基于 MRI 的放射组学分析预测肺腺癌患者胸腰椎转移中 EGFR 突变的价值

MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients.

机构信息

Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, P.R. China.

Department of Biophysics, School of Fundamental Sciences, China Medical University, Shenyang, P.R. China.

出版信息

Med Phys. 2021 Sep;48(9):5142-5151. doi: 10.1002/mp.15137. Epub 2021 Aug 6.

DOI:10.1002/mp.15137

PMID:34318502

Abstract

PURPOSE

This study aims to develop and evaluate multi-parametric MRI-based radiomics for preoperative identification of epidermal growth factor receptor (EGFR) mutation, which is important in treatment planning for patients with thoracic spinal metastases from primary lung adenocarcinoma.

METHODS

A total of 110 patients were enrolled between January 2016 and March 2019 as a primary cohort. A time-independent validation cohort was conducted containing 52 patients consecutively enrolled from July 2019 to April 2021. The patients were pathologically diagnosed with thoracic spinal metastases from primary lung adenocarcinoma; all underwent T1-weighted (T1W), T2-weighted (T2W), and T2-weighted fat-suppressed (T2FS) MRI scans of the thoracic spinal. Handcrafted and deep learning-based features were extracted and selected from each MRI modality, and used to build the radiomics signature. Various machine learning classifiers were developed and compared. A clinical-radiomics nomogram integrating the combined rad signature and the most important clinical factor was constructed with receiver operating characteristic (ROC), calibration, and decision curves analysis (DCA) to evaluate the prediction performance.

RESULTS

The combined radiomics signature derived from the joint of three modalities can effectively classify EGFR mutation and EGFR wild-type patients, with an area under the ROC curve (AUC) of 0.886 (95% confidence interval [CI]: 0.826-0.947, SEN =0.935, SPE =0.688) in the training group and 0.803 (95% CI: 0.682-0.924, SEN = 0.700, SPE = 0.818) in the time-independent validation group. The nomogram incorporating the combined radiomics signature and smoking status achieved the best prediction performance in the training (AUC = 0.888, 95% CI: 0.849-0.958, SEN = 0.839, SPE = 0.792) and time-independent validation (AUC = 0.821, 95% CI: 0.692-0.929, SEN = 0.667, SPE = 0.909) cohorts. The DCA confirmed potential clinical usefulness of our nomogram.

CONCLUSION

Our study demonstrated the potential of multi-parametric MRI-based radiomics on preoperatively predicting the EGFR mutation. The proposed nomogram model can be considered as a new biomarker to guide the selection of individual treatment strategies for patients with thoracic spinal metastases from primary lung adenocarcinoma.

摘要

目的

本研究旨在开发和评估基于多参数 MRI 的放射组学，用于术前识别表皮生长因子受体（EGFR）突变，这对于原发性肺腺癌胸脊柱转移患者的治疗计划非常重要。

方法

本研究共纳入了 110 例患者，他们于 2016 年 1 月至 2019 年 3 月作为主要队列进行了前瞻性研究。此外，还进行了时间独立的验证队列研究，该队列纳入了 52 例连续患者，他们于 2019 年 7 月至 2021 年 4 月间就诊。所有患者均经病理检查确诊为原发性肺腺癌胸脊柱转移；所有患者均接受了 T1 加权（T1W）、T2 加权（T2W）和 T2 加权脂肪抑制（T2FS）的胸脊柱 MRI 扫描。从每个 MRI 模态中提取和选择了手工和基于深度学习的特征，并用于构建放射组学特征。开发并比较了各种机器学习分类器。通过受试者工作特征（ROC）曲线、校准和决策曲线分析（DCA），构建了一个整合联合放射特征和最重要临床因素的临床-放射组学列线图，以评估预测性能。

结果

来自三个模态联合的联合放射组学特征可以有效地区分 EGFR 突变和 EGFR 野生型患者，在训练组中的 ROC 曲线下面积（AUC）为 0.886（95%置信区间 [CI]：0.826-0.947，SEN=0.935，SPE=0.688），在时间独立验证组中的 AUC 为 0.803（95% CI：0.682-0.924，SEN=0.700，SPE=0.818）。纳入联合放射组学特征和吸烟状况的列线图在训练组（AUC=0.888，95%CI：0.849-0.958，SEN=0.839，SPE=0.792）和时间独立验证组（AUC=0.821，95%CI：0.692-0.929，SEN=0.667，SPE=0.909）中均获得了最佳预测性能。DCA 证实了我们的列线图具有潜在的临床应用价值。