Yan Yan Yu1,2, Rui Zhang1, Rui Tong Dong1, Qi Yun Hu1, Tao Yu1, Fan Liu1, Ya Hong Luo1, Yue Dong1. 1. 1 Department of Radiology, Cancer Hospital of China Medical University, Liaoning cancer hospital & institute Shenyang , Liaoning , China. 2. 2 Graduate School of Dalian Medical University , Dalian, Liaoning , China.
Abstract
OBJECTIVES: To investigate the prediction value of a radiomics model based on apparent diffusion coefficient (ADC) maps for pelvic lymph node metastasis (PLNM) in patients with stage IB-IIA cervical squamous cell carcinoma (CSCC). METHODS: A total of 153 stage IB-IIA CSCC patients who underwent preoperative MRI including DWI from January 2015 to October 2017 were retrospectively studied and divided into a training cohort ( n = 102) and a validation cohort ( n = 51). Radiomics features were extracted from the ADC maps. The one-way ANOVA method, Mann-Whitney U test and Pearson's correlation analysis were used for selecting radiomics features. Logistic regression analyses were used to develop the model. ROC analyses were used to evaluate the prediction performance of the model. RESULTS: Clinical stage, tumor diameter, and MR-reported lymph node (LN) status were significantly associated with LN status ( p < 0.05 for both the training and validation cohorts). The radiomics model, which incorporated clinical stage, MR-reported LN status, and grey-level non-uniformity, showed good predictive performance in the training group (AUC 0.864; 95% CI, 0.782 - 0.924) and the validation group (AUC 0.870; 95% CI, 0.747 - 0.948). The performance of the radiomics model was significantly better than that of each predictive factor alone. CONCLUSION: The presented radiomics model, a non-invasive preoperative prediction tool, has the potential to have more predictive efficacy than clinical and radiological factors for differentiating between metastatic and non-metastatic lymph nodes. ADVANCES IN KNOWLEDGE: A radiomics model derived from the ADC maps of primary lesions demonstrated good performance for predicting PLNM in stage IB-IIA CSCC patients and may help to improve clinical decision-making.
OBJECTIVES: To investigate the prediction value of a radiomics model based on apparent diffusion coefficient (ADC) maps for pelvic lymph node metastasis (PLNM) in patients with stage IB-IIA cervical squamous cell carcinoma (CSCC). METHODS: A total of 153 stage IB-IIA CSCC patients who underwent preoperative MRI including DWI from January 2015 to October 2017 were retrospectively studied and divided into a training cohort ( n = 102) and a validation cohort ( n = 51). Radiomics features were extracted from the ADC maps. The one-way ANOVA method, Mann-Whitney U test and Pearson's correlation analysis were used for selecting radiomics features. Logistic regression analyses were used to develop the model. ROC analyses were used to evaluate the prediction performance of the model. RESULTS: Clinical stage, tumor diameter, and MR-reported lymph node (LN) status were significantly associated with LN status ( p < 0.05 for both the training and validation cohorts). The radiomics model, which incorporated clinical stage, MR-reported LN status, and grey-level non-uniformity, showed good predictive performance in the training group (AUC 0.864; 95% CI, 0.782 - 0.924) and the validation group (AUC 0.870; 95% CI, 0.747 - 0.948). The performance of the radiomics model was significantly better than that of each predictive factor alone. CONCLUSION: The presented radiomics model, a non-invasive preoperative prediction tool, has the potential to have more predictive efficacy than clinical and radiological factors for differentiating between metastatic and non-metastatic lymph nodes. ADVANCES IN KNOWLEDGE: A radiomics model derived from the ADC maps of primary lesions demonstrated good performance for predicting PLNM in stage IB-IIA CSCC patients and may help to improve clinical decision-making.
Authors: Jacques Ferlay; Isabelle Soerjomataram; Rajesh Dikshit; Sultan Eser; Colin Mathers; Marise Rebelo; Donald Maxwell Parkin; David Forman; Freddie Bray Journal: Int J Cancer Date: 2014-10-09 Impact factor: 7.396
Authors: Chintan Parmar; Ralph T H Leijenaar; Patrick Grossmann; Emmanuel Rios Velazquez; Johan Bussink; Derek Rietveld; Michelle M Rietbergen; Benjamin Haibe-Kains; Philippe Lambin; Hugo J W L Aerts Journal: Sci Rep Date: 2015-06-05 Impact factor: 4.379