Accuracy of multidetector-row CT in diagnosing lymph node metastasis in patients with gastric cancer

A clinicopathologic feature-based nomogram for preoperative estimation of splenic hilar lymph node metastasis in advanced proximal gastric cancer without invasion of the greater curvature

BACKGROUND

The indications for splenic hilar lymph node dissection in advanced proximal gastric cancer without invasion of the greater curvature are controversial. We aimed to develop a preoperative nomogram for individualized prediction of splenic hilar lymph node metastasis in non-greater curvature advanced proximal gastric cancer.

METHODS

From January 2014 to December 2021, 558 patients with non-greater curvature advanced proximal gastric cancer who underwent D2 lymphadenectomy (including splenic hilar lymph node) were retrospectively analyzed and divided into a training cohort (n = 361) and validation cohort (n = 197), depending on the admission time. A preoperative predictive nomogram of splenic hilar lymph node metastasis was established based on independent predictors identified by multivariate analysis, and the performance and prognostic value were confirmed.

RESULTS

In the training and validation cohorts, 48 (13.3%) and 24 patients (12.2%) had pathologically confirmed splenic hilar lymph node metastasis, respectively. Tumor located in the posterior wall, tumor size ≥5 cm, Borrmann type IV, and splenic hilar lymph node lymphadenectasis on computed tomography were preoperative factors independently associated with splenic hilar lymph node metastasis. The nomogram developed based on these four parameters had a high concordance index of 0.850 (95% confidence interval, 0.793-0.907) and 0.825 (95% confidence interval, 0.743-0.908) in the training and validation cohorts, respectively, with well-fitting calibration plots and better net benefits in the decision curve analysis. In addition, disease-free survival and overall survival were significantly shorter in the high-risk group, with hazard ratios of 3.660 (95% confidence interval, 2.228-6.011; log-rank P < .0001) and 3.769 (95% confidence interval, 2.279-6.231; log-rank P < .0001), respectively.

CONCLUSION

The nomogram has good performance in predicting the risk of splenic hilar lymph node metastasis in non-greater curvature advanced proximal gastric cancer preoperatively, which can help surgeons make rational clinical decisions.

Preoperative Risk Stratification for Gastric Cancer: The Establishment of Dual-Energy CT-Based Radiomics Using Prospective Datasets at Two Centers

RATIONALE AND OBJECTIVES

To evaluate the performance of dual-energy CT (DECT)-based radiomics models for identifying high-risk histopathologic phenotypes-serosal invasion (pT4a), lymph node metastasis (LNM), lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer.

MATERIAL AND METHODS

This prospective bi-center study recruited histologically confirmed gastric adenocarcinoma patients who underwent triple-phase enhanced DECT before gastrectomy between January 2021 and July 2023. Radiomics features were extracted from polychromatic/monochromatic (40 keV, 100 keV)/iodine images at arterial/venous/delay phase, respectively. Predictive features were selected in the training dataset using logistic regression classifier, and trained models were applied to the external validation dataset. Performances of clinical models, conventional contrast enhanced CT (CECT) models and DECT models were evaluated using areas under the receiver operating characteristic curve (AUCs).

RESULTS

In total, 503 patients were recruited: 396 at training dataset (60.1 ± 10.8 years, 110 females, 286 males) and 107 at validation dataset (61.4 ± 9.5 years, 29 females, 78 males). DECT models dichotomizing pT4a, LNM, LVI, and PNI achieved AUCs of 0.891, 0.817, 0.834, and 0.889, respectively, in the validation dataset, similar with the CECT models. In the training dataset, compared to the CECT model, the DECT model provided increased performance for identifying pT4a, LNM, LVI (all P＜0.05), and similar performance for stratifying PNI (P = 0.104). The DECT models was associated with patient disease-free survival (all P＜0.05).

CONCLUSION

DECT radiomics can stratify patients preoperatively according to high-risk histopathologic phenotypes for gastric cancer and are associated with patient disease-free survival in the training dataset.

Diagnostic performance of Node Reporting and Data System (Node-RADS) for regional lymph node staging of gastric cancer by CT

OBJECTIVES

Diagnostic performance of imaging for regional lymph node assessment in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. At the same time, there is an increasing demand for structured reporting using Reporting and Data Systems (RADS) to standardize oncological imaging. We aimed at investigating the diagnostic performance of Node-RADS compared to the use of various individual criteria for assessing regional lymph nodes in gastric cancer using histopathology as reference.

