Borrmann Type IV Gastric Cancer: Computed Tomography Features and Corresponding Pathological Findings.

OBJECTIVE: We aimed to analyze the association between computed tomography (CT) features and the corresponding pathological findings in Borrmann type IV (BT-4) gastric cancers and explore the pathological basis of the characteristic CT features. METHODS: This retrospective study included 84 patients with BT-4 gastric cancers who underwent contrast-enhanced CT and surgical resection. Preoperative CT features were evaluated, including the major location, range, circumferential invasion, perigastric fat infiltration, enlarged lymph nodes, layered enhancement, degree of enhancement, and peak enhanced phase. Postoperative pathological findings were also recorded. Differences in CT features according to different World Health Organization types, surgical margin, adjacent organ invasion, and peritoneal status were assessed using the χ 2 or Fisher exact test (n < 5). RESULTS: The most common World Health Organization type of BT-4 gastric cancer was poorly cohesive carcinoma (65.5%), which tended to show circumferential invasion, fewer enlarged lymph nodes, and layered enhancement. Although 82 patients with BT-4 gastric cancer (97.6%) had positive lymph nodes, only 26 (31.0%) had enlarged lymph nodes. Lesions originating from the gastroesophageal junction had a higher rate of positive margins ( P < 0.05). Adjacent organ invasion was more likely to occur in lesions with perigastric fat infiltration ( P < 0.05). Patients with circumferential invasion tended to show peritoneal metastasis ( P < 0.05). CONCLUSIONS: The characteristic CT features of BT-4 gastric cancer may be attributed to the corresponding pathological findings. Recognizing the association between CT features and pathological findings may help evaluate the aggressiveness of BT-4 gastric cancers.

Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer.

Objectives: To explore whether the preoperative CT radiomics can predict the status of microsatellite instability (MSI) in colorectal cancer (CRC) patients and identify the region with the most stable and high-efficiency radiomics features. Methods: This retrospective study involved 230 CRC patients with preoperative computed tomography scans and available MSI status between December 2019 and October 2021. Image segmentation and radiomic feature extraction were performed as follows. First, slices with the maximum tumor area (region of interest, ROI) were manually contoured. Subsequently, each ROI was shrunk inward by 1, 2, and 3 mm, respectively, where the remaining ROIs were considered as the internal region of the tumor (named as IROI1, IROI2, and IROI3), and the shrunk regions were considered as marginal regions of the tumor (named as MROI1, MROI2, and MROI3). Finally, radiomics features were extracted from each of the ROI. The intraclass correlation coefficient and least absolute shrinkage and selection operator method were used to choose the most reliable and relevant features of MSI status. Clinical, radiomics, and combined clinical radiomics models have been established. Calibration curve and decision curve analyses (DCA) were generated to explore the correction effect and assess the clinical applicability of the above models, respectively. Results: In the testing cohort, the radiomics model based on IROI3 yielded the highest average area under the curve (AUC) value of 0.908, compared with the remaining radiomics models. Additionally, hypertension and N stage were considered as clinically independent factors of MSI status. The combined clinical radiomics model achieved excellent diagnostic efficacy (AUC: 0.928; sensitivity: 0.840; specificity: 0.867) in the testing cohort, as well as favorable calibration and clinical utility by calibration curve and DCA analyses. Conclusions: The IROI3 model, which is based on a 3-mm shrink in the largest areas of the tumor, could noninvasively reflect the heterogeneity and genetic instability within the tumor. This suggests that it is an important biomarker for the preoperative prediction of MSI status. The model can extract more robust and effective radiomics features, which lays a foundation for the radiomics study of hollow organs, such as in CRC.

Development and validation of multivariate models integrating preoperative clinicopathological and radiographic findings to predict HER2 status in gastric cancer.

