Value of texture analysis on apparent diffusion coefficient maps in the preoperative prediction of histological grade of tongue and mouth floor squamous cell carcinoma
10.3760/cma.j.issn.1005?1201.2019.04.008
- VernacularTitle:术前表观扩散系数图纹理分析预测舌和口底鳞状细胞癌组织学分级的价值
- Author:
Jiliang REN
1
;
Ying YUAN
;
Di DONG
;
Yiqian SHI
;
Xiaofeng TAO
Author Information
1. 上海交通大学医学院附属第九人民医院放射科200011
- Keywords:
Squamous cell carcinoma of head and neck;
Diffusion weighted imaging;
Texture analysis;
Grade
- From:
Chinese Journal of Radiology
2019;53(4):281-285
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of texture analysis on ADC maps in the preoperative prediction of histological grade of tongue and mouth floor squamous cell carcinoma (SCC). Methods Forty?nine pathologically confirmed tongue and mouth floor SCC with definite grading from May 2015 to June 2018 were retrospectively analyzed, including 21 cases of gradeⅠ, 21 cases of gradeⅡand 7 cases of gradeⅢ. All subjects underwent preoperative MRI examination with DWI included. Two doctors delineated whole tumor region of interest and extracted texture parameters by the 3D Slicer software, including 8 histogram parameters, 11 grey?level co?occurrence matrix (GLCM) parameters and 7 gray?level run?length matrix (GLRLM) parameters. Intraclass correlation coefficient (ICC) was used to evaluate the inter?observer delineation agreement, and the texture parameters with excellent reproducibility (ICC>0.8) were used for analysis only. Mann?Whitney U test was used to compare the differences of ADC texture parameters between grade Ⅰ and grade Ⅱ?Ⅲ SCCs. Stepwise logistic regression was used to determine the independent predictors and to build combined model. ROC analysis was used to explore the performance of texture parameter and model in predicting histological grade of tongue and mouth floor SCCs. Pearson correlation coefficient was used to evaluate the correlation between texture parameters with statistical significance. Results (1) Excellent inter?observer delineation agreement (ICC: 0.81-0.98) was observed in 69.23% (18/26) texture parameters, including 6 histogram parameters, 7 GLCM parameters and 5 GLRLM parameters. (2) Among histogram parameters, significantly higher 10 percentile ADC value (ADC10) and significantly lower energy and entropy were shown in gradeⅠcompared with gradeⅡandⅢSCCs (all P<0.05). Among GLCM parameters, significantly lower joint entropy, difference entropy, sum entropy, difference variance, difference average and contrast were shown in grade Ⅰ SCCs (all P<0.05). Among GLRLM parameters, significantly lower gray?level nonuniformity and run?length nonuniformity were shown in gradeⅠSCCs (all P<0.05). ADC10 and entropy were identified as independent predictors. The ADC10 and entropy were 960(913, 1 178)×10?6mm2/s and 4.32(4.06, 4.76) in gradeⅠSCCs, and 888(816, 987)×10?6mm2/s and 4.88(4.57, 5.29) in gradeⅡ?ⅢSCCs respectively. The area under ROC curve (AUC) of ADC10, entropy and combined model were 0.72, 0.75, 0.81. (3) Significant correlation (|r|≥0.5) was observed among 52.73% (29/55)texture parameters with statistical significance. Conclusion Texture analysis on ADC maps can provide more quantitative information, which can be more accurately in discriminating grade Ⅰfrom gradeⅡ?Ⅲtongue and mouth floor SCCs.