METHODS

In this retrospective single-center study, consecutive 91 patients (median age, 66 years, range 33-91 years, 54 men) with CT scans and histologically proven gastric adenocarcinoma were assessed using Node-RADS assigning scores from 1 to 5 for the likelihood of regional lymph node metastases. Additionally, different Node-RADS criteria as well as subcategories of altered border contour (lobulated, spiculated, indistinct) were assessed individually. Sensitivity, specificity, and Youden's index were calculated for Node-RADS scores, and all criteria investigated. Interreader agreement was calculated using Cohen's kappa.

RESULTS

Among all criteria, best performance was found for Node-RADS scores ≥ 3 and ≥ 4 with a sensitivity/specificity/Youden's index of 56.8%/90.7%/0.48 and 48.6%/98.1%/0.47, respectively, both with substantial interreader agreement (κ = 0.73 and 0.67, p < 0.01). Among individual criteria, the best performance was found for short-axis diameter of 10 mm with sensitivity/specificity/Youden's index of 56.8%/87.0%/0.44 (κ = 0.65, p < 0.01).

CONCLUSION

This study shows that structured reporting of combined size and configuration criteria of regional lymph nodes in gastric cancer slightly improves overall diagnostic performance compared to individual criteria including short-axis diameter alone. The results show an increase in specificity and unchanged sensitivity.

CLINICAL RELEVANCE STATEMENT

The results of this study suggest that Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer.

KEY POINTS

• Assessment of lymph nodes in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. • Node-RADS in gastric cancer improves overall diagnostic performance compared to individual criteria including short-axis diameter. • Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer.

Prediction of lymph node metastasis in advanced gastric adenocarcinoma based on dual-energy CT radiomics: focus on the features of lymph nodes with a short axis diameter ≥6 mm

Objective

To explore the value of the features of lymph nodes (LNs) with a short-axis diameter ≥6 mm in predicting lymph node metastasis (LNM) in advanced gastric adenocarcinoma (GAC) based on dual-energy CT (DECT) radiomics.

Materials and methods

Data of patients with GAC who underwent radical gastrectomy and LN dissection were retrospectively analyzed. To ensure the correspondence between imaging and pathology, metastatic LNs were only selected from patients with pN3, nonmetastatic LNs were selected from patients with pN0, and the short-axis diameters of the enrolled LNs were all ≥6 mm. The traditional features of LNs were recorded, including short-axis diameter, long-axis diameter, long-to-short-axis ratio, position, shape, density, edge, and the degree of enhancement; univariate and multivariate logistic regression analyses were used to establish a clinical model. Radiomics features at the maximum level of LNs were extracted in venous phase equivalent 120 kV linear fusion images and iodine maps. Intraclass correlation coefficients and the Boruta algorithm were used to screen significant features, and random forest was used to build a radiomics model. To construct a combined model, we included the traditional features with statistical significance in univariate analysis and radiomics scores (Rad-score) in multivariate logistic regression analysis. Receiver operating curve (ROC) curves and the DeLong test were used to evaluate and compare the diagnostic performance of the models. Decision curve analysis (DCA) was used to evaluate the clinical benefits of the models.

Results

This study included 114 metastatic LNs from 36 pN3 cases and 65 nonmetastatic LNs from 28 pN0 cases. The samples were divided into a training set (n=125) and a validation set (n=54) at a ratio of 7:3. Long-axis diameter and LN shape were independent predictors of LNM and were used to establish the clinical model; 27 screened radiomics features were used to build the radiomics model. LN shape and Rad-score were independent predictors of LNM and were used to construct the combined model. Both the radiomics model (area under the curve [AUC] of 0.986 and 0.984) and the combined model (AUC of 0.970 and 0.977) outperformed the clinical model (AUC of 0.772 and 0.820) in predicting LNM in both the training and validation sets. DCA showed superior clinical benefits from radiomics and combined models.

Conclusion

The models based on DECT LN radiomics features or combined traditional features have high diagnostic performance in determining the nature of each LN with a short-axis diameter of ≥6 mm in advanced GAC.

The largest lymph node defined response to neoadjuvant chemotherapy can predict long-term prognosis in locally advanced gastric cancer

BACKGROUND

The assessment of tumor response to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC) remain challenging. We aimed to explore the potential role of peri-NACT change of the largest lymph node (LN) and primary tumor (P-T) in the prediction of tumor response and patient overall survival (OS) in LAGC.

METHODS

A cohort of LAGC patients who underwent NACT followed by radical surgery from a prospective clinical trial were retrospectively analyzed. The percentage change of the largest LN and P-T from initial to post-NACT Computed Tomography (CT) were measured. Tumor response was defined by the change of LN (LN-response) and P-T (P-T-response), respectively. A multivariate Cox model was constructed to examine if P-T- and LN-determined response had significant predictive ability for OS when adjusting with other possible prognostic factors.

