CT volumetry for gastric carcinoma: association with TNM stage

Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer

OBJECTIVE

This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III).

METHODS

This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA).

RESULTS

A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility.

CONCLUSION

The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

Multi-modal analysis for accurate prediction of preoperative stage and indications of optimal treatment in gastric cancer

BACKGROUND

Accurate preoperative clinical staging of gastric cancer helps determine therapeutic strategies. However, no multi-category grading models for gastric cancer have been established. This study aimed to develop multi-modal (CT/EHRs) artificial intelligence (AI) models for predicting tumor stages and optimal treatment indication based on preoperative CT images and electronic health records (EHRs) in patients with gastric cancer.

METHODS

This retrospective study enrolled 602 patients with a pathological diagnosis of gastric cancer from Nanfang hospital retrospectively and divided them into training (n = 452) and validation sets (n = 150). A total of 1326 features were extracted of which 1316 radiomic features were extracted from the 3D CT images and 10 clinical parameters were obtained from electronic health records (EHRs). Four multi-layer perceptrons (MLPs) whose input was the combination of radiomic features and clinical parameters were automatically learned with the neural architecture search (NAS) strategy.

RESULTS

Two two-layer MLPs identified by NAS approach were employed to predict the stage of the tumor showed greater discrimination with the average ACC value of 0.646 for five T stages, 0.838 for four N stages than traditional methods with ACC of 0.543 (P value = 0.034) and 0.468 (P value = 0.021), respectively. Furthermore, our models reported high prediction accuracy for the indication of endoscopic resection and the preoperative neoadjuvant chemotherapy with the AUC value of 0.771 and 0.661, respectively.

CONCLUSIONS

Our multi-modal (CT/EHRs) artificial intelligence models generated with the NAS approach have high accuracy for tumor stage prediction and optimal treatment regimen and timing, which could facilitate radiologists and gastroenterologists to improve diagnosis and treatment efficiency.

Imaging in Gastric Cancer: Current Practice and Future Perspectives

Gastric cancer represents one of the most common oncological causes of death worldwide. In order to treat patients in the best possible way, the staging of gastric cancer should be accurate. In this regard, endoscopy ultrasound (EUS) has been considered the reference standard for tumor (T) and nodal (N) statuses in recent decades. However, thanks to technological improvements, computed tomography (CT) has gained an important role, not only in the assessment of distant metastases (M status) but also in T and N staging. In addition, magnetic resonance imaging (MRI) can contribute to the detection and staging of primary gastric tumors thanks to its excellent soft tissue contrast and multiple imaging sequences without radiation-related risks. In addition, MRI can help with the detection of liver metastases, especially small lesions. Finally, positron emission tomography (PET) is still considered a useful diagnostic tool for the staging of gastric cancer patients, with a focus on nodal metastases and peritoneal carcinomatosis. In addition, it may play a role in the treatment of gastric cancer in the coming years thanks to the introduction of new labeling peptides. This review aims to summarize the most common advantages and pitfalls of EUS, CT, MRI and PET in the TNM staging of gastric cancer patients.

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.

Differentiating T1a-T1b from T2 in gastric cancer lesions with three different measurement approaches based on contrast-enhanced T1W imaging at 3.0 T

Background

To explore the diagnostic value of three different measurement approaches in differentiating T1a–T1b from T2 gastric cancer (GC) lesions.

Methods

A total of 95 consecutive patients with T1a–T2 stage of GC who performed preoperative MRI were retrospectively enrolled between January 2017 and November 2020. The parameters MRI T stage (subjective evaluation), thickness, maximum area and volume of the lesions were evaluated by two radiologists. Specific indicators including AUC, optimal cutoff, sensitivity, specificity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), positive predictive value (PPV) and negative predictive value (NPV) of MRI T stage, thickness, maximum area and volume for differentiating T1a–T1b from T2 stage lesions were calculated. The ROC curves were compared by the Delong test. Decision curve analysis (DCA) was used to evaluate the clinical benefit.

Results

The ROC curves for thickness (AUC = 0.926), maximum area (AUC = 0.902) and volume (AUC = 0.897) were all significantly better than those of the MRI T stage (AUC = 0.807) in differentiating T1a–T1b from T2 lesions, with p values of 0.004, 0.034 and 0.041, respectively. The values corresponding to the thickness (including AUC, sensitivity, specificity, accuracy, PPV, NPV, PLR and NLR) were all higher than those corresponding to the MRI T stage, maximum area and volume. The DCA curves indicated that the parameter thickness could provide the highest clinical benefit if the threshold probability was above 35%.

