Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer.
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TLDR
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.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 0825 (95% CI: 0732-0919) The radiomics nomogram that incorporated radiomics signature and MRI-reported LN status also showed good calibration and discrimination in both sets, with AUCs of 0865 (95% CI: 0794-0936) and 0861 (95% CI: 0733-0990), 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 makingread more
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Journal ArticleDOI
Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients.
Chunli Li,Jiandong Yin +1 more
TL;DR: In this article, a radiomics nomogram, incorporating rad-score based on features from the T2WI and ADC images, and clinical factors, has favorable predictive performance for preoperative prediction of LN metastasis in patients with rectal cancer.
Journal ArticleDOI
Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer
Xijia Deng,Meiling Liu,Jianqing Sun,Min Li,Daihong Liu,Lan Li,Jiayang Fang,Xiaoxia Wang,Jiuquan Zhang +8 more
TL;DR: The presented radiomics prediction models demonstrated potential to noninvasively differentiate LNM and VEGF expression in cervical cancer.
Journal ArticleDOI
The role of lymph nodes in cervical cancer: incidence and identification of lymph node metastases-a literature review.
Ester P Olthof,Maaike A. van der Aa,Judit A. Adam,Lukas J.A. Stalpers,Hans H B Wenzel,Jacobus van der Velden,Constantijne H. Mom +6 more
TL;DR: In this paper, a review of the existing knowledge on the identification of lymph node metastases in cervical cancer is presented, where diffusion-weighted magnetic resonance imaging has the highest sensitivity and 2-deoxy-2-[18F]fluoro-Dglucose positron emission computed tomography the highest specificity.
Journal ArticleDOI
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.
TL;DR: In this paper, a random forest model was established and optimised based on the open source toolkit scikit-learn to evaluate its predictive capability of the treatment effect of neoadjuvant chemotherapy-radiation therapy on advanced cervical cancer.
Journal ArticleDOI
Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer.
Handong Li,Miaochen Zhu,Lian Jian,Feng Bi,Xiaoye Zhang,Chao Fang,Ying Wang,Jing Wang,Na-Yiyuan Wu,Xiaoping Yu +9 more
TL;DR: In this paper, the incremental value of radiomics when added to the FIGO stage in predicting overall survival (OS) in patients with cervical cancer was investigated, where the authors used Pearson correlation coefficient analysis and Relief were used to detect the most discriminatory features.
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