RESULTS

Of the 41 patients, 28 (68.3%) was defined as LN-responders to NACT, and 17 (41.5%) patients was defined as P-T-responders. When the cohort was stratified by LN response standard, LN-responders showed a significant longer median OS than LN-nonresponders (p = 0.031, 20.6 vs 16.6 months). When stratified by primary tumor response, no significant difference in OS was observed between P-T-responders and P-T-nonresponders (p = 0.377, 18.5 vs 19.0 months). In the multivariate analysis, number of positive LN (p = 0.004, hazard ratio [HR] = 1.284), recurrence (p = 0.024, HR =3556), LN shrinkage (p = 0.022, HR = 0.930) and LN-response (p = 0.033, HR = 0.008) were observed with independent association with OS.

CONCLUSIONS

Peri-NACT change of the largest LN could reflect tumor response to NACT, and LN-defined response was useful in predicting the long-term prognosis (OS) of LAGC patients who underwent NACT followed by radical surgery.

The role of computed tomography features in assessing response to neoadjuvant chemotherapy in locally advanced gastric cancer

OBJECTIVE

To compare the computed tomography (CT) images of patients with locally advanced gastric cancer (GC) before and after neoadjuvant chemotherapy (NAC) in order to identify CT features that could predict pathological response to NAC.

METHODS

We included patients with locally advanced GC who underwent gastrectomy after NAC from September 2016 to September 2021. We retrieved and collected the patients' clinicopathological characteristics and CT images before and after NAC. We analyzed CT features that could differentiate responders from non-responders and established a logistic regression equation based on these features.

RESULTS

We included 97 patients (69 [71.1%] men; median [range] age, 60 [26-75] years) in this study, including 66 (68.0%) responders and 31 (32.0%) non-responders. No clinicopathological variable prior to treatment was significantly associated with pathological response. Out of 16 features, three features (ratio of tumor thickness reduction, ratio of reduction of primary tumor attenuation in arterial phase, and ratio of reduction of largest lymph node attenuation in venous phase) on logistic regression analysis were used to establish a regression equation that demonstrated good discrimination performance in predicting pathological response (area under receiver operating characteristic curve 0.955; 95% CI, 0.911-0.998).

CONCLUSION

Logistic regression equation based on three CT features can help predict the pathological response of patients with locally advanced GC to NAC.

LncRNA expression signature identified using genome-wide transcriptomic profiling to predict lymph node metastasis in patients with stage T1 and T2 gastric cancer

BACKGROUND

Lymph node (LN) status is vital to evaluate the curative potential of relatively early gastric cancer (GC; T1-T2) treatment (endoscopic or surgery). Currently, there is a lack of robust and convenient methods to identify LN metastasis before therapeutic decision-making.

METHODS

Genome-wide expression profiles of long noncoding RNA (lncRNA) in primary T1 gastric cancer data from The Cancer Genome Atlas (TCGA) was used to identify lncRNA expression signature capable of detecting LN metastasis of GC and establish a 10-lncRNA risk-prediction model based on deep learning. The performance of the lncRNA panel in diagnosing LN metastasis was evaluated both in silico and clinical validation methods. In silico validation was conducted using TCGA and Asian Cancer Research Group (ACRG) datasets. Clinical validation was performed on T1 and T2 patients, and the panel's efficacy was compared with that of traditional tumor markers and computed tomography (CT) scans.

RESULTS

Profiling of genome-wide RNA expression identified a panel of lncRNA to predict LN metastasis in T1 stage gastric cancer (AUC = 0.961). A 10-lncRNA risk-prediction model was then constructed, which was validated successfully in T1 and T2 datasets (TCGA, AUC = 0.852; ACRG, AUC = 0.834). Thereafter, the clinical performance of the lncRNA panel was validated in clinical cohorts (T1, AUC = 0.812; T2, AUC = 0.805; T1 + T2, AUC = 0.764). Notably, the panel demonstrated significantly better performance compared with CT and traditional tumor markers.

CONCLUSIONS

The novel 10-lncRNA could diagnose LN metastasis robustly in relatively early gastric cancer (T1-T2), with promising clinical potential.

Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.

