The effects of volume of interest delineation on MRI-based radiomics analysis: evaluation with two disease groups
The effects of volume of interest delineation on MRI-based radiomics analysis: evaluation with two disease groups
复制标题
感兴趣体积描绘对基于 MRI 的放射组学分析的影响：对两个疾病组的评估
DOI：
10.1186/s40644-019-0276-7
复制
发表时间：
2019-12-21
期刊：
影响因子：
4.9
通讯作者：
Feng, Qianjin
中科院分区：
文献类型：
--
作者：
Zhang, Xiao;Zhong, Liming;Feng, Qianjin
BackgroundManual delineation of volume of interest (VOI) is widely used in current radiomics analysis, suffering from high variability. The tolerance of delineation differences and possible influence on each step of radiomics analysis are not clear, requiring quantitative assessment. The purpose of our study was to investigate the effects of delineation of VOIs on radiomics analysis for the preoperative prediction of metastasis in nasopharyngeal carcinoma (NPC) and sentinel lymph node (SLN) metastasis in breast cancer.MethodsThis study retrospectively enrolled two datasets (NPC group: 238 cases; SLN group: 146 cases). Three operations, namely, erosion, smoothing, and dilation, were implemented on the VOIs accurately delineated by radiologists to generate diverse VOI variations. Then, we extracted 2068 radiomics features and evaluated the effects of VOI differences on feature values by the intra-class correlation coefficient (ICC). Feature selection was conducted by Maximum Relevance Minimum Redundancy combined with 0.632+ bootstrap algorithms. The prediction performance of radiomics models with random forest classifier were tested on an independent validation cohort by the area under the receive operating characteristic curve (AUC).ResultsThe larger the VOIs changed, the fewer features with high ICCs. Under any variation, SLN group showed fewer features with ICC ≥ 0.9 compared with NPC group. Not more than 15% top-predictive features identical to the accurate VOIs were observed across feature selection. The differences of AUCs of models derived from VOIs across smoothing or dilation with 3 pixels were not statistically significant compared with the accurate VOIs (p> 0.05) except for T2-weighted fat suppression images (smoothing: 0.845 vs. 0.725,p= 0.001; dilation: 0.800 vs. 0.725,p= 0.042). Dilation with 5 and 7 pixels contributed to remarkable AUCs in SLN group but the opposite in NPC group. The radiomics models did not perform well when tested by data from other delineations.ConclusionsDifferences in delineation of VOIs affected radiomics analysis, related to specific disease and MRI sequences. Differences from smooth delineation or expansion with 3 pixels width around the tumors or lesions were acceptable. The delineation for radiomics analysis should follow a predefined and unified standard.
