Differentiation of early gastric cancer with ulceration and resectable advanced gastric cancer using multiphasic dynamic multidetector CT

Enhancement Patterns in Differentiated and Undifferentiated Gastric Carcinoma: Multiphasic Contrast-Enhanced Computed Tomography Versus Histopathology

Background Gastric adenocarcinoma (GCA) poses a significant global health burden due to its prevalence and high morbidity and mortality rates. GCA is classified into three main histological types: well-differentiated (intestinal type), poorly differentiated (diffuse type), and mixed or indeterminate forms. These types vary in causes, epidemiology, and genetics, with the diffuse type often associated with the worst prognosis. Endoscopic biopsy is the primary method for characterization, but it has its limitations. There is potential in using contrast-enhanced computed tomography (CT) to differentiate between histological subtypes of gastric adenocarcinoma, which could aid subtype differentiation. Building on this, our study aims to assess CT's effectiveness in distinguishing between broad histological groups of gastric adenocarcinoma based on enhancement patterns, contributing to improved diagnostic accuracy Objective Our research focuses on evaluating the effectiveness of multiphasic contrast-enhanced computed tomography (CECT) in distinguishing between the three broad histopathological subtypes of gastrointestinal cancers. Methods This study was a prospective, analytical observational study that was approved and carried out in our institutional tertiary care hospital. Consecutive individuals who had undergone endoscopic-guided biopsy and demonstrated histological evidence of GCA were taken into consideration for participation in the study. In order to complete the clinical staging process, further multiphasic CT scans were carried out on each of the fifty patients and were categorised accordingly based on the findings of histopathology. Results In the differentiated type, segmental distribution was: 5.5% upper segment, 16.7% middle segment, 66.7% lower segment, and 11.1% diffuse type. Esophageal involvement was 5.6%, duodenal involvement was similar, and lymph node involvement was approximately 38.8%. TNM staging: 38.8% IIIB, 22.2% III, 27.8% IVA, and 11.1% IVB. In the undifferentiated type, segmental distribution: 6.2% upper segment, 31.2% middle segment, 50.0% lower segment, and 12.5% diffuse type. Esophageal involvement was around 6.25%, duodenal involvement was 18.75%, and lymph node involvement was about 71.8%. TNM staging: 34.4% IIIB, 21.8% III, 28.1% IVA, and 15.6% IVB. Conclusion Multiphasic CT evaluations provide valuable insights into the prognostic aspects of gastric carcinomas by assessing peak enhancement. Differentiated tumors typically exhibit arterial phase enhancement, while undifferentiated tumors show venous phase enhancement, reflecting their microvascular architecture. Recent studies emphasize the importance of understanding gastric carcinoma characteristics for diagnosis and prognosis. Our research aligns with this, revealing distinct contrast enhancement patterns between differentiated and undifferentiated types. However, discrepancies in histological classifications and contrast enhancement patterns across studies warrant further investigation. Integrating histopathological and radiological insights is essential for accurate diagnosis and treatment planning.

Radiomics Analysis to Predict Lymphovascular Invasion of Gastric Cancer Based on Iodine-Based Material Decomposition Images and Virtual Monoenergetic Images

OBJECTIVE

This study aimed to investigate the utility of virtual monoenergetic images (VMIs) and iodine-based material decomposition images (IMDIs) in the assessment of lymphovascular invasion (LVI) in gastric cancer (GC) patients.

METHODS

A total of 103 GC patients who underwent dual-energy spectral computed tomography preoperatively were enrolled. The LVI status was confirmed by pathological analysis. The radiomics features obtained from the 70 keV VMI and IMDI were used to build radiomics models. Independent clinical factors for LVI were identified and used to build the clinical model. Then, combined models were constructed by fusing clinical factors and radiomics signatures. The predictive performance of these models was evaluated.

RESULTS

The computed tomography-reported N stage was an independent predictor of LVI, and the areas under the curve (AUCs) of the clinical model in the training group and testing group were 0.750 and 0.765, respectively. The radiomics models using the VMI signature and IMDI signature and combining these 2 signatures outperformed the clinical model, with AUCs of 0.835, 0.855, and 0.924 in the training set and 0.838, 0.825, and 0.899 in the testing set, respectively. The model combined with the computed tomography-reported N stage and the 2 radiomics signatures achieved the best performance in the training (AUC, 0.925) and testing (AUC, 0.961) sets, with a good degree of calibration and clinical utility for LVI prediction.

CONCLUSIONS

The preoperative assessment of LVI in GC is improved by radiomics features based on VMI and IMDI. The combination of clinical, VMI-, and IMDI-based radiomics features effectively predicts LVI and provides support for clinical treatment decisions.

Extracellular volume fraction determined by equilibrium contrast-enhanced computed tomography: correlation with histopathological findings in gastric cancer

PURPOSE

To assess the relationship between histopathological features of gastric cancer and the extracellular volume fraction (ECV) measured by preoperative equilibrium contrast-enhanced computed tomography (CECT).

MATERIALS AND METHODS

The study group consisted of 66 patients with surgically resected gastric adenocarcinoma who underwent preoperative multiphasic CECT. Tumor ECVs were calculated using region-of-interest measurements within the gastric cancer and aorta of each case on unenhanced and equilibrium-phase images. The relationship between the mean ECV values and clinicopathological parameters was examined by univariate analysis. Parameters showing a significant difference in the former test were further tested by linear regression and receiver operating characteristic (ROC) curve analyses.

RESULTS

In the univariate analysis, the values of venous invasion (p = 0.0487) and tumor infiltration (INF) pattern (p < 0.0001) were significantly correlated with the tumor ECV. INF was significantly correlated (β = 0.57, p < 0.0001) in the linear regression analysis. The tumor ECV showed better diagnostic accuracy for predicting INF (INFa/b vs INFc), and the area under the ROC curve value was 0.89.

CONCLUSION

Tumor ECV determined by equilibrium CECT is significantly correlated with the pathological INF of gastric cancer.

Are radiomic spleen features useful for assessing the differentiation status of advanced gastric cancer?

Background

The differentiation status of gastric cancer is related to clinical stage, treatment and prognosis. It is expected to establish a radiomic model based on the combination of gastric cancer and spleen to predict the differentiation degree of gastric cancer. Thus, we aim to determine whether radiomic spleen features can be used to distinguish advanced gastric cancer with varying states of differentiation.

