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• Clinical Article •
A neural network radiomics model for diagnosing lymph node metastasis in cervical cancer
LING Rennan  YANG Ruofeng  YI Qinqin  RAO Zibin  YANG Yi  JIN Hongtao  CHENG Lixin 

Cite this article as: Ling RN, Yang RF, Yi QQ, et al. A neural network radiomics model for diagnosing lymph node metastasis in cervical cancer[J]. Chin J Magn Reson Imaging, 2021, 12(10): 16-21. DOI:10.12015/issn.1674-8034.2021.10.004.


[Abstract] Objective To construct a noninvasive multiparametric MRI-based radiomics model to predict lymph node metastasis in cervical cancer. Materials andMethods One hundred and seventy-eight cases of cervical cancer were analyzed retrospectively. While 9 clinical-pathological features were extracted, and 3 features were extracted into the model by analysis of variance (ANOVA). The volume of interest (VOI) was drawn by two observers respectively. Finally, 428 radiomics features of every tumor sample were extracted. Radiomics characteristics combined with various clinical-pathological characteristics were used to build the models of 428, 437 and 431 dimensions. The neural network model and support vector machine model are constructed and evaluated by torch and sklearn of Python library. Intraclass correlation coefficient (ICC) was used to evaluate inter observer reliability. Classification accuracy, sensitivity, specificity and area under the receiver operating characteristics curve (AUC) were used to measure the performance of the model. Youden index evaluated the performance of a dichotomous diagnostic test and the authenticity, by metrics.roc in sklearn_ROC curve.Results The average ICC was 0.819, which was highly reliable of the radiomics features performed by the two radiologists. The average ICC was 0.796, which was highly reproducibility of the radiomics features performed by one radiologist. The AUC of 431 dimension neural network model in test was 0.882, and the accuracy, sensitivity and specificity were 0.810, 0.840 and 0.741 respectively, which are better than other models.Conlusions The multiparametric MRI radiomics model based on neural network can be leveraged as a noninvasive diagnosis marker to predict LMN in cervical cancer.
[Keywords] cervical cancer；lymph node；metastasis；radiomics；machine learning

LING Rennan1   YANG Ruofeng2   YI Qinqin1   RAO Zibin1   YANG Yi3   JIN Hongtao4   CHENG Lixin5*  

1 Department of Radiology, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, 1st Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518020, China

2 John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China

3 Department of Gynecology,Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, 1st Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518020, China

4 Department of Pathology, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, 1st Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518020, China

5 Department of Critical Care Medicine, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, 1st Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518020, China

Cheng LX, E-mail: easonlcheng@gmail.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Medical Scientific Research Foundation of Guangdong Province of China (NO. B2020004)
Received  2021-05-07
Accepted  2021-07-01
DOI: 10.12015/issn.1674-8034.2021.10.004
Cite this article as: Ling RN, Yang RF, Yi QQ, et al. A neural network radiomics model for diagnosing lymph node metastasis in cervical cancer[J]. Chin J Magn Reson Imaging, 2021, 12(10): 16-21. DOI:10.12015/issn.1674-8034.2021.10.004.

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