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Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

To investigate whether texture features from tumor and peritumoral areas based on sequence combinations can differentiate between low- and non-low-grade meningiomas.

Methods

Consecutive patients diagnosed with meningioma by surgery (77 low-grade and 28 non-low-grade meningiomas) underwent preoperative magnetic resonance imaging including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI). Manual segmentation of the tumor area was performed to extract texture features. Segmentation of the peritumoral area was performed for peritumoral high-signal intensity (PHSI) on T2WI. Principal component analysis was performed to fuse the texture features to principal components (PCs), and PCs of each sequence of the tumor and peritumoral areas were compared between low- and non-low-grade meningiomas. Only PCs with statistical significance were used for the model construction using a support vector machine algorithm. k-fold cross-validation with receiver operating characteristic curve analysis was used to evaluate diagnostic performance.

Results

Two, one, and three PCs of T1WI, apparent diffusion coefficient (ADC), and CE-T1WI, respectively, for the tumor area, were significantly different between low- and non-low-grade meningiomas, while PCs of T2WI for the tumor area and PCs for the peritumoral area were not. No significant differences were observed in PHSI. Among models of sequence combination, the model with PCs of ADC and CE-T1WI for the tumor area showed the highest area under the curve (0.84).

Conclusion

The model with PCs of ADC and CE-T1WI for the tumor area showed the highest diagnostic performance for differentiating between low- and non-low-grade meningiomas. Neither PHSI nor PCs in the peritumoral area showed added value.

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Data availability

No.

Code availability

N/A.

The authors have nothing to disclose.

Abbreviations

ADC:

Apparent diffusion coefficient

CE-T1WI:

Contrast-enhanced T1WI

DWI:

Diffusion-weighted imaging

MRI:

Magnetic resonance imaging

PHSI:

Peritumoral high-signal intensity

PCA:

Principal component analysis

PC:

Principal component

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

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Acknowledgements

The authors thank Mamiko Yamazaki and Akari Akiyama in Tohoku University for their kind support.

Funding

This research was supported by JSPS KAKENHI (22K07763).

Author information

Author notes

  1. Teiji Tominaga and Kei Takase are coequal last authors.

Authors and Affiliations

  1. Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan

    Naoko Mori, Shunji Mugikura, Yo Oguma, Li Li & Kei Takase

  2. Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan

    Toshiki Endo, Hidenori Endo, Akira Ito, Masayuki Kanamori & Teiji Tominaga

  3. Department of Neurosurgery, Tohoku Medical and Pharmaceutical University, Sendai, Japan

    Toshiki Endo

  4. Department of Neurosurgery, Kohnan Hospital, Sendai, Japan

    Hidenori Endo

  5. Department of Anatomic Pathology, Tohoku University Graduate School of Medicine, Sendai, Japan

    Mika Watanabe

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Contributions

Conceptualization: [Naoko Mori], [Shunji Mugikura], and [Toshiki Endo]; methodology: [Shunji Mugikura] and [Naoko Mori]; formal analysis and investigation: [Hidenori Endo], [Li Li], and [Akira Ito]; writing—original draft preparation: [Naoko Mori] and [Yo Oguma]; writing—review and editing: [Shunji Mugikura] and [Kei Takase]; funding acquisition: [Naoko Mori]; resources: [Toshiki Endo], [Hidenori Endo], [Mika Watanabe], and [Masayuki Kanamori]; and supervision: [Teiji Tominaga] and [Kei Takase].

Corresponding author

Correspondence to Naoko Mori.

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Our institutional review board (IRB) approved this retrospective study.

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Appendix

Appendix

Table 6 Texture features of T1WI, T2WI, and CE-T1WI for the tumor and peritumoral area
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Table 7 Texture features of apparent diffusion coefficient for the tumor and peritumoral area
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Table 8 Values used for the imputation of missing values of T1WI, T2WI, and CE-T1WI in patients without peritumoral high-signal intensity
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Table 9 Values used for the imputation of missing values of apparent diffusion coefficient in patients without peritumoral high signal intensity
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Table 10 Comparison of PCs of each sequence for the tumor area between low- and non-low-grade meningiomas in patients with peritumoral high-signal intensity
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Table 11 Comparison of PCs of each sequence for the peritumoral area between low- and non-low-grade meningiomas in patients with peritumoral high-signal intensity
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Table 12 Loadings of PCs of ADC for the tumor area
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Table 13 Loadings of PCs of CE-T1WI for the tumor area
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Mori, N., Mugikura, S., Endo, T. et al. Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area. Neuroradiology 65, 257–274 (2023). https://doi.org/10.1007/s00234-022-03045-1

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