Conclusions

Thickness may provide an efficient approach to rapidly distinguish T1a–T1b from T2 stage GC lesions.

Tumor size measured by multidetector CT in resectable colon cancer: correlation with regional lymph node metastasis and N stage

Background

Lymph node metastasis (LNM) is a risk factor for poor long-term outcomes and a prognostic factor for disease-free survival in colon cancer. Preoperative lymph node status evaluation remains a challenge. The purpose of this study is to determine whether tumor size measured by multidetector computed tomography (MDCT) could be used to predict LNM and N stage in colon cancer.

Material and methods

One hundred six patients with colon cancer who underwent radical surgery within 1 week of MDCT scan were enrolled. Tumor size including tumor length (Tlen), tumor maximum diameter (Tdia), tumor maximum cross-sectional area (Tare), and tumor volume (Tvol) were measured to be correlated with pathologic LNM and N stage using univariate logistic regression analysis, multivariate logistic analysis, and receiver operating characteristic (ROC) curve analysis.

Results

The inter- and intraobserver reproducibility of Tlen (intraclass correlation coefficient [ICC] = 0.94, 0.95, respectively), Tdia (ICC = 0.81, 0.93, respectively), Tare (ICC = 0.97, 0.91, respectively), and Tvol (ICC = 0.99, 0.99, respectively) parameters measurement are excellent. Univariate logistic regression analysis showed that there were significant differences in Tlen, Tdia, Tare, and Tvol between positive and negative LNM (p < 0.001, 0.001, < 0.001, < 0.001, respectively). Multivariate logistic regression analysis revealed that Tvol was independent risk factor for predicting LNM (odds ratio, 1.082; 95% confidence interval for odds ratio, 1.039, 1.127, p<0.001). Tlen, Tdia, Tare, and Tvol could distinguish N0 from N1 stage (p < 0.001, 0.041, < 0.001, < 0.001, respectively), N0 from N2 (all p < 0.001), N0 from N1-2 (p < 0.001, 0.001, < 0.001, < 0.001, respectively), and N0-1 from N2 (p < 0.001, 0.001, < 0.001, < 0.001, respectively). The area under the ROC curve (AUC) was higher for Tvol than that of Tlen, Tdia, and Tare in identifying LNM (AUC = 0.83, 0.82, 0.69, 0.79), and distinguishing N0 from N1 stage (AUC = 0.79, 0.78, 0.63, 0.74), N0 from N2 stage (AUC = 0.92, 0.89, 0.80, 0.89, respectively), and N0-1 from N2 stage (AUC = 0.84, 0.79, 0.76, 0.83, respectively).

Conclusion

Tumor size was correlated with regional LNM in resectable colon cancer. In particularly, Tvol showed the most potential for noninvasive preoperative prediction of regional LNM and N stage.

Comparison of CT Volumetry and RECIST to Predict the Treatment Response and Overall Survival in Gastric Cancer Liver Metastases

Purpose

The aim of this study was to compare the diameter and volume of liver metastases on CT images in relation to overall survival and tumor response in patients with gastric cancer liver metastases (GCLM) treated with chemotherapy.

Materials and Methods

We recruited 43 patients with GCLM who underwent chemotherapy as a first-line treatment. We performed a three-dimensional quantification of the metastases for each patient. An independent survival analysis using the Response Evaluation Criteria in Solid Tumors (RECIST) was performed and compared to volumetric measurements. Overall survival was evaluated using Kaplan-Meier analysis and compared using Cox proportional hazard ratios following univariate analyses.

Results

When patients were classified as responders or non-responders based on volumetric criteria, the median overall survival was 23.6 months [95% confidence interval (CI), 8.63–38.57] and 7.6 months (95% CI, 3.78–11.42), respectively (p = 0.039). The volumetric analysis and RECIST of the non-progressing and progressing groups showed similar results based on the Kaplan-Meier method (p = 0.006) and the Cox proportional hazard model (p = 0.008).

Conclusion

Volumetric assessment of liver metastases could be an alternative predictor of overall survival for patients with GCLM treated with chemotherapy.