Materials and methods

January 2019 to January 2021, we retrospectively analyzed 147 patients with advanced gastric cancer confirmed by pathology. The clinical data were reviewed and analyzed. Three radiomics predictive models were built from radiomics features based on gastric cancer (GC), spleen (SP) and combination of two organ position (GC+SP) images. Then, three Radscores (GC, SP and GC+SP) were obtained. A nomogram was developed to predict differentiation statue by incorporating GC+SP Radscore and clinical risk factors. The area under the curve (AUC) of operating characteristics (ROC) and calibration curves were assessed to evaluate the differential performance of radiomic models based on gastric cancer and spleen for advanced gastric cancer with different states of differentiation (poorly differentiated group and non- poorly differentiated group).

Results

There were 147 patients evaluated (mean age, 60 years ± 11SD, 111 men). Univariate and multivariate logistic analysis identified three clinical features (age, cTNM stage and CT attenuation of spleen arterial phase) were independent risk factors for the degree of differentiation of GC (p =0.004,0.000,0.020, respectively). The clinical radiomics (namely, GC+SP+Clin) model showed powerful prognostic ability in the training and test cohorts with AUCs of 0.97 and 0.91, respectively. The established model has the best clinical benefit in diagnosing GC differentiation.

Conclusion

By combining radiomic features (GC and spleen) with clinical risk factors, we develop a radiomic nomogram to predict differentiation status in patients with AGC, which can be used to guide treatment decisions.

Classification of tumor from computed tomography images: A brain-inspired multisource transfer learning under probability distribution adaptation

Preoperative diagnosis of gastric cancer and primary gastric lymphoma is challenging and has important clinical significance. Inspired by the inductive reasoning learning of the human brain, transfer learning can improve diagnosis performance of target task by utilizing the knowledge learned from the other domains (source domain). However, most studies focus on single-source transfer learning and may lead to model performance degradation when a large domain shift exists between the single-source domain and target domain. By simulating the multi-modal information learning and transfer mechanism of human brain, this study designed a multisource transfer learning feature extraction and classification framework, which can enhance the prediction performance of the target model by using multisource medical data (domain). First, this manuscript designs a feature extraction network that takes the maximum mean difference based on the Wasserstein distance as an adaptive measure of probability distribution and extracts the domain-specific invariant representations between source and target domain data. Then, aiming at the random generation of parameters bringing uncertainties to prediction accuracy and generalization ability of extreme learning machine network, the 1-norm regularization is used to implement sparse constraints of the output weight matrix and improve the robustness of the model. Finally, some experiments are carried out on the data of two medical centers. The experimental results show that the area under curves (AUCs) of the method are 0.958 and 0.929 in the two validation cohorts, respectively. The method in this manuscript can provide doctors with a better diagnostic reference, which has certain practical significance.

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer

BACKGROUND

The histological differentiation grades of gastric cancer (GC) are closely related to treatment choices and prognostic evaluation. Radiomics from dual-energy spectral CT (DESCT) derived iodine-based material decomposition (IMD) images may have the potential to reflect histological grades.

METHODS

A total of 103 patients with pathologically proven GC (low-grade in 40 patients and high-grade in 63 patients) who underwent preoperative DESCT were enrolled in our study. Radiomic features were extracted from conventional polychromatic (CP) images and IMD images, respectively. Three radiomic predictive models (model-CP, model-IMD, and model-CP-IMD) based on solely CP selected features, IMD selected features and CP coupled with IMD selected features were constructed. The clinicopathological data of the enrolled patients were analyzed. Then, we built a combined model (model-Combine) developed with CP-IMD and clinical features. The performance of these models was evaluated and compared.

RESULTS

Model-CP-IMD achieved better AUC results than both model-CP and model-IMD in both cohorts. Model-Combine, which combined CP-IMD radiomic features, pT stage, and pN stage, yielded the highest AUC values of 0.910 and 0.912 in the training and testing cohorts, respectively. Model-CP-IMD and model-Combine outperformed model-CP according to decision curve analysis.

CONCLUSION

DESCT-based radiomics models showed reliable diagnostic performance in predicting GC histologic differentiation grade. The radiomic features extracted from IMD images showed great promise in terms of enhancing diagnostic performance.

A computed tomography-based preoperative risk scoring system to distinguish lymphoepithelioma-like gastric carcinoma from non-lymphoepithelioma-like gastric carcinoma

Purpose

The aim of this study was to develop a preoperative risk scoring model for distinguishing lymphoepithelioma-like gastric carcinoma (LELGC) from non-LELGC based on contrast-enhanced computed tomography (CT) images.

Methods

Clinicopathological features and CT findings of patients with LELGC and non-LELGC in our hospital from January 2016 to July 2022 were retrospectively analyzed and compared. A preoperative risk stratification model and a risk scoring system were developed using logistic regression.

Results

Twenty patients with LELGC and 40 patients with non-LELGC were included in the training cohort. Significant differences were observed in Epstein–Barr virus (EBV) infection and vascular invasion between the two groups (p < 0.05). Significant differences were observed in the distribution of location, enhancement pattern, homogeneous enhancement, CT-defined lymph node status, and attenuations in the non-contrast, arterial, and venous phases (all p < 0.05). Enhancement pattern, CT-defined lymph node status, and attenuation in venous phase were independent predictors of LELGC. The optimal cutoff score of distinguishing LELGC from non-LELGC was 3.5. The area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of risk identification model in the training cohort were 0.904, 87.5%, 80.0%, and 85.0%, respectively. The area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of risk identification model in the validation cohort were 0.705 (95% CI 0.434–0.957), 75.0%, 63.6%, and 66.7%, respectively.

Conclusion

A preoperative risk identification model based on CT imaging data could be helpful for distinguishing LELGC from non-LELGC.

A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma

Objective

This study aims to differentiate preoperative Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL) by transfer learning radiomics nomogram (TLRN) with whole slide images of GC as source domain data.

Materials and Methods

This study retrospectively enrolled 438 patients with histopathologic diagnoses of Borrmann type IV GC and PGL. They received CT examinations from three hospitals. Quantitative transfer learning features were extracted by the proposed transfer learning radiopathomic network and used to construct transfer learning radiomics signatures (TLRS). A TLRN, which integrates TLRS, clinical factors, and CT subjective findings, was developed by multivariate logistic regression. The diagnostic TLRN performance was assessed by clinical usefulness in the independent validation set.

Results

The TLRN was built by TLRS and a high enhanced serosa sign, which showed good agreement by the calibration curve. The TLRN performance was superior to the clinical model and TLRS. Its areas under the curve (AUC) were 0.958 (95% confidence interval [CI], 0.883–0.991), 0.867 (95% CI, 0.794–0.922), and 0.921 (95% CI, 0.860–0.960) in the internal and two external validation cohorts, respectively. Decision curve analysis (DCA) showed that the TLRN was better than any other model. TLRN has potential generalization ability, as shown in the stratification analysis.

