Peritumoral ultrasound-driven multimodal AI model for predicting lymphovascular space invasion and prognosis in cervical cancer.

purposeAccurate preoperative assessment of lymphovascular space invasion (LVSI) in cervical cancer (CC) remains challenging despite its importance for treatment planning and risk stratification. This study aimed to develop and evaluate a hybrid model integrating peritumoral ultrasound radiomics features, deep learning features, and clinical data for preoperative prediction of LVSI in CC. Additionally, we assessed the model's prognostic value for disease-free survival (DFS) and explored LVSI-associated molecular pathways using data from The Cancer Genome Atlas (TCGA).

methodsIn a retrospective cohort (n = 241) and a prospective cohort (n = 51), radiomics features were extracted from tumor and peritumoral regions of interest on ultrasound images. Least absolute shrinkage and selection operator regression and machine learning classifiers generated a radiomics score (Rad-score). Simultaneously, transfer learning with pre-trained convolutional neural networks constructed a deep learning score (DL-score). A hybrid model integrating clinical factors, Rad-score, and DL-score was developed. Model performance was evaluated using the area under the curve (AUC). Furthermore, prognostic value (Kaplan-Meier analysis), model interpretability using Shapley Additive Explanations (SHAP), and biological pathways were assessed.

resultsOverall, 292 women were included: 188 in the training set, 53 in the internal validation set, and 51 in the prospective testing set. The hybrid model achieved the highest performance (AUC: 0.79, 95%CI: 0.66-0.92; sensitivity: 0.85, 95%CI: 0.71-0.98) in the prospective testing set, outperforming the single-modality models. Exploratory survival analysis showed shorter DFS in the high-risk group (P < 0.05). SHAP analysis identified the DL-score as the primary predictor. Biologically, the Wnt/β-catenin pathway was significantly enriched in LVSI-positive patients.

conclusionsThe hybrid model, evaluated in a prospective cohort, showed potential for preoperative LVSI prediction and DFS risk stratification in CC. TCGA analysis showed Wnt/β-catenin enrichment in LVSI-positive tumors, providing complementary biological context. Further validation in larger independent multicenter cohorts is needed before clinical application.