Dataset Information

[Radiomics models based on non-enhanced MRI can differentiate chondrosarcoma from enchondroma].

ABSTRACT: OBJECTIVE:To develop and validate radiomics models based on non-enhanced magnetic resonance (MR) imaging for differentiating chondrosarcoma from enchondroma. METHODS:We retrospectively evaluated a total of 68 patients (including 27 with chondrosarcoma and 41 with enchondroma), who were randomly divided into training group (n=46) and validation group (n=22). Radiomics features were extracted from T1WI and T2WI-FS sequences of the whole tumor by two radiologists independently and selected by Low Variance, Univariate feature selection, and least absolute shrinkage and selection operator (LASSO). Radiomics models were constructed by multivariate logistic regression analysis based on the features from T1WI and T2WI-FS sequences. The receiver-operating characteristics (ROC) curve and intraclass correlation coefficient (ICC) analyses of the radiomics models and conventional MR imaging were performed to determine their diagnostic accuracy. RESULTS:The ICC value for interreader agreement of the radiomics features ranged from 0.779 to 0.923, which indicated good agreement. Ten and 11 features were selected from the T1WI and T2WI-FS sequences to construct radiomics models, respectively. The areas under the curve (AUCs) of T1WI and T2WI-FS models were 0.990 and 0.925 in training group and 0.915 and 0.855 in the validation group, respectively, showing no significant differences between the two sequence-based models (P>0.05). In all the cases, the AUCs of the two radiomics models based on T1WI and T2WI-FS sequences and conventional MR imaging were 0.955, 0.901 and 0.569, respectively, demonstrating a significantly higher diagnostic accuracy of the two sequence-based radiomics models than conventional MR imaging (P<0.01). CONCLUSIONS:The radiomics models based on T1WI and T2WI-FS non-enhanced MR imaging can be used for the differentiation of chondrosarcoma from enchondroma.

SUBMITTER: Pan J

PROVIDER: S-EPMC7225098 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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[Radiomics models based on non-enhanced MRI can differentiate chondrosarcoma from enchondroma].

Pan Jielin J Jiang Yunping Y Zhan Yingying Y Zuo Panli P Fang Yijie Y Li Shaolin S Hong Guobin G

Nan fang yi ke da xue xue bao = Journal of Southern Medical University 20200401 4

<h4>Objective</h4>To develop and validate radiomics models based on non-enhanced magnetic resonance (MR) imaging for differentiating chondrosarcoma from enchondroma.<h4>Methods</h4>We retrospectively evaluated a total of 68 patients (including 27 with chondrosarcoma and 41 with enchondroma), who were randomly divided into training group (<i>n</i>=46) and validation group (<i>n</i>=22). Radiomics features were extracted from T<sub>1</sub>WI and T<sub>2</sub>WI-FS sequences of the whole tumor by t ...[more]

PMID: 32895139

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Project description:BackgroundWe conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).MethodsForty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal-Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models.ResultsThe high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences).ConclusionsRadiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.

Dataset Information

[Radiomics models based on non-enhanced MRI can differentiate chondrosarcoma from enchondroma].

Publications

[Radiomics models based on non-enhanced MRI can differentiate chondrosarcoma from enchondroma].

Similar Datasets

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