Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software

Abstract

ObjectiveThe purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software.Materials and MethodsMR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic.ResultsOur study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC < 0.5). Most first order statistics and morphometric features showed moderate-to-high NDR (4 > NDR ≥1), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant.ConclusionThe use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.

Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer

“…To evaluate the correlation between all features, pair-wise Spearman correlation coefficients were computed ( Fig 6 ). Pair-wise correlation coefficients with an absolute value larger than 0.95 were regarded as very redundant [ 15 ]. For correlated features, the feature with the largest mean absolute correlation was removed, reducing the feature set to 40 non-redundant features ( Fig 7 ).…”

“…One common approach for narrowing the feature set is to apply a combination of different methods in a sequential manner [ 9 , 14 , 15 , 30 , 31 ] to remove features that are non-informative or redundant. In the current study, we applied two steps to reduce the initial feature set of 83 features to 40 informative and non-redundant features.…”

“…Once the NDR is calculated for each feature, a cutoff value is chosen as a means to remove the least informative features. In general, the cutoff value is chosen arbitrarily and may be set to a higher or lower value [ 9 , 15 ]. For the second step, highly correlated features were removed.…”

Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer

“…Second, the manual method was applied to image segmentation although the satisfactory inter-and intra-observer reproducibility of radiomics features extraction was achieved. Moreover, over 90% of features had good reproducibility [31]. However, the automated method for image segmentation may have high stability [32,33].…”

The human battle with glioblastoma multiforme: A true challenge

“…Diffusion Tensor Imaging (DTI) is commonly used to identify tumor infiltration into the parenchyma of the brain by providing a picture of the actual neurons of the brain in relation to the tumor. This imaging modality, along with neuronavigation guidance, are used intraoperatively during resection of the tumors for maximal patient safety and outcome [9][10][11][12][13][14][15][16][17][18][19][20].…”