AI Article Synopsis
- The study focuses on developing a radiomic signature from MRI scans to predict disease-free survival (DFS) in patients with locally advanced cervical cancer who received chemoradiotherapy.
- It involved analyzing MRI data from 263 patients and used statistical methods like LASSO and Cox regression to create a signature highlighting four key features associated with poorer DFS.
- Results showed that this radiomic signature performed better in predicting DFS compared to traditional clinical models, suggesting its potential as a valuable non-invasive prognostic tool for high-risk patients.
Article Abstract
Unlabelled: Prognostic biomarkers that can reliably predict the disease-free survival (DFS) of locally advanced cervical cancer (LACC) are needed for identifying those patients at high risk for progression, who may benefit from a more aggressive treatment. In the present study, we aimed to construct a multiparametric MRI-derived radiomic signature for predicting DFS of LACC patients who underwent concurrent chemoradiotherapy (CCRT).
Methods: This multicenter retrospective study recruited 263 patients with International Federation of Gynecology and Obetrics (FIGO) stage IB-IVA treated with CCRT for whom pretreatment MRI scans were performed. They were randomly divided into two groups: primary cohort (n = 178) and validation cohort (n = 85). The LASSO regression and Cox proportional hazard regression were conducted to construct the radiomic signature (RS). According to the cutoff of the RS value, patients were dichotomized into low- and high-risk groups. Pearson's correlation and Kaplan-Meier analysis were conducted to evaluate the association between the RS and DFS. The RS, the clinical model incorporating FIGO stage and lymph node metastasis by the multivariate Cox proportional hazard model, and a combined model incorporating RS and clinical model were constructed to estimate DFS individually.
Results: The final radiomic signature consisted of four radiomic features: T2W, ADC, ADC, and ADC. Higher RS was significantly associated with worse DFS in the primary and validation cohorts (both p<0.001). The RS demonstrated better prognostic performance in predicting DFS than the clinical model in both cohorts (C-index, 0.736-0.758 for RS, and 0.603-0.649 for clinical model). However, the combined model showed no significant improvement (C-index, 0.648, 95% CI, 0.571-0.685).
Conclusions: The present study indicated that the multiparametric MRI-derived radiomic signature could be used as a non-invasive prognostic tool for predicting DFS in LACC patients.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8821662 | PMC |
| http://dx.doi.org/10.3389/fonc.2021.812993 | DOI Listing |
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