Survival and adequate preoperative staging in patients undergoing gastric cancer surgery at a Peruvian Police Hospital

INTRODUCTION

Gastric cancer is the fifth most common malignant neoplasm and the third leading cause of cancer-related death worldwide. In Peru, its incidence is 15.8 per 100,000 population, and it is associated with high mortality rates, especially in areas with low socioeconomic status. The aim of this study was to compare preoperative, postoperative, and anatomopathological staging results and their relation to disease recurrence and survival.

METHODS

We conducted a retrospective cohort study of patients undergoing surgery for gastric cancer with a definitive postoperative anatomopathological diagnosis from 2005 to 2014 at the Hospital Nacional Luis N. Sáenz. Statistical analyses included descriptive and correlation statistics using the κ index, determination of associations between preoperative and postoperative staging and surgical reintervention and recurrence using the χ²  test, as well as Kaplan Meier survival analysis.

RESULTS

There was little correlation between preoperative staging and final anatomopathological diagnosis, while there was a good correlation with postoperative staging. A significant association was found between preoperative staging and cancer recurrence. In the survival analysis, survival was lower among patients with underestimated staging.

CONCLUSIONS

The survival of patients with gastric cancer can be affected by an overestimation of preoperative staging, therefore improvements in preoperative staging could lengthen the survival of patients undergoing gastric cancer surgery.

CT-detected extramural venous invasion is corelated with presence of lymph node metastasis and progression-free survival in gastric cancer

OBJECTIVE

This study aimed to investigate if CT-detected extramural venous invasion (ctEMVI) was associated with the presence of lymph node metastasis (LNM) and survival outcomes in patients with gastric cancer.

METHODS

We retrospectively reviewed 105 patients with pathologically proved gastric cancer who underwent pre-operative CT examinations and received radical gastrectomy with extended lymphadenectomy. Differences in CT characteristics between the LNM-positive and -negative groups were assessed by two observers. Binary logistic regression analysis was performed to determine the risk factors of lymph node metastasis in gastric cancer. Progression-free survival analysis was performed by Kaplan-Meier method.

RESULTS

Two observers reached good inter-reader agreements in ctEMVI and ctN status with κ values of 0.711 and 0.751, respectively. The frequency of ctEMVI-positive status was 58.1% (61/105) in patients with gastric cancer. The LNM-positive group showed higher possibility of ctEMVI-positive status (81.7% vs 26.7%, p＜0.001), larger tumor volume (mean volume, 40.77 vs 22.09 mL, p＜0.001), poor tumor margin (45.0% vs 26.7% , p = 0.054) and high enhancement on arterial phase (43.3% vs 26.7%, p = 0.023) and venous phase (60.0% vs 44.4%, p = 0.048), than LNM-negative group. In multivariate analysis, ctEMVI status and tumor volume were identified as independent risk factors for lymph node metastasis with odds ratio (OR) of 9.804 (95% CI, 3.076－31.246; p＜0.001) and 1.030 (95% CI, 1.001－1.060; p = 0.044). CT-detected EMVI presented better diagnostic efficiency for lymph node metastasis than CT-defined N status, with sensitivity (81.7% vs 70.0%), specificity (73.3% vs 71.1%), accuracy (78.1% vs 70.5), PPV (80.3% vs 76.4%), and NPV (75.0% vs 64.0%), respectively. Kaplan-Meier curves showed that patients with positive ctEMVI findings has lower PFS rate than patients with negative ctEMVI findings (Log-rank test, p = 0.007).

CONCLUSION

CT-detected EMVI was significantly associated with lymph node metastasis and progression free survival in patients with gastric cancer. Compared to CT-defined N status, ctEMVI provided superior diagnostic performance to predict pathologic Nstatus.

ADVANCES IN KNOWLEDGE

Our study proved that CT-detected EMVI is a promising imaging marker to predict lymph node metastasis and poor prognosis, which may contribute to the precise evaluation of gastric cancer before surgery.

The Correlation Between Computed Tomography Volumetry and Prognosis of Advanced Gastric Cancer Treated with Neoadjuvant Chemotherapy

Purpose

To investigate the feasibility and utility of computer tomography (CT) volumetry in evaluating the tumor response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) patients.

