Differential Diagnosis Between Subcutaneous Hemangioma and Kaposiform Hemangioendothelioma via Different Ultrasonography-Based Radiomics Models
10.3969/j.issn.1005-5185.2024.07.016
- VernacularTitle:不同超声影像组学模型鉴别皮下组织血管瘤和卡波西型血管内皮瘤
- Author:
Yaning NIU
1
,
2
,
3
;
Yihang YU
;
Yubin GONG
;
Jian DONG
;
Jing ZHAO
;
Gang WU
Author Information
1. 河南大学人民医院超声科,河南 郑州 450003
2. 河南省人民医院超声科,河南 郑州 450003
3. 上海交通大学医学院附属瑞金医院妇产科,上海 200025
- Keywords:
Hemangioma;
Kaposiform hemangioendothelioma;
Ultrasonography;
Radiomics;
Algorithms
- From:
Chinese Journal of Medical Imaging
2024;32(7):721-725
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To identify hemangioma(HE)and Kaposiform hemangioendothelioma(KHE)by constructing two ultrasonography-based radiomics models to evaluate the application value of two models in distinguishing HE from KHE,and to compare the diagnostic efficiency of two models.Materials and Methods A total of 90 lesions of subcutaneous HE or KHE confirmed clinically or pathologically from Henan Provincial People's Hospital from August 2020 to May 2022,were retrospectively analyzed.Imaging features were extracted by using Pyradiomics and screened out by the least absolute shrinkage and selection operator algorithm.Support vector machine and random forest were used to construct the radiomics models.Then the diagnostic efficacy of different models was compared.Results Based on the selected 10 radiomics features,the area under the curve,accuracy,sensitivity,specificity,positive and negative prediction the training group and validation group of the support vector machine model were 0.902(95%CI 0.887-0.917),92.1%,85.0%,92.3%,90.9%,93.5%and 0.827(95%CI 0.787-0.856),85.2%,70.0%,94.1%,90.9%,85.0%,respectively;and those in the training group and validation group of the random forest model were 0.960(95%CI 0.938-0.983),98.4%,96.4%,97.8%,98.1%,97.2%and 0.742(95%CI 0.699-0.785),77.8%,57.1%,82.3%,79.6%,62.5%,respectively.The area under the curve between two models in the training group and validation group was statistically significant(Z=-3.306,-2.009;P<0.05).Conclusion Ultrasonography-based radiomics can distinguish HE from KHE,support vector machine model shows more stable diagnostic performance in small sample data.
