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Pancreas adenocarcinoma CT texture analysis: comparison of 3D and 2D tumor segmentation techniques

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Abstract

Purpose

To determine equivalency of multi-slice 3D CTTA and single slice 2D CTTA of pancreas adenocarcinoma.

Methods

This retrospective study was research ethics board approved. Untreated pancreas adenocarcinomas were segmented on CT in 128 consecutive patients. Tumor segmentation was compared using two techniques: 3D segmentation by contouring all visible tumor in a 3D volume, and 2D segmentation using only a single axial image. First-order CTTA features including mean, minimum, maximum Hounsfield units (HU), standard deviation, skewness, kurtosis, entropy, and second-order gray-level co-occurrence matrix (GLCM) features homogeneity, contrast, correlation, entropy and dissimilarity were extracted. Median values were compared using the Mann–Whitney U test with Holm–Bonferroni correction. Kendall’s Rank Correlation Tau assessed for correlation, and agreement was calculated using intraclass correlation coefficients (ICC) using a two-way model with single rating and absolute agreement. Statistical significance defined as P < 0.05.

Results

The median values of CTTA features differed significantly between 3 and 2D segmentations for all of the evaluated features except for mean attenuation, standard deviation and skewness (P = 0.2979 each). 3D and 2D segmentations had moderate correlation for mean attenuation (R = 0.69, P < 0.01), while all other features demonstrated poor to fair correlation. Agreement between 3 and 2D segmentations was good for mean attenuation (ICC: 0.87, P < 0.01), moderate for minimum (ICC: 0.65, P < 0.01) and standard deviation (ICC: 0.56, P < 0.01), and poor for all other features.

Conclusion

While pancreas adenocarcinoma CTTA features obtained using 3D and 2D segmentation have multiple associations with clinically relevant outcomes, these segmentation techniques are likely not interchangeable other than for mean HU.

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Funding

This research did not receive any funding.

Author information

Authors and Affiliations

  1. Department of Diagnostic Imaging, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, McMaster University, 711 Concession Street, Hamilton, ON, L8V 1C3, Canada

    Ameya Kulkarni & Christian B. van der Pol

  2. McMaster University, Hamilton, Canada

    Ameya Kulkarni & Christian B. van der Pol

  3. Department of Medical Imaging, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada

    Ivan Carrion-Martinez

  4. Vector Institute for Artificial Intelligence, Toronto, ON, Canada

    Kiret Dhindsa

  5. Research and High-Performance Computing, McMaster University, Hamilton, ON, Canada

    Kiret Dhindsa

  6. Thunder Bay Regional Health Sciences Centre, 980 Oliver Rd, Thunder Bay, ON, P7B 6V4, Canada

    Amer A. Alaref & Radu Rozenberg

  7. Northern Ontario School of Medicine (NOSM), Thunder Bay, Canada

    Amer A. Alaref & Radu Rozenberg

Authors
  1. Ameya Kulkarni
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  2. Ivan Carrion-Martinez
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  3. Kiret Dhindsa
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  4. Amer A. Alaref
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  5. Radu Rozenberg
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  6. Christian B. van der Pol
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Contributions

Below is a description of each individual author’s contributions: Ameya Kulkarni, MBBD, MD, collected data by reviewing imaging and medical records, performed lesion segmentation and led drafting and revisions of the manuscript. Ivan Carrion-Martinez identified imaging exams, collected data by reviewing imaging and medical records. Contributed toward developing study concept and protocol including reference standard. Contributed to drafting and revisions of the manuscript. Kiret Dhindsa, PhD contributed toward statistical analysis, reviewed the methodology and helped with developing the study concept and drafting of the manuscript. Amer A. Alaref, MD contributed toward developing study concept and protocol including the reference standard and contributed to drafting and revisions of the manuscript. Radu Rozenberg, MD contributed toward developing study concept and protocol including the reference standard and contributed to drafting and revisions of the manuscript. Christian B. van der Pol, MD obtained REB approval, devised study concept and protocol, established team, identified imaging exams, collected data by reviewing imaging and medical records, supported statistical analysis, led drafting of the manuscript.

Corresponding author

Correspondence to Christian B. van der Pol.

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The authors declared that they have no conflict of interest.

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Institutional Review Board approval was obtained for this study.

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Kulkarni, A., Carrion-Martinez, I., Dhindsa, K. et al. Pancreas adenocarcinoma CT texture analysis: comparison of 3D and 2D tumor segmentation techniques. Abdom Radiol 46, 1027–1033 (2021). https://doi.org/10.1007/s00261-020-02759-1

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  • DOI: https://doi.org/10.1007/s00261-020-02759-1

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