Dataset Information

Nomogram for Stability Stratification of Small Intracranial Aneurysm Based on Clinical and Morphological Risk Factors.

ABSTRACT: Background and Purpose: Stability stratification of intracranial aneurysms (IAs) is crucial for individualized clinical management, especially for small IAs. We aim to develop and validate a nomogram based on clinical and morphological risk factors for individualized instability stratification of small IAs. Methods: Six hundred fifty-eight patients with unstable (n = 293) and stable (n = 416) IAs <7 mm were randomly divided into derivation and validation cohorts. Twelve clinical risk factors and 18 aneurysm morphological risk factors were extracted. Combined with important risk factors, a clinical-morphological predictive nomogram was developed. The nomogram performance was evaluated in the derivation and the validation cohorts in terms of discrimination, calibration, and clinical usefulness. Results: Five independent instability-related risk factors were included in the nomogram: location, irregularity, side/bifurcation type, flow angle, and height-to-width ratio. In the derivation cohort, the area under the curve (95% CI) of the nomogram was 0.803 (95% CI, 0.764-0.842), and good agreement between predicted instability risk and actual instability status could be detected in the calibration plot. The nomogram also exhibited good discriminations and calibration in the validation cohort: the area under the curve (95% CI) was 0.744 (95% CI, 0.677-0.812). Small IAs with scores <90 were considered to have low risk of instability, and those with scores of 90 or greater were considered to have high risk of instability. Conclusions: The nomogram based on clinical and morphological risk factors can be used as a convenient tool to facilitate individualized decision-making in the management of small IAs.

SUBMITTER: Zhu W

PROVIDER: S-EPMC7845661 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Nomogram for Stability Stratification of Small Intracranial Aneurysm Based on Clinical and Morphological Risk Factors.

Zhu Wei W Li Wenqiang W Tian Zhongbin Z Zhang Mingqi M Zhang Yisen Y Wang Kun K Zhang Ying Y Yang Xinjian X Liu Jian J

Frontiers in neurology 20210115

Background and Purpose: Stability stratification of intracranial aneurysms (IAs) is crucial for individualized clinical management, especially for small IAs. We aim to develop and validate a nomogram based on clinical and morphological risk factors for individualized instability stratification of small IAs. Methods: Six hundred fifty-eight patients with unstable (n = 293) and stable (n = 416) IAs <7 mm were randomly divided into derivation and validation cohorts. Twel ...[more]

PMID: 33519680

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Project description:ObjectiveRebleeding is recognized as the main cause of mortality after intracranial aneurysm rupture. Though timely intervention can prevent poor prognosis, there is no agreement on the surgical priority and choosing medical treatment for a short period after rupture. The aim of this study was to investigate the risk factors related to the rebleeding after admission and establish predicting models for better clinical decision-making.MethodsThe patients with ruptured intracranial aneurysms (RIAs) between January 2018 and September 2020 were reviewed. All patients fell to the primary and the validation cohort by January 2020. The hemodynamic parameters were determined through the computational fluid dynamics simulation. Cox regression analysis was conducted to identify the risk factors of rebleeding. Based on the independent risk factors, nomogram models were built, and their predicting accuracy was assessed by using the area under the curves (AUCs).ResultA total of 577 patients with RIAs were enrolled in this present study, 86 patients of them were identified as undergoing rebleeding after admission. Thirteen parameters were identified as significantly different between stable and rebleeding aneurysms in the primary cohort. Cox regression analysis demonstrated that six parameters, including hypertension [hazard ratio (HR), 2.54; P = 0.044], bifurcation site (HR, 1.95; P = 0.013), irregular shape (HR, 4.22; P = 0.002), aspect ratio (HR, 12.91; P < 0.001), normalized wall shear stress average (HR, 0.16; P = 0.002), and oscillatory stress index (HR, 1.14; P < 0.001) were independent risk factors related to the rebleeding after admission. Two nomograms were established, the nomogram including clinical, morphological, and hemodynamic features (CMH nomogram) had the highest predicting accuracy (AUC, 0.92), followed by the nomogram including clinical and morphological features (CM nomogram; AUC, 0.83), ELAPSS score (AUC, 0.61), and PHASES score (AUC, 0.54). The calibration curve for the probability of rebleeding showed good agreement between prediction by nomograms and actual observation. In the validation cohort, the discrimination of the CMH nomogram was superior to the other models (AUC, 0.93 vs. 0.86, 0.71 and 0.48).ConclusionWe presented two nomogram models, named CMH nomogram and CM nomogram, which could assist in identifying the RIAs with high risk of rebleeding.