A thirty-three gene-based signature predicts lymph node metastasis and prognosis in patients with gastric cancer

Recently, several studies have indicated the great potential of gene expression signature of the primary tumor in predicting lymph node metastasis; however, few current gene biomarkers can predict lymph node status and prognosis in gastric cancer (GC). Thus, we used the RNA-seq data from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes between pathological lymph node-negative (pN0) and positive (pN+) patients and to establish a gene signature that could predict lymph node metastasis. Meanwhile, the robustness of identified gene signatures was validated in an independent dataset Asian Cancer Research Group (n = 300). In this study, our thirty-three gene-based signature was highly correlated with lymph node metastasis and could successfully discriminate pN + patients in the training set (Area under the receiver operating characteristic curve = 0.951). Moreover, Disease-free survival (P = 0.0029) and overall survival (P = 0.026) were significantly worse in high-risk compared with low-risk patients overall and when confined to pN0 patients only (P < 0.0001). Of note, this gene signature also proved useful in predicting lymph node status and survival in the validation cohort. The present study suggests a thirty-three gene-based signature that could effectively predict lymph node metastasis and prognosis in GC.

Hemostatic Radiotherapy for Gastric Cancer: MRI as an Alternative to Endoscopy for Post-Treatment Evaluation

INTRODUCTION

Pretreatment diagnosis by diffusion-weighted magnetic resonance imaging (DW-MRI) is useful to determine the effect of chemotherapy for gastric cancer. Here, we investigated the relationship among DW-MRI, endoscopy, and tumor markers.

PATIENTS

Eight patients underwent hemostatic radiotherapy (RT) for gastric cancer in this prospective study from 2019 to 2021. The patients completed MRI, endoscopy, and blood tests before RT; MRI, endoscopy, and blood tests 1 month after RT; and MRI and blood tests 3 months after RT. Correlations between changes in apparent diffusion coefficient (ADC) derived from DW-MRI and the tumor marker carcinoembryonic antigen (CEA) were investigated.

RESULTS

Univariate analysis of overall survival showed that sex and chemotherapy treatment were statistically significant factors. The CEA values before and 1 month after RT decreased significantly. There was no statistical difference between the CEA value 1 and 3 months after RT. The ADC value before and 1 month after RT increased significantly but not between 1 and 3 months after RT. Comparing the ratio of ADC before RT to 1 (or 3) month(s) after RT with that of CEA before RT to 1 (or 3) month(s) after RT, we found an inverse relationship between the two ratios.

CONCLUSIONS

Therefore, changes in ADC and CEA are correlated. Additionally, 3 months after RT, the decrease in ADC appeared earlier than the decrease in CEA. ADC may indicate a biological change earlier than CEA, and the ratios of ADC and CEA may be important factors. These aspects warrant further confirmation in a larger sample population.

[Radiologic evaluation of lymph nodes in cancer patients]

BACKGROUND

In solid tumors, the detection of locoregional lymph node metastases is of decisive importance not only for the prognosis but also for selecting the correct treatment. Various noninvasive imaging methods or, classically, lymph node dissection are available for this purpose.

OBJECTIVE

This article presents the general principles of noninvasive lymph node diagnostics and discusses the value of the clinically available imaging modalities, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). In addition, recent new technical developments of each modality are highlighted.

MATERIAL AND METHODS

Literature search and summary of the clinical and scientific experience of the authors.

RESULTS

The available imaging procedures are divided into (1) morphological (US, CT, MRI) and (2) functional modalities (PET, special MRI). The former capture structural lymph node parameters, such as size and shape, while the latter address properties that go beyond morphology (e.g. glucose metabolism). The high diagnostic accuracy required for future treatment algorithms will require a combination of both aspects.

DISCUSSION/CONCLUSION

Currently, none of the available modalities have sufficient accuracy to replace lymph node dissection in all oncological scenarios. One of the major challenges for interdisciplinary oncological research is to define the optimal interaction between imaging and lymph node dissection for different malignancies and tumor stages.

Construction of a nomogram for preoperative prediction of the risk of lymph node metastasis in early gastric cancer

Background

The status of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) is particularly important for the formulation of clinical treatment. The purpose of this study was to construct a nomogram to predict the risk of LNM in EGC before operation.

Methods

Univariate analysis and logistic regression analysis were used to determine the independent risk factors for LNM. The independent risk factors were included in the nomogram, and the prediction accuracy, discriminant ability and clinical practicability of the nomogram were evaluated by the receiver operating characteristic curve (ROC), calibration curve and clinical decision curve (DCA), and 100 times ten-fold cross-validation was used for internal validation.

