A meta-analysis of the association between adjuvant chemoradiotherapy and disease-free survival in gastric cancer according to the histology

Yıldırım, Hasan Çağrı; Güven, Deniz Can; Akyildiz, Arif; Yalçın, Şuayib; Dizdar, Ömer

doi:10.1007/s11845-023-03297-7

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…It has been shown that postoperative recurrence rate is higher in diffuse gastric cancer than intestinal type [ 3 , 6 ], and 5-year overall survival rate is higher in intestinal gastric cancer than diffuse [ 3 , 5 ]. Adjuvant chemoradiotherapy improves disease-free survival in patients with intestinal gastric cancer, but not in patients with diffuse gastric cancer after D2 resection [ 7 ]. In addition, patients showed different sensitivities to chemotherapy according to Lauren classification, with diffuse patients showing a higher efficiency to chemotherapy [ 8 ].…”

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confidence: 99%

Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map

Qin

et al. 2023

Insights Imaging

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Objective To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer. Methods A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance. Results Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802). Conclusion The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer. Critical relevance statement The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis. Graphical abstract

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