Automated Prediction of Neoadjuvant Chemoradiotherapy Response in Locally Advanced Cervical Cancer Using Hybrid Model-Based MRI Radiomics

被引:2
|
作者
Yang, Hua [1 ,2 ]
Xu, Yinan [3 ]
Dong, Mohan [4 ]
Zhang, Ying [1 ]
Gong, Jie [1 ]
Huang, Dong [5 ]
He, Junhua [6 ]
Wei, Lichun [1 ]
Huang, Shigao [1 ]
Zhao, Lina [1 ]
机构
[1] Air Force Med Univ, Xijing Hosp, Dept Radiat Oncol, Xian 710032, Peoples R China
[2] Xi An Jiao Tong Univ, Affiliated Hosp 1, Dept Radiat Oncol, Xian 710061, Peoples R China
[3] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Peoples R China
[4] Air Force Med Univ, Xijing Hosp, Dept Med Educ, Xian 710032, Peoples R China
[5] Air Force Med Univ, Dept Mil Biomed Engn, Xian 710012, Peoples R China
[6] Air Force Med Univ, Hosp 986, Dept Radiat Oncol, Xian 710054, Peoples R China
关键词
automated prediction; MRI radiomics; radiotherapy response; cervical cancer;
D O I
10.3390/diagnostics14010005
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: This study aimed to develop a model that automatically predicts the neoadjuvant chemoradiotherapy (nCRT) response for patients with locally advanced cervical cancer (LACC) based on T2-weighted MR images and clinical parameters. Methods: A total of 138 patients were enrolled, and T2-weighted MR images and clinical information of the patients before treatment were collected. Clinical information included age, stage, pathological type, squamous cell carcinoma (SCC) level, and lymph node status. A hybrid model extracted the domain-specific features from the computational radiomics system, the abstract features from the deep learning network, and the clinical parameters. Then, it employed an ensemble learning classifier weighted by logistic regression (LR) classifier, support vector machine (SVM) classifier, K-Nearest Neighbor (KNN) classifier, and Bayesian classifier to predict the pathologic complete response (pCR). The area under the receiver operating characteristics curve (AUC), accuracy (ACC), true positive rate (TPR), true negative rate (TNR), and precision were used as evaluation metrics. Results: Among the 138 LACC patients, 74 were in the pCR group, and 64 were in the non-pCR group. There was no significant difference between the two cohorts in terms of tumor diameter (p = 0.787), lymph node (p = 0.068), and stage before radiotherapy (p = 0.846), respectively. The 109-dimension domain features and 1472-dimension abstract features from MRI images were used to form a hybrid model. The average AUC, ACC, TPR, TNR, and precision of the proposed hybrid model were about 0.80, 0.71, 0.75, 0.66, and 0.71, while the AUC values of using clinical parameters, domain-specific features, and abstract features alone were 0.61, 0.67 and 0.76, respectively. The AUC value of the model without an ensemble learning classifier was 0.76. Conclusions: The proposed hybrid model can predict the radiotherapy response of patients with LACC, which might help radiation oncologists create personalized treatment plans for patients.
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页数:12
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