Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

Abstract

Objective: To develop and validate a radiomics predictive model based on multiparameter MR imaging features and clinical features to predict lymph node metastasis (LNM) in patients with cervical cancer. Material and Methods: A total of 168 consecutive patients with cervical cancer from two centers were enrolled in our retrospective study. A total of 3,930 imaging features were extracted from T2-weighted (T2W), ADC, and contrast-enhanced T1-weighted (cT1W) images for each patient. Four-step procedures, mainly minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) regression, were applied for feature selection and radiomics signature building in the training set from center I (n = 115). Combining clinical risk factors, a radiomics nomogram was then constructed. The models were then validated in the external validation set comprising 53 patients from center II. The predictive performance was determined by its calibration, discrimination, and clinical usefulness. Results: The radiomics signature derived from the combination of T2W, ADC, and cT1W images, composed of six LN-status-related features, was significantly associated with LNM and showed better predictive performance than signatures derived from either of them alone in both sets. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN-negative subgroup, with AUC of 0.825 (95% CI: 0.732-0.919). The radiomics nomogram that incorporated radiomics signature and MRI-reported LN status also showed good calibration and discrimination in both sets, with AUCs of 0.865 (95% CI: 0.794-0.936) and 0.861 (95% CI: 0.733-0.990), respectively. Decision curve analysis confirmed its clinical usefulness. Conclusion: The proposed MRI-based radiomics nomogram has good performance for predicting LN metastasis in cervical cancer and may be useful for improving clinical decision making.

Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer

“…Radiomics has been involved in many aspects of cervical cancer, including prediction of tumor staging (21,22), histological grading (23,24), lymphovascular space invasion (LVI) (25), LNM (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36), treatment response (37)(38)(39)(40)(41)(42), and outcome (43)(44)(45)(46)(47)(48) based on multimodal imaging tools (e.g., CT, MRI, and PET/CT). In clinical practice, the issues of most concern may be the evaluation of LNM, treatment response, and survival prediction.…”

“…Song et al combined the Radscore and morphological features of lymph nodes to assess the LNM in cervical cancer patients (26). Some studies developed radiomic models based on Radscore and clinical factors (e.g., MRIreported lymph node status and FIGO stage) for predicting LNM in cervical cancer (27)(28)(29)(30)(31)(32)(33)(34)(35)(36). Treatment response to neoadjuvant or concurrent chemotherapy may have a significant impact on patient management by identifying tailored approaches for patient subgroups to achieve a better clinical outcome.…”

Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients

“…It can provide more metabolic and biological information than conventional imaging methods (17). Previous studies have shown that radiomic features derived from MRI or computed tomography (CT) data have the potential to predict LN metastasis in such malignancies as breast cancer, cervical cancer, and bladder cancer (18)(19)(20). For the rectal cancer, some previous studies demonstrated that the histogram features from T2-weighted imaging (T2WI) and the texture features from apparent diffusion coefficient (ADC) maps can help predict lymph node metastasis (21,22).…”

Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images

“…Traditional medical imaging mode based on morphological changes limits the image analysis to a range of visual judgement. However, the resolution of human eyes is limited; thus, detecting all fine and tiny features in images is difficult [ 11 – 13 ]. The predictive capability of traditional imaging techniques for tumour response to treatment is still extremely limited.…”