Journal
EUROPEAN RADIOLOGY
Volume 32, Issue 10, Pages 7087-7097Publisher
SPRINGER
DOI: 10.1007/s00330-022-08858-5
Keywords
Gallbladder carcinoma; Recurrence; Radiomics; Computed tomography; Prediction modeling
Funding
- National Natural Science Foundation of China [81572975]
- Key Research and Development Project of Science and Technology Department of Zhejiang [2015C03053]
- Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei province [CXPJJH11900009-07]
- Zhejiang Provincial Program for the Cultivation of High-level Innovative Health talents
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This study aimed to develop and validate a radiomics signature to estimate recurrence-free survival in GBC patients. The results showed that the radiomics signature and combined nomogram could assist in predicting RFS in GBC patients, demonstrating good prediction performance.
Objectives Gallbladder carcinoma (GBC) is the most common and aggressive biliary tract malignancy with high postoperative recurrence rates. This single-center study aimed to develop and validate a radiomics signature to estimate GBC recurrence-free survival (RFS). Methods This study retrospectively included 204 consecutive patients with pathologically diagnosed GBC and were randomly divided into development (n = 142) and validation (n = 62) cohorts (7:3). The radiomics features of tumor were extracted from preoperative contrast-enhanced CT imaging for each patient. In the development cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression was employed to develop a radiomics signature for RFS prediction. The patients were stratified into high-score or low-score groups according to their median value of radiomics score. A nomogram was established using multivariable Cox regression by incorporating significant pathological predictors and radiomics signatures. Results The radiomics signature based on 12 features could discriminate high-risk patients with poor RFS. Multivariate Cox analysis revealed that pT3/4 stage (hazard ratio, [HR] = 2.691), pN2 stage (HR = 3.60), poor differentiation grade (HR = 2.651), and high radiomics score (HR = 1.482) were independent risk variables associated with worse RFS and were incorporated to construct a nomogram. The nomogram displayed good prediction performance in estimating RFS with AUC values of 0.895, 0.935, and 0.907 at 1, 3, and 5 years, respectively. Conclusions The radiomics signature and combined nomogram may assist in predicting RFS in GBC patients.
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