Frontiers in immunology · 2026
An integrated multimodal model for early prediction of high-risk recurrence phenotype indicative of poor disease-free survival in stage IA NSCLC: a multicenter study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Lai Linqiang, Wang Yanji, Jiang Jiangle, Yang Dengfa, Wu Jinying, Xie Liangjun, Wu Jiahao, Mao Ruolan, Zhang Dengke, Wang Xiangxue, Ming Wenlong, Tu Jianfei
Аннотация
OBJECTIVES: To develop and validate a preoperative multimodal model that predicts a high-risk recurrence phenotype indicative of poor disease-free survival (DFS), in order to stratify patients with stage IA non-small cell lung cancer (NSCLC).
MATERIALS AND METHODS: This retrospective multicenter study enrolled 342 stage IA NSCLC patients from three independent centers. The high-risk recurrence phenotype (indicative of poor DFS) was defined by postoperative pathology as the presence of spread through air spaces (STAS), lymphovascular invasion (LVI), a predominant solid/micropapillary/complex glandular pattern, or a > 5% solid/micropapillary component. Deep learning features were extracted from preoperative biopsy whole-slide images (WSI) using the UNI foundation model, and CT morphological and textural features were extracted from preoperative chest CT. An early-fusion multimodal model integrating clinical, radiomics, and pathomics features was developed and evaluated with five-fold cross-validation. The Kaplan-Meier method with log-rank tests was used to assess associations between the model-predicted risk and DFS. Logistic regression identified clinical predictors of high-risk pathology. Model interpretability and clinical utility were examined with SHapley Additive exPlanations (SHAP) and calibration analysis, respectively.
RESULTS: The multimodal model achieved higher discriminative performance than each single-modality model in both the internal and external test sets. In the internal test set, it yielded an AUC of 0.76 (95% CI 0.58-0.90); in external validation, AUCs were 0.69 (95% CI 0.55-0.82) in Center 2 and 0.86 (95% CI 0.74-0.96) in Center 3. Multivariable analysis identified solid tumor density as the only independent predictor (OR = 5.13, 95% CI 1.53-17.24; P = 0.008). Model-stratified high-risk patients showed significantly inferior DFS in both the training (2-year DFS 78% vs. 99%; log-rank P < 0.0001) and external (3-year DFS 86% vs. 100%; log-rank P = 0.003) cohorts.
CONCLUSION: The multimodal model consistently predicted the high-risk recurrence phenotype across multiple centers. This phenotype may serve as a pragmatic indicator of poor DFS to guide earlier individualized treatment decisions, including adjuvant therapy and intensified surveillance, in stage IA NSCLC.
MATERIALS AND METHODS: This retrospective multicenter study enrolled 342 stage IA NSCLC patients from three independent centers. The high-risk recurrence phenotype (indicative of poor DFS) was defined by postoperative pathology as the presence of spread through air spaces (STAS), lymphovascular invasion (LVI), a predominant solid/micropapillary/complex glandular pattern, or a > 5% solid/micropapillary component. Deep learning features were extracted from preoperative biopsy whole-slide images (WSI) using the UNI foundation model, and CT morphological and textural features were extracted from preoperative chest CT. An early-fusion multimodal model integrating clinical, radiomics, and pathomics features was developed and evaluated with five-fold cross-validation. The Kaplan-Meier method with log-rank tests was used to assess associations between the model-predicted risk and DFS. Logistic regression identified clinical predictors of high-risk pathology. Model interpretability and clinical utility were examined with SHapley Additive exPlanations (SHAP) and calibration analysis, respectively.
RESULTS: The multimodal model achieved higher discriminative performance than each single-modality model in both the internal and external test sets. In the internal test set, it yielded an AUC of 0.76 (95% CI 0.58-0.90); in external validation, AUCs were 0.69 (95% CI 0.55-0.82) in Center 2 and 0.86 (95% CI 0.74-0.96) in Center 3. Multivariable analysis identified solid tumor density as the only independent predictor (OR = 5.13, 95% CI 1.53-17.24; P = 0.008). Model-stratified high-risk patients showed significantly inferior DFS in both the training (2-year DFS 78% vs. 99%; log-rank P < 0.0001) and external (3-year DFS 86% vs. 100%; log-rank P = 0.003) cohorts.
CONCLUSION: The multimodal model consistently predicted the high-risk recurrence phenotype across multiple centers. This phenotype may serve as a pragmatic indicator of poor DFS to guide earlier individualized treatment decisions, including adjuvant therapy and intensified surveillance, in stage IA NSCLC.