Prediction of parametrial invasion of early cervical squamous cell carcinoma: combining apparent diffusion coefficient and greatest primary lesion dimension with hematological variables

Background: Cervical cancer is the fourth most common malignant tumor in women, with cervical squamous cell carcinoma (CSCA) being the most prevalent subtype. Parametrial invasion (PMI) significantly impacts treatment decisions for CSCA patients. Current methods for predicting PMI rely on postoperative histopathological evaluation, which may delay optimal therapy. Combining imaging and hematological markers could improve preoperative risk stratification. Methods: This retrospective study included 134 patients with early stage CSCA who underwent 3.0 T magnetic resonance imaging (MRI) and preoperative hematological examination. We assessed the apparent diffusion coefficient (ADC) value, greatest tumor dimension, squamous cell carcinoma antigen (SCC-Ag) level, neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR). Patients were categorized into PMI-positive (n = 22) and PMI-negative (n = 112) groups based on postoperative findings. Receiver operating characteristic (ROC) curves evaluated individual and combined parameters for predicting PMI. Results: Among the 134 patients, PMI was detected in 22 cases, which represented 16.42% of the total population. The ADC value in the PMI-positive group was lower than that in the PMI-negative group, while the greatest dimension and the median SCC-Ag values were higher in the PMI-positive group than in the PMI-negative group. However, there was no significant difference in NLR or PLR between the two groups. The areas under the curve for ADC, greatest dimension, SCC-Ag level, and all three parameters combined were 0.787, 0.765, 0.673 and 0.880, respectively, for predicting PMI. Conclusions: Combining ADC, greatest dimension, and SCC-Ag provides superior accuracy for predicting PMI in early CSCA. This multimodal approach may enable preoperative risk assessment, potentially guiding individualized treatment strategies and reducing unnecessary costs.