Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types

Zhang, Yupeng; Zhang, Baorui; Liang, Fei; Liang, Shikai; Zhang, Yuxiang; Peng, Yan; Ma, Chao; Liu, Aihua; Guo, Feng; Jiang, Chuhan

doi:10.1007/s00330-018-5747-x

Cited by 56 publications

(32 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML makes it possible to handle complex and numerous data, however, the optimal ML for radiomics is under debated 36 , 37 and the selection of ML depends on researchers' preference. 11 , 12 Although tree‐based models (eg, RF and Adaboost) combined with feature reduction were reported to be more preferable, 8 , 38 the SVM combined with the linear SVM preprocessor was observed to be the best in our study. The core reason is the nonlinear nature of medical problems; along with nonlinear kernels, which could transform linear input into nonlinear input, SVM has been reported to have the ability to solve nonlinear problems, 8 , 39 , 40 similar to tree‐based models.…”

Section: Discussionmentioning

confidence: 61%

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

et al. 2021

View full text Add to dashboard Cite

Introduction: Predicting the postoperative neurological function of cervical spondylotic myelopathy (CSM) patients is generally based on conventional magnetic resonance imaging (MRI) patterns, but this approach is not completely satisfactory. This study utilized radiomics, which produced advanced objective and quantitative indicators, and machine learning to develop, validate, test, and compare models for predicting the postoperative prognosis of CSM.Materials and methods: In total, 151 CSM patients undergoing surgical treatment and preoperative MRI was retrospectively collected and divided into good/poor outcome groups based on postoperative modified Japanese Orthopedic Association (mJOA) scores. The datasets obtained from several scanners (an independent scanner) for the training (testing) cohort were used for cross-validation (CV). Radiological models based on the intramedullary hyperintensity and compression ratio were constructed with 14 binary classifiers. Radiomic models based on 237 robust radiomic features were constructed with the same 14 binary classifiers in combination with 7 feature reduction methods, resulting in 98 models. The main outcome measures were the area under the receiver operating characteristic curve (AUROC) and accuracy.Results: Forty-one (11) radiomic models were superior to random guessing during CV (testing), with significant increased AUROC and/or accuracy (P AUROC < .05 and/or P accuracy < .05). One radiological model performed better than random guessing during CV (P accuracy < .05). In the testing cohort, the linear SVM preprocessor + SVM, the best radiomic model (AUROC: 0.74 ± 0.08, accuracy: 0.73 ± 0.07), overperformed the best radiological model (P AUROC = .048). Conclusion: Radiomic features can predict postoperative spinal cord function in CSMpatients. The linear SVM preprocessor + SVM has great application potential in building radiomic models.Meng-Ze Zhang and Han-Qiang Ou-Yang contributed equally to this work.

show abstract

Section: Discussionmentioning

confidence: 61%

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

et al. 2021

View full text Add to dashboard Cite

show abstract

“…These filter-based methods were frequently applied in studies [ 35 ]. The features were ranked using the above feature selection methods based on joint mutual information (JMI) [ 35 – 37 ], redundancy and relevance (MRMR) [ 37 – 39 ], ANOVA F-value (SKB, SP) [ 37 , 38 ], p -value (WLCX) [ 35 , 37 , 39 ], respectively. For each feature selection method, different number of selected features ( n = 5, 10, 15, ..., 100) were selected for further classifications.…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma

Tang

Yang

et al. 2021

BMC Cancer

View full text Add to dashboard Cite

Background Radiomics may provide more objective and accurate predictions for extrahepatic cholangiocarcinoma (ECC). In this study, we developed radiomics models based on magnetic resonance imaging (MRI) and machine learning to preoperatively predict differentiation degree (DD) and lymph node metastasis (LNM) of ECC. Methods A group of 100 patients diagnosed with ECC was included. The ECC status of all patients was confirmed by pathology. A total of 1200 radiomics features were extracted from axial T1 weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and apparent diffusion coefficient (ADC) images. A systematical framework considering combinations of five feature selection methods and ten machine learning classification algorithms (classifiers) was developed and investigated. The predictive capabilities for DD and LNM were evaluated in terms of area under precision recall curve (AUPRC), area under the receiver operating characteristic (ROC) curve (AUC), negative predictive value (NPV), accuracy (ACC), sensitivity, and specificity. The prediction performance among models was statistically compared using DeLong test. Results For DD prediction, the feature selection method joint mutual information (JMI) and Bagging Classifier achieved the best performance (AUPRC = 0.65, AUC = 0.90 (95% CI 0.75–1.00), ACC = 0.85 (95% CI 0.69–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.88 (95% CI 0.64–0.97)), and the radiomics signature was composed of 5 selected features. For LNM prediction, the feature selection method minimum redundancy maximum relevance and classifier eXtreme Gradient Boosting achieved the best performance (AUPRC = 0.95, AUC = 0.98 (95% CI 0.94–1.00), ACC = 0.90 (95% CI 0.77–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.94 (95% CI 0.72–0.99)), and the radiomics signature was composed of 30 selected features. However, these two chosen models were not significantly different to other models of higher AUC values in DeLong test, though they were significantly different to most of all models. Conclusion MRI radiomics analysis based on machine learning demonstrated good predictive accuracies for DD and LNM of ECC. This shed new light on the noninvasive diagnosis of ECC.

show abstract

“…Early and accurate diagnosis of AVM-related hematomas is crucial for guiding treatment decisions, for instance, deciding whether or not to embolize the nidus to avoid rebleeding. Zhang and colleagues [66] performed a radiomics study to differentiate between acute ICH (< 6 h) of diverse etiologies on NCCT. The hypothesis driving this research is that AVM-related hematomas embedded in malformed vasculature are more heterogeneous in composition and could be identified through quantitative radiomic analysis.…”

Section: Diagnosis Of Stroke Lesionsmentioning

confidence: 99%

Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges

et al. 2021

View full text Add to dashboard Cite

Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types

Cited by 56 publications

References 29 publications

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma

Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges

Contact Info

Product

Resources

About

Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types

Cited by 56 publications

References 29 publications

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T2*‐weighted images of cervical spondylotic myelopathy

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T2*‐weighted images of cervical spondylotic myelopathy

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma

Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges

Contact Info

Product

Resources

About

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy