Int J Stomatol

Int J Stomatol ›› 2023, Vol. 50 ›› Issue (5): 506-513.doi: 10.7518/gjkq.2023090

• Oral Oncology • Previous Articles     Next Articles

Differentiation of pleomorphic adenoma and adenolymphoma of parotid gland by CT morphological features, gender and radiomics

Yu Dongyang1,2(),Li Shaodong1(),Han Lei2,Shan Ben2,Liu Yong2,Zhao Zhengyu2   

  1. 1.Dept. of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, China
    2.Dept. of Radiology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an 223002, China
  • Received:2022-11-15 Revised:2023-03-10 Online:2023-09-01 Published:2023-09-01
  • Contact: Shaodong Li E-mail:501285395@qq.com;13852003378@163.com
  • Supported by:
    2021 Huai’an Health Research Project(HAWJ202116)

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Abstract:

Objective This study aimed to explore the differential application of pleomorphic adenoma (PA) and adenolymphoma (AL) in parotid gland on the basis of the morphological characteristics of CT plain scan and gender combined with radiomic model. Methods The morphological features of 56 cases of PA and 49 cases of AL confirmed by pathology were analyzed retrospectively. The morphological characteristics of shape, boundary, cystic degeneration, multiple occurrence, and location of the tumors were observed and analyzed. Six kinds of radiologic features of tumors in CT plain scan images were extracted and analyzed, including gray histogram, absolute gradient, gray-level co-occurrence matrix, autoregressive model, gray-level run length matrix, and wavelet transform. They were used to screen the statistically significant radiomic characteristic parameters between groups. The radial basis function kernel (RBFK), polynomial kernel (PK), and linear kernel (LK) of the support vector machine (SVM) classification model were established for the screened radiomic features. A joint model combined with gender and morphological features was also established. The receiver operator characteristic curve was used to evaluate the diagnostic efficiency of classification models and joint model. Results A total of 12 features were screened out from 287 radiomic feature parameters to establish classification models. The classification model with RBFK as core had the highest diagnostic efficiency, and the corresponding sensitivity, specificity, accuracy, and area under the curve (AUC) were 90.2%, 82.5%, 89.6%, and 0.883, respectively. PA was more common in women, whereas AL was more common in men. AL was more prone to multiple and cystic degeneration than PA (P<0.05). No significant difference was observed between the two groups in terms of boundary, shape, and the location of the tumor (P>0.05). The sensitivity, specificity, accuracy, and AUC of the combined model based on the radiomic characteristics of RBFK, gender, and morphological characteristics (multiple and cystic changes) were 95.1%, 87.6%, 92.8%, and 0.963, respectively. Conclusion The combination of morphological characteristics based on radiomics characteristics, gender and morphological characteristics could effectively distinguish PA and AL before operation.

Key words: parotid gland, pleomorphic adenoma, adenolymphoma, morphology, radiomics, CT

CLC Number: 

  • R 782

TrendMD: 

Tab 1

Comparison of general clinical data and CT morphological features between two groups of patients"

患者及病灶特征PA组（n=56）AL组（n=49）t值或χ2值P值
年龄47.8±15.153.6±15.7-1.9140.058
性别（男/女）14/4238/1128.8710.000
多发（有/无）12/4421/285.5680.018
病灶数（个）6675
囊变（有/无）16/5031/444.6150.032
边界（清晰/不清晰）59/771/41.3570.244
部位（浅叶/非浅叶）52/1464/111.0310.310
形状（规则/不规则）48/1861/141.4820.223

Fig 1

Use MaZda ver.4.6 software to segment ROI along the edge of the lesion"

Tab 2

Comparison of feature parameters screened by LASSO between PA and AL M（QL，QU)"

组学特征参数PA组（n=66）AL组（n=75）P值
S（2，0）Correlat18.36（12.62，36.00）29.00（22.30，41.02）0.022
Pere.90%119.00（89.56，137.90）59.07（47.90，76.80）<0.001
45dgr_Fraction0.72（0.51，0.81）0.57（0.49，0.73）0.009
Horzl_GLevNonU1767.81（956.55，1987.81）1250.73（951.09，1681.70）0.037
S（4，4）SumVarnc597.76（509.49，821.29）326.08（299.70，422.50）0.028
GrSkewness6.92（5.41，7.35）5.38（4.02，5.99）0.006
Teta41.81（1.02，2.52）0.93（0.79，1.05）<0.001
135dr_GLevNonU0.76（0.60，0.85）0.62（0.52，0.69）0.015
S（5，5）SumOfSqs0.41（0.35，0.49）0.33（0.29，0.40）0.019
Horzl_ShrtREmp0.66（0.52，0.71）0.58（0.51，0.67）0.024
Sigma0.05（0.02，0.09）0.11（0.06，0.18）<0.001
Pere.10%47.67（37.84，54.29）59.81（40.35，67.66）0.032

Fig 2

LASSO model screening for omics features"

Fig 3

12 omics features screened by LASSO model"

Tab 3

Effectiveness of SVM classification model based on radiomics, CT morphological features, and gender construction"

模型灵敏度/%特异度/%准确率/%AUC
性别91.869.177.60.794
CT形态特征81.779.282.30.825
放射组学（RBFK）90.282.589.60.883
联合95.187.692.80.963

Fig 4

Gender, CT morphological features (a) and omics features (b) effectiveness of SVM classification model constructed and diagnostic efficacy of joint model (c)"

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