Predictive value and potential association of PET/CT radiomics on lymph node metastasis of cervical cancer

Abstract

Objective: Due to the information-rich nature of positron emission tomography/computed tomography (PET/CT) images, we hope to explore radiomics features that could distinguish metastatic lymph nodes from hypermetabolic benign lymph nodes, in addition to conventional indicators. Methods: PET/CT images of 106 patients with early-stage cervical cancer from 2019 to 2021 were retrospectively analyzed. The tumor lesions and lymph node regions of PET/CT images were outlined with SeeIt, and then radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select features. The final selected radiomics features of lymph nodes were used as predictors to construct a machine learning model to predict lymph node metastasis. Results: We determined two morphological coefficient characteristics of cervical lesions (shape - major axis length and shape - mesh volume), one first order characteristics of lymph nodes (first order - 10 percentile) and two grey-level co-occurrence matrix (GLCM) characteristics of lymph nodes (GLCM - id and GLCM - inverse variance) were closely related to lymph node metastasis. Finally, a neural network was constructed based on the radiomic features of the lymph nodes. The area under the curve of receiver operating characteristic (AUC-ROC) of the model was 0.983 in the training set and 0.860 in the test set. Conclusion: We constructed and demonstrated a neural network based on radiomics features of PET/CT to evaluate the risk of single lymph node metastasis in early-stage cervical cancer.