The combination of trastuzumab and chemotherapy is recommended as first-line therapy for patients with human epidermal growth factor receptor 2 (HER2) positive advanced gastric cancers (GCs). Successful trastuzumab-induced targeted therapy should be based on the assessment of HER2 overexpression. This study aimed to evaluate the feasibility of multivariate models based on hematological parameters, endoscopic biopsy, and computed tomography (CT) findings for assessing HER2 overexpression in GC. This retrospective study included 183 patients with GC, and they were divided into primary (n = 137) and validation (n = 46) cohorts at a ratio of 3:1. Hematological parameters, endoscopic biopsy, CT morphological characteristics, and CT value-related and texture parameters of all patients were collected and analyzed. The mean corpuscular hemoglobin concentration value, morphological type, 3 CT value-related parameters, and 22 texture parameters in three contrast-enhanced phases differed significantly between the two groups (all p < 0.05). Multivariate models based on the regression analysis and support vector machine algorithm achieved areas under the curve of 0.818 and 0.879 in the primary cohort, respectively. The combination of hematological parameters, CT morphological characteristics, CT value-related and texture parameters could predict HER2 overexpression in GCs with satisfactory diagnostic efficiency. The decision curve analysis confirmed the clinical utility.

Clinicopathological features and CT findings of papillary gastric adenocarcinoma.

PURPOSE: This study aimed to analyze the clinicopathological and computed tomography (CT) findings of papillary gastric adenocarcinoma and to evaluate the feasibility of the multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas. METHODS: This retrospective study included 22 patients with papillary gastric adenocarcinoma and 88 patients with tubular adenocarcinoma. The demographic data, tumor markers, histopathological information, CT morphological characteristics, and CT value-related parameters of all patients were collected and analyzed. The multivariate model based on regression analysis was performed to improve the diagnostic efficacy for discriminating papillary gastric adenocarcinomas preoperatively. The diagnostic performance of the established nomogram was evaluated by receiver operating characteristic curve analysis. RESULTS: The distribution of age, carcinoembryonic antigen, differentiation degree, neural invasion, human epidermal growth factor receptor 2 overexpression, P53 mutation status, 4 CT morphological characteristics, and 10 CT valued-related parameters differed significantly between papillary gastric adenocarcinoma and tubular adenocarcinoma groups (all p < 0.05). The established multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas preoperatively achieved the area under the curve of 0.920. CONCLUSION: There existed differences in clinicopathological features and CT findings between papillary gastric adenocarcinomas and tubular adenocarcinomas. The combination of demographic data, tumor markers, CT morphological characteristics, and CT value-related parameters could discriminate papillary gastric adenocarcinomas preoperatively with satisfactory diagnostic efficiency.

Integrative Predictive Models of Computed Tomography Texture Parameters and Hematological Parameters for Lymph Node Metastasis in Lung Adenocarcinomas.

OBJECTIVES: The aims of the study were to integrate characteristics of computed tomography (CT), texture, and hematological parameters and to establish predictive models for lymph node (LN) metastasis in lung adenocarcinoma. METHODS: A total of 207 lung adenocarcinoma cases with confirmed postoperative pathology and preoperative CT scans between February 2017 and April 2019 were included in this retrospective study. All patients were divided into training and 2 validation cohorts chronologically in the ratio of 3:1:1. The χ2 test or Fisher exact test were used for categorical variables. The Shapiro-Wilk test and Mann-Whitney U test were used for continuous variables. Logistic regression and machine learning algorithm models based on CT characteristics, texture, and hematological parameters were used to predict LN metastasis. The performance of the multivariate models was evaluated using a receiver operating characteristic curve; prediction performance was evaluated in the validation cohorts. Decision curve analysis confirmed its clinical utility. RESULTS: Logistic regression analysis demonstrated that pleural thickening (P = 0.013), percentile 25th (P = 0.033), entropy gray-level co-occurrence matrix 10 (P = 0.019), red blood cell distribution width (P = 0.012), and lymphocyte-to-monocyte ratio (P = 0.049) were independent risk factors associated with LN metastasis. The area under the curve of the predictive model established using the previously mentioned 5 independent risk factors was 0.929 in the receiver operating characteristic analysis. The highest area under the curve was obtained in the training cohort (0.777 using Naive Bayes algorithm). CONCLUSIONS: Integrative predictive models of CT characteristics, texture, and hematological parameters could predict LN metastasis in lung adenocarcinomas. These findings may provide a reference for clinical decision making.

