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• Clinical Article •
Predicting lymph-vascular space invasion in cervical cancer based on MR-T2WI with deep learning and radiomic features combined with clinical features
LIN Baojin  LONG Xianfeng  WU Zhaoxia  LIANG Lili  LU Zihong  GAN Wutian  ZHU Chaohua 

Cite this article as: LIN B J, LONG X F, WU Z X, et al. Predicting lymph-vascular space invasion in cervical cancer based on MR-T2WI with deep learning and radiomic features combined with clinical features[J]. Chin J Magn Reson Imaging, 2024, 15(3): 130-136. DOI:10.12015/issn.1674-8034.2024.03.021.


[Abstract] Objective To explore the value of preoperative prediction of cervical cancer lymph-vascular space invasion (LVSI) by combining deep transfer learning features based on MR-T2WI, radiomic features, and clinical characteristics.Materials and Methods Data of 178 patients with cervical cancer by postoperative pathology, including 70 cases with LVSI (+) and 108 cases with LVSI (-) were retrospectively analyzed. The patients were divided into training set [n=142, including 54 LVSI (+) and 88 LVSI (-)] and test set [n=36, including 16 LVSI (+) and 20 LVSI (-)] at a ratio of 8∶2. Univariate logistic regression analysis was conducted on clinical factors to identify independent predictors for LVSI (+) cases. The deep transfer learning (DTL) method and traditional radiomics methods were used to extract the DTL features and radiomics features from the lesions in the sagittal T2WI images. This led to the construction of a deep transfer learning feature dataset, a radiomics feature dataset, and a dataset that merges the DTL features with the radiomics features. Each feature dataset in the training set underwent feature dimension reduction using t-tests, Pearson analysis, and least absolute shrinkage and selection operator (LASSO) regression. The best of these were used to construct radiomics models [radiomics (Rad) model, DTL model, Fusion model of Rad and DTL features (Rad+DTL) model], and the optimal radiomics model was selected. A joint model was constructed based on the best model's radiomics score and independent clinical factors, and a nomogram was drawn. The calibration of the model was evaluated using calibration curves, and the application value of the model was assessed using decision curve analysis.Results Lymph node metastasis and the neutrophil-to-lymphocyte ratio were identified as independent predictors (P<0.05) with LVSI (+). The Rad+DTL model was determined as the optimal radiomics model. The combined model exhibited a higher AUC in the training set compared to the Rad+DTL model (0.984 vs. 0.966, P<0.05), and in the testing set (0.912 vs. 0.759, P=0.05). The combined model showed higher calibration accuracy and greater clinical net benefit.Conclusions The combination of DTL features from MR-T2WI, radiomics features, and clinical characteristics can effectively predict LVSI in cervical cancer.
[Keywords] cervical cancer；lymph-vascular space invasion；radiomics；magnetic resonance imaging；deep transfer learning

LIN Baojin1, 2   LONG Xianfeng1   WU Zhaoxia2   LIANG Lili1   LU Zihong3   GAN Wutian1   ZHU Chaohua1*  

1 Department of Radiation Therapy Physics Laboratory, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, China

2 Tsinghua University, Beijing 100084, China

3 Department of Radiation Therapy, Peking University Cancer Hospital, Beijing 100142, China

Corresponding author: ZHU C H, E-mail: th0624@163.com

Conflicts of interest   None.

Received  2023-11-30
Accepted  2024-02-23
DOI: 10.12015/issn.1674-8034.2024.03.021
Cite this article as: LIN B J, LONG X F, WU Z X, et al. Predicting lymph-vascular space invasion in cervical cancer based on MR-T2WI with deep learning and radiomic features combined with clinical features[J]. Chin J Magn Reson Imaging, 2024, 15(3): 130-136. DOI:10.12015/issn.1674-8034.2024.03.021.

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