Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion

Duan, Chongfeng; Liu, Fang; Gao, Song; Zhao, Jian; Niu, Lei; Li, Nan; Song, Lixin; Wang, Gang; Zhou, Xiaoming; Ren, Yande; Xu, Wenjian; Liu, Xuejun

doi:10.1007/s00062-021-01040-2

Cited by 10 publications

(10 citation statements)

References 28 publications

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“…Considering the limited number of studies conducted by using ML in prediction models in dentistry, it is difficult to compare our study with the existing studies. Although most ML-based prediction models used in the field of medicine demonstrated improved performance compared to traditional statistical methods [43][44][45], not all showed a higher performance than regression models developed in these studies. For example, Sampa et al [43] developed a blood uric acid prediction model using multiple ML algorithms and reported that the boosted decision tree model showed improved performance compared to the traditional linear regression model.…”

Section: Discussionmentioning

confidence: 73%

“…This shows that not all ML-based prediction models show higher performance than traditional regression models, and the outcome varies based on the variable characteristics, the database used, and disease characteristics to be predicted. Nevertheless, there is no doubt that ML algorithms such as XGBoost and random forest are very powerful classifiers in many cases [43][44][45].…”

Section: Discussionmentioning

confidence: 99%

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Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms

Park

Kim

Choi

2021

IJERPH

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In this study, we developed machine learning-based prediction models for early childhood caries and compared their performances with the traditional regression model. We analyzed the data of 4195 children aged 1–5 years from the Korea National Health and Nutrition Examination Survey data (2007–2018). Moreover, we developed prediction models using the XGBoost (version 1.3.1), random forest, and LightGBM (version 3.1.1) algorithms in addition to logistic regression. Two different methods were applied for variable selection, including a regression-based backward elimination and a random forest-based permutation importance classifier. We compared the area under the receiver operating characteristic (AUROC) values and misclassification rates of the different models and observed that all four prediction models had AUROC values ranging between 0.774 and 0.785. Furthermore, no significant difference was observed between the AUROC values of the four models. Based on the results, we can confirm that both traditional logistic regression and ML-based models can show favorable performance and can be used to predict early childhood caries, identify ECC high-risk groups, and implement active preventive treatments. However, further research is essential to improving the performance of the prediction model using recent methods, such as deep learning.

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms

Park

Kim

Choi

2021

IJERPH

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“…KNN is a conceptually simple but powerful algorithm that is easy to use, interpret and implement [28]. It has been widely used in previous studies [29][30][31]. Furthermore, we found that XGBoost had the highest ACC in the training group.…”

Section: Discussionmentioning

confidence: 67%

Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma

et al. 2023

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Objectives Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years. Methods 486 patients (LM = 200, non-LM = 286) with histologically proven OS were retrospectively analyzed and divided into a training set (n = 389) and a validation set (n = 97). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. Results The radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (tradscore = -5.829, χ2ALP = 97.137, tsize = -3.437, P < 0.01). Multivariable LR analyses showed that ALP was an important indicator for predicting LM of OS (odds ratio [OR] = 7.272, P < 0.001). Among the eight models, the SVM-based clinical-radiomics model had the best performance in the validation set (AUC = 0.807, ACC = 0.784). Conclusion The clinical-radiomics model had good performance in predicting LM in OS, which would be helpful in clinical decision-making.

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“…Ten studies comprising a total of 1525 patients were quantitatively analyzed for hematoma expansion after cerebral hemorrhage using the radiomics method (4,6,7,(17)(18)(19)(20)(21)(22)(23). The pooled Se, Sp, PPV, NPV, and DAR were 0.771 (0.710-0.832), 0.743 (0.684-0.801), 0.612 (0.448-0.737), 0.863 (0.815-0.912), and 0.748 (0.707-0.788), respectively (Figure 3).…”

Section: Diagnostic Test Meta-analysis 411 Radiomics Modelmentioning

confidence: 99%

Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis

Jiang

Wang

et al. 2023

Front. Oncol.

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BackgroundThis meta-analysis aimed to assess the efficacy of radiomics using non-enhanced computed tomography (NCCT) for predicting hematoma expansion in patients with spontaneous intracerebral hemorrhage.MethodsThroughout the inception of the project to April 11, 2022, a comprehensive search was conducted on PubMed, Embase, and Cochrane Central Register of Controlled Trials. The methodological quality of studies in this analysis was assessed by the radiomics quality scoring system (RQS). A meta-analysis of radiomic studies based on NCCT for predicting hematoma expansion in patients with intracerebral hemorrhage was performed. The efficacy of the radiomics approach and non-contrast CT markers was compared using network meta-analysis (NMA).ResultsTen articles comprising a total of 1525 patients were quantitatively analyzed for hematoma expansion after cerebral hemorrhage using radiomics. Based on the included studies, the mean RQS was 14.4. The AUC value (95% confidence interval) of the radiomics model was 0.80 (0.76-0.83). Five articles comprising 846 patients were included in the NMA. The results synthesized according to Bayesian NMA revealed that the predictive ability of the radiomics model outperformed most of the NCCT biomarkers.ConclusionsThe NCCT-based radiomics approach has the potential to predict hematoma expansion. Compared to NCCT biomarkers, we recommend a radiomics approach. Standardization of the radiomics approach is required for further clinical implementation.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=324034, identifier [CRD42022324034].

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Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion

Cited by 10 publications

References 28 publications

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms

Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma

Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis

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