ArticleJournal of thoracic disease2026
Development and validation of a collagen score-based prediction model for neoadjuvant chemoimmunotherapy efficacy in locally advanced esophageal squamous cell carcinoma.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The relationship between collagen features (CFs) in the tumor microenvironment (TME) of esophageal cancer (EC) and the therapeutic response to neoadjuvant chemoimmunotherapy (nCIT) remains unclear. This study aimed to develop and validate a novel model based on CFs to predict the therapeutic response of patients with locally advanced EC to nCIT. Methods: In this retrospective study, 79 newly diagnosed patients with resectable locally advanced EC were enrolled and randomly divided into a training cohort (54 patients) and a validation cohort (25 patients) at a 7:3 ratio. Multiphoton imaging (MPI) was used to quantitatively analyze collagen in pretreatment biopsy tissues. Least absolute shrinkage and selection operator (LASSO) regression was applied to select significant CFs and calculate the collagen score. Univariate and multivariate analyses were performed to verify the predictive value of the collagen score for a good pathological response (GR). Results: LASSO regression identified collagen-related parameters in EC biopsy specimens: F1 (collagen percentage area), F2 (fiber density), F8 (mean of histograms), F9 (mean of histograms), F102 (Gabor_variance_90°_1 scale), F117 (Gabor_mean_150°_2 scale), and F138 (Gabor_variance_90°_4 scale), on the basis of which a formula was established to calculate the collagen score. Univariate analysis revealed a significant difference in the collagen score between the GR and non-GR groups (P<0.001), and multivariate analysis confirmed that the collagen score was an independent risk factor (P=0.001). These results were validated in the validation cohort (P=0.009 for univariate analysis, P=0.02 for multivariate analysis). For predicting the GR, the area under the curve (AUC) values of the collagen score in the experimental and validation cohorts were 0.819 [95% confidence interval (CI): 0.704-0.935] and 0.820 (95% CI: 0.645-0.995), respectively. Conclusions: MPI can be used to quantify the TME of EC on the basis of a collagen score, which has a certain ability to predict the efficacy of nCIT.
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