Prediction of serosal invasion in gastric cancer: development and validation of multivariate models integrating preoperative clinicopathological features and radiographic findings based on late arterial phase CT images.

BACKGROUND: To develop and validate multivariate models integrating endoscopic biopsy, tumor markers, and CT findings based on late arterial phase (LAP) to predict serosal invasion in gastric cancer (GC). METHODS: The preoperative differentiation degree, tumor markers, CT morphological characteristics, and CT value-related and texture parameters of 154 patients with GC were analyzed retrospectively. Multivariate models based on regression analysis and machine learning algorithms were performed to improve the diagnostic efficacy. RESULTS: The differentiation degree, carbohydrate antigen (CA) 199, CA724, CA242, and multiple CT findings based on LAP differed significantly between T1-3 and T4 GCs in the primary cohort (all P < 0.05). Multivariate models based on regression analysis and random forest achieved AUCs of 0.849 and 0.865 in the primary cohort, respectively. CONCLUSION: We developed and validated multivariate models integrating endoscopic biopsy, tumor markers, CT morphological characteristics, and CT value-related and texture parameters to predict serosal invasion in GCs and achieved favorable performance.

Gastric poorly cohesive carcinoma: differentiation from tubular adenocarcinoma using nomograms based on CT findings in the 40 s late arterial phase.

OBJECTIVES: To summarise the CT findings of gastric poorly cohesive carcinoma (PCC) in the 40 s late arterial phase and differentiate it from tubular adenocarcinoma (TAC) using an integrative nomogram. METHODS: A total of 241 patients including 59 PCCs, 109 TACs, and 73 other type gastric cancers were enrolled. Thirteen CT morphological characteristics of each lesion in the late arterial phase were evaluated. In addition, CT value-related parameters were extracted from ROIs encompassing the area of greatest enhancement on four-phase CT images. Nomograms based on regression models were built to discriminate PCCs from TACs and from non-PCCs. ROC curve analysis was performed to assess the diagnostic efficiency. RESULTS: Six morphological characteristics, 10 CT value-related parameters, and the enhanced curve types differed significantly among the above three groups in the primary cohort (all p < 0.05). The paired comparison revealed that 10 CT value-related parameters differed significantly between PCCs and TACs (all p < 0.05). The AUC of the nomogram based on the multivariate model for discriminating PCCs from TACs was 0.954, which was confirmed in the validation cohort (AUC = 0.895). The AUC of another nomogram for discriminating PCCs from non-PCCs was 0.938, which was confirmed in the validation cohort (AUC = 0.880). CONCLUSIONS: In the 40 s late arterial phase, the morphological characteristics and CT value-related parameters were significantly different among PCCs, TACs, and other types. PCCs were prone to manifest mucosal line interruption, diffuse thickening, infiltrative growth, and slow-rising enhanced curve (Type A). Furthermore, multivariate models were useful in discriminating PCCs from TACs and other types. KEY POINTS: • Multiple morphological characteristics and CT value-related parameters differed significantly between gastric PCCs and TACs in the 40 s late arterial phase. • The nomogram integrating morphological characteristics and CT value-related parameters in the 40 s late arterial phase had favourable performance in discriminating PCCs from TACs. • More useful information can be derived from 40 s late arterial phase CT images; thus, a more accurate evaluation can be made in clinical practice.

Development and Validation of Multivariate Models Integrating Preoperative Clinicopathological Parameters and Radiographic Findings Based on Late Arterial Phase CT Images for Predicting Lymph Node Metastasis in Gastric Cancer.