Conclusions

The proposed TLRN based on gastric WSIs may help preoperatively differentiate PGL from Borrmann type IV GC.

Borrmann type IV gastric cancer, primary gastric lymphoma, transfer learning, whole slide image, deep learning.

Assessment of gastric wall structure using ultra-high-resolution computed tomography

PURPOSE

To evaluate the image quality of ultra-high-resolution CT (U-HRCT) in the comparison among four different reconstruction methods, focusing on the gastric wall structure, and to compare the conspicuity of a three-layered structure of the gastric wall between conventional HRCT (C-HRCT) and U-HRCT.

METHOD

Our retrospective study included 48 patients who underwent contrast-enhanced U-HRCT. Quantitative analyses were performed to compare image noise of U-HRCT between deep-learning reconstruction (DLR) and other three methods (filtered back projection: FBP, hybrid iterative reconstruction: Hybrid-IR, and Model-based iterative reconstruction: MBIR). The mean overall image quality scores were also compared between the DLR and other three methods. In addition, the mean conspicuity scores for the three-layered structure of the gastric wall at five regions were compared between C-HRCT and U-HRCT.

RESULTS

The mean noise of U-HRCT with DLR was significantly lower than that with the other three methods (P < 0.001). The mean overall image quality scores with DLR images were significantly higher than those with the other three methods (P < 0.001). Regarding the comparison between C-HRCT and U-HRCT, the mean conspicuity scores for the three-layered structure of the gastric wall on U-HRCT were significantly better than those on C-HRCT in the fornix (5 [5-5] vs. 3.5 [3-4], P < 0.001), body (4 [3.25-5] vs. 4 [3-4], P = 0.039), angle (5 [4-5] vs. 3 [2-4], P < 0.001), and antral posterior (4 [3.25-5] vs. 2 [2-4], P < 0.001), except for antral anterior (4 [3-5] vs. 3 [3-4], P = 0.230) CONCLUSION: U-HRCT using DLR improved the image noise and overall image quality of the gastric wall as well as the conspicuity of the three-layered structure, suggesting its utility for the evaluation of the anatomical details of the gastric wall structure.

Intra-examiner and inter-examiner reliability of rehabilitative ultrasound imaging for lumbar multifidus and anterolateral abdominal muscles in females with recurrent low back pain: an observational, cross-sectional study

Aim: To examine the reliability of rehabilitative ultrasound imaging performed to measure the thickness of the transverse abdominis, internal oblique, external oblique, and lumbar multifidus muscles in females with recurrent low back pain. Material and methods: A sample of 15 women was recruited. Two independent examiners recorded the thickness of their deep abdominal and spinal muscles by rehabilitative ultrasound imaging. Imaging scans of the transverse abdominis, internal oblique, and external oblique muscles were performed in the supine position and in the midaxillary line, between the lower edge of the ribcage and the iliac crest. Imaging of the lumbar multifidus was done in the prone position and at the level of the L5/S1 zygapophyseal joints. Imaging scans were performed bilaterally in rest and contraction, three times by the first examiner (at baseline, after two hours, and one week later) and once by the second examiner. Results: Good to excellent within-session intra-rater (ICC = 0.76, 0.97), good to excellent between-session intra-rater reliability (ICC = 0.73, 0.93), and good to excellent inter-rater reliability (ICC = 0.73, 0.98) were obtained. Conclusions: The results showed that rehabilitative ultrasound imaging can be used as an excellent reliable instrument by one or two examiners to measure the thickness of the transverse abdominis, internal oblique, external oblique and lumbar multifidus muscles in females with recurrent low back pain.

Performance of quantitative CT texture analysis in differentiation of gastric tumors

PURPOSE

To examine the computed tomography (CT) images of patients with a diagnosis of gastric tumor by texture analysis and to investigate its place in differential diagnosis.

MATERIALS AND METHODS

Contrast enhanced venous phase CT images of 163 patients with pathological diagnosis of gastric adenocarcinoma (n = 125), gastric lymphoma (n = 12) and gastrointestinal stromal tumors (n = 26) were retrospectively analyzed. Pixel size adjustment, gray-level discretization and gray-level normalization procedures were applied as pre-processing steps. Region of interest (ROI) was determined from the axial slice that represented the largest lesion area and a total of 40 texture features were calculated for each patient. Texture features were compared between the tumor subtypes and between adenocarcinoma grades. Statistically significant texture features were combined into a single parameter by logistic regression analysis. The sensitivity and specificity of these features and the combined parameter were measured to differentiate tumor subtypes by receiver-operating characteristic curve (ROC) analysis.

RESULTS

Classifications between adenocarcinoma versus lymphoma, adenocarcinoma vs. gastrointestinal stromal tumor (GIST) and well-differentiated adenocarcinoma versus poorly differentiated adenocarcinoma using texture features yielded successful results with high sensitivity (98, 91, 96%, respectively) and specificity (75, 77, 80%, respectively).

CONCLUSIONS

CT texture analysis is a non-invasive promising method for classifying gastric tumors and predicting gastric adenocarcinoma differentiation.

Computed tomography radiomics to predict EBER positivity in Epstein-Barr virus-associated gastric adenocarcinomas: a retrospective study

BACKGROUND

The relevance of Epstein-Barr virus (EBV) in gastric carcinoma has been represented by the existence of EBV-encoded small RNA (EBER) in the tumor cells and has prognostic significance in gastric cancer, while gastric adenocarcinoma represents the most frequently occurring gastric malignancy.

PURPOSE

To observe the capacity of radiomic features extracted from contrast-enhanced computed tomography (CE-CT) images to differentiate EBER-positive gastric adenocarcinoma from EBER-negative ones.

MATERIAL AND METHODS

A total of 54 patients with gastric adenocarcinoma (EBER-positive: 27, EBER-negative: 27) were retrospectively examined. Radiomic imaging features were extracted from all regions of interest (ROI) delineated by two experienced radiologists on late arterial phase CT images. We distinguished related radiomic features through the two-tailed t test and applied them to construct a decision tree model to evaluate whether EBER in situ hybridization positive had appeared.

