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Objectives: To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1–2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification. Methods: A total of 151 pathologically confirmed 1–2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance. Results: The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1–2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1–2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively. Conclusions: Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1–2-cm gGISTs. Key Points: • The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1–2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1–2-cm gGISTs. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a preoperative risk stratification model for predicting disease-free survival (DFS) based on contrast-enhanced multidetector computed tomography (ceMDCT) images in patients with gastric cancer (GC) undergoing radical surgery. Methods: We retrospectively enrolled patients with GC who underwent ceMDCT followed by radical surgery. A preoperative risk stratification model was constructed (including risk factor selection, risk status scoring, and risk level assignment) using Cox proportional hazard regression and log-rank analyses in the training cohort; the model was tested in the validation cohort. A nomogram was used to compare the preoperative risk stratification model with a postoperative DFS prediction model. Results: A total of 462 patients (training/validation: 271/191) were included. The ceMDCT features of T category (score of 0 or 2), N category (0, 1, 2, or 3), extramural vessel invasion (0 or 2), and tumor location (0 or 1) were selected to construct the preoperative risk stratification model, with 4 risk levels defined based on risk score. There were significant differences in DFS among the risk levels in both cohorts (p < 0.001). The predictive value of the preoperative model was similar to that of the postoperative model, with concordance indices of 0.791 (95% CI, 0.743–0.837) and 0.739 (95% CI, 0.666–0.812), respectively, in the training cohort and 0.762 (95% CI, 0.696–0.828) and 0.738 (95% CI, 0.684–0.792), respectively, in the validation cohort. Conclusion: A preoperative risk stratification model based on ceMDCT images could be used to predict DFS and thus classify GC cases into various risk levels. [ABSTRACT FROM AUTHOR]

Purpose: Preoperative and noninvasive prognosis evaluation remains challenging for gastric cancer. Novel preoperative prognostic biomarkers should be investigated. This study aimed to develop multidetector‐row computed tomography (MDCT)‐guided prognostic models to direct follow‐up strategy and improve prognosis. Methods: A retrospective dataset of 353 gastric cancer patients were enrolled from two centers and allocated to three cohorts: training cohort (n = 166), internal validation cohort (n = 83), and external validation cohort (n = 104). Quantitative radiomic features were extracted from MDCT images. The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature. A radiomic nomogram was established by integrating the radiomic signature and significant clinical risk factors. We also built a preoperative tumor‐node‐metastasis staging model for comparison. All models were evaluated considering the abilities of risk stratification, discrimination, calibration, and clinical use. Results: In the two validation cohorts, the established four‐feature radiomic signature showed robust risk stratification power (P = 0.0260 and 0.0003, log‐rank test). The radiomic nomogram incorporated radiomic signature, extramural vessel invasion, clinical T stage, and clinical N stage, outperforming all the other models (concordance index = 0.720 and 0.727) with good calibration and decision benefits. Also, the 2‐yr disease‐free survival (DFS) prediction was most effective (time‐dependent area under curve = 0.771 and 0.765). Moreover, subgroup analysis indicated that the radiomic signature was more sensitive in risk stratifying patients with advanced clinical T/N stage. Conclusions: The proposed MDCT‐guided radiomic signature was verified as a prognostic factor for gastric cancer. The radiomic nomogram was a noninvasive auxiliary model for preoperative individualized DFS prediction, holding potential in promoting treatment strategy and clinical prognosis. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the correlation of the mitotic index (MI) of 1–5 cm gastric gastrointestinal stromal tumors (gGISTs) with CT-identified morphological and first-order radiomics features, incorporating subgroup analysis based on tumor size.We enrolled 344 patients across four institutions, each pathologically diagnosed with 1–5 cm gGISTs and undergoing preoperative contrast-enhanced CT scans. Univariate and multivariate analyses were performed to investigate the independent CT morphological high-risk features of MI. Lesions were categorized into four subgroups based on their pathological LD: 1–2 cm (n = 69), 2–3 cm (n = 96), 3–4 cm (n = 107), and 4–5 cm (n = 72). CT morphological high-risk features of MI were evaluated in each subgroup. In addition, first-order radiomics features were extracted on CT images of the venous phase, and the association between these features and MI was