A Clinical-Radiomics Nomogram for Functional Outcome Predictions in Ischemic Stroke

Wang, Hao; Sun, Yi; Wu, Pu-Yeh; Lin, Jixian; Zhao, Jing; Song, Bin

doi:10.1007/s40120-021-00263-2

Cited by 35 publications

(31 citation statements)

References 32 publications

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“…Therefore, more objective and accurate analytical methods of [ 18 F]FDG PET/CT images were needed in neuroblastoma patients, especially in high-risk sub-group patients. Radiomics transforms medical images into quantitative indexes through high-throughput extraction by data-assessment algorithms for predicting important clinical outcomes [ 27 ]. After this research, the R_model showed a moderate power for predicting recurrence, with AUCs of 0.813 in the training set and 0.869 in the test set.…”

Section: Discussionmentioning

confidence: 99%

Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma

et al. 2022

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Background This retrospective study aimed to develop and validate a combined model based [18F]FDG PET/CT radiomics and clinical parameters for predicting recurrence in high-risk pediatric neuroblastoma patients. Methods Eighty-four high-risk neuroblastoma patients were retrospectively enrolled and divided into training and test sets according to the ratio of 3:2. [18F]FDG PET/CT images of the tumor were segmented by 3D Slicer software and the radiomics features were extracted. The effective features were selected by the least absolute shrinkage and selection operator to construct the radiomics score (Rad_score). And the radiomics model (R_model) was constructed based on Rad_score for prediction of recurrence. Then, univariate and multivariate analyses were used to screen out the independent clinical risk parameters and construct the clinical model (C_model). A combined model (RC_model) was developed based on the Rad_score and independent clinical risk parameters and presented as radiomics nomogram. The performance of the above three models was assessed by the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results Seven radiomics features were selected for building the R_model. The AUCs of the C_model in training and test sets were 0.744 (95% confidence interval [CI], 0.595–0.874) and 0.750 (95% CI, 0.577–0.904), respectively. The R_model yielded AUCs of 0.813 (95% CI, 0.685–0.916) and 0.869 (95% CI, 0.715–0.985) in the training and test sets, respectively. The RC_model demonstrated the largest AUCs of 0.889 (95% CI, 0.794–0.963) and 0.892 (95% CI, 0.758–0.992) in the training and test sets, respectively. DCA demonstrated that RC_model added more net benefits than either the C_model or the R_model for predicting recurrence in high-risk pediatric neuroblastoma. Conclusions The combined model performed well for predicting recurrence in high-risk pediatric neuroblastoma, which can facilitate disease follow-up and management in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma

et al. 2022

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“…The current results prove that with the radiomics technique, the essential temporal features of the impaired tissues could be captured. It is challenging to estimate clinical outcomes only by considering the radiomics features of lesions [ 29 ]. Multiple factors are associated with the functional outcome, as well as the features of the lesion itself.…”

Section: Discussionmentioning

confidence: 99%

“…For example, it is used to investigate tumor heterogeneity [ 19 , 20 ] and in clinical decision support systems to improve treatment decision making and accelerate advancements of clinical decision support systems in cancer medicine [ 21 , 22 , 23 , 24 , 25 , 26 ]. However, in the field of stroke, only a few studies have explored the role of radiomics in diagnosing ischemic stroke [ 27 ], penumbra-based prognosis assessment [ 28 ], and functional prediction [ 29 ]. For example, Tang et al [ 28 ] used a Lasso model to achieve multiclassification prediction with a maximum AUC of 0.77 by using radiomics features extracted from infarct and penumbra in CBF and ADC.…”

Section: Introductionmentioning

confidence: 99%

Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome

et al. 2022

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Background: Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke. Methods: SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF. Results: For 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47. Conclusions: The above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary.

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“…Some important risk factors previously reported such as coagulopathy were also included in multivariate regression. Collinearity was assessed via the variance inflation factor (VIF), and features with VIF values > 10 were excluded [ 24 ]. Variables for inclusion were carefully chosen to ensure the parsimony of the final model.…”

Section: Methodsmentioning

confidence: 99%

A Clinical Predictive Nomogram for Traumatic Brain Parenchyma Hematoma Progression

et al. 2021

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Introduction: Acute traumatic intraparenchymal hematoma (tICH) expansion is a major cause of clinical deterioration after brain contusion. Here, an accurate prediction tool for acute tICH expansion is proposed. Methods: A multicenter hospital-based study for multivariable prediction model was conducted among patients (889 patients in a development dataset and 264 individuals in an external validation dataset) with initial and follow-up computed tomography (CT) imaging for tICH volume evaluation. Semi-automated software was employed to assess tICH expansion. Two multivariate predictive models for acute tICH expansion were developed and externally validated. Results: A total of 198 (22.27%) individuals had remarkable acute tICH expansion. The novel Traumatic Parenchymatous Hematoma Expansion Aid (TPHEA) model retained several variables, including age, coagulopathy, baseline tICH volume, time to baseline CT time, subdural hemorrhage, a novel imaging marker of multihematoma fuzzy sign, and an inflammatory index of monocyte-to-lymphocyte ratio. Compared with multihematoma fuzzy sign, monocyte-to-lymphocyte ratio, and the basic model, the TPHEA model exhibited optimal discrimination, calibration, and clinical net benefits for patients with acute tICH expansion. A TPHEA nomogram was subsequently introduced from this model to facilitate clinical Jiangtao Sheng and Weiqiang Chen contributed equally to this work.

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A Clinical-Radiomics Nomogram for Functional Outcome Predictions in Ischemic Stroke

Cited by 35 publications

References 32 publications

Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma

Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma

Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome

A Clinical Predictive Nomogram for Traumatic Brain Parenchyma Hematoma Progression

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