Zhongguo aizheng zazhi (Jun 2021)
Prediction of lymph node metastasis of cervical cancer based on multi-sequence MRI and multi-system imaging omics model
Abstract
Background and purpose: It is of great clinical value to search for early biomarkers that can be used to accurately evaluate lymph node metastasis before surgery. This study aimed to investigate the value of magnetic resonance imaging (MRI) omics parameters in predicting cervical cancer lymph node metastasis, and to establish and verify an imaging omics model for preoperative prediction of cervical cancer lymph node metastasis. Methods: The clinical data of 202 patients with non lymph node metastasis and lymph node metastasis of cervical cancer confirmed by postoperative pathological examination in Fudan University Shanghai Cancer Center from June 2015 to September 2019 were retrospectively analyzed. MRI images were selected as T2 weighted images (T2WI) and T1 contrast + (T1C+). Itk-snap software was used for three-dimensional manual segmentation of cervical cancer tumor regions. Through Pyradiomics, an open source Python package, and a Python programming platform Jupyter, imaging omics features were extracted through ten image type systems and 6 feature systems. Among a total of 202 patients with cervical cancer, 104 had no lymph node metastasis, and 98 had lymph node metastasis. Imaging features were extracted from each patient in each group, including 1 923 features from the lymph node metastasis group and no lymph node metastasis group of T2WI sequence, 1 923 features from the lymph node metastasis group and no lymph node metastasis group of T1C+ sequence, and 3 846 features from the lymph node metastasis group and no lymph node metastasis group of T2WI combined with T2WI-T1C+ sequence. Imaging omics label was established and validated by machine learning model. Finally, area under curve (AUC), accuracy, positive predictive value (PPV) and negative predictive value (NPV) of the training set and the test set were used as the quantitative performance of the imaging omics label. Results: The T2WI sequence selected the features in the first 14 for classifier training, with the AUC of the training set=0.810 and the AUC of the test set =0.773. For T1C+ sequence, the first 16 features of feature sequencing were selected for classifier training, with AUC=0.819 in the training set and AUC=0.781 in the test set. In T2WI combined with T1C+sequence, the first 16 features of feature sequencing were selected for classifier training, with AUC=0.841 in the training set and AUC=0.803 in the test set. Conclusion: T2WI combined with T1C+ sequential imaging omics model has a good efficacy in predicting lymph node metastasis of early cervical cancer.
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