investigated.Tumor size (p = 0.04, odds ratio, 1.41; 95% confidence interval: 1.01–1.96) and invasive margin (p < 0.01, odds ratio, 4.55; 95% confidence interval: 1.77–11.73) emerged as independent high-risk features for MI > 5 of 1–5 cm gGISTs from multivariate analysis. In the subgroup analysis, the invasive margin was correlated with MI > 5 in 3–4 cm and 4–5 cm gGISTs (p = 0.02, p = 0.03), and potentially correlated with MI > 5 in 2–3 cm gGISTs (p = 0.07). The energy was the sole first-order radiomics feature significantly correlated with gGISTs of MI > 5, displaying a strong correlation with CT-detected tumor size (Pearson’s ρ = 0.85, p < 0.01).The invasive margin stands out as the sole independent CT morphological high-risk feature for 1–5 cm gGISTs after tumor size-based subgroup analysis, overshadowing intratumoral morphological characteristics and first-order radiomics features.QuestionHow can accurate preoperative risk stratification of gGISTs be achieved to support treatment decision-making?FindingsInvasive margins may serve as a reliable marker for risk prediction in gGISTs up to 5 cm, rather than surface ulceration, irregular shape, necrosis, or heterogeneous enhancement.Clinical relevanceFor gGISTs measuring up to5 cm, preoperative prediction of the metastatic risk could help select patients who could be treated by endoscopic resection, thereby avoiding overtreatment.Methods: To investigate the correlation of the mitotic index (MI) of 1–5 cm gastric gastrointestinal stromal tumors (gGISTs) with CT-identified morphological and first-order radiomics features, incorporating subgroup analysis based on tumor size.We enrolled 344 patients across four institutions, each pathologically diagnosed with 1–5 cm gGISTs and undergoing preoperative contrast-enhanced CT scans. Univariate and multivariate analyses were performed to investigate the independent CT morphological high-risk features of MI. Lesions were categorized into four subgroups based on their pathological LD: 1–2 cm (n = 69), 2–3 cm (n = 96), 3–4 cm (n = 107), and 4–5 cm (n = 72). CT morphological high-risk features of MI were evaluated in each subgroup. In addition, first-order radiomics features were extracted on CT images of the venous phase, and the association between these features and MI was investigated.Tumor size (p = 0.04, odds ratio, 1.41; 95% confidence interval: 1.01–1.96) and invasive margin (p < 0.01, odds ratio, 4.55; 95% confidence interval: 1.77–11.73) emerged as independent high-risk features for MI > 5 of 1–5 cm gGISTs from multivariate analysis. In the subgroup analysis, the invasive margin was correlated with MI > 5 in 3–4 cm and 4–5 cm gGISTs (p = 0.02, p = 0.03), and potentially correlated with MI > 5 in 2–3 cm gGISTs (p = 0.07). The energy was the sole first-order radiomics feature significantly correlated with gGISTs of MI > 5, displaying a strong correlation with CT-detected tumor size (Pearson’s ρ = 0.85, p < 0.01).The invasive margin stands out as the sole independent CT morphological high-risk feature for 1–5 cm gGISTs after tumor size-based subgroup analysis, overshadowing intratumoral morphological characteristics and first-order radiomics features.QuestionHow can accurate preoperative risk stratification of gGISTs be achieved to support treatment decision-making?FindingsInvasive margins may serve as a reliable marker for risk prediction in gGISTs up to 5 cm, rather than surface ulceration, irregular shape, necrosis, or heterogeneous enhancement.Clinical relevanceFor gGISTs measuring up to5 cm, preoperative prediction of the metastatic risk could help select patients who could be treated by endoscopic resection, thereby avoiding overtreatment.Results: To investigate the correlation of the mitotic index (MI) of 1–5 cm gastric gastrointestinal stromal tumors (gGISTs) with CT-identified morphological and first-order radiomics features, incorporating subgroup analysis based on tumor size.We enrolled 344 patients across four institutions, each pathologically diagnosed with 1–5 cm gGISTs and undergoing preoperative contrast-enhanced CT scans. Univariate and multivariate analyses were performed to investigate the independent CT morphological high-risk features of MI. Lesions were categorized into four subgroups based on their pathological LD: 1–2 cm (n = 69), 2–3 cm (n = 96), 3–4 cm (n = 107), and 4–5 cm (n = 72). CT morphological high-risk features of MI were evaluated in each subgroup. In addition, first-order radiomics features were extracted on CT images of the venous phase, and the association between these features and MI was investigated.Tumor size (p = 0.04, odds ratio, 1.41; 95% confidence interval: 1.01–1.96) and invasive margin (p < 0.01, odds ratio, 4.55; 95% confidence interval: 1.77–11.73) emerged as independent high-risk features for MI > 5 of 1–5 cm gGISTs from multivariate analysis. In the subgroup analysis, the invasive margin was correlated with MI > 5 in 3–4 cm and 4–5 cm gGISTs (p = 0.02, p = 0.03), and potentially correlated with MI > 5 in 2–3 cm gGISTs (p = 0.07). The energy was the sole first-order radiomics feature significantly correlated with gGISTs of MI > 5, displaying a strong correlation with CT-detected tumor size (Pearson’s ρ = 0.85, p < 0.01).The invasive margin stands out as the sole independent CT