RESULTS

Nine radiomics features were significantly related to EBER in situ hybridization status (P < 0.05), four of which were used to build the decision tree through backward elimination: Correlation_ AllDirection_offset7, Correlation_ angle135_offset7, RunLengthNonuniformity_ AllDirection_offset1_SD, and HighGreyLevelRunEmphasis_ AllDiretion_offset1_SD. The decision tree model consisted of seven decision nodes and six terminal nodes, three of which demonstrated positive EBER in situ hybridization. The specificity, sensitivity, and accuracy of the model were 84%, 80%, and 81.7%, respectively. The area under the curve of the decision tree model was 0.87.

CONCLUSION

Radiomics based on CE-CT could be applied to predict EBER in situ hybridization status preoperatively in patients with gastric adenocarcinoma.

A CT-based Radiomics Model for Prediction of Lymph Node Metastasis in Early Stage Gastric Cancer

RATIONALE AND OBJECTIVES

To develop and validate a CT-based radiomics model for preoperative prediction of lymph node metastasis (LNM) in early stage gastric cancer (EGC).

MATERIALS AND METHODS

Four hundred and sixty-three consecutive EGC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. The predictive performance of radiomics signature was tested in the training and testing cohorts. Multivariate logistic regression analysis was conducted to build a radiomics-based model combined radiomics signature and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness.

RESULTS

The radiomics signature comprised six robust features showed significant association with LNM in both cohorts. A radiomics model that incorporated radiomics signature and CT-reported lymph node status showed good calibration and discrimination in the training cohort (AUC = 0.91) and testing cohort (AUC = 0.89). Decision curve analysis confirmed the clinical utility of this model.

CONCLUSION

The CT-based radiomics model showed favorable accuracy for prediction of LNM in EGC and may help to improve clinical decision-making.

A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer

OBJECTIVE

This study aimed to preoperatively differentiate primary gastric lymphoma from Borrmann type IV gastric cancer by heterogeneity nomogram based on routine contrast-enhanced computed tomographic images.

METHODS

We enrolled 189 patients from 2 hospitals (90 in the training cohort and 99 in the validation cohort). Subjective findings, including high-enhanced mucosal sign, high-enhanced serosa sign, nodular or an irregular outer layer of the gastric wall, and perigastric fat infiltration, were assessed to construct a subjective finding model. A deep learning model was developed to segment tumor areas, from which 1680 three-dimensional heterogeneity radiomic parameters, including first-order entropy, second-order entropy, and texture complexity, were extracted to build a heterogeneity signature by least absolute shrinkage and selection operator logistic regression. A nomogram that integrates heterogeneity signature and subjective findings was developed by multivariate logistic regression. The diagnostic performance of the nomogram was assessed by discrimination and clinical usefulness.

RESULTS

High-enhanced serosa sign and nodular or an irregular outer layer of the gastric wall were identified as independent predictors for building the subjective finding model. High-enhanced serosa sign and heterogeneity signature were significant predictors for differentiating the 2 groups (all, P < 0.05). The area under the curve with heterogeneity nomogram was 0.932 (95% confidence interval, 0.863-0.973) in the validation cohort. Decision curve analysis and stratified analysis confirmed the clinical utility of the heterogeneity nomogram.

CONCLUSIONS

The proposed heterogeneity radiomic nomogram on contrast-enhanced computed tomographic images may help differentiate primary gastric lymphoma from Borrmann type IV gastric cancer preoperatively.

Utility of the gastric window in computed tomography for differentiation of early gastric cancer (T1 stage) from muscularis involvement (T2 stage)

OBJECTIVE

To analyze the diagnostic value of using the gastric window in computed tomography for differentiation of early gastric cancer (T1 stage) from muscularis involvement (T2 stage).

MATERIALS AND METHODS

All patients with pathologically confirmed T1 stage and T2 stage gastric cancer and who underwent endoscopic resection or gastrectomy at our institution from January 2011 to November 2018 were examined. Each patient received an enhanced CT scan of the abdomen before the operation. T staging of tumors based on the CT scans was performed independently by two radiologists using the gastric window (width 150-200 HU, level 80-100 HU) and the abdominal window (width 350-400 HU, level 50 HU).

RESULTS

Use of the gastric window to diagnose stage T1 EGC led to an accuracy of 88.9% for observer1 and 91.5% for observer2; use of the abdominal window led to an accuracy of 53.6% for observer1 and 51.6% (38/106) for observer2. Use of the gastric window to diagnose stage T2 led to an accuracy of 85.6% for observer1 and 82.4% for observer2; use of the abdominal window led to an accuracy of 52.3% for both observer1 and observer2. For observer1, use of the gastric window had a diagnostic accuracy of 69.2% for stage T1a and 62.5% for stage T1b; for observer2, the diagnostic accuracy was 65.1% for stage T1a and 67.0% for stage T1b. A Kappa test indicated moderate and substantial inter-observer agreement for T staging with gastric window (κ = 0.598, P < 0.001) and abdominal window (κ = 0.745, P < 0.001).

CONCLUSION

Use of the gastric window in computed tomography provided more accurate staging for T1 and T2 stages of gastric cancer than the conventional abdominal window.

CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer

PURPOSE

This work aimed to develop and validate a deep learning radiomics model for evaluating serosa invasion in gastric cancer.

MATERIALS AND METHODS

A total of 572 gastric cancer patients were included in this study. Firstly, we retrospectively enrolled 428 consecutive patients (252 in the training set and 176 in the test set I) with pathological confirmed T3 or T4a. Subsequently, 144 patients who were clinically diagnosed cT3 or cT4a were prospectively allocated to the test set II. Histological verification was based on the surgical specimens. CT findings were determined by a panel of three radiologists. Conventional hand-crafted features and deep learning features were extracted from three phases CT images and were utilized to build radiomics signatures via machine learning methods. Incorporating the radiomics signatures and CT findings, a radiomics nomogram was developed via multivariable logistic regression. Its diagnostic ability was measured using receiver operating characteristiccurve analysis.

RESULTS

The radiomics signatures, built with support vector machine or artificial neural network, showed good performance for discriminating T4a in the test I and II sets with area under curves (AUCs) of 0.76-0.78 and 0.79-0.84. The nomogram had powerful diagnostic ability in all training, test I and II sets with AUCs of 0.90 (95 % CI, 0.86-0.94), 0.87 (95 % CI, 0.82-0.92) and 0.90 (95 % CI, 0.85-0.96) respectively. The net reclassification index revealed that the radiomics nomogram had significantly better performance than the clinical model (p-values < 0.05).

CONCLUSIONS

The deep learning radiomics model based on CT images is effective at discriminating serosa invasion in gastric cancer.

