基于影像组学特征预测早期宫颈癌淋巴结转移
Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer.
机构信息
Dalian Medical University, Dalian, P.R. China.
Cancer Hospital of China Medical University, Shenyang, P.R. China.
出版信息
J Magn Reson Imaging. 2019 Jan;49(1):304-310. doi: 10.1002/jmri.26209. Epub 2018 Aug 13.
BACKGROUND
Lymph node metastasis (LNM) is the principal risk factor for poor outcomes in early-stage cervical cancer. Radiomics may offer a noninvasive way for predicting the stage of LNM.
PURPOSE
To evaluate a radiomic signature of LN involvement based on sagittal T contrast-enhanced (CE) and T MRI sequences.
STUDY TYPE
Retrospective.
POPULATION
In all, 143 patients were randomly divided into two primary and validation cohorts with 100 patients in the primary cohort and 43 patients in the validation cohort.
FIELD STRENGTH/SEQUENCE: T CE and T MRI sequences at 3T.
ASSESSMENT
The gold standard of LN status was based on histologic results. A radiologist with 10 years of experience used the ITK-SNAP software for 3D manual segmentation. A senior radiologist with 15 years of experience validated all segmentations. The area under the receiver operating characteristics curve (ROC AUC), classification accuracy, sensitivity, and specificity were used between LNM and non-LNM groups.
STATISTICAL TESTS
A total of 970 radiomic features and seven clinical characteristics were extracted. Minimum redundancy / maximum relevance and support vector machine algorithms were applied to select features and construct a radiomic signature. The Mann-Whitney U-test and the chi-square test were used to test the performance of clinical characteristics and potential prognostic outcomes. The results were used to assess the quantitative discrimination performance of the SVM-based radiomic signature.
RESULTS
The radiomic signatures allowed good discrimination between LNM and non-LNM groups. The ROC AUC was 0.753 (95% confidence interval [CI], 0.656-0.850) in the primary cohort and 0.754 (95% CI, 0584-0.924) in the validation cohort.
DATA CONCLUSIONS
A multiple-sequence MRI radiomic signature can be used as a noninvasive biomarker for preoperative assessment of LN status and potentially influence the therapeutic decision-making in early-stage cervical cancer patients.
LEVEL OF EVIDENCE
3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:304-310.
背景
淋巴结转移（LNM）是早期宫颈癌预后不良的主要危险因素。放射组学可能提供一种非侵入性的方法来预测 LNM 的分期。
目的
基于矢状面 T 增强（CE）和 T 磁共振成像（MRI）序列评估 LNM 受累的放射组学特征。
研究类型
回顾性。
人群
共 143 例患者被随机分为两组，一组为原发性队列，另一组为验证性队列，每组 100 例。
磁场强度/序列：3T 时的 T CE 和 T MRI 序列。
评估
LN 状态的金标准基于组织学结果。一位具有 10 年经验的放射科医生使用 ITK-SNAP 软件进行 3D 手动分割。一位具有 15 年经验的高级放射科医生验证了所有分割。在 LNM 和非 LNM 组之间使用受试者工作特征曲线（ROC）下面积（AUC）、分类准确性、敏感性和特异性。
统计学检验
共提取了 970 个放射组学特征和 7 个临床特征。采用最小冗余/最大相关性和支持向量机算法选择特征并构建放射组学特征。采用曼-惠特尼 U 检验和卡方检验检验临床特征和潜在预后结果的性能。结果用于评估基于 SVM 的放射组学特征的定量判别性能。
结果
放射组学特征可很好地区分 LNM 和非 LNM 组。原发性队列的 ROC AUC 为 0.753（95%置信区间 [CI]，0.656-0.850），验证性队列的 ROC AUC 为 0.754（95% CI，0.584-0.924）。
数据结论
多序列 MRI 放射组学特征可作为术前评估 LN 状态的非侵入性生物标志物，并可能影响早期宫颈癌患者的治疗决策。
证据水平
3 级技术功效：2 级 J. Magn. Reson. Imaging 2019;49:304-310.
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