RATIONALE AND OBJECTIVES: To develop and validate multivariate models integrating endoscopic biopsy, tumor markers, computed tomography (CT) morphological characteristics based on late arterial phase (LAP), and CT value-related and texture parameters to predict lymph node (LN) metastasis in gastric cancers (GCs). MATERIALS AND METHODS: The preoperative differentiation degree based on biopsy, 6 tumor markers, 8 CT morphological characteristics based on LAP, 18 CT value-related parameters, and 35 CT texture parameters of 163 patients (111 men and 52 women) with GC were analyzed retrospectively. The differences in parameters between N (-) and N (+) GCs were analyzed by the Mann-Whitney U test. Diagnostic performance was obtained by receiver operating characteristic (ROC) curve analysis. Multivariate models based on regression analysis and machine learning algorithms were performed to improve diagnostic efficacy. RESULTS: The differentiation degree, carbohydrate antigen (CA) 199 and CA242, 5 CT morphological characteristics, and 22 CT texture parameters showed significant differences between N (-) and N (+) GCs in the primary cohort (all p < 0.05). The multivariate model integrating clinicopathological parameters and radiographic findings based on regression analysis achieved areas under the ROC curve (AUCs) of 0.936 and 0.912 in the primary and validation cohorts, respectively. The model generated by the support vector machine algorithm achieved AUCs of 0.914 and 0.948, respectively. CONCLUSION: We developed and validated multivariate models integrating endoscopic biopsy, tumor markers, CT morphological characteristics based on LAP, and CT texture parameters to predict LN metastasis in GCs and achieved satisfactory performance.

Preoperative T2-weighted MR imaging texture analysis of gastric cancer: prediction of TNM stages.

PURPOSE: To explore the capability of algorithms to build multivariate models integrating morphological and texture features derived from preoperative T2-weighted magnetic resonance (MR) images of gastric cancer (GC) to evaluate tumor- (T), node- (N), and metastasis- (M) stages. METHODS: A total of 80 patients at our hospital who underwent abdominal MR imaging and were diagnosed with GC from December 2011 to November 2016 were retrospectively included. Texture features were calculated using T2-weighted images with a manual region of interest. Morphological characteristics were also evaluated. Classifiers and regression analyses were used to build multivariate models. Receiver operating characteristic (ROC) curve analysis was performed to assess diagnostic efficacy. RESULTS: There were 8, 10, and 3 texture parameters that showed significant differences in GCs at different overall (I-II vs. III-IV), T (1-2 vs. 3-4), and N (- vs. +) stages (all p < 0.05), respectively. Mild thickening was more common in stages I-II, T1-2, and N- GCs (all p < 0.05). An irregular outer contour was more commonly observed in stages III-IV (p = 0.001) and T3-4 (p = 0.001) GCs. T3-4 and N+ GCs tended to be thickening type lesions (p = 0.005 and 0.032, respectively). The multivariate models using the naive bayes algorithm showed the highest diagnostic efficacy in predicting T and N stages (area under the ROC curves [AUC] = 0.900 and 0.863, respectively), and the model based on regression analysis had the best predictive performance in overall staging (AUC = 0.839). CONCLUSION: Multivariate models combining morphological characteristics with texture parameters based on machine learning algorithms were able to improve diagnostic efficacy in predicting the overall, T, and N stages of GCs.

Differentiating gastric cancer and gastric lymphoma using texture analysis (TA) of positron emission tomography (PET).