Improved Visualization of Gastric Cancer and Increased Diagnostic Performance in Lesion Depiction and Depth Identification Using Monoenergetic Reconstructions from a Novel Dual-Layer Spectral Detector CT

RATIONALE AND OBJECTIVES

To determine the optimal keV for the visualization of gastric cancer and to investigate its value in depicting lesions and in identifying depth invasion using virtual monoenergetic images (VMIs) on a novel dual-layer spectral detector CT.

MATERIALS AND METHODS

Eighty-two gastric cancer patients were retrospectively enrolled, and 41 patients who did not undergo surgery were evaluated for image quality in VMIs at different keVs (40 keV-70 keV with 10 keV increments) and in conventional 120 kVp polyenergetic images (PEIs) reconstructed from the portal venous phase. Objective image quality was assessed by the contrast-to-noise ratio of the gastric cancer, while subjective performance was compared using a 5-point Likert scale. Another 41 patients who underwent surgery were examined to compare the diagnostic performance of the VMIs taken at the optimal keV and that of the 120 kVp-PEIs.

RESULTS

The contrast-to-noise ratio of gastric cancer at 40 keV (10.4 ± 4.6) was the highest among all the VMIs and was significantly superior to that of the 120 kVp-PEIs (3.5 ± 1.5, p < 0.001). Gastric-specific image quality was rated highest for the 40 keV-VMIs (4.92 ± 0.26), which was significantly superior to that of the 120 kVp-PEIs (4.15 ± 0.82, p < 0.001). In the diagnostic group, there were 13 pT1, 10 pT2, 9 pT3, and 9 pT4 gastric cancer patients. Compared with the 120 kVp-PEIs, the VMIs at 40 keV tended to have a higher detection rate of gastric cancer (82.9% vs. 92.7%, respectively, p = 0.125) and a significantly improved diagnostic accuracy in the T stage (from 41.5% to 78.11%, respectively) (p < 0.001), particularly in pT1 patients, whose diagnostic accuracy was improved by 53.8% (7.7% vs. 61.5%, respectively, p = 0.016).

CONCLUSION

VMIs at 40 keV performed the best, both objectively and subjectively, for gastric cancer, leading to improved lesion depiction and higher T stage accuracy.

A CT-based radiomics signature for evaluating tumor infiltrating Treg cells and outcome prediction of gastric cancer

Background

Tumor infiltrating regulatory T (TITreg) cells are highly infiltrated in gastric cancer (GC) and associated with worse prognosis of GC patients. We aim to develop and validate a radiomics signature for evaluation of TITreg cells and outcome prediction of GC patients.

Methods

A total of 165 GC patients from three independent cohorts were enrolled in this retrospective study. The abundance of TITreg cells were evaluated by using multispectral immunohistochemical analysis and CIBERSORT algorithm. The radiomics features were extracted by using PyRadiomics software and the radiomics signature was generated by using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The receiver operator characteristic (ROC) curves were applied to assess the performance of radiomics signature for estimating TITreg cells. Univariable and multivariable Cox regression analysis were used for identifying risk factor of overall survival (OS). The prognostic value of the radiomics signature and the TITreg cells were evaluated by using the Kaplan-Meier method and log-rank test.

Results

Six robust features were selected for building the radiomics signature. The radiomics signature showed good ability for estimating TITreg in the training, validation and testing cohort, with area under the curve (AUC) of 0.884, 0.869 and 0.847, respectively. Multivariable Cox regression analysis showed that the radiomics signature was an independent risk factor of unfavorable OS of GC patients.

Conclusions

The proposed CT-based radiomics signature is a promising non-invasive biomarker of TITreg cells and outcome prediction of GC patients.

Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment

PURPOSE

The aim of the study is to explore the role of computed tomography texture analysis (CT-TA) for predicting clinical T and N stages and tumor grade before neoadjuvant chemotherapy treatment in gastric cancer (GC) patients during the preoperative period.

MATERIALS AND METHODS

CT images of 114 patients with GC were included in this retrospective study. Following pre-processing steps, textural features were extracted using MaZda software in the portal venous phase. We evaluated and analyzed texture features of six principal categories for differentiating between T stages (T1,2 vs T3,4), N stages (N+ vs N-) and grades (low-intermediate vs. high). Classification was performed based on texture parameters with high model coefficients in linear discriminant analysis (LDA).

RESULTS

Dimension-reduction steps yielded five textural features for T stage, three for N stage and two for tumor grade. The discriminatory capacities of T stage, N stage and tumor grade were 90.4%, 81.6% and 64.5%, respectively, when LDA algorithm was employed.

CONCLUSION

CT-TA yields potentially useful imaging biomarkers for predicting the T and N stages of patients with GC and can be used for preoperative evaluation before neoadjuvant treatment planning.

A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer

OBJECTIVE

To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer.

METHODS

This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts.

RESULTS

The radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704-0.894] in the training cohort and 0.771 (95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful.

CONCLUSIONS

We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.

Feasibility of using two-dimensional axial computed tomography in pretreatment decision making for patients with early gastric cancer

Computed tomography (CT) is widely used in the pretreatment period of early gastric cancer (EGC). Only few studies have reported low accuracy of CT imaging for T and N staging in patients with EGC. However, owing to the limited number of studies, the value of CT imaging for EGC staging is not well known. Thus, we conducted a retrospective cross-sectional study regarding the associations among submucosal invasion, lymph node metastasis, and CT findings.The medical records of patients with EGC who had surgery or endoscopic resection were reviewed in a single center from January 2011 to December 2016. We evaluated the histological type, invasion depth, and lymph node (LN) metastasis on the basis of two-dimensional CT findings.We enrolled 1544 patients. Submucosal (SM) invasion was related to tumor size, histological type, and wall thickening or enhancement on CT images. Deep SM invasion (>500 μm) was also related to tumor size, poorly differentiated type, and abnormal CT findings (wall thickening, enhancement, and central depression). Among the patients with LN reactive positivity (0.5-1 cm), those who were female and had a tumor invasion of >1000 μm showed a higher prevalence of LN metastasis. The false-negative LN group had a higher prevalence of large tumors (>3 cm), poor differentiation, and SM invasion than the true-negative group.Wall thickening, enhancement, and central depression on CT images might be related to SM invasion. Patients with any positive CT findings needs more attention when performing ESD.

CT gastrography "wall-carving technique" of gastric cancer: impact of contrast enhancement based on layer depth

PURPOSE

Wall-carving technique (WC) is a special volume rendering technique of three-dimensional CT gastrography that can illustrate the enhancement of gastric wall at an arbitrary depth. We conducted the present study to evaluate the impact of contrast enhancement based on layer depth on WC of gastric cancer and to correlate them with pathological findings.