Results

33 (11.3%) cases of AGC were pathologically confirmed as LNM. In multivariate analysis, T stage, presence of enlarged lymph nodes on CT examination, carbohydrate antigen 199 (CA199), undifferentiated histological type and systemic inflammatory response index (SIRI) were risk factors for LNM. The area under the ROC curve of the nomogram was 0.86, the average area under the ROC curve of the 100-fold ten-fold cross-validation was 0.85, and the P value of the Hosmer-Lemeshow test was 0.60. In addition, the clinical decision curve, net reclassification index (NRI) and Integrated Discriminant Improvement Index (IDI) showed that the nomogram had good clinical utility.

Conclusions

We found that SIRI is a novel biomarker for preoperative prediction of LNM in EGC, and constructed a nomogram for preoperative prediction of the risk of LNM in EGC, which is helpful for the formulation of the clinical treatment strategies.

One 3D VOI-based deep learning radiomics strategy, clinical model and radiologists for predicting lymph node metastases in pancreatic ductal adenocarcinoma based on multiphasic contrast-enhanced computer tomography

Purpose

We designed to construct one 3D VOI-based deep learning radiomics strategy for identifying lymph node metastases (LNM) in pancreatic ductal adenocarcinoma on the basis of multiphasic contrast-enhanced computer tomography and to assist clinical decision-making.

Methods

This retrospective research enrolled 139 PDAC patients undergoing pre-operative arterial phase and venous phase scanning examination between 2015 and 2021. A primary group (training group and validation group) and an independent test group were divided. The DLR strategy included three sections. (1) Residual network three dimensional-18 (Resnet 3D-18) architecture was constructed for deep learning feature extraction. (2) Least absolute shrinkage and selection operator model was used for feature selection. (3) Fully connected network served as the classifier. The DLR strategy was applied for constructing different 3D CNN models using 5-fold cross-validation. Radiomics scores (Rad score) were calculated for distinguishing the statistical difference between negative and positive lymph nodes. A clinical model was constructed by combining significantly different clinical variables using univariate and multivariable logistic regression. The manifestation of two radiologists was detected for comparing with computer-developed models. Receiver operating characteristic curves, the area under the curve, accuracy, precision, recall, and F1 score were used for evaluating model performance.

Results

A total of 45, 49, and 59 deep learning features were selected via LASSO model. No matter in which 3D CNN model, Rad score demonstrated the deep learning features were significantly different between non-LNM and LNM groups. The AP+VP DLR model yielded the best performance in predicting status of lymph node in PDAC with an AUC of 0.995 (95% CI:0.989-1.000) in training group; an AUC of 0.940 (95% CI:0.910-0.971) in validation group; and an AUC of 0.949 (95% CI:0.914-0.984) in test group. The clinical model enrolled the histological grade, CA19-9 level and CT-reported tumor size. The AP+VP DLR model outperformed AP DLR model, VP DLR model, clinical model, and two radiologists.

Conclusions

The AP+VP DLR model based on Resnet 3D-18 demonstrated excellent ability for identifying LNM in PDAC, which could act as a non-invasive and accurate guide for clinical therapeutic strategies. This 3D CNN model combined with 3D tumor segmentation technology is labor-saving, promising, and effective.

Impact of tumor-related factors and inter-institutional heterogeneity on preoperative T staging for gastric cancer

Background: To improve the diagnostic accuracy of preoperative T staging in gastric cancer, the authors evaluated tumor-related factors that might affect the diagnosis. Materials & methods: The authors analyzed the data of cT2-4b gastric cancer patients enrolled in the prospective, multicenter JCOG1302A study. They used contrast-enhanced computed tomography to analyze the association between tumor-related factors and the diagnostic accuracy of T3-4b staging for gastric cancer. Results: Among 876 cT3-4b tumors, the diagnostic accuracy was relatively low in the lower third of the stomach compared with those in the upper or middle. A multivariable analysis revealed that accuracy was higher in the lesser curvature or entire circumference region than in other areas (p < 0.001), in macroscopic types 3/5 than in types 0/1/2 (p = 0.003) and in the undifferentiated histological type than in the differentiated type (p = 0.011). Conclusion: The authors found tumor-related factors affecting preoperative T staging by enhanced computed tomography.

Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma

Background. The aim of this study was to identify the most accurate computed-tomography (CT) dimensional criteria of loco-regional lymph nodes (LNs) for detecting nodal metastases in gastric cancer (GC) patients. Methods. Staging CTs of surgically resected GC were jointly reviewed by two radiologists, considering only loco-regional LNs with a long axis (LA) ≥ 5 mm. For each nodal group, the short axis (SA), volume and SA/LA ratio of the largest LN, the sum of the SAs of all LNs, and the mean of the SA/LA ratios were plotted in ROC curves, taking the presence/absence of metastases at histopathology for reference. On a per-patient basis, the sums of the SAs of all LNs, and the sums of the SAs, volumes, and SA/LA ratios of the largest LNs in all nodal groups were also plotted, taking the presence/absence of metastatic LNs in each patient for reference. Results. Four hundred and forty-three nodal groups were harvested during surgery from 107 patients with GC, and 173 (39.1%) were metastatic at histopathology. By nodal group, the sum of the SAs showed the best Area Under the Curve (AUC), with a sensitivity/specificity of 62.4/72.6% using Youden’s index with a >8 mm cutoff. In the per-patient analysis, the sum of the SAs of all LNs in the loco-regional nodal groups showed the best AUC with a sensitivity/specificity of 65.6%/83.7%, using Youden’s index with a >39 mm cutoff. Conclusion. In patients with GC, the sum of the SAs of all the LNs at staging CT is the best predictor among dimensional LNs criteria of both metastatic invasion of the nodal group and the presence of metastatic LNs.

A novel DNA methylation signature associated with lymph node metastasis status in early gastric cancer

Background

Lymph node metastasis (LNM) is an important factor for both treatment and prognosis of early gastric cancer (EGC). Current methods are insufficient to evaluate LNM in EGC due to suboptimal accuracy. Herein, we aim to identify methylation signatures for LNM of EGC, facilitate precision diagnosis, and guide treatment modalities.

Methods

For marker discovery, genome-wide methylation sequencing was performed in a cohort (marker discovery) using 47 fresh frozen (FF) tissue samples. The identified signatures were subsequently characterized for model development using formalin-fixed paraffin-embedded (FFPE) samples by qPCR assay in a second cohort (model development cohort, n = 302, training set: n = 151, test set: n = 151). The performance of the established model was further validated using FFPE samples in a third cohorts (validation cohort, n = 130) and compared with image-based diagnostics, conventional clinicopathology-based model (conventional model), and current standard workups.

Results

Fifty LNM-specific methylation signatures were identified de novo and technically validated. A derived 3-marker methylation model for LNM diagnosis was established that achieved an AUC of 0.87 and 0.88, corresponding to the specificity of 80.9% and 85.7%, sensitivity of 80.6% and 78.1%, and accuracy of 80.8% and 83.8% in the test set of model development cohort and validation cohort, respectively. Notably, this methylation model outperformed computed tomography (CT)-based imaging with a superior AUC (0.88 vs. 0.57, p < 0.0001) and individual clinicopathological features in the validation cohort. The model integrated with clinicopathological features demonstrated further enhanced AUCs of 0.89 in the same cohort. The 3-marker methylation model and integrated model reduced 39.4% and 41.5% overtreatment as compared to standard workups, respectively.

Conclusions

A novel 3-marker methylation model was established and validated that shows diagnostic potential to identify LNM in EGC patients and thus reduce unnecessary gastrectomy in EGC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01219-x.

Application of Machine Learning Algorithms to Predict Lymph Node Metastasis in Early Gastric Cancer

Objective

This study aimed to establish the best early gastric cancer lymph node metastasis (LNM) prediction model through machine learning (ML) to better guide clinical diagnosis and treatment decisions.

Methods

We screened gastric cancer patients with T1a and T1b stages from 2010 to 2015 in the Surveillance, Epidemiology and End Results (SEER) database and collected the clinicopathological data of patients with early gastric cancer who were treated with surgery at the Second Affiliated Hospital of Nanchang University from January 2014 to December 2016. At the same time, we applied 7 ML algorithms—the generalized linear model (GLM), RPART, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), regularized dual averaging (RDA), and the neural network (NNET)—and combined them with patient pathological information to develop the best prediction model for early gastric cancer lymph node metastasis. Among the SEER set, 80% were randomly selected to train the models, while the remaining 20% were used for testing. The data from the Second Affiliated Hospital were considered as the external verification set. Finally, we used the AUROC, F1-score value, sensitivity, and specificity to evaluate the performance of the model.

Results

The tumour size, tumour grade, and depth of tumour invasion were independent risk factors for early gastric cancer LNM. Comprehensive comparison of the prediction model performance of the training set and test set showed that the RDA model had the best prediction performance (F1-score = 0.773; AUROC = 0.742). The AUROC of the external validation set was 0.73.

Conclusions

Tumour size, tumour grade, and depth of tumour invasion were independent risk factors for early gastric cancer LNM. ML predicted LNM risk more accurately, and the RDA model had the best predictive performance and could better guide clinical diagnosis and treatment decisions.