Patients and Methods

One hundred and seventeen Patients with AGC who received NAC followed by R0 resection between January 2006 and December 2012 were included. Tumor volumes were quantified using OsiriX software. The volume reduction rate (VRR) was calculated as follows: VRR = [(pre-chemotherapy total volume) − (post-chemotherapy total volume)]/(pre-chemotherapy total volume) × 100%. The optimal cut-off VRR for differentiating favorable from unfavorable prognosis was determined by receiver operating characteristic (ROC) analysis. Overall survival was calculated using Kaplan-Meier analysis and values were compared using the Log-rank test. Multivariate analysis was determined by the Cox proportional regression model.

Results

The optimal cut-off VRR was 31.95% according to ROC analysis, with a sensitivity of 70.4% and a specificity of 71.7%. Based on the cut-off VRR, patients were divided into the VRR-High (VRR ≥ 31.95%, n = 63) and VRR-Low (VRR < 31.95%, n = 54) groups. The VRR-Low group exhibited a worse prognosis than that of the VRR-High group (HR, 2.85; 95% CI, 1.69–4.82, P < 0.001), with 3-year survival rates of 40.7% and 79.4%, and 5-year survival rates of 31.5% and 63.5%, respectively.

Conclusion

CT volumetry is a feasible and reliable method for assessing the tumor response to NAC in patients with AGC.

Volumetric analysis at abdominal CT: oncologic and non-oncologic applications

Volumetric analysis is an objective three-dimensional assessment of a lesion or organ that may more accurately depict the burden of complex objects compared to traditional linear size measurement. Small changes in linear size are amplified by corresponding changes in volume, which could have significant clinical implications. Though early methods of calculating volumes were time-consuming and laborious, multiple software platforms are now available with varying degrees of user-software interaction ranging from manual to fully automated. For the assessment of primary malignancy and metastatic disease, volumetric measurements have shown utility in the evaluation of disease burden prior to and following therapy in a variety of cancers. Additionally, volume can be useful in treatment planning prior to resection or locoregional therapies, particularly for hepatic tumours. The utility of CT volumetry in a wide spectrum of non-oncologic pathology has also been described. While clear advantages exist in certain applications, some data have shown that volume is not always the superior method of size assessment and the associated labor intensity may not be worthwhile. Further, lack of uniformity among software platforms is a challenge to widespread implementation. This review will discuss CT volumetry and its potential oncologic and non-oncologic applications in abdominal imaging, as well as advantages and limitations to this quantitative technique.

CT volumetry can potentially predict the local stage for gastric cancer after chemotherapy

PURPOSE

We aimed to evaluate the value of CT tumor volumetry for predicting T and N stages of gastric cancer after chemotherapy, with pathologic results as the reference standard.

METHODS

This study retrospectively evaluated 42 patients diagnosed with gastric cancer, who underwent chemotherapy followed by surgery. Pre- and post-treatment CT tumor volumes (VT) were measured in portal venous phase and volume reduction ratios were calculated. Correlations between pre- and post-treatment VT, reduction ratio, and pathologic stages were analyzed. Receiver operator characteristic (ROC) analyses were also performed to assess diagnostic performance for prediction of downstaging to T0-2 stage and N0 stage.

RESULTS

Pretreatment VT, post-treatment VT, and VT reduction ratio were significantly correlated with T stage (rs=0.329, rs=0.546, rs= -0.422, respectively). Post-treatment VT and VT reduction ratio were significantly correlated with N stage (rs=0.442 and rs= -0.376, respectively). Pretreatment VT, post-treatment VT, and VT reduction ratio were significantly different between T0-2 and T3,4 stage tumors (P = 0.05, P < 0.001, and P = 0.002, respectively). The differences between N0 and ≥N1 groups were also statistically significant (P = 0.005 for post-treatment VT, P = 0.016 for VT reduction ratio, respectively). The area under the ROC curve (AUC) for identification of T0-2 groups was 0.70 for pretreatment VT, 0.88 for post-treatment VT, and 0.82 for VT reduction ratio, respectively. AUC was 0.78 for post-treatment VT and 0.74 for VT reduction ratio for identification of N0 groups.

CONCLUSION

CT tumor volumetry, particularly post-treatment measurement of VT, is potentially valuable for predicting histopathologic T and N stages after chemotherapy in patients with gastric cancer.