METHODS

The subjects of this retrospective study consisted of 36 patients with advanced gastric cancer (22 men, 14 women; age range, 39-90 years; median, 67 years) who underwent contrast-enhanced CT before surgery. WC images of arterial phase were divided into first and second layer. Two radiologists in consensus evaluated the contrast enhancement of WC images for each layer and correlated with pathologic factors.

RESULTS

Twenty-six (72%) of the gastric cancers showed a well-enhanced lesion in the first layer at the arterial phase on WC images, and 18 (50%) showed a well-enhanced lesion in the second layer. The study of second layers showed that the well-enhanced group had significantly more cases of differentiated type histology and intermediate stroma than the normally to poorly enhanced group (p = 0.008 and 0.0026).

CONCLUSION

The contrast enhancement on WC of gastric cancer showed a significant relationship with pathological factors based on layer depth.

The reliability and concurrent validity of a new iPhone® application for measuring active lumbar spine flexion and extension range of motion in patients with low back pain

Objective: To investigate the reliability and validity of an iPhone® application (iHandy® Level) for measuring active lumbar flexion-extension range of motion (ROM) in chronic nonspecific low back pain (CNLBP) patients. Methods: Fifteen CNLBP patients were recruited. The participants stood in a relaxed position and the T12-L1 and S1-S2 spinal levels were identified through palpation and were marked on the skin. Two blinded examiners used a gravity-based inclinometer and the application in order to measure ROM. The instruments were lined up appropriately and the participants were asked to perform maximum lumbar flexion following by maximum extension. First, each examiner placed the instruments over the T12-L1 level and then over the S1-S2 level during the movements. In order to calculate flexion-extension ROM, the measurement which was obtained from T12-L1 was subtracted from S1-S2. Intraclass correlation coefficient (ICC) models (3, k) and (2, k) were used in order to determine the intrarater and inter-rater reliability, respectively. The Spearman's correlation coefficients (r_s ) and Bland-Altman plots were used in order to examine the validity. Results: Fair-to-excellent intrarater (ICC = 0.39-0.89) and moderate-to-good inter-rater reliability (ICC = 0.55-0.77) were observed using the inclinometer. Moreover, poor-to-good intrarater (ICC = 0.30-70) and inter-rater (ICC = 0.13-0.70) reliability were found with the application. The Spearman's correlation coefficients demonstrated low-to-moderate associations between the measures of the two instruments (r_s ≥ 0.22). The Bland-Altman plots indicated that there was a significant difference between the instruments for measuring flexion ROM. The difference was not significant for measuring extension ROM. Conclusion: The iHandy® Level application does not have sufficient validity for measuring active lumbar flexion ROM in CNLBP patients.

An Intelligent Clinical Decision Support System for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

PURPOSE

The aim of this study was to develop and validate a computational clinical decision support system (DSS) on the basis of CT radiomics features for the prediction of lymph node (LN) metastasis in gastric cancer (GC) using machine learning-based analysis.

METHODS

Clinicopathologic and CT imaging data were retrospectively collected from 490 patients who were diagnosed with GC between January 2002 and December 2016. Radiomics features were extracted from venous-phase CT images. Relevant features were selected, ranked, and modeled using a support vector machine classifier in 326 training and validation data sets. A model test was performed independently in a test set (n = 164). Finally, a head-to-head comparison of the diagnostic performance of the DSS and that of the conventional staging criterion was performed.

RESULTS

Two hundred ninety-seven of the 490 patients examined had histopathologic evidence of LN metastasis, yielding a 60.6% metastatic rate. The area under the curve for predicting LN+ was 0.824 (95% confidence interval, 0.804-0.847) for the DSS in the training and validation data and 0.764 (95% confidence interval, 0.699-0.833) in the test data. The calibration plots showed good concordance between the predicted and observed probability of LN+ using the DSS approach. The DSS was better able to predict LN metastasis than the conventional staging criterion in the training and validation data (accuracy 76.4% versus 63.5%) and in the test data (accuracy 71.3% versus 63.2%) CONCLUSIONS: A DSS based on 13 "worrisome" radiomics features appears to be a promising tool for the preoperative prediction of LN status in patients with GC.

Test-retest reliability of sit-to-stand and stand-to-sit analysis in people with and without chronic non-specific low back pain

BACKGROUND

Sit-to-stand (STD) and stand-to-sit (SIT) analysis can provide information on functional independence in daily activities in patients with low back pain (LBP). However, in order for measurements to be clinically useful, data on psychometric properties should be available.

OBJECTIVES

The main purpose was to investigate intra-rater reliability of STD and SIT tasks in participants with and without chronic non-specific LBP (CNLBP). The second purpose was to detect any differences in lumbar spine and hips sagittal plane kinematics and coordination between asymptomatic individuals and CNLBP patients during STD and SIT.

DESIGN

Cross-sectional study.

METHOD

Twenty-three CNLBP patients and 23 demographically-matched controls were recruited. Ten markers were placed on specific anatomical landmarks. Participants were asked to perform STD and SIT at a preferred speed. Peak flexion angles, mean angular velocities, lumbar to hip movement ratios, and relative phase angles were measured. The procedure was repeated after 2 h and 6-8 days. Differences between two groups were analyzed using independent t-test. Intraclass correlation coefficient (ICC 3,k), standard error of measurement (SEM), and limits of agreement (LOAs) were also estimated.

RESULTS

The ICC values showed moderate to excellent intra-rater reliability, with relatively low SEM values (≤10.17°). The 95% LOAs demonstrated that there were no differences between the measured parameters. Furthermore, CNLBP patients had limited sagittal plane angles, smaller angular velocities, and lumbar-hip dis-coordination compared to asymptomatic participants.

CONCLUSIONS

The results indicated moderate to excellent test-retest reliability of STD and SIT analysis. Moreover, CNLBP patients had altered kinematics during STD and its reverse.

Preoperative CT texture analysis of gastric cancer: correlations with postoperative TNM staging

AIM

To explore the role of computed tomography (CT) texture analysis in predicting pathologic stage of gastric cancers.

MATERIALS AND METHODS

Preoperative enhanced CT images of 153 patients (112 men, 41 women) with gastric cancers were reviewed retrospectively. Regions of interest (ROIs) were manually drawn along the margin of the lesion on the section where it appeared largest on the arterial and venous CT images, which yielded texture parameters, including mean, maximum frequency, mode, skewness, kurtosis, and entropy. Correlations between texture parameters and pathological stage were analysed with Spearman's correlation test. The diagnostic performance of CT texture parameters in differentiating different stages was evaluated using receiver operating characteristic (ROC) analysis.

