Cervical cancer (CC) poses a significant threat to women’s health, emphasizing the critical need for accurate preoperative prediction of lymph node metastasis (LNM).

Tao Wang and the team aimed to focus on developing and validating a novel predictive model based on MRI radiomics. Their aim was to enhance the ability to detect LNM in CC patients, thereby improving treatment planning and patient outcomes.

Researchers performed an inclusive analysis, incorporating 86 CC patients into the training group and 38 into the testing group retrospectively. Radiomics features were extracted from MRI T2WI, T2WI-SPAIR, and axial apparent diffusion coefficient (ADC) sequences.

Selected features from the training group were utilized to construct a radiomics scoring model. Relevant LNM-related risk factors were identified through univariate and multivariate logistic regression analyses. The resulting predictive model underwent validation in the testing cohort.

About 16 features were chosen for constructing a radiomics scoring model, revealing significant associations between LNM and worse differentiation (P < 0.001), advanced International Federation of Gynecology and Obstetrics (FIGO) stages (P = 0.03), and elevated radiomics score from combined MRI sequences (P = 0.01).

The predictive model’s equation was determined as -0.0493-2.1410 × differentiation level + 7.7203 × radiomics score of combined sequences + 1.6752 × FIGO stage. In the training cohort, the respective area under the curve (AUC) values were 0.656 for the T2WI radiomics score, 0.664 for T2WI-SPAIR radiomics score, 0.658 for ADC radiomics score, 0.835 for combined sequence radiomics score, and 0.923 for the predictive model. In the testing cohort, these corresponding AUC values were: 0.643, 0.525, 0.513, 0.826, and 0.82, respectively.

The study concluded that the MRI radiomics-based model demonstrated favorable accuracy in predicting lymph node metas (LNM) in cervical cancer patients. Relative to individual MRI sequence-based radiomics scores, this predictive model showed superior diagnostic accuracy.

The study was sponsored by the Program of Jiangsu Traditional Chinese Medicine Development.

Source: https://pubmed.ncbi.nlm.nih.gov/38365759/

Wang T, Li YY, Ma NN, et al. (2024). “A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer.” World J Surg Oncol. 2024 Feb 17;22(1):55. doi: 10.1186/s12957-024-03333-5. PMID: 38365759; PMCID: PMC10873981.