AI Article Synopsis
- Cervical cancer (CC) is a significant health issue for women, making the prediction of lymph node metastasis (LNM) crucial for treatment planning; this study focuses on creating a predictive model using MRI radiomics to identify LNM in CC patients.
- The research involved 124 patients, splitting them into training (86) and testing (38) groups, extracting key MRI features to develop a scoring model that incorporates crucial risk factors related to LNM, with validation done on the testing cohort.
- The model demonstrated strong predictive capabilities, achieving a high accuracy rate (AUC of 0.923) in the training group and a respectable performance (AUC of 0.82) in the testing group, indicating its
Article Abstract
Background: Cervical cancer (CC) is a common malignancy of the female reproductive tract, and preoperative prediction of lymph node metastasis (LNM) is essential. This study aims to design and validate a magnetic resonance imaging (MRI) radiomics-based predictive model capable of detecting LNM in patients diagnosed with CC.
Methods: This retrospective analysis incorporated 86 and 38 CC patients into the training and testing groups, respectively. Radiomics features were extracted from MRI T2WI, T2WI-SPAIR, and axial apparent diffusion coefficient (ADC) sequences. Selected features identified in the training group were then used to construct a radiomics scoring model, with relevant LNM-related risk factors having been identified through univariate and multivariate logistic regression analyses. The resultant predictive model was then validated in the testing cohort.
Results: In total, 16 features were selected for the construction of a radiomics scoring model. LNM-related risk factors included worse differentiation (P < 0.001), more advanced International Federation of Gynecology and Obstetrics (FIGO) stages (P = 0.03), and a higher radiomics score from the combined MRI sequences (P = 0.01). The equation for the predictive model was as follows: -0.0493-2.1410 × differentiation level + 7.7203 × radiomics score of combined sequences + 1.6752 × FIGO stage. The respective area under the curve (AUC) values for the T2WI radiomics score, T2WI-SPAIR radiomics score, ADC radiomics score, combined sequence radiomics score, and predictive model were 0.656, 0.664, 0.658, 0.835, and 0.923 in the training cohort, while these corresponding AUC values were 0.643, 0.525, 0.513, 0.826, and 0.82 in the testing cohort.
Conclusions: This MRI radiomics-based model exhibited favorable accuracy when used to predict LNM in patients with CC. Relative to the use of any individual MRI sequence-based radiomics score, this predictive model yielded superior diagnostic accuracy.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10873981 | PMC |
| http://dx.doi.org/10.1186/s12957-024-03333-5 | DOI Listing |
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