RESULTS

Maximum frequency in the arterial phase and mean, maximum frequency, mode in the venous phase correlated positively with T stage, N stage, and overall stage (all p<0.05) of gastric cancer. Entropy in the venous phase also correlated positively with N stage (p=0.009) and overall stage (p=0.032). Skewness in the arterial phase had the highest area under the ROC curve (AUC) of 0.822 in identifying early from advanced gastric cancers. Multivariate analysis identified four parameters, including maximum frequency, skewness, entropy in the venous phase, and differentiation degree from biopsy, for predicting lymph node metastasis of gastric cancer. The multivariate model could distinguish gastric cancers with and without lymph node metastasis with an AUC of 0.892.

CONCLUSION

Multiple CT texture parameters, especially those in the venous phase, correlated well with pathological stage and hold great potential in predicting lymph node metastasis of gastric cancers.

[A radiomics-based model for differentiation between benign and malignant gastrointestinal stromal tumors]

OBJECTIVE

To establish a model for discrimination between benign and malignant gastrointestinal stromal tumors (GIST) by analyzing the texture features extracted from computed tomography (CT) images.

METHODS

The CT datasets were collected from 110 patients with GIST (including 80 as the training cohort and 30 as the validation cohort). Feature set reduction was executed with the 0.632 + bootstrap method in the initial feature set followed by stepwise forward feature selection in the feature subset, and the classification model was generated by logistic regression.

RESULTS

The 6-texture-featurebased classification model successfully discriminated between benign and malignant GIST in both the training and validation cohorts with AUCs of 0.93 and 0.91, sensitivity of 0.88 and 0.87, specificity of 0.85 and 0.86, and accuracy of 0.87 and 0.86 in the two cohorts, respectively.

CONCLUSIONS

This classification model established by radiomics analysis is capable of discrimination between benign and malignant GIST to provide assistance in preoperative diagnosis of GIST.

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

Objective

To study the value of high enhanced serosa sign on contrast-enhanced computed tomography (CT) in differentiating T3 from T4a gastric cancer in different Lauren classification.

Methods

This study included 276 consecutive patients with surgically confirmed pT3 or pT4a gastric cancers. The pre-operative CT images were reviewed by two radiologists blinded. The demonstration of the high enhanced serosa on CT between T3 and T4a was compared with chi-square test. The diagnostic performance of this sign on CT in the differentiation of T4a from T3 in different Lauren classification was calculated.

Results

The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the judgement of serosa invasion using the high enhanced serosa sign on CT was 74.6%, 63.7%, 83.6%, 76.0% and 73.8% by one radiologist and 76.4%, 66.1%, 84.9%, 78.1% and 75.4% by the other radiologist. Compared to the intestinal-type, the sensitivity of the judgement of serosa invasion using the high enhanced serosa sign on CT in diffuse-type was significant higher (80% in both readers), while the specificity trended to be lower (65.9% and 80.5%, respectively). There is no significant difference in the accuracy of diagnosis between intestinal-type and diffuse-type of gastric cancers (the P-values of two radiologists were 0.968, 0.591, respectively). The combination of the high enhanced serosa sign with conventional CT signs is significant different in diagnosis of T3 and T4a (P<0.001). The diagnostic accuracy was increased in both radiologists after the combination. The two readers achieved substantial agreement, with Kappa coefficient of 0.63, P<0.001.

Conclusions

The high enhanced serosa sign on CT is associated with serosa involvement. The sensitivity of the judgement of serosa invasion using this sign on CT in diffuse-type was significant higher than that in intestinal-type.

Histopathologic diversity of gastric cancers: Relationship between enhancement pattern on dynamic contrast-enhanced CT and histological type

PURPOSE

To evaluate the diagnostic value of contrast-enhanced computed tomography gastrography (CE-CTG) to predict the histological type of gastric cancer.

MATERIALS AND METHODS

We analyzed 47 consecutive patients with resectable advanced gastric cancer preoperatively evaluated by multiphasic dynamic contrast-enhanced CT. Two radiologists independently reviewed the CT images and they determined the peak enhancement phase, and then measured the CT attenuation value of the gastric lesion for each phase. The histological types of gastric cancers were assigned to three groups as differentiated-type, undifferentiated-type, and mixed-type. We compared the peak enhancement phase of the three types and compared the CT attenuation values in each phase.

RESULTS

The peak enhancement was significantly different between the three types of gastric cancers for both readers (reader 1, p=0.001; reader 2, p=0.009); most of the undifferentiated types had peak enhancement in the delayed phase. The CT attenuation values of undifferentiated type were significantly higher than those of differentiated or mixed type in the delayed phase according to both readers (reader 1, p=0.002; reader 2, p=0.004).

CONCLUSION

CE-CTG could provide helpful information in diagnosing the histological type of gastric cancers preoperatively.

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.

CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma

PURPOSE

To evaluate the value of CT-based radiomics signature for differentiating Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL).

MATERIALS AND METHODS

40 patients with Borrmann type IV GC and 30 patients with PGL were retrospectively recruited. 485 radiomics features were extracted and selected from the portal venous CT images to build a radiomics signature. Subjective CT findings, including gastric wall peristalsis, perigastric fat infiltration, lymphadenopathy below the renal hila and enhancement pattern, were assessed to construct a subjective findings model. The radiomics signature, subjective CT findings, age and gender were integrated into a combined model by multivariate analysis. The diagnostic performance of these three models was assessed with receiver operating characteristics curves (ROC) and were compared using DeLong test.

RESULTS

The subjective findings model, the radiomics signature and the combined model showed a diagnostic accuracy of 81.43% (AUC [area under the curve], 0.806; 95% CI [confidence interval]: 0.696-0.917; sensitivity, 63.33%; specificity, 95.00%), 84.29% (AUC, 0.886 [95% CI: 0.809-0.963]; sensitivity, 86.67%; specificity, 82.50%), 87.14% (AUC, 0.903 [95%CI: 0.831-0.975]; sensitivity, 70.00%; specificity, 100%), respectively. There were no significant differences in AUC among these three models (P=0.051-0.422).

CONCLUSION

Radiomics analysis has the potential to accurately differentiate Borrmann type IV GC from PGL.

