features showed the best predictive performance, with an accuracy of 91%, precision of 85%, and AUC of 0.88. Machine learning using combined clinical and texture features may have the potential to predict recurrence in PSPF meningiomas.
| S-EPMC9032338 | biostudies-literature
Project description:BackgroundThe ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM.MethodsA prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis.FindingsThe final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (c-statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome.InterpretationThe PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria.FundingThis work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847.
| S-EPMC9596305 | biostudies-literature
Project description:BackgroundNephrolithiasis is a common complication of primary hyperparathyroidism (PHPT), and the recurrence of nephrolithiasis in patients with PHPT is also an urgent concern. What is worse, there is a scarcity of recommended evaluation to predict the risk of nephrolithiasis recurrence in patients with PHPT. This study was aimed to develop and validate a nomogram to facilitate risk assessment in patients with PHPT.MethodsA total of 197 patients with PHPT were retrospectively included in this study from September 2016 to August 2021. Patients' demographic data, blood test parameters, urinalysis, stone parameters, and surgical intervention were collected. Extracted variables were submitted to a least absolute shrinkage and selection operator (LASSO) regression model. A nomogram was built and validated according to the area under the curve (AUC) value, calibration curve, and decision curve analysis.ResultsAccording to the LASSO regression and logistic regression analyses, five predictors were derived from 22 variables: creatinine, uric acid, bilateral stone, multiplicity, and surgery. The AUC and concordance index of the training cohort and validation cohort were 0.829 and 0.856, and 0.827 and 0.877, respectively. The calibration curve analysis and the decision curve analysis showed that the nomogram had an adequate prediction accuracy.ConclusionWe built a useful nomogram model to predict the risk of nephrolithiasis recurrence in patients with PHPT. This would assist clinicians to provide appropriate advices and managements for these patients.
| S-EPMC9470877 | biostudies-literature
Project description:The heterogeneity of immune cells and metabolic pathways in hepatocellular carcinoma (HCC) patients has not been fully elucidated, leading to diverse clinical outcomes. Accurately distinguishing different HCC subtypes and recommending appropriate treatments is are highly important. In this study, we conducted a comprehensive analysis of 28 immune cells and 85 metabolic pathways in the TCGA-LIHC and GSE14520 datasets. Metabolism-related first principal component (MRPC1) and cytotoxic T lymphocyte (CTL) infiltration were used to assess the metabolic and immune infiltration levels of HCC patients, respectively. These two quantifiable indicators were then used to construct an immune‒metabolic classifier, which categorized HCC patients into three distinct groups. The potential biological mechanisms were explored through multiomics analysis, revealing that group S1 exhibited high metabolic activity and a high level of immune infiltration, that group S2 presented a low level of immune infiltration, and that group S3 presented low metabolic activity. This new immune‒metabolic classifier was well validated in a pancancer cohort of 9296 patients. The efficacy of multiple treatment approaches was assessed in relation to different immune‒metabolic groups, indicating that group S1 patients may benefit from immunotherapy, that group S2 patients are suitable for transcatheter arterial chemoembolization (TACE), and that group S3 patients are appropriate candidates for tyrosine kinase inhibitors. In conclusion, this immune‒metabolic classifier is anticipated to address the differences in treatment efficacy among HCC patients due to the heterogeneity of the tumor microenvironment, and to help refine the individualized treatment choices for clinical patients.
| S-EPMC11407989 | biostudies-literature
Project description:Loco-regional recurrence in 50% of oral squamous cell carcinoma (OSCC) patients poses major challenge for oncologists. Lack of biomarkers that can predict disease aggressiveness and recurrence risk makes the scenario more dismal. On the basis of our earlier global proteomic analyses we identified five differentially expressed proteins in OSCC. This study aimed to develop protein biomarkers-based prognostic risk prediction model for OSCC. Sub-cellular expression of five proteins, S100A7, heterogeneous nuclear ribonucleoproteinK (hnRNPK), prothymosin α (PTMA), 14-3-3ζ and 14-3-3σ was analyzed by immunohistochemistry in test set (282 Indian OSCCs and 209 normal tissues), correlated with clinic-pathological parameters and clinical outcome over 12 years to develop a risk model for prediction of recurrence-free survival. This risk classifier was externally validated in 135 Canadian OSCC and 96 normal tissues. Biomarker signature score based on PTMA, S100A7 and hnRNPK was associated with recurrence free survival of OSCC patients (hazard ratio=1.11; 95% confidence interval 1.08, 1.13, P<0.001, optimism-corrected c-statistic=0.69) independent of clinical parameters. Biomarker signature score stratified OSCC patients into high- and low-risk groups with significant difference for disease recurrence. The high-risk group had median survival 14 months, and 3-year survival rate of 30%, whereas low-risk group survival probability did not reach 50%, and had 3-year survival rate of 71%. As a powerful predictor of 3-year recurrence-free survival in OSCC patients, the newly developed biomarkers panel risk classifier will facilitate patient counseling for personalized treatment.
| S-EPMC4491610 | biostudies-literature