[Deep transfer learning radiomics model based on temporal bone CT for assisting in the diagnosis of inner ear malformations].
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi
; 38(6): 547-552, 2024 Jun.
Article
in Zh
| MEDLINE
| ID: mdl-38858123
ABSTRACT
Objective:
To evaluate the diagnostic efficacy of traditional radiomics, deep learning, and deep learning radiomics in differentiating normal and inner ear malformations on temporal bone computed tomographyï¼CTï¼.Methods:
A total of 572 temporal bone CT data were retrospectively collected, including 201 cases of inner ear malformation and 371 cases of normal inner ear, and randomly divided into a training cohortï¼n=458ï¼ and a test cohortï¼n=114ï¼ in a ratio of 4â¶1. Deep transfer learning features and radiomics features were extracted from the CT images and feature fusion was performed to establish the least absolute shrinkage and selection operator. The CT results interpretated by two chief otologists from the National Clinical Research Center for Otorhinolaryngological Diseases served as the gold standard for diagnosis. The model performance was evaluated using receiver operating characteristicï¼ROCï¼, and the accuracy, sensitivity, specificity, and other indicators of the models were calculated. The predictive power of each model was compared using the Delong test.Results:
1 179 radiomics features were obtained from traditional radiomics, 2 048 deep learning features were obtained from deep learning, and 137 features fusion were obtained after feature screening and fusion of the two. The area under the curveï¼AUCï¼ of the deep learning radiomics model on the test cohort was 0.964 0ï¼95%CI 0.931 4-0.996 8ï¼, with an accuracy of 0.922, sensitivity of 0.881, and specificity of 0.945. The AUC of the radiomics features alone on the test cohort was 0.929 0ï¼95%CI 0.882 2-0.974 9ï¼, with an accuracy of 0.878, sensitivity of 0.881, and specificity of 0.877. The AUC of the deep learning features alone on the test cohort was 0.947 0ï¼95%CI 0.898 2-0.994 8ï¼, with an accuracy of 0.913, sensitivity of 0.810, and specificity of 0.973. The results indicated that the prediction accuracy and AUC of the deep learning radiomics model are the highest. The Delong test showed that the differences between any two models did not reach statistical significance.Conclusion:
The feature fusion model can be used for the differential diagnosis of normal and inner ear malformations, and its diagnostic performance is superior to radiomics or deep learning models alone.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Temporal Bone
/
Tomography, X-Ray Computed
/
Deep Learning
/
Ear, Inner
Limits:
Female
/
Humans
/
Male
Language:
Zh
Journal:
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi
Year:
2024
Document type:
Article