Diffuse-type gastric cancer: specific enhancement pattern on multiphasic contrast-enhanced computed tomography

PURPOSE

To evaluate the enhancement pattern of diffuse-type gastric cancers (DGCs) on multiphasic contrast-enhanced computed tomography gastrography (CECTG).

METHODS AND MATERIALS

We studied 21 consecutive clinically diagnosed DGC patients who underwent CECTG. Gastric distension was obtained using effervescent granules. CT images were obtained 40 s (arterial phase) and 240 s (delayed phase) after injection of a nonionic contrast material. Two radiologists reviewed the CT images and analyzed layers and enhancement patterns. The readers evaluated the enhancement degree (mild, moderate, or marked) and calculated CT attenuation values by placing circular regions of interest (ROIs) within each layer of the lesion. The CT findings of 11 operated cases were correlated with pathological results.

RESULTS

Most lesions were double-layered in the arterial phase, with a moderately enhanced inner layer and a mildly enhanced outer layer, and single-layered in the delayed phase. The mean attenuation value of the inner layer (146 ± 32.8 HU) was significantly higher than that of the outer layer (80.4 ± 15.5 HU) in the arterial phase (p = 0.0001). In the pathological analysis, wall stratification was preserved in nine cases and not preserved in two cases.

CONCLUSION

Most DGCs showed a double-layered pattern in the arterial phase and a single-layered pattern with moderate enhancement in the delayed phase.

Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: Comparison with tumour regression grade at final histology

PURPOSE

An accurate prediction of tumour response to therapy is fundamental in oncology, so as to prompt personalised treatment options if needed. The aim of this study was to investigate the ability of preoperative texture analysis from multi-detector computed tomography (MDCT) in the prediction of the response rate to neo-adjuvant therapy in patients with gastric cancer.

MATERIAL AND METHODS

Thirty-four patients with biopsy-proven gastric cancer were examined by MDCT before neo-adjuvant therapy, and treated with radical surgery after treatment completion. Tumour regression grade (TRG) at final histology was also assessed. Image features from texture analysis were quantified, with and without filters for fine to coarse textures. Patients with TRG 1-3 were considered responders while TRG 4-5 as non- responders. The response rate to neo-adjuvant therapy was assessed both at univariate and multivariate analysis.

RESULTS

Fourteen parameters were significantly different between the two subgroups at univariate analysis; in particular, entropy and compactness (higher in responders) and uniformity (lower in responders). According to our model, the following parameters could identify non-responders at multivariate analysis: entropy (≤6.86 with a logarithm of Odds Ratio - Log OR -: 4.11; p=0.003); range (>158.72; Log OR: 3.67; p=0.010) and root mean square (≤3.71; Log OR: 4.57; p=0.005). Entropy and three-dimensional volume were not significantly correlated (r=0.06; p=0.735).

CONCLUSION

Pre-treatment texture analysis can potentially provide important information regarding the response rate to neo-adjuvant therapy for gastric cancer, improving risk stratification.

Can lymphovascular invasion be predicted by preoperative multiphasic dynamic CT in patients with advanced gastric cancer?

OBJECTIVES

To determine whether multiphasic dynamic CT can preoperatively predict lymphovascular invasion (LVI) in advanced gastric cancer (AGC).

METHODS

278 patients with AGC who underwent preoperative multiphasic dynamic CT were retrospectively recruited. Tumour CT attenuation difference between non-contrast and arterial (Δ_AP), portal (Δ_PP) and delayed phase (Δ_DP), tumour-spleen attenuation difference in the portal phase (Δ_T-S), tumour contrast enhancement ratios (CERs), tumour-to-spleen ratio (TSR) and tumour volumes were obtained. All CT-derived parameters and clinicopathological variables associated with LVI were analysed by univariate analysis, followed by multivariate and receiver operator characteristics (ROC) analysis. Associations between CT predictors for LVI and histopathological characteristics were evaluated by the chi-square test.

RESULTS

Δ_PP (OR, 1.056; 95% CI: 1.032-1.080) and Δ_T-S (OR, 1.043; 95% CI: 1.020-1.066) are independent predictors for LVI in AGC. Δ_PP, Δ_T-S and their combination correctly predicted LVI in 74.8% (AUC, 0.775; sensitivity, 88.6%; specificity, 54.1%), 68.7% (AUC, 0.747; sensitivity, 68.3%; specificity, 69.4%) and 71.7% (AUC, 0.800; sensitivity, 67.6%; specificity, 77.8%), respectively. There were significant associations between CT predictors for LVI with tumour histological differentiation and Lauren classification.

CONCLUSION

Multiphasic dynamic CT provides a non-invasive method to predict LVI in AGC through quantitative enhancement measurement.

KEY POINTS

• Lymphovascular invasion rarely can be evaluated preoperatively in advanced gastric cancer (AGC). • Δ _PP and Δ _T-S were independent predictors for LVI in patients with AGC. • Δ _PP and Δ _T-S showed acceptable predictive performance for LVI. • Combination of Δ _PP and Δ _T-S improved predictive performance for LVI. • Multiphasic dynamic CT may be a useful adjunct for detecting LVI preoperatively.

Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker

OBJECTIVES

To investigate the association between preoperative texture analysis from multidetector computed tomography (MDCT) and overall survival in patients with gastric cancer.

METHODS

Institutional review board approval and informed consent were obtained. Fifty-six patients with biopsy-proved gastric cancer were examined by MDCT and treated with surgery. Image features from texture analysis were quantified, with and without filters for fine to coarse textures. The association with survival time was assessed using Kaplan-Meier and Cox analysis.

RESULTS

The following parameters were significantly associated with a negative prognosis, according to different thresholds: energy [no filter] - Logarithm of relative risk (Log RR): 3.25; p = 0.046; entropy [no filter] (Log RR: 5.96; p = 0.002); entropy [filter 1.5] (Log RR: 3.54; p = 0.027); maximum Hounsfield unit value [filter 1.5] (Log RR: 3.44; p = 0.027); skewness [filter 2] (Log RR: 5.83; p = 0.004); root mean square [filter 1] (Log RR: - 2.66; p = 0.024) and mean absolute deviation [filter 2] (Log RR: - 4.22; p = 0.007).

CONCLUSIONS

Texture analysis could increase the performance of a multivariate prognostic model for risk stratification in gastric cancer. Further evaluations are warranted to clarify the clinical role of texture analysis from MDCT.

KEY POINTS

• Textural analysis from computed tomography can be applied in gastric cancer. • Preoperative non-invasive texture features are related to prognosis in gastric cancer. • Texture analysis could help to evaluate the aggressiveness of this tumour.