Significance of Lymph Node Metastasis in the Treatment of Gastric Cancer and Current Challenges in Determining the Extent of Metastasis

The stomach exhibits abundant lymphatic flow, and metastasis to lymph nodes is common. In the case of gastric cancer, there is a regularity to the spread of lymph node metastasis, and it does not easily metastasize outside the regional nodes. Furthermore, when its extent is limited, nodal metastasis of gastric cancer can be cured by appropriate lymph node dissection. Therefore, identifying and determining the extent of lymph node metastasis is important for ensuring accurate diagnosis and appropriate surgical treatment in patients with gastric cancer. However, precise detection of lymph node metastasis remains difficult. Most nodal metastases in gastric cancer are microscopic metastases, which often occur in small-sized lymph nodes, and are thus difficult to diagnose both preoperatively and intraoperatively. Preoperative nodal diagnoses are mainly made using computed tomography, although the specificity of this method is low because it is mainly based on the size of the lymph node. Furthermore, peripheral nodal metastases cannot be palpated intraoperatively, nodal harvesting of resected specimens remains difficult, and the number of lymph nodes detected vary greatly depending on the skill of the technician. Based on these findings, gastrectomy with prophylactic lymph node dissection is considered the standard surgical procedure for gastric cancer. In contrast, several groups have examined the value of sentinel node biopsy for accurately evaluating nodal metastasis in patients with early gastric cancer, reporting high sensitivity and accuracy. Sentinel node biopsy is also important for individualizing and optimizing the extent of uniform prophylactic lymph node dissection and determining whether patients are indicated for function-preserving curative gastrectomy, which is superior in preventing post-gastrectomy symptoms and maintaining dietary habits. Notably, advancements in surgical treatment for early gastric cancer are expected to result in individualized surgical strategies with sentinel node biopsy. Chemotherapy for advanced gastric cancer has also progressed, and conversion gastrectomy can now be performed after downstaging, even in cases previously regarded as inoperable. In this review, we discuss the importance of determining lymph node metastasis in the treatment of gastric cancer, the associated difficulties, and the need to investigate strategies that can improve the diagnosis of lymph node metastasis.

Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study

BACKGROUND

Specific diagnosis and treatment of gastric cancer (GC) require accurate preoperative predictions of lymph node metastasis (LNM) at individual stations, such as estimating the extent of lymph node dissection. This study aimed to develop a radiomics signature based on preoperative computed tomography (CT) images, for predicting the LNM status at each individual station.

METHODS

We enrolled 1506 GC patients retrospectively from two centers as training (531) and external (975) validation cohorts, and recruited 112 patients prospectively from a single center as prospective validation cohort. Radiomics features were extracted from preoperative CT images and integrated with clinical characteristics to construct nomograms for LNM prediction at individual lymph node stations. Performance of the nomograms was assessed through calibration, discrimination and clinical usefulness.

RESULTS

In training, external and prospective validation cohorts, radiomics signature was significantly associated with LNM status. Moreover, radiomics signature was an independent predictor of LNM status in the multivariable logistic regression analysis. The radiomics nomograms revealed good prediction performances, with AUCs of 0.716-0.871 in the training cohort, 0.678-0.768 in the external validation cohort and 0.700-0.841 in the prospective validation cohort for 12 nodal stations. The nomograms demonstrated a significant agreement between the actual probability and predictive probability in calibration curves. Decision curve analysis showed that nomograms had better net benefit than clinicopathologic characteristics.

CONCLUSION

Radiomics nomograms for individual lymph node stations presented good prediction accuracy, which could provide important information for individual diagnosis and treatment of gastric cancer.

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.

Identifying multiple swollen lymph nodes on preoperative computed tomography is associated with poor prognosis along with pathological extensive nodal metastasis in locally advanced gastric cancer

BACKGROUND

Advanced gastric cancer with extensive lymph node (LN) metastasis is associated with poor outcomes even after R0 gastrectomy. Although multi-detector row computed tomography (MDCT) is the basis of preoperative LN staging, the diagnostic accuracy of pathologically extensive LN metastasis detection by MDCT remains unsatisfactory.

METHODS

We retrospectively evaluated diagnostic accuracy for pN2/3 disease by size and number of depicted LNs on MDCT in a single-center cohort of 421 patients with pT2-4 gastric carcinoma. The positive predictive value (PPV) was determined based on the number and short-axis diameter (SAD) of identified LNs, and oncological outcomes were also evaluated according to clinical LN status and pN categories.