CT volumetry for gastric adenocarcinoma: association with lymphovascular invasion and T-stages

Purpose

To determine whether gross tumor volume of resectable gastric adenocarcinoma on multidetector computed tomography could predict presence of lymphovascular invasion and T-stages.

Results

Gross tumor volume increased with the lymphovascular invasion (r = 0.426, P < 0.0001) and T stage (r = 0.656, P < 0.0001). Univariate analysis showed gross tumor volume could predict lymphovascular invasion (P < 0.0001). Multivariate analyses indicated gross tumor volume as an independent risk factor of lymphovascular invasion (P = 0.026, odds ratio = 2.284). The Mann-Whitney U test showed gross tumor volume could distinguish T2 from T3, T1 from T2–T4a, T1–T2 from T3–T4a and T1–T3 from T4a (P = 0.000). In the development cohort, gross tumor volume could predict lymphovascular invasion (cutoff, 15.92 cm³; AUC, 0.760), and distinguish T2 from T3 (cutoff, 10.09 cm³; AUC, 0.828), T1 from T2-T4a (cutoff, 8.20 cm³; AUC, 0.860), T1-T2 from T3-T4a (cutoff, 15.88 cm³; AUC, 0.883), and T1-T3 from T4a (cutoff, 21.53 cm³; AUC, 0.834). In validation cohort, gross tumor volume could predict presence of lymphovascular invasion (AUC, 0.742), and distinguish T2 from T3 (AUC, 0.861), T1 from T2-T4a (AUC, 0.859), T1–T2 from T3–T4a (AUC, 0.875), and T1–T3 from T4a (AUC, 0.773).

Materials and Methods

360 consecutive patients with gastric adenocarcinoma were retrospectively identified. Gross tumor volume was evaluated on multidetector computed tomography images. Statistical analysis was performed to determine whether gross tumor volume could predict presence of lymphovascular invasion and T-stages. Cutoffs of gross tumor volume were first investigated in 212 patients and then validated in an independent 148 patients using area under the receiver operating characteristic curve (AUC) for predicting lymphovascular invasion and T-stages.

Conclusions

Gross tumor volume of resectable gastric adenocarcinoma at multidetector computed tomography demonstrated capability in predicting lymphovascular invasion and distinguishing T-stages.

CT volumetric measurement of colorectal cancer helps predict tumor staging and prognosis

Purpose

To evaluate feasibility of CT colonography (CTC) volumetry of colorectal cancer (CRC) and its correlation with disease stage and patients’ survival.

Materials and methods

CTC volumetry was performed for 126 patients who underwent preoperative CTC. Reproducibility of tumor volume (Tvol) between two readers was assessed. One-way ANOVA and ROC analysis evaluated correlation between Tvol and pTNM staging. ROC analysis compared diagnostic performance to predict pTNM staging between Tvol and radiologist. Kaplan-Meier test compared overall survival.

Results

Reproducibility among readers was excellent (interclass correlation = 0.9829). Mean Tvol showed an incremental trend with T stage and Tvol of pT4b stage was significantly larger than other stages (P<0.0001). Az value (0.780) of Tvol to predict pT4b stage was significantly larger than that (0.591) of radiologist (P = 0.004). However, Tvol was not significantly different according to pN stage. Az values (0.723~0.857) of Tvol to predict M1 or M1b were comparable to those (0.772~0.690) of radiologist (P>0.05). Smaller tumor burden (≤12.85cm³), ≤T3, N0, M0 stages, and curative surgery were significantly associated with patients’ longer survival (P<0.05).

Conclusion

CT volumetry has a limited value to predict N stage; however, it may outperform the radiologist’s performance when predicting pT4b and M1b stage and can be a useful prognostic marker.

Organ Atrophy Induced by Sorafenib and Sunitinib - Quantitative Computed Tomography (CT) Evaluation of the Pancreas, Thyroid Gland and Spleen

Background

To evaluate organ atrophy induced by sorafenib and sunitinib, we retrospectively reviewed the CT scans of renal cell carcinoma (RCC) patients receiving molecular targeted therapy (MTT) using sorafenib or sunitinib, and performed volumetric analysis of the pancreas, thyroid gland, and spleen.

