Abstract
Objective
To determine the possible influence of segmentation margin on each step (feature reproducibility, selection, and classification) of the machine learning (ML)-based high-dimensional quantitative computed tomography (CT) texture analysis (qCT-TA) of renal clear cell carcinomas (RcCCs).
Materials and methods
For this retrospective study, 47 patients with RcCC were included from a public database. Two segmentations were obtained by two radiologists for each tumour: (i) contour-focused and (ii) margin shrinkage of 2 mm. Texture features were extracted from original, filtered, and transformed CT images. Feature selection was done using a correlation-based algorithm. The ML classifier was k-nearest neighbours. Classifications were performed with and without using synthetic minority over-sampling technique. Reference standard was nuclear grade (low versus high). Intraclass correlation coefficient (ICC), Pearson’s correlation coefficient, Wilcoxon signed-ranks test, and McNemar’s test were used in the analysis.
Results
The segmentation with margin shrinkage of 2 mm (772 of 828; 93.2%) yielded more texture features with excellent reproducibility (ICC ≥ 0.9) than contour-focused segmentation (714 of 828; 86.2%), p < 0.0001. The feature selection algorithms resulted in different feature subsets for two segmentation datasets with only one common feature. All ML-based models based on contour-focused segmentation (area under the curve [AUC] range, 0.865–0.984) performed better than those with margin shrinkage of 2 mm (AUC range, 0.745–0.887), p < 0.05.
Conclusions
Each step of the ML-based high-dimensional qCT-TA was susceptible to a slight change of 2 mm in segmentation margin. Despite yielding fewer features with excellent reproducibility, use of the contour-focused segmentation provided better classification performance for distinguishing nuclear grade.
Key Points
• Each step of a machine learning (ML)-based high-dimensional quantitative computed tomography texture analysis (qCT-TA) is sensitive to even a slight change of 2 mm in segmentation margin.
• Despite yielding fewer texture features with excellent reproducibility, performing the segmentation focusing on the outermost boundary of the tumours provides better classification performance in ML-based qCT-TA of renal clear cell carcinomas for distinguishing nuclear grade.
• Findings of an ML-based high-dimensional qCT-TA may not be reproducible in clinical practice even using the same feature selection algorithm and ML classifier unless the possible influence of the segmentation margin is considered.
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Abbreviations
- AUC:
-
Area under the curve
- CE-CT:
-
Contrast-enhanced computed tomography
- CT:
-
Computed tomography
- ICC:
-
Intraclass correlation coefficient
- k-NN:
-
k-nearest neighbours
- LoG:
-
Laplacian of Gaussian
- NN:
-
Nearest neighbours
- qCT-TA:
-
Quantitative computed tomography texture analysis
- qTA:
-
Quantitative texture analysis
- RCC:
-
Renal cell carcinoma
- RcCC:
-
Renal clear cell carcinoma
- TCGA-KIRC:
-
The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma
- WEKA:
-
Waikato environment for knowledge analysis
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Acknowledgements
The abstract of this study has also been submitted to the European Congress of Radiology (ECR) 2019 (Control Number 19-P-339-ECR). The authors acknowledge that they have previously used this public database (The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma [TCGA-KIRC]) in different context.
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The scientific guarantor of this publication is Burak Kocak, MD.
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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
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One of the authors (Burak Kocak, MD) has significant statistical expertise.
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Written informed consent was not required for this study because all patients’ data included in this study are publicly and freely available for scientific purposes (The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma [TCGA-KIRC]).
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Institutional Review Board approval was not required because all patients’ data included in this study are publicly and freely available for scientific purposes (The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma [TCGA-KIRC]).
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The authors acknowledge that they have previously used this public database (The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma [TCGA-KIRC]) in different context.
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• retrospective
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• performed using a publicly and freely available database
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Kocak, B., Ates, E., Durmaz, E.S. et al. Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas. Eur Radiol 29, 4765–4775 (2019). https://doi.org/10.1007/s00330-019-6003-8
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DOI: https://doi.org/10.1007/s00330-019-6003-8