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Erschienen in:
24.05.2022 | Head and Neck
CT-based radiomics signature analysis for evaluation of response to induction chemotherapy and progression-free survival in locally advanced hypopharyngeal carcinoma
Erschienen in: European Radiology | Ausgabe 11/2022
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Objectives
To establish and validate a CT radiomics model for prediction of induction chemotherapy (IC) response and progression-free survival (PFS) among patients with locally advanced hypopharyngeal carcinoma (LAHC).
Methods
One hundred twelve patients with LAHC (78 in training cohort and 34 in validation cohort) who underwent contrast-enhanced CT (CECT) scans prior to IC were enrolled. Least absolute shrinkage and selection operator (LASSO) was used to select the crucial radiomic features in the training cohort. Radiomics signature and clinical data were used to build a radiomics nomogram to predict individual response to IC. Kaplan–Meier analysis and log-rank test were used to evaluate ability of radiomics signature in progression-free survival risk stratification.
Results
The radiomics signature consisted of 6 selected features from the arterial and venous phases of CECT images and demonstrated good performance in predicting the IC response in both two cohorts. The radiomics nomogram showed good discriminative performance, and the C-index of nomogram was 0.899 (95% confidence interval (CI), 0.831–0.967) and 0.775 (95% CI, 0.591–0.959) in the training and validation cohorts, respectively. Survival analysis indicated that low-risk and high-risk groups defined by the value of radiomics signature had significant difference in PFS (3-year PFS 66.4% vs 29.7%, p < 0.001).
Conclusions
Multiparametric CT-based radiomics model could be useful for predicting treatment response and PFS in patients with LAHC who underwent IC.
Key Points
• CT radiomics can predict IC response and progression-free survival in hypopharyngeal carcinoma.
• We combined significant radiomics signature with clinical predictors to establish a nomogram to predict individual response to IC.
• Radiomics signature could divide patients into the high-risk and low-risk groups based on the PFS.