Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions.
Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions.
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DOI：
10.3389/fonc.2022.934108
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发表时间：
2022
影响因子：
4.7
通讯作者：
Jiang, Jian
中科院分区：
文献类型：
--
作者：
Li, Linghao;Gu, Lili;Kang, Bin;Yang, Jiaojiao;Wu, Ying;Liu, Hao;Lai, Shasha;Wu, Xueting;Jiang, Jian
To compare the performance of different imaging classifiers in the prospective diagnosis of prostate diseases based on multiparameter MRI. A total of 238 patients with pathological outcomes were enrolled from September 2019 to July 2021, including 142 in the training set and 96 in the test set. After the regions of interest were manually segmented, decision tree (DT), Gaussian naive Bayes (GNB), XGBoost, logistic regression, random forest (RF) and support vector machine classifier (SVC) models were established on the training set and tested on the independent test set. The prospective diagnostic performance of each classifier was compared by using the AUC, F1-score and Brier score. In the patient-based data set, the top three classifiers of combined sequences in terms of the AUC were logistic regression (0.865), RF (0.862), and DT (0.852); RF “was significantly different from the other two classifiers (P =0.022, P =0.005), while logistic regression and DT had no statistical significance (P =0.802). In the lesions-based data set, the top three classifiers of combined sequences in terms of the AUC were RF (0.931), logistic regression (0.922) and GNB (0.922). These three classifiers were significantly different from. The results of this experiment show that radiomics has a high diagnostic efficiency for prostate lesions. The RF classifier generally performed better overall than the other classifiers in the experiment. The XGBoost and logistic regression models also had high classification value in the lesions-based data set.
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影响因子：
5.2
作者：
Bourbonne V;Jaouen V;Nguyen TA;Tissot V;Doucet L;Hatt M;Visvikis D;Pradier O;Valéri A;Fournier G;Schick U
通讯作者：
Schick U
影响因子：
5.2
作者：
Fiz F;Viganò L;Gennaro N;Costa G;La Bella L;Boichuk A;Cavinato L;Sollini M;Politi LS;Chiti A;Torzilli G
通讯作者：
Torzilli G
影响因子：
8.2
作者：
Stabile, Armando;Dell'Oglio, Paolo;Briganti, Alberto
通讯作者：
Briganti, Alberto
惠勒放射学分期系统：利用多参数磁共振成像改善前列腺癌局部分期的回顾性队列分析
影响因子：
--
作者：
Russo, Filippo;Manfredi, Matteo;Porpiglia, Francesco
通讯作者：
Porpiglia, Francesco
影响因子：
--
作者：
Salvaggio, Giuseppe;Calamia, Mauro;Lagalla, Roberto
通讯作者：
Lagalla, Roberto