[1]潘洁琳,姜云萍,占颖莺,等.基于MRI平扫的影像组学模型鉴别软骨肉瘤与内生软骨瘤[J].南方医科大学学报,2020,(04):483-490.[doi:10.12122/j.issn.1673-4254.2020.04.06]
 Radiomics models based on non-enhanced MRI can differentiate chondrosarcoma fromenchondroma[J].Journal of Southern Medical University,2020,(04):483-490.[doi:10.12122/j.issn.1673-4254.2020.04.06]
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基于MRI平扫的影像组学模型鉴别软骨肉瘤与内生软骨瘤()
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《南方医科大学学报》[ISSN:1673-4254/CN:44-1627/R]

卷:
期数:
2020年04期
页码:
483-490
栏目:
出版日期:
2020-04-30

文章信息/Info

Title:
Radiomics models based on non-enhanced MRI can differentiate chondrosarcoma from enchondroma
作者:
潘洁琳姜云萍占颖莺左盼莉方义杰李绍林洪国斌
关键词:
磁共振成像软骨肉瘤内生软骨瘤影像组学鉴别诊断
Keywords:
magnetic resonance imaging chondrosarcoma enchondroma radiomics differential diagnosis
DOI:
10.12122/j.issn.1673-4254.2020.04.06
文献标志码:
A
摘要:
目的 探讨基于MRI平扫构建的影像组学模型用于鉴别诊断软骨肉瘤与内生软骨瘤的价值。方法 回顾性分析68例软骨源性肿瘤(软骨肉瘤27例,内生软骨瘤41例),将其随机分配到训练组(n=46)与验证组(n=22)。首先由2名放射科医师独立提取平扫T1WI和T2WI-FS序列中肿瘤所有层面的影像组学特征,采用组内相关系数(ICC)评价2名医师提取组学特征的一致性;然后使用方差选择法、单变量特征选择、最小绝对收缩与选择算子算法(LASSO)对组学特征进行筛选和降维,使用多因素逻辑回归分析构建基于T1WI和T2WI-FS序列的组学模型,采用受试者工作特征曲线(ROC)评估组学模型的诊断效能, 并与放射科医师采用常规MR序列的诊断效能进行对比。结果 2名放射科医师独立提取患者T1WI和T2WI-FS序列影像组学特征的一致性良好(ICC值范围为0.779~0.923)。在T1WI序列筛选出10个组学特征,在T2WI-FS序列筛选出11个组学特征,两个序列的组 学模型在训练组中AUC分别为0.990和0.925;在验证组中AUC分别0.915和0.855,模型之间的诊断效能差异均无统计学意义(P>0.05)。在所有病例中,T1WI、T2WI-FS序列组学模型与常规MRI诊断的AUC分别为0.955、0.901、0.569,基于两个序列的组学模型诊断准确性均高于放射科医生的诊断效能(P<0.001)。结论 基于MRI平扫T1WI和T2WI-FS序列构建的影像组学模型能用于鉴别诊断软骨肉瘤与内生软骨瘤。
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). Conclusion The radiomics models based on T1WI and T2WI-FS non-enhanced MR imaging can be used for the differentiation of chondrosarcoma from enchondroma.

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更新日期/Last Update: 2020-04-30