Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification

Liu, Qinglin; Jiang, Peng; Jiang, Yuhua; Ge, Huijian; Li, Shaolin; Jin, Hengwei; Liu, Peng; Li, YouXiang

doi:10.1038/s41598-021-93286-6

Cited by 5 publications

(11 citation statements)

References 29 publications

(51 reference statements)

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“…A model including features that have been well investigated could be more reliable (8). In our nomogram model, all risk factors were confirmed by previous studies, including as follows: IAs size (19), location, shape (18,20), diabetes history, and neck width. Similar to the former conclusion (21,22), our study indicated that IAs located at PC, ACOA and POCA represented a dangerous signature of rupture.…”

Section: Discussionsupporting

confidence: 77%

“…Nonetheless, establishing a prospective cohort of patients with IAs is not reasonable enough to some extent. On the one hand, we cannot ignore these patients with high rupture risk IA(s), which was evaluated by present risk assessment methods ( 18 ). On the other hand, if only taking the remaining IAs with relatively low risk into observation and investigation, it will bring a great selection bias to the research.…”

Section: Discussionmentioning

confidence: 99%

“…Thirdly, for the sake of improving the convenience of our model, we ignored some factors associated with IAs rupture, such as hemodynamic parameters ( 28 ), patterns of wall enhancement ( 29 ). Besides, some important morphological parameters, especially size ratio and aspect ratio ( 10 , 18 ), have not been included in our study. These may exert some influence on the performance of our model.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Development and External Validation of a Dynamic Nomogram With Potential for Risk Assessment of Ruptured Multiple Intracranial Aneurysms

et al. 2022

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Background and PurposeAbout 20.1% of intracranial aneurysms (IAs) carriers are multiple intracranial aneurysms (MIAs) patients with higher rupture risk and worse prognosis. A prediction model may bring some potential benefits. This study attempted to develop and externally validate a dynamic nomogram to assess the rupture risk of each IA among patients with MIA.MethodWe retrospectively analyzed the data of 262 patients with 611 IAs admitted to the Hunan Provincial People's Hospital between November 2015 and November 2021. Multivariable logistic regression (MLR) was applied to select the risk factors and derive a nomogram model for the assessment of IA rupture risk in MIA patients. To externally validate the nomogram, data of 35 patients with 78 IAs were collected from another independent center between December 2009 and May 2021. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical utility.ResultSize, location, irregular shape, diabetes history, and neck width were independently associated with IA rupture. The nomogram showed a good discriminative ability for ruptured and unruptured IAs in the derivation cohort (AUC = 0.81; 95% CI, 0.774–0.847) and was successfully generalized in the external validation cohort (AUC = 0.744; 95% CI, 0.627–0.862). The nomogram was calibrated well, and the decision curve analysis showed that it would generate more net benefit in identifying IA rupture than the “treat all” or “treat none” strategies at the threshold probabilities ranging from 10 to 60% both in the derivation and external validation set. The web-based dynamic nomogram calculator was accessible on https://wfs666.shinyapps.io/onlinecalculator/.ConclusionExternal validation has shown that the model was the potential to assist clinical identification of dangerous aneurysms after longitudinal data evaluation. Size, neck width, and location are the primary risk factors for ruptured IAs.

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Section: Discussionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Development and External Validation of a Dynamic Nomogram With Potential for Risk Assessment of Ruptured Multiple Intracranial Aneurysms

et al. 2022

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“…The ICC was used to determine the consistency of the extracted features, and intra- and inter-observer variability was analyzed via calculating the Dice similarity coefficient (DSC). The DSC between two observers (JW and YL) ( Table S2A ) and between the two observations of the first observer (JW) ( Table S2B ) was 0.87 ± 0.05 and 0.86 ± 0.06, respectively, which was considered to achieve a good agreement ( 21 ). The remaining VOIs were delineated by the first radiologist (JW).…”

Section: Methodsmentioning

confidence: 98%

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

et al. 2023

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PurposeExploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis.MethodsThis retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models.ResultsFive clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical–radiographic model (P=0.049).ConclusionsThe proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.

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“…Nomograms, as a statistical model based on different clinical variables, have been designed to predict the complications, prognosis, and survival rate of various diseases [10][11][12] . The nomogram approach has shown good prediction and prognostic evaluation functions for cancer, sepsis, and other diseases.…”

Section: Introductionmentioning

confidence: 99%

A prediction nomogram based on machine learning methods for acute respiratory distress syndrome in patients with acute pancreatitis: A retrospective study

Wang

Song

et al. 2022

Preprint

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To construct a predictive nomograph for acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS) admitted to the intensive care unit (ICU) using machine learning methods. This study was designed as a retrospective investigation of data from patients enrolled with AP. These patients were divided into a training cohort and a testing cohort. Machine learning methods were used to select independent predictive factors and to establish a nomogram model, and the feasibility of the nomogram model was evaluated by the test set. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the discriminatory ability, precision, and clinical applicability of the model. A total of 427 patients were included, 344 of whom were in the training cohort and 83 in the testing cohort. The support vector machine (SVM) model showed the best performance among the six machine-learning models. Age, sex, the sequential organ failure score (SOFA), C-reactive protein (CRP), platelet count (PLT), total serum bilirubin (TBIL), and direct bilirubin (DBIL) levels were the best predictors for patients with AP presenting ARDS through the SVM learning model. These seven variables were incorporated to construct a nomogram. The C-index of the model was 0.7977 in the training cohort and 0.8484 in the testing cohort. The calibration curve for predictive probability showed that the nomogram-based predictions were in good agreement with the actual observations. The DCA plot demonstrated a good net benefit for this model, and external validation confirmed its reliability. The prediction nomogram constructed based on the SVM model in this study can effectively predict the probability of AP complicated by ARDS.

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Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification

Cited by 5 publications

References 29 publications

Development and External Validation of a Dynamic Nomogram With Potential for Risk Assessment of Ruptured Multiple Intracranial Aneurysms

Development and External Validation of a Dynamic Nomogram With Potential for Risk Assessment of Ruptured Multiple Intracranial Aneurysms

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

A prediction nomogram based on machine learning methods for acute respiratory distress syndrome in patients with acute pancreatitis: A retrospective study

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