BACKGROUND: Texture analysis (TA) can quantify intra-tumor heterogeneity using standard medical images. The present study aimed to assess the application of positron emission tomography (PET) TA in the differential diagnosis of gastric cancer and gastric lymphoma. METHODS: The pre-treatment PET images of 79 patients (45 gastric cancer, 34 gastric lymphoma) between January 2013 and February 2018 were retrospectively reviewed. Standard uptake values (SUVs), first-order texture features, and second-order texture features of the grey-level co-occurrence matrix (GLCM) were analyzed. The differences in features among different groups were analyzed by the two-way Mann-Whitney test, and receiver operating characteristic (ROC) analysis was used to estimate the diagnostic efficacy. RESULTS: InertiaGLCM was significantly lower in gastric cancer than that in gastric lymphoma (4975.61 vs. 11,425.30, z = -3.238, P = 0.001), and it was found to be the most discriminating texture feature in differentiating gastric lymphoma and gastric cancer. The area under the curve (AUC) of inertiaGLCM was higher than the AUCs of SUVmax and SUVmean (0.714 vs. 0.649 and 0.666, respectively). SUVmax and SUVmean were significantly lower in low-grade gastric lymphoma than those in high grade gastric lymphoma (3.30 vs. 11.80, 2.40 vs. 7.50, z = -2.792 and -3.007, P = 0.005 and 0.003, respectively). SUVs and first-order grey-level intensity features were not significantly different between low-grade gastric lymphoma and gastric cancer. EntropyGLCM12 was significantly lower in low-grade gastric lymphoma than that in gastric cancer (6.95 vs. 9.14, z = -2.542, P = 0.011) and had an AUC of 0.770 in the ROC analysis of differentiating low-grade gastric lymphoma and gastric cancer. CONCLUSIONS: InertiaGLCM and entropyGLCM were the most discriminating features in differentiating gastric lymphoma from gastric cancer and low-grade gastric lymphoma from gastric cancer, respectively. PET TA can improve the differential diagnosis of gastric neoplasms, especially in tumors with similar degrees of fluorodeoxyglucose uptake.

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer.

OBJECTIVES: To evaluate the predictive value of CT radiomics features derived from the primary tumor in discriminating occult peritoneal metastasis (PM) in advanced gastric cancer (AGC). METHODS: Preoperative CT images of 233 patients with AGC were retrospectively analyzed. The region of interest (ROI) was manually drawn along the margin of the lesion on the largest slice of venous CT images, and a total of 539 quantified features were extracted automatically. The intra-class correlation coefficient (ICC) and the absolute correlation coefficient (ACC) were calculated for selecting influential features. A multivariate logistic regression model was constructed based on the training cohort, and the testing cohort validated the reliability of the model. Additionally, another model based on the preoperative clinic-pathological features was also developed. The comparison of the diagnostic performance between the two models was performed using ROC analysis and the Akaike information criterion (AIC) value. RESULTS: Six radiomics features (ID_Energy, LoG(0.5)_Energy, Compactness2, Max Diameter, Orientation, and Surface Area Density) differed significantly between AGCs with and without PM and performed well in distinguishing AGCs with PM from those without PM in the primary cohort (AUC = 0.618-0.658). The radiomics model showed a higher AUC value than each single radiomics feature in the primary cohort (0.741 vs. 0.618-0.658) and similar diagnosis performance in the validation cohort. The radiomics model showed slightly worse diagnostic efficacy than the clinic-pathological model (AUC, 0.724 vs. 0.762). CONCLUSION: Venous CT radiomics analysis based on the primary tumor provided valuable information for predicting occult PM in AGCs. KEY POINTS: • Venous CT radiomics analysis provided valuable information for predicting occult peritoneal metastases in advanced gastric cancer. • CT-based T stage was an independent predictive factor of occult peritoneal metastases in advanced gastric cancer. • A radiomics model showed slightly worse diagnostic efficacy than a clinic-pathological model.

CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers.

To investigate the ability of CT texture analysis to assess and predict the expression statuses of E-cadherin, Ki67, VEGFR2 and EGFR in gastric cancers, the enhanced CT images of 139 patients with gastric cancer were retrospectively reviewed. The region of interest was manually drawn along the margin of the lesion on the largest slice in the arterial and venous phases, which yielded a series of texture parameters. Our results showed that the standard deviation, width, entropy, entropy (H), correlation and contrast from the arterial and venous phases were significantly correlated with the E-cadherin expression level in gastric cancers (all P < 0.05). The skewness from the arterial phase and the mean and autocorrelation from the venous phase were negatively correlated with the Ki67 expression level in gastric cancers (all P < 0.05). The width, entropy and contrast from the venous phase were positively correlated with the VEGFR2 expression level in gastric cancers (all P < 0.05). No significant correlation was found between the texture features and EGFR expression level. CT texture analysis, which had areas under the receiver operating characteristic curve (AUCs) ranging from 0.612 to 0.715, holds promise in predicting E-cadherin, Ki67 and VEGFR2 expression levels in gastric cancers.

