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Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas

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.

Funding

The authors state that this work has not received any funding.

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Corresponding author

Correspondence to Burak Kocak.

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Guarantor

The scientific guarantor of this publication is Burak Kocak, MD.

Conflict of interest

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.

Statistics and biometry

One of the authors (Burak Kocak, MD) has significant statistical expertise.

Informed consent

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]).

Ethical approval

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]).

Study subjects or cohorts overlap

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.

Methodology

• retrospective

• experimental

• 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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Keywords

  • Multidetector computed tomography
  • Radiomics
  • Clear cell renal cell carcinoma
  • Machine learning
  • Artificial intelligence