Project description:BACKGROUND:Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients. PURPOSE:To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk. STUDY TYPE:Retrospective. POPULATION:Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21). FIELD STRENGTH/SEQUENCE:3.0T MRI/T2 -weighted (T2 W), multi-b-value diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences. ASSESSMENT:Radiomics features were extracted from the T2 W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine-based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle-invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics-clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve. STATISTICAL TESTS:Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction. RESULTS:Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics-clinical nomogram than using the radiomics or clinical model alone. DATA CONCLUSION:The proposed radiomics-clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence. LEVEL OF EVIDENCE:3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893-1904.

Project description:Objective: The aim of the present study is to investigate the potential morphological and hemodynamic risk factors related to intracranial aneurysms (IAs) rupture and establish a system to stratify the risk of IAs rupture to help the clinical decision-making. Methods: Patients admitted to our hospital for single-IAs were selected from January 2012 and January 2018. A propensity score matching was conducted to match patients. The morphological parameters were obtained from high solution CTA images, and the hemodynamic parameters were obtained in accordance with the outcomes of computational fluid dynamics (CFDs) simulation. Differences in the morphologic and hemodynamic parameters were compared. The significant parameters were selected to establish a novel scoring system (Intracranial Aneurysm Rupture Score, IARS). The comparison was drawn between the discriminating accuracy of IARS and the Rupture Resemblance Score (RRS) system to verify the value of IARS. Then, a group of patients with unruptured IAs was stratified into the high risk and low risk groups by IARS and RRS system separately and was followed up for 18-27 months to verify the value of IARS. The outcome of different stratifications was compared. Results: The matching process yielded 167 patients in each group. Differences of statistical significance were found in aneurysm length (p = 0.001), perpendicular height (H) (p < 0.001), aspect ratio (AR) (p < 0.001), size ratio (SR) (p < 0.001), deviated angle (DA) (p < 0.001), normalized average wall shear stress (NWSSa) (p < 0.001), wall shear stress gradient (WSSG) (p < 0.001), low shear area ratio (LSAR) (p = 0.01), and oscillatory shear index (OSI) (p = 0.01). Logistic regression analysis further demonstrated that SR, DA, NWSSa, LSAR, and OSI were the independent risk factors of IAs rupture. SR, DA, LSAR, and OSI were finally selected to establish the IARS. Our present IARS showed a higher discriminating value (AUC 0.81 vs. 0.77) in comparison with the RRS (SR, NWSSa, and OSI). After follow-up, seven patients were subject to IAs rupture. 5/26 in high risk group stratified by IARS, yet 7/57 in high risk group stratified by RRS. The accuracy of IARS was further verified (19.2% vs. 12.3%, AUC for the IARS and the RRS was 0.723 and 0.673, respectively). Conclusion: SR, DA, NWSSa, LSAR, and OSI were considered the independent risk factors of IAs rupture. Our novel IARS showed higher accuracy in discriminating IA rupture in comparison with RRS.

Dataset Information

Nomogram for Stability Stratification of Small Intracranial Aneurysm Based on Clinical and Morphological Risk Factors.

Publications

Nomogram for Stability Stratification of Small Intracranial Aneurysm Based on Clinical and Morphological Risk Factors.

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