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.
Highlights
Cervical cancer is the fourth most common cancer worldwide and ranks second as a cause of cancer-related death among women in developing countries, including China [1, 2]
The radiomics signature derived from the combination of T2W, apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted (cT1W) images, composed of six LN-status-related features, was significantly associated with Lymph node metastasis (LNM) and showed better predictive performance than signatures derived from either of them alone in both sets
The radiomics nomogram that incorporated radiomics signature and Magnetic resonance imaging (MRI)-reported LN status showed good calibration and discrimination in both sets, with AUCs of 0.865 and 0.861, respectively
Summary
Cervical cancer is the fourth most common cancer worldwide and ranks second as a cause of cancer-related death among women in developing countries, including China [1, 2]. Lymph node metastasis (LNM) is one of the important determinants for prognosis and treatment planning [3, 4]. Patients without LNM in early-stage cervical cancer show a high 5-year survival rate of 90%, while the 5-year survival rate rapidly deteriorated in patients with LNM, with only 65% [4, 5]. Radical hysterectomy and pelvic lymph node dissection (PLND) are the conventional curative treatment options for stage IB–IIA cervical cancers, recommended by the International Federation of Gynecology and Obstetrics (FIGO) guidelines. Approximately 10%−30% of patients with early-stage cervical cancer harbor LNM [6, 7]. Radical trachelectomy, an emerging fertility-sparing treatment for cervical cancer, was not eligible for patients with LNM [9]. Accurate prediction of LNM is crucial for treatment strategy decision and predicting prognosis of patients with cervical cancer
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