An <scp>MRI</scp>‐Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors

Zhang, Han; Wang, Hexiang; Hao, Dapeng; Wan, Guangyao; Zhang, Jun; Liu, Shunli; Zhang, Yu; Xu, Deguang

doi:10.1002/jmri.27298

Cited by 17 publications

(20 citation statements)

References 32 publications

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“…Second, all radiomics features extracted from the VOI segmentations performed by the two radiologists were assessed for intra-observer and inter-observer agreement using the intraclass correlation coefficient (ICC). Radiomics features with intra- and inter-observer ICCs ≥0.75 were accepted as having good reproducibility in a previous article [ 31 ]; therefore, radiomics features with ICCs ≥0.75 were considered to be robust features.…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

et al. 2022

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Background The purpose of this study was to investigate and validate multiparametric magnetic resonance imaging (MRI)-based machine learning classifiers for early identification of poor responders after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). Methods Patients with LARC who underwent nCRT were included in this retrospective study (207 patients). After preprocessing of multiparametric MRI, radiomics features were extracted and four feature selection methods were used to select robust features. The selected features were used to build five machine learning classifiers, and 20 (four feature selection methods × five machine learning classifiers) predictive models for the screening of poor responders were constructed. The predictive models were evaluated according to the area under the curve (AUC), F1 score, accuracy, sensitivity, and specificity. Results Eighty percent of all predictive models constructed achieved an AUC of more than 0.70. A predictive model using a support vector machine classifier with the minimum redundancy maximum relevance (mRMR) selection method followed by the least absolute shrinkage and selection operator (LASSO) selection method showed superior prediction performance, with an AUC of 0.923, an F1 score of 88.14%, and accuracy of 91.03%. The predictive performance of the constructed models was not improved by ComBat compensation. Conclusions In rectal cancer patients who underwent neoadjuvant chemoradiotherapy, machine learning classifiers with radiomics features extracted from multiparametric MRI were able to accurately discriminate poor responders from good responders. The techniques should provide additional information to guide patient-tailored treatment.

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Section: Methodsmentioning

confidence: 99%

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

et al. 2022

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“…The result showed that radiomics was a valuable tool to help radiologists distinguish COVID-19 from influenza virus pneumonia. The nomogram had been widely applied to predict clinical diseases, such as the differentiation of benign and malignant cancers, cancer recurrence, and lymph node metastasis (22)(23)(24), and it was also commonly used in COVID-19 pneumonia diagnosis (20,21). In our research, the radiomics nomogram was also developed to predict COVID-19 pneumonia and aimed to illustrate the relationship between Radscore and the risk of COVID-19 pneumonia graphically.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia

Lin

Liu

Deng

et al. 2021

Front. Public Health

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Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia.Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by the Max-Relevance and Min-Redundancy algorithm and the least absolute shrinkage and selection operator method. The radiomics model was built using the multivariate backward stepwise logistic regression. A nomogram of the radiomics model was established, and the decision curve showed the clinical usefulness of the radiomics nomogram.Results: The radiomics features, consisting of nine selected features, were significantly different between COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample [area under the receiver operating characteristic curve (AUC), 0.909; 95% confidence interval (CI), 0.859–0.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753–1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful.Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.

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“…Xiao et al (39) found that the apparent diffusion coefficient (ADC) had a good performance in differentiating between benign and malignant tumors with AUC of 0.754 and accuracy of 68.6%. Zhang et al (40) analyzed clinical parameters and MRI-based radiomics features of 197 sinonasal tumor patients, and found that the radiomic nomogram with an AUC of 0.91 can effectively predict malignant sinonasal tumors. The research results of this study are basically consistent with previously published relevant research results.…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al. ( 40 ) analyzed clinical parameters and MRI-based radiomics features of 197 sinonasal tumor patients, and found that the radiomic nomogram with an AUC of 0.91 can effectively predict malignant sinonasal tumors. The research results of this study are basically consistent with previously published relevant research results.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Nomograms Based on Multi-Parametric MRI for Preoperative Differential Diagnosis of Malignant and Benign Sinonasal Tumors: A Two-Centre Study

Zhang

Wang

et al. 2021

Front. Oncol.

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ObjectivesTo investigate the efficacy of multi-parametric MRI-based radiomics nomograms for preoperative distinction between benign and malignant sinonasal tumors.MethodsData of 244 patients with sinonasal tumor (training set, n=192; test set, n=52) who had undergone pre-contrast MRI, and 101 patients who underwent post-contrast MRI (training set, n=74; test set, n=27) were retrospectively analyzed. Independent predictors of malignancy were identified and their performance were evaluated. Seven radiomics signatures (RSs) using maximum relevance minimum redundancy (mRMR), and the least absolute shrinkage selection operator (LASSO) algorithm were established. The radiomics nomograms, comprising the clinical model and the RS algorithms were built: one based on pre-contrast MRI (RNWOC); the other based on pre-contrast and post-contrast MRI (RNWC). The performances of the models were evaluated with area under the curve (AUC), calibration, and decision curve analysis (DCA) respectively.ResultsThe efficacy of the clinical model (AUC=0.81) of RNWC was higher than that of the model (AUC=0.76) of RNWOC in the test set. There was no significant difference in the AUC of radiomic algorithms in the test set. The RS-T1T2 (AUC=0.74) and RS-T1T2T1C (RSWC, AUC=0.81) achieved a good distinction efficacy in the test set. The RNWC and the RNWOC showed excellent distinction (AUC=0.89 and 0.82 respectively) in the test set. The DCA of the nomograms showed better clinical usefulness than the clinical models and radiomics signatures.ConclusionsThe radiomics nomograms combining the clinical model and RS can be accurately, safely and efficiently used to distinguish between benign and malignant sinonasal tumors.

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An MRI‐Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors

Cited by 17 publications

References 32 publications

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia

Radiomics Nomograms Based on Multi-Parametric MRI for Preoperative Differential Diagnosis of Malignant and Benign Sinonasal Tumors: A Two-Centre Study

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