ArticleFrontiers in oncology2024
Radiomic and dosimetric parameter-based nomogram predicts radiation esophagitis in patients with non-small cell lung cancer undergoing combined immunotherapy and radiotherapy.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- A multicenter study of automatic segmentation-based multimodal fusion integrating radiomics, deep learning, and clinical parameters for prostate cancer detection.Abdominal radiology (New York) · 2026Article
- Immune cell involvement in ulcer repair in radiation esophagitis and its role in the TGF-β1/p38MAPK/FN axis (Review).Molecular and clinical oncology · 2025Review
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Abstract
Background: The combination of immune checkpoint inhibitors (ICIs) and radiotherapy (RT) may increase the risk of radiation esophagitis (RE). This study aimed to establish and validate a new nomogram to predict RE in patients with non-small cell lung cancer (NSCLC) undergoing immunochemotherapy followed by RT (ICI-RT). Methods: The 102 eligible patients with NSCLC treated with ICI-RT were divided into training (n = 71) and validation (n = 31) cohorts. Clinicopathologic features, dosimetric parameters, inflammatory markers, and radiomic score (Rad-score) were included in the univariate logistic regression analysis, and factors with Results: A total of 38 (37.3%) patients developed RE. Univariate and multivariate analyses identified the following independent predictors of RE: a maximum dose delivered to the esophagus >58.4 Gy, a mean esophagus dose >13.3 Gy, and the Rad-score. The AUCs of the nomogram in the training and validation cohorts were 0.918 (95% confidence interval [CI]: 0.824-1.000) and 0.833 (95% CI: 0.697-0.969), respectively, indicating good discrimination. The calibration curves showed good agreement between the predicted occurrence of RE and the actual observations. The decision curve showed a satisfactory positive net benefit at most threshold probabilities, suggesting a good clinical effect. Conclusions: We developed and validated a nomogram based on imaging histological features and RT dosimetric parameters. This model can effectively predict the occurrence of RE in patients with NSCLC treated using ICI-RT.
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