MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy

Abstract

Background To investigate the magnetic resonance imaging (MRI)-based radiomics value in predicting the survival of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT). Methods A total of 185 patients (training group: n = 128; testing group: n = 57) with LACSC treated with CCRT between January 2014 and December 2018 were retrospectively enrolled in this study. A total of 400 radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient map, arterial- and delayed-phase contrast-enhanced MRI. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression was applied to select radiomics features and clinical characteristics that could independently predict progression-free survival (PFS) and overall survival (OS). The predictive capability of the prediction model was evaluated using Harrell’s C-index. Nomograms and calibration curves were then generated. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison. Results The radiomics score achieved significantly better predictive performance for the estimation of PFS (C-index, 0.764 for training and 0.762 for testing) and OS (C-index, 0.793 for training and 0.750 for testing), compared with the 2018 FIGO staging system (C-index for PFS, 0.657 for training and 0.677 for testing; C-index for OS, 0.665 for training and 0.633 for testing) and clinical-predicting model (C-index for PFS, 0.731 for training and 0.725 for testing; C-index for OS, 0.708 for training and 0.693 for testing) (P < 0.05). The combined model constructed with T stage, lymph node metastasis position, and radiomics score achieved the best performance for the estimation of PFS (C-index, 0.792 for training and 0.809 for testing) and OS (C-index, 0.822 for training and 0.785 for testing), which were significantly higher than those of the radiomics score (P < 0.05). Conclusions The MRI-based radiomics score could provide effective information in predicting the PFS and OS in patients with LACSC treated with CCRT. The combined model (including MRI-based radiomics score and clinical characteristics) showed the best prediction performance.

Development and validation of a radiomics-based nomogram for the prediction of postoperative malnutrition in stage IB1-IIA2 cervical carcinoma

“…The combined nomogram prediction model incorporating radiomic features and clinical parameters yielded better performance (training cohort, AUC = 0.943; validation cohort, AUC = 0.923) than other prediction models (41). Similar studies have demonstrated that radiomics-based nomogram has robust performance in predicting lymph node metastasis (42) and survival (43) in patients with CC. Some limitations are present in this research.…”

Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram

“…Prior research has demonstrated the potential utility of radiology in predicting survival of cervical cancer patients, with reported accuracy higher than that of traditional clinical factors [31][32][33][34]. However, the sample sizes of these studies were relatively small and did not focus on early cervical cancer cases treated with surgery.…”

MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer

“…Furthermore, MRI-based CC radiomic tumor profiles have been linked to increased risk of recurrence or death in a few recent studies. [22][23][24][25][26] The latter studies (comprising 183-248 patients) report moderateto-high performance metrics for predicting disease-free/ progression-free survival based on radiomic signatures from various combinations of MRI-sequences (T2WI, DWI, and CE T1WI). Their reported AUCs (training/validation cohort) are in the range of 0.73-0.86/0.66-0.81 [22][23][24] and C-indexes (training/validation cohort) in the range of 0.74-0.79/0.67-0.81.…”

“…[15][16][17][18][19][20][21] Furthermore, MRIbased radiomic signatures have recently been recognized as valuable biomarkers for predicting recurrence and survival in CC patients. [22][23][24][25][26] The purpose of this study was to investigate whether pretreatment MRI radiomic whole-volume tumor profiling based on T2WI and DWI may aid in prognostication in CC. Furthermore, we aimed to compare the prognostic performance of radiomic signatures with conventional clinical markers and explore the potential added value of radiomic signatures for guiding therapeutic strategy in CC.…”