Material/Methods

Thirteen RCC patients receiving MTT were assigned as the evaluation cases (MTT group), while thirteen additional RCC patients not receiving MTT were retrieved as the Control group. We evaluated the baseline and follow-up CT studies. The volume of the three organs estimated by CT volumetry was compared between the baseline and follow-up CTs. The atrophic ratio of the organ volume in the follow-up CT to that in the baseline CT was calculated, and compared between the MTT and Control groups.

Results

All measured organs in the MTT group showed statistically significant volume loss, while no significant change was observed in the Control group. Mean atrophic ratio in the MTT group was 0.74, 0.58, and 0.82 for the pancreas, thyroid and spleen, respectively. The differences in atrophic ratios between both groups were all statistically significant (P<0.05).

Conclusions

Single-agent sorafenib or sunitinib therapy induced statistically significant atrophy in the pancreas, thyroid, and spleen.

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.

Tumor volume of resectable gastric adenocarcinoma on multidetector computed tomography: association with N categories

OBJECTIVE

To determine whether the gross tumor volume of resectable gastric adenocarcinoma on multidetector computed tomography could predict the presence of regional lymph node metastasis and could determine N categories.

MATERIALS AND METHODS

A total of 202 consecutive patients with gastric adenocarcinoma who had undergone gastrectomy 1 week after contrast-enhanced multidetector computed tomography were retrospectively identified. The gross tumor volume was evaluated on multidetector computed tomography images. Univariate and multivariate analyses were performed to determine whether the gross tumor volume could predict regional lymph node metastasis, and the Mann-Whitney U test was performed to compare the gross tumor volume among N categories. Additionally, a receiver operating characteristic analysis was performed to identify the accuracy of the gross tumor volume in differentiating N categories.

RESULTS

The gross tumor volume could predict regional lymph node metastasis (p<0.0001) in the univariate analysis, and the multivariate analyses indicated that the gross tumor volume was an independent risk factor for regional lymph node metastasis (p=0.005, odds ratio=1.364). The Mann-Whitney U test showed that the gross tumor volume could distinguish N0 from the N1-N3 categories, N0-N1 from N2-N3, and N0-N2 from N3 (all p<0.0001). In the T1-T4a categories, the gross tumor volume could differentiate N0 from the N1-N3 categories (cutoff, 12.3 cm3), N0-N1 from N2-N3 (cutoff, 16.6 cm3), and N0-N2 from N3 (cutoff, 24.6 cm3). In the T4a category, the gross tumor volume could differentiate N0 from the N1-N3 categories (cutoff, 15.8 cm3), N0-N1 from N2-N3 (cutoff, 17.8 cm3), and N0-N2 from N3 (cutoff, 24 cm3).

CONCLUSION

The gross tumor volume of resectable gastric adenocarcinoma on multidetector computed tomography could predict regional lymph node metastasis and N categories.

Relationship between expression of NADPH oxidase 2 and invasion and prognosis of human gastric cancer

AIM: To assess the expression and prognostic value of nicotinamide adenine dinucleotide phosphate oxidase 2 (NOX2) in gastric cancer, and its correlation with vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR).

METHODS: Tumor and adjacent tissues were obtained from 123 patients who underwent radical surgery for gastric cancer at Renmin Hospital of Wuhan University from 2008-2009. The expression of NOX2, VEGF, EGFR and CD68 in tumor tissues was detected by immunohistochemistry. The expression of NOX2 in gastric cancer and adjacent tissues was detected by Western blot analysis. Spearman^’s correlation was performed to elucidate the relationship of NOX2 with VEGF and EGFR. The Kaplan-Meier method was used to calculate survival time, and the log-rank test was used to evaluate differences in survival. Cox‘s proportional hazards regression model was applied in a stepwise manner to analyze the independent prognostic factors.

RESULTS: NOX2 exhibited positive expression in 47.2% (58/123) of the gastric cancer tissues. Western blot analysis revealed that NOX2 was up-regulated in tumor tissues compared to the adjacent tissue [39.0% (48/123)]. Immunohistochemistry staining revealed that CD68, which is a specific marker of macrophages, and NOX expression presented a similar localization and staining intensity. The expression of NOX2 was positively correlated with that of VEGF and EGFR. Comparison of the 5-year survival rates of the NOX2 positive and NOX2 negative groups showed that the NOX2 positive group presented a poor prognosis.

CONCLUSION: NOX2 positively correlates with the levels of VEGF and EGFR. NOX2 may be used as a new biomarker and a potential therapeutic target for gastric cancer.