Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the ... View Full Text


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Article Info

DATE

2022-08-31

AUTHORS

Naoko Mori, Shunji Mugikura, Toshiki Endo, Hidenori Endo, Yo Oguma, Li Li, Akira Ito, Mika Watanabe, Masayuki Kanamori, Teiji Tominaga, Kei Takase

ABSTRACT

PurposeTo investigate whether texture features from tumor and peritumoral areas based on sequence combinations can differentiate between low- and non-low-grade meningiomas.MethodsConsecutive 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.ResultsTwo, 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).ConclusionThe 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. More... »

PAGES

1-18

References to SciGraph publications

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    21 schema:description PurposeTo investigate whether texture features from tumor and peritumoral areas based on sequence combinations can differentiate between low- and non-low-grade meningiomas.MethodsConsecutive 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.ResultsTwo, 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).ConclusionThe 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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    25 schema:keywords CE-T1WI
    26 MethodsConsecutive patients
    27 PurposeTo
    28 ResultsTwo
    29 T1-weighted imaging
    30 T1WI
    31 T2-weighted imaging
    32 T2WI
    33 algorithm
    34 analysis
    35 apparent diffusion coefficient
    36 area
    37 characteristic curve analysis
    38 coefficient
    39 combination
    40 component analysis
    41 components
    42 construction
    43 contrast-enhanced T1WI
    44 curve analysis
    45 curves
    46 diagnostic performance
    47 differences
    48 diffusion coefficient
    49 diffusion-weighted imaging
    50 features
    51 grade meningiomas
    52 grading
    53 grading of meningiomas
    54 high diagnostic performance
    55 high signal intensity
    56 highest area
    57 imaging
    58 intensity
    59 machine algorithm
    60 magnetic resonance
    61 manual segmentation
    62 meningiomas
    63 model
    64 model construction
    65 patients
    66 performance
    67 peritumoral area
    68 preoperative magnetic resonance
    69 principal component analysis
    70 principal components
    71 receiver
    72 resonance
    73 segmentation
    74 sequence
    75 sequence combinations
    76 significance
    77 significant differences
    78 statistical significance
    79 support vector machine algorithm
    80 surgery
    81 texture
    82 texture features
    83 tumor area
    84 tumors
    85 values
    86 vector machine algorithm
    87 schema:name Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area
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