基于机器学习的外周神经鞘瘤良恶性鉴别：多中心研究。

Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study.

机构信息

Department of Neurosurgery, Stanford University, Stanford, California, USA.

Department of Radiology, Stanford University, Stanford, California, USA.

出版信息

Neurosurgery. 2021 Aug 16;89(3):509-517. doi: 10.1093/neuros/nyab212.

DOI:10.1093/neuros/nyab212

PMID:34131749

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8364819/

Abstract

BACKGROUND

Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications.

OBJECTIVE

To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs.

METHODS

We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers.

RESULTS

A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P = .001).

CONCLUSION

Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.

摘要

背景

良性和恶性周围神经鞘瘤（PNST）的临床影像学鉴别具有重要的管理意义。

目的

开发并评估机器学习方法以区分良性和恶性 PNST。

方法

我们在 3 家机构中确定了接受治疗的 PNST，并从钆增强 T1 加权磁共振成像（MRI）序列中提取了高维放射组学特征。采用 70:30 的比例随机选择训练集和测试集。使用定量成像特征管道的 PyRadiomics 包自动提取了 900 个图像特征。还纳入了临床数据，包括年龄、性别、神经遗传综合征存在、自发性疼痛和运动障碍。使用稀疏回归分析选择特征，并通过梯度提升建模进一步优化特征以优化诊断的曲线下面积（AUC）。我们评估了基于放射组学的分类器在包含和不包含临床特征时的性能，并将性能与人类读者进行了比较。

结果

共纳入 95 例恶性和 171 例良性 PNST。最终的分类器模型包括 21 个影像学和临床特征。在测试集上，灵敏度、特异性和 AUC 分别为 0.676、0.882 和 0.845。使用影像学和临床特征，人类专家的总体灵敏度、特异性和 AUC 分别为 0.786、0.431 和 0.624。分类器的 AUC 明显优于人类专家（P=0.002）。人类专家在统计学上并不优于无信息率，而分类器则优于无信息率（P=0.001）。