Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study

Wang, Shuxing1; Liu, Xiaowen1; Wu, Yu2; Jiang, Changsi3; Luo, Yan3; Tang, Xue3; Wang, Rui3; Zhang, Xiaochun3; Gong, Jingshan3

2023-10-26

10.3389/fonc.2023.1252074

FRONTIERS IN ONCOLOGY   影响因子和分区

2234-943X

IntroductionLymphovascular space invasion (LVSI) is associated with lymph node metastasis and poor prognosis in cervical cancer. In this study, we investigated the potential of radiomics, derived from magnetic resonance (MR) images using habitat analysis, as a non-invasive surrogate biomarker for predicting LVSI in cervical cancer.MethodsThis retrospective study included 300 patients with cervical cancer who underwent surgical treatment at two centres (centre 1 = 198 and centre 2 = 102). Using the k-means clustering method, contrast-enhanced T1-weighted imaging (CE-T1WI) images were segmented based on voxel and entropy values, creating sub-regions within the volume ofinterest. Radiomics features were extracted from these sub-regions. Pearson correlation coefficient and least absolute shrinkage and selection operator LASSO) regression methods were used to select features associated with LVSI in cervical cancer. Support vector machine (SVM) model was developed based on the radiomics features extracted from each sub-region in the training cohort.ResultsThe voxels and entropy values of the CE-T1WI images were clustered into three sub-regions. In the training cohort, the AUCs of the SVM models based on radiomics features derived from the whole tumour, habitat 1, habitat 2, and habitat 3 models were 0.805 (95% confidence interval [CI]: 0.745-0.864), 0.873(95% CI: 0.824-0.922), 0.869 (95% CI: 0.821-0.917), and 0.870 (95% CI: 0.821-0.920), respectively. Compared with whole tumour model, the predictive performances of habitat 3 model was the highest in the external test cohort (0.780 [95% CI: 0.692-0.869]).ConclusionsThe radiomics model based on the tumour sub-regional habitat demonstrated superior predictive performance for an LVSI in cervical cancer than that of radiomics model derived from the whole tumour.

cervical cancer LVSI radiomics habitat machine learning

SCI

英语

通讯

Oncology

Oncology

WOS:001099239900001

FRONTIERS MEDIA SA

Web of Science

1.Jinan Univ, Clin Med Coll 2, Shenzhen, Peoples R China
2.Guangzhou Women & Childrens Med Ctr, Dept Radiol, Guangzhou, Peoples R China
3.Southern Univ Sci & Technol, Clin Med Coll Jinan Univ 2, Shenzhen Peoples Hosp, Dept Radiol,Affiliated Hosp 1, Shenzhen, Peoples R China

Wang, Shuxing,Liu, Xiaowen,Wu, Yu,et al. Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study[J]. FRONTIERS IN ONCOLOGY,2023,13.

Wang, Shuxing.,Liu, Xiaowen.,Wu, Yu.,Jiang, Changsi.,Luo, Yan.,...&Gong, Jingshan.(2023).Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study.FRONTIERS IN ONCOLOGY,13.

Wang, Shuxing,et al."Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study".FRONTIERS IN ONCOLOGY 13(2023).