Application of CT texture analysis in predicting histopathological characteristics of gastric cancers.

OBJECTIVES: To explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers. METHODS: Preoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed. RESULTS: Mean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, -0.231 ~ -0.324, 0.228 ~ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = -0.265, -0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion. CONCLUSION: CT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers. KEY POINTS: • CT texture analysis is noninvasive and effective for gastric cancer. • Portal venous CT images correlated significantly with differentiation degree and Lauren classification. • Standard deviation, entropy and minimum attenuation in arterial phase reflect vascular invasion.

Role of intravoxel incoherent motion MR imaging in preoperative assessing HER2 status of gastric cancers.

PURPOSE: To explore the role of intravoxel incoherent motion (IVIM) magnetic resonance (MR) imaging in evaluating human epidermal growth factor receptor 2 (HER2) status of gastric cancers preoperatively. RESULTS: The apparent diffusion coefficient (ADC) and pure diffusion coefficient (D) values correlated positively with HER2 scores of gastric cancers significantly (r = 0.276, P = 0.048; r = 0.481, P < 0.001, respectively). The ADC and D values of HER2 positive gastric cancers were significantly higher than those of HER2 negative tumors (P = 0.033, 0.007, respectively). With a cut-off value of 1.321 and 1.123 × 10-3 mm2/sec, the ADC and D values could distinguish HER2 positive gastric cancers from HER2 negative ones with an area under the curve of 0.733 and 0.762, respectively (P = 0.023, 0.011, respectively). MATERIALS AND METHODS: Fifty-three patients with gastric cancers underwent IVIM MR imaging preoperatively. The values of ADC, D, pseudo diffusion coefficient (D*) and perfusion related fraction (f) of the lesions were obtained. Partial correlation test including tumor volume was performed to analyze correlations between IVIM values and HER2 scores excluding the impact of tumor size. IVIM parameters of gastric cancers with different HER2 status were compared using independent samples t test. Diagnostic performance of IVIM parameters in distinguishing HER2 positive gastric cancers from negative ones was tested with receiver operating characteristic analysis. CONCLUSIONS: We confirmed the feasibility of IVIM MR imaging in preoperative assessment of HER2 status of gastric cancers, which might make up the shortfall of biopsy and facilitate personalized treatment for patients with gastric cancers.

Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps.

PURPOSE: To investigate the efficacy of histogram analysis of the entire tumor volume in apparent diffusion coefficient (ADC) maps for differentiating between histological grades in gastric cancer. MATERIALS AND METHODS: Seventy-eight patients with gastric cancer were enrolled in a retrospective 3.0T magnetic resonance imaging (MRI) study. ADC maps were obtained at two different b values (0 and 1000 sec/mm2 ) for each patient. Tumors were delineated on each slice of the ADC maps, and a histogram for the entire tumor volume was subsequently generated. A series of histogram parameters (eg, skew and kurtosis) were calculated and correlated with the histological grade of the surgical specimen. The diagnostic performance of each parameter for distinguishing poorly from moderately well-differentiated gastric cancers was assessed by using the area under the receiver operating characteristic curve (AUC). RESULTS: There were significant differences in the 5th , 10th , 25th , and 50th percentiles, skew, and kurtosis between poorly and well-differentiated gastric cancers (P < 0.05). There were correlations between the degrees of differentiation and histogram parameters, including the 10th percentile, skew, kurtosis, and max frequency; the correlation coefficients were 0.273, -0.361, -0.339, and -0.370, respectively. Among all the histogram parameters, the max frequency had the largest AUC value, which was 0.675. CONCLUSION: Histogram analysis of the ADC maps on the basis of the entire tumor volume can be useful in differentiating between histological grades for gastric cancer. LEVEL OF EVIDENCE: 4 J. Magn. Reson. Imaging 2017;45:440-449.