Identification of osteoid and chondroid matrix mineralization in primary bone tumors using a deep learning fusion model based on CT and clinical features: a multi-center retrospective study.
10.12122/j.issn.1673-4254.2024.12.18
- Author:
Caolin LIU
1
;
Qingqing ZOU
2
;
Menghong WANG
1
;
Qinmei YANG
1
;
Liwen SONG
1
;
Zixiao LU
1
;
Qianjin FENG
2
;
Yinghua ZHAO
1
Author Information
1. Department of Radiology, Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics of Guangdong Province), Guangzhou 510630, China.
2. School of Biomedical Engineering, Southern Medical University (Guangdong Provincial Key Laboratory of Medical Image Processing), Guangzhou 510515, China.
- Publication Type:English Abstract
- Keywords:
chondroid matrix mineralization;
computed tomography;
deep learning;
osteoid matrix mineralization;
primary bone tumor
- MeSH:
Humans;
Deep Learning;
Bone Neoplasms/diagnostic imaging*;
Retrospective Studies;
Tomography, X-Ray Computed/methods*;
Neural Networks, Computer;
Male;
Female;
ROC Curve;
Algorithms
- From:
Journal of Southern Medical University
2024;44(12):2412-2420
- CountryChina
- Language:Chinese
-
Abstract:
METHODS:We retrospectively collected CT scan data from 276 patients with pathologically confirmed primary bone tumors from 4 medical centers in Guangdong Province between January, 2010 and August, 2021. A convolutional neural network (CNN) was employed as the deep learning architecture. The optimal baseline deep learning model (R-Net) was determined through transfer learning, and an optimized model (S-Net) was obtained through algorithmic improvements. Multivariate logistic regression analysis was used to screen the clinical features such as sex, age, mineralization location, and pathological fractures, which were then connected with the imaging features to construct the deep learning fusion model (SC-Net). The diagnostic performance of the SC-Net model and machine learning models were compared with radiologists' diagnoses, and their classification performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score.
RESULTS:In the external test set, the fusion model (SC-Net) achieved the best performance with an AUC of 0.901 (95% CI: 0.803-1.00), an accuracy of 83.7% (95% CI: 69.3%-93.2%) and an F1 score of 0.857, and outperformed the S-Net model with an AUC of 0.818 (95% CI: 0.694-0.942), an accuracy of 76.7% (95% CI: 61.4%-88.2%), and an F1 score of 0.828. The overall classification performance of the fusion model (SC-Net) exceeded that of radiologists' diagnoses.
CONCLUSIONS:The deep learning fusion model based on multi-center CT images and clinical features is capable of accurate classification of osseous and chondroid matrix mineralization and may potentially improve the accuracy of clinical diagnoses of osteogenic versus chondrogenic primary bone tumors.
