Abstract
ObjectivesThe aim was to determine whether the dual-energy CT radiomics model derived from an iodine map (IM) has incremental diagnostic value for the model based on 120-kV equivalent mixed images (120 kVp) in preoperative restaging of serosal invasion with locally advanced gastric cancer (LAGC) after neoadjuvant chemotherapy (NAC).MethodsA total of 155 patients (110 in the training cohort and 45 in the testing cohort) with LAGC who had standard NAC before surgery were retrospectively enrolled. All CT images were analyzed by two radiologists for manual classification. Volumes of interests (VOIs) were delineated semi-automatically, and 1,226 radiomics features were extracted from every segmented lesion in both IM and 120 kVp images, respectively. Spearman’s correlation analysis and the least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented for filtering unstable and redundant features and screening out vital features. Two predictive models (120 kVp and IM-120 kVp) based on 120 kVp selected features only and 120 kVp combined with IM selected features were established by multivariate logistic regression analysis. We then build a combination model (ComModel) developed with IM-120 kVp signature and ycT. The performance of these three models and manual classification were evaluated and compared.ResultThree radiomics models showed great predictive accuracy and performance in both the training and testing cohorts (ComModel: AUC: training, 0.953, testing, 0.914; IM-120 kVp: AUC: training, 0.953, testing, 0.879; 120 kVp: AUC: training, 0.940, testing, 0.831). All these models showed higher diagnostic accuracy (ComModel: 88.9%, IM-120 kVp: 84.4%, 120 kVp: 80.0%) than manual classification (68.9%) in the testing group. ComModel and IM-120 kVp model had better performances than manual classification both in the training (both p<0.001) and testing cohorts (p<0.001 and p=0.034, respectively).ConclusionsDual-energy CT-based radiomics models demonstrated convincible diagnostic performance in differentiating serosal invasion in preoperative restaging for LAGC. The radiomics features derived from IM showed great potential for improving the diagnostic capability.
Highlights
Among all of the preoperative clinical factors, including sex, location of the tumor, Borrmann type and tumor markers were not significantly associated with serosal invasion after univariate analysis except ycT, which was determined by radiologists as positive or negative
Eight features were selected during least absolute shrinkage and selection operator (LASSO) from 120 kVp images
We developed and validated an iodine maps (IM)-120 kVp radiomics model, which was superior to the radiomics model built by conventional 120 kVp, indicating the discrimination value of iodine from Dual-energy computed tomography (CT) (DECT) for serosal invasion in GC patients after Neoadjuvant chemotherapy (NAC)
Summary
Endoscopic ultrasound (EUS) and computed tomography (CT) are the most frequently used methods for preoperative staging of gastric cancer and the accuracy varies among different studies: 78%– 92% and 77%–89% for T staging and 57%–91% and 71%–90% for N staging for EUS and CT respectively [7,8,9,10]. No diagnostic modality has been accepted as an effective method for restaging, in T-restaging, which was once regarded as too weak for clinical decision-making. The accurate assessment of clinical T-restaging, with the invasion of serosa after NAC, is critical for operative decision-making, as well as to evaluate prognosis. Improving the accuracy of restaging of serosal invasion after NAC is critical
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