Abdominal radiology (New York) · 2026
Peritumoral ultrasound-driven multimodal AI model for predicting lymphovascular space invasion and prognosis in cervical cancer.
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Shen Hui, Jiang Yuting, Sun Yueting, Zheng Qiao, Zhang Lihe, He Miao, Wu Lihong, Liu Junfeng, Du Liu, Xie Hongning
Аннотация
PURPOSE: Accurate 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).
METHODS: In 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.
RESULTS: Overall, 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.
CONCLUSIONS: The 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.
METHODS: In 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.
RESULTS: Overall, 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.
CONCLUSIONS: The 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.