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. 2021 Mar 12;21(1):29.
doi: 10.1186/s40644-021-00398-3.

Value of conventional magnetic resonance imaging texture analysis in the differential diagnosis of benign and borderline/malignant phyllodes tumors of the breast

Xiaoguang Li  1 , Nianping Jiang  1 , Chunlai Zhang  1 , Xiangguo Luo  1 , Peng Zhong  2 , Jingqin Fang  3
Affiliations

Value of conventional magnetic resonance imaging texture analysis in the differential diagnosis of benign and borderline/malignant phyllodes tumors of the breast

Xiaoguang Li et al. Cancer Imaging. .

Abstract

Background: The purpose of this study was to determine the potential value of magnetic resonance imaging (MRI) texture analysis (TA) in differentiating between benign and borderline/malignant phyllodes tumors of the breast.

Methods: The preoperative MRI data of 25 patients with benign phyllodes tumors (BPTs) and 19 patients with borderline/malignant phyllodes tumors (BMPTs) were retrospectively analyzed. A gray-level histogram and gray-level cooccurrence matrix (GLCM) were used for TA with fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) images, and 2- and 7-min postcontrast T1W images on dynamic contrast-enhanced MRI (DCE-T1WI2min and DCE-T1WI7min) between BPTs and BMPTs. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve analysis was carried out to evaluate diagnostic efficiency.

Results: For ADC images, the texture parameters angular second moment (ASM), correlation, contrast, entropy and the minimum gray values of ADC images (ADCMinimum) showed significant differences between the BPT group and BMPT group (all p<0.05). The parameter entropy of FS-T2WI and the maximum gray values and kurtosis of the tumor solid region of DCE-T1WI7min also showed significant differences between these two groups. Except for ADCMinimum, angular second moment of FS-T2WI (FS-T2WIASM), and the maximum gray values of DCE-T1WI7min (DCE-T1WI7min-Maximum) of the tumor solid region, the AUC values of other positive texture parameters mentioned above were greater than 0.75. Binary logistic regression analysis demonstrated that the contrast of ADC images (ADCContrast) and entropy of FS-T2WI (FS-T2WIEntropy) could be considered independent texture variables for the differential diagnosis of BPTs and BMPTs. Combined, the AUC of these parameters was 0.891 (95% CI: 0.793-0.988), with a sensitivity of 84.2% and a specificity of up to 89.0%.

Conclusion: Texture analysis could be helpful in improving the diagnostic efficacy of conventional MR images in differentiating BPTs and BMPTs.

Keywords: Differential diagnosis; Magnetic resonance imaging; Phyllodes tumors; Texture analysis.

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Conflict of interest statement

The authors declare that they have no competing interest.

Figures

Fig. 1
Fig. 1
Flowchart for the case accrual process
Fig. 2
Fig. 2
Schematic diagram of the ROI. ImageJ software was used to select the layer with the maximum tumor are from the FS-T2WI (a) and manually delineate the tumor boundary as much as possible, which was automatically copied to the DWI (b), ADC image (c), DCE-T1WI2min (d), and DCE-T1WI7min (e) of the same tumor layer. Note that the red part in the lower right corner of the DCE-T1WI7min (e) represents the solid tumor region
Fig. 3
Fig. 3
ROC curves of independent variables and the combination of texture parameters from the logistic regression model for differentiating BPTs from BMPTs
See this image and copyright information in PMC

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References

    1. Bendifallah S, Canlorbe G. [common benign breast tumors including fibroadenoma, phyllodes tumors, and papillary lesions: guidelines]. J Gynecol Obstet. Biol Reprod (Paris) 2015;44(10):1017–1029. - PubMed
    1. Spitaleri G, Toesca A, Botteri E, Bottiglieri L, Rotmensz N, Boselli S, et al. Breast phyllodes tumor: a review of literature and a single center retrospective series analysis. Crit Rev Oncol Hematol. 2013;88(2):427–436. doi: 10.1016/j.critrevonc.2013.06.005. - DOI - PubMed
    1. Tan PH, Thike AA, Tan WJ, Thu MM, Busmanis I, Li H, et al. Predicting clinical behaviour of breast phyllodes tumours: a nomogram based on histological criteria and surgical margins. J Clin Pathol. 2012;65(1):69–76. doi: 10.1136/jclinpath-2011-200368. - DOI - PubMed
    1. Tan BY, Acs G, Apple SK, Badve S, Bleiweiss IJ, Brogi E, et al. Phyllodes tumours of the breast: a consensus review. Histopathology. 2016;68(1):5–21. doi: 10.1111/his.12876. - DOI - PMC - PubMed
    1. Pezner RD, Schultheiss TE, Paz IB. Malignant phyllodes tumor of the breast: local control rates with surgery alone. Int J Radiat Oncol Biol Phys. 2008;71(3):710–713. doi: 10.1016/j.ijrobp.2007.10.051. - DOI - PubMed