MRI 纹理模型预测胃肠道间质瘤有丝分裂指数和危险度分级。
MRI Texture-Based Models for Predicting Mitotic Index and Risk Classification of Gastrointestinal Stromal Tumors.
机构信息
Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China.
Department of Intervention, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China.
出版信息
J Magn Reson Imaging. 2021 Apr;53(4):1054-1065. doi: 10.1002/jmri.27390. Epub 2020 Oct 9.
BACKGROUND
Treatment regimens and prognoses of gastrointestinal stromal tumors (GIST) are quite different for tumors in different risk categories. Accurate preoperative grading of tumors is important for avoiding under- or overtreatment.
PURPOSE
To develop and validate an MRI texture-based model to predict the mitotic index and its risk classification.
STUDY TYPE
Retrospective.
POPULATION
Ninety-one patients with histologically-confirmed GIST; 64 patients in a training cohort, and 27 patients in a test cohort.
FIELD STRENGTH/SEQUENCE: T -weighted imaging (T WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) at 1.5T.
ASSESSMENT
GIST images were manually segmented by two independent radiologists using ITK-SNAP software and MRI features were extracted using Pyradiomics. Two pathologists reviewed the tissue specimens of the tumors to identify the mitotic index and risk classification in consensus.
STATISTICAL TESTS
The least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. A logistic regression model was established based on the radiomic score (radscore), tumor location, and maximum diameter to predict tumor classification and develop a nomogram. Receiver operator characteristic (ROC) curves were used to evaluate the ability of the nomogram to distinguish between two tumors with different risk classifications, and a calibration curve was used to evaluate the consistency between the predicted risk and the actual risk.
RESULTS
The texture signature achieved high efficacy in predicting the mitotic index area under the curve ([AUC], 0.906; 95% confidence interval [CI]: 0.813, 0.961). A nomogram for prediction of the risk classification of GIST, which incorporated this texture signature together with maximum tumor diameter and location, allowed good discrimination in the training cohort (AUC, 0.878; 95% CI: 0.769, 0.960) and the validation cohort (AUC, 0.903; 95% CI: 0.732, 0.922).
DATA CONCLUSION
The texture-based model can be used to predict GIST mitotic index and risk classification preoperatively.
LEVEL OF EVIDENCE
TECHNICAL EFFICACY STAGE
背景
胃肠道间质瘤（GIST）的治疗方案和预后因肿瘤处于不同风险类别而有很大差异。准确的术前肿瘤分级对于避免过度或不足治疗至关重要。
目的
开发和验证一种基于 MRI 纹理的模型，以预测有丝分裂指数及其风险分类。
研究类型
回顾性。
人群
91 例经组织学证实的 GIST 患者；64 例患者入训练队列，27 例患者入测试队列。
磁场强度/序列：1.5T 下 T 加权成像（TWI）、弥散加权成像（DWI）和动态对比增强三维容积内插屏气检查（3D-VIBE）。
评估
两名独立的放射科医生使用 ITK-SNAP 软件手动对 GIST 图像进行分割，并使用 Pyradiomics 提取 MRI 特征。两名病理学家通过共识审查肿瘤组织标本，以确定有丝分裂指数和风险分类。
统计学检验
使用最小绝对收缩和选择算子（LASSO）回归方法选择纹理特征。基于放射组学评分（radscore）、肿瘤位置和最大直径建立逻辑回归模型，以预测肿瘤分类并制定诺莫图。使用受试者工作特征（ROC）曲线评估诺莫图区分具有不同风险分类的两个肿瘤的能力，并使用校准曲线评估预测风险与实际风险之间的一致性。
结果
纹理特征在预测有丝分裂指数方面具有较高的功效，ROC 曲线下面积（AUC）为 0.906（95%置信区间[CI]：0.813，0.961）。一个纳入该纹理特征、最大肿瘤直径和位置的 GIST 风险分类预测诺莫图，在训练队列中具有良好的区分能力（AUC，0.878；95%CI：0.769，0.960）和验证队列中（AUC，0.903；95%CI：0.732，0.922）。
数据结论
基于纹理的模型可用于预测 GIST 有丝分裂指数和术前风险分类。
证据水平
2。
技术功效分期
3。
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