morphological high-risk feature for 1–5 cm gGISTs after tumor size-based subgroup analysis, overshadowing intratumoral morphological characteristics and first-order radiomics features.QuestionHow can accurate preoperative risk stratification of gGISTs be achieved to support treatment decision-making?FindingsInvasive margins may serve as a reliable marker for risk prediction in gGISTs up to 5 cm, rather than surface ulceration, irregular shape, necrosis, or heterogeneous enhancement.Clinical relevanceFor gGISTs measuring up to5 cm, preoperative prediction of the metastatic risk could help select patients who could be treated by endoscopic resection, thereby avoiding overtreatment.Conclusions: To investigate the correlation of the mitotic index (MI) of 1–5 cm gastric gastrointestinal stromal tumors (gGISTs) with CT-identified morphological and first-order radiomics features, incorporating subgroup analysis based on tumor size.We enrolled 344 patients across four institutions, each pathologically diagnosed with 1–5 cm gGISTs and undergoing preoperative contrast-enhanced CT scans. Univariate and multivariate analyses were performed to investigate the independent CT morphological high-risk features of MI. Lesions were categorized into four subgroups based on their pathological LD: 1–2 cm (n = 69), 2–3 cm (n = 96), 3–4 cm (n = 107), and 4–5 cm (n = 72). CT morphological high-risk features of MI were evaluated in each subgroup. In addition, first-order radiomics features were extracted on CT images of the venous phase, and the association between these features and MI was investigated.Tumor size (p = 0.04, odds ratio, 1.41; 95% confidence interval: 1.01–1.96) and invasive margin (p < 0.01, odds ratio, 4.55; 95% confidence interval: 1.77–11.73) emerged as independent high-risk features for MI > 5 of 1–5 cm gGISTs from multivariate analysis. In the subgroup analysis, the invasive margin was correlated with MI > 5 in 3–4 cm and 4–5 cm gGISTs (p = 0.02, p = 0.03), and potentially correlated with MI > 5 in 2–3 cm gGISTs (p = 0.07). The energy was the sole first-order radiomics feature significantly correlated with gGISTs of MI > 5, displaying a strong correlation with CT-detected tumor size (Pearson’s ρ = 0.85, p < 0.01).The invasive margin stands out as the sole independent CT morphological high-risk feature for 1–5 cm gGISTs after tumor size-based subgroup analysis, overshadowing intratumoral morphological characteristics and first-order radiomics features.QuestionHow can accurate preoperative risk stratification of gGISTs be achieved to support treatment decision-making?FindingsInvasive margins may serve as a reliable marker for risk prediction in gGISTs up to 5 cm, rather than surface ulceration, irregular shape, necrosis, or heterogeneous enhancement.Clinical relevanceFor gGISTs measuring up to5 cm, preoperative prediction of the metastatic risk could help select patients who could be treated by endoscopic resection, thereby avoiding overtreatment.Key Points: To investigate the correlation of the mitotic index (MI) of 1–5 cm gastric gastrointestinal stromal tumors (gGISTs) with CT-identified morphological and first-order radiomics features, incorporating subgroup analysis based on tumor size.We enrolled 344 patients across four institutions, each pathologically diagnosed with 1–5 cm gGISTs and undergoing preoperative contrast-enhanced CT scans. Univariate and multivariate analyses were performed to investigate the independent CT morphological high-risk features of MI. Lesions were categorized into four subgroups based on their pathological LD: 1–2 cm (n = 69), 2–3 cm (n = 96), 3–4 cm (n = 107), and 4–5 cm (n = 72). CT morphological high-risk features of MI were evaluated in each subgroup. In addition, first-order radiomics features were extracted on CT images of the venous phase, and the association between these features and MI was investigated.Tumor size (p = 0.04, odds ratio, 1.41; 95% confidence interval: 1.01–1.96) and invasive margin (p < 0.01, odds ratio, 4.55; 95% confidence interval: 1.77–11.73) emerged as independent high-risk features for MI > 5 of 1–5 cm gGISTs from multivariate analysis. In the subgroup analysis, the invasive margin was correlated with MI > 5 in 3–4 cm and 4–5 cm gGISTs (p = 0.02, p = 0.03), and potentially correlated with MI > 5 in 2–3 cm gGISTs (p = 0.07). The energy was the sole first-order radiomics feature significantly correlated with gGISTs of MI > 5, displaying a strong correlation with CT-detected tumor size (Pearson’s ρ = 0.85, p < 0.01).The invasive margin stands out as the sole independent CT morphological high-risk feature for 1–5 cm gGISTs after tumor size-based subgroup analysis, overshadowing intratumoral morphological characteristics and first-order radiomics features.QuestionHow can accurate preoperative risk stratification of gGISTs be achieved to support treatment decision-making?FindingsInvasive margins may serve as a reliable marker for risk prediction in gGISTs up to 5 cm, rather than surface ulceration, irregular shape, necrosis, or heterogeneous enhancement.Clinical relevanceFor gGISTs measuring up to5 cm, preoperative prediction of the metastatic risk could help select patients who could be treated by endoscopic resection, thereby avoiding overtreatment. [ABSTRACT FROM AUTHOR]