MDCT features in the differentiation of T4a gastric cancer from less-advanced gastric cancer: significance of the hyperattenuating serosa sign

Kim, T U; Kim, S; Lee, J W; Lee, N K; Jeon, Tae Yong; Park, D Y

doi:10.1259/bjr.20130290

Cited by 22 publications

(18 citation statements)

References 27 publications

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“…The AJCC proposed preoperative clinical TNM staging to guide attending physicians in making preliminary treatment decisions, but a lack of consistency in the initial clinical evaluation has been documented, including non-standardized radiological reports (5). Kim et al reported that the accuracy of CT in distinguishing T3 and T4 GC was only 60%, similar to the diagnostic accuracy of the traditional model in the present study (ACC: training set, 0.764; test set, 0.750) (18). In a multicenter prospective study, the preoperative clinical stage and postoperative pathological diagnosis of 4,534 cases of GC were compared.…”

Section: Discussionmentioning

confidence: 49%

Potential Value of Radiomics in the Identification of Stage T3 and T4a Esophagogastric Junction Adenocarcinoma Based on Contrast-Enhanced CT Images

Guo

et al. 2021

Front. Oncol.

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PurposeThis study was designed to evaluate the predictive performance of contrast-enhanced CT-based radiomic features for the personalized, differential diagnosis of esophagogastric junction (EGJ) adenocarcinoma at stages T3 and T4a.MethodsTwo hundred patients with T3 (n = 44) and T4a (n = 156) EGJ adenocarcinoma lesions were enrolled in this study. Traditional computed tomography (CT) features were obtained from contrast-enhanced CT images, and the traditional model was constructed using a multivariate logistic regression analysis. A radiomic model was established based on radiomic features from venous CT images, and the radiomic score (Radscore) of each patient was calculated. A combined nomogram diagnostic model was constructed based on Radscores and traditional features. The diagnostic performances of these three models (traditional model, radiomic model, and nomogram) were assessed with receiver operating characteristics curves. Sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and areas under the curve (AUC) of models were calculated, and the performances of the models were evaluated and compared. Finally, the clinical effectiveness of the three models was evaluated by conducting a decision curve analysis (DCA).ResultsAn eleven-feature combined radiomic signature and two traditional CT features were constructed as the radiomic and traditional feature models, respectively. The Radscore was significantly different between patients with stage T3 and T4a EGJ adenocarcinoma. The combined nomogram performed the best and has potential clinical usefulness.ConclusionsThe developed combined nomogram might be useful in differentiating T3 and T4a stages of EGJ adenocarcinoma and may facilitate the decision-making process for the treatment of T3 and T4a EGJ adenocarcinoma.

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Section: Discussionmentioning

confidence: 49%

Potential Value of Radiomics in the Identification of Stage T3 and T4a Esophagogastric Junction Adenocarcinoma Based on Contrast-Enhanced CT Images

Guo

et al. 2021

Front. Oncol.

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“…Because of the decreased number of tumor cells, edema, fibrosis, and chronic inflammation after chemotherapy ( 36 ), the blurred border of lesions seriously hinders accurate judgment. By extracting high-throughput quantitative imaging features, radiomics can characterize the spatial relationships and consistency of signal intensities within the tumor region ( 18 ), showing their utility in discriminating serosal invasion in preoperative staging ( 37 , 38 ). In summary, our radiomics models performed well in accurately classifying serosal invasion not only in the training cohort, with AUCs of 0.940, 0.953 and 0.953 for the 120 kVp model, IM-120 kVp model and ComModel, respectively, but also in the testing cohort, with the AUCs of 0.831, 0.879 and 0.914, respectively.…”

Section: Discussionmentioning

confidence: 99%

The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images

et al. 2021

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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.

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“…The assessment of serosa invasion before the surgery has a crucial significance because for locally AGC with serosal invasion, neoadjuvant chemotherapy is being used more frequently to downstage the tumor and treat micrometastases (7,8). Moreover, differentiation between T3 and T4a gastric carcinoma is very important to determine the appropriate procedure of surgery and expect the prognosis, especially for the risk of peritoneal dissemination.…”

Section: Original Articlementioning

confidence: 99%

“…Moreover, differentiation between T3 and T4a gastric carcinoma is very important to determine the appropriate procedure of surgery and expect the prognosis, especially for the risk of peritoneal dissemination. For T4a gastric cancer staging, the reported computed tomography (CT) accuracy varies considerably, ranging from 77.8% to 93.5% (6)(7)(8). The reported CT criteria for T4a gastric cancer are extraluminal extension of the gastric wall and haziness of the perigastric fat (8).…”

Section: Original Articlementioning

confidence: 99%

“…For T4a gastric cancer staging, the reported computed tomography (CT) accuracy varies considerably, ranging from 77.8% to 93.5% (6)(7)(8). The reported CT criteria for T4a gastric cancer are extraluminal extension of the gastric wall and haziness of the perigastric fat (8). The paragastric inflammatory strands may mimic the cancer infiltration out of the serosa and lead to the overstaging of T3 tumors during the preoperative staging of gastric cancers.…”

Section: Original Articlementioning

confidence: 99%

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Feasibility of differentiating T3 from T4a gastric cancer in different Lauren classification by determining serosa invasion: Diagnostic performance of high enhanced serosa sign

Sun

Tang

Chen

et al. 2018

Chinese Journal of Cancer Research

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MDCT features in the differentiation of T4a gastric cancer from less-advanced gastric cancer: significance of the hyperattenuating serosa sign

Abstract: The hyperattenuating serosa sign is associated with gastric cancer with invading the serosa and can facilitate planning of the optimal pre-operative evaluation and treatment.

Cited by 22 publications

References 27 publications

Potential Value of Radiomics in the Identification of Stage T3 and T4a Esophagogastric Junction Adenocarcinoma Based on Contrast-Enhanced CT Images

Potential Value of Radiomics in the Identification of Stage T3 and T4a Esophagogastric Junction Adenocarcinoma Based on Contrast-Enhanced CT Images

The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images

Feasibility of differentiating T3 from T4a gastric cancer in different Lauren classification by determining serosa invasion: Diagnostic performance of high enhanced serosa sign

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