RESULTS

The PPV for detecting pN2/3 disease rose with the SAD value cut-off for one LN, reaching 84.6% at 10 mm with no further increase at 15 mm. However, the SAD cut-off value plateaued at 8 mm (91.3%) when at least two measurable LNs were identified on MDCT. Patients with two measurable LNs with SAD≥8 mm had significantly poorer 5-year overall and recurrence-free survival than patients with fewer than two measurable LNs in the pN2-3 disease. On multivariate analysis, two measurable LNs with SAD≥8 mm was an independent prognostic factor for overall and relapse-free survivals.

CONCLUSION

Locally advanced gastric cancer with two measurable LNs with SAD≥8 mm on preoperative MDCT is highly associated with pN2/3 disease and poorer outcomes with upfront surgery. This criterion might be a reasonable indicator for identifying candidates for neoadjuvant treatment of advanced gastric cancer.

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.

The value of restaging CT following neoadjuvant chemotherapy for resectable gastric cancer. A population-based study

Background

Response evaluation following neoadjuvant chemotherapy (NAC) in gastric cancer is debated. The aim of this study was to investigate the value of UICC-downstaging as mode of response evaluation following a MAGIC-style regimen of NAC.

Methods

Retrospective, population-based study on consecutive patients with resectable gastric adenocarcinoma receiving NAC from 2007 to 2016. CT-scan was obtained at diagnosis (rTNM) and repeated following NAC (yrTNM) to evaluate response in terms of downstaging. Further, yrTNM stage was crosstabulated to pathologic stage (ypTNM) to depict correlation between radiologic and pathologic assessment.

Results

Of 171 patients receiving NAC, 169 were available for response evaluation. For TNM-stages, 43% responded, 50% had stable disease and 7% progressed at CT. Crosstabulating yrTNM stage to ypTNM stage, 24% had concordant stages, with CT overstaging 38% and understaging 38% of the tumours, Cohen kappa ƙ = 0,06 (95%CI 0.004–0.12). Similar patterns of discordance were found for T-stages and N-stages separately. For M-category, restaging CT detected 12 patients with carcinomatosis, with an additional 14 diagnosed with carcinomatosis only at operation. No patient developed parenchymal or extra abdominal metastases, and none developed locally non-resectable tumour during delivery of NAC. Restaging CT with response evaluation was not able to stratify patients into groups of different long-term survival rates based on response mode.

Conclusions

Routine CT-scan following NAC is of limited value. Accuracy of CT staging compared to final pathologic stage is poor, and radiologic downstaging as measure of response evaluation is unreliable and unable to discriminate long-term survival rates based on response mode.

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.

Radiomics analysis of CT imaging for differentiating gastric neuroendocrine carcinomas from gastric adenocarcinomas

PURPOSE

To develop and evaluate a CT-based radiomics nomogram for differentiating gastric neuroendocrine carcinomas (NECs) from gastric adenocarcinomas (ADCs).

METHODS

CT images of 63 patients with gastric NECs were collected retrospectively, and 63 patients with gastric ADCs were selected as the control group. Univariate analysis was used to identify the significant factors of clinical characteristics and CT findings for differentiating gastric NECs from ADCs. Radiomics analysis was applied to CT images of unenhanced, arterial phase and venous phase, respectively. A radiomics nomogram incorporating the radiomics signature and the subjective CT findings was developed and its diagnostic ability was evaluated. The diagnostic performances of CT findings model, radiomics signature and radiomics nomogram were compared using DeLong test.

RESULTS

The tumor margin and lymph node (LN) metastasis were independent predictors for differentiating gastric NECs from ADCs. The radiomics signature based on venous phase presented superior AUC of 0.798 [95 % confidence interval (CI), 0.657-0.938] in validation cohort. The nomogram incorporated the radiomics signature, tumor margin and LN metastasis showed AUCs of 0.821 (95 %CI: 0.725-0.895) in the primary cohort and 0.809 (95 %CI: 0.649-0.918) in the validation cohort. Moreover, the radiomics nomogram showed good discrimination and calibration. The diagnostic performance of CT findings model was significantly lower than that of radiomics nomogram (p =  0.001) and radiomics signature (p = 0.025).

CONCLUSIONS

Radiomics analysis exhibited good performance in differentiating gastric NECs from ADCs, and the radiomics nomogram may have significant clinical implications on preoperative detection of gastric malignant tumors.

Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: A systematic review and meta-analysis

PURPOSE

Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies.

METHODOLOGY

Embase (PubMed, MEDLINE), Science Direct and IEEE Xplore databases were searched to identify eligible studies published between January 2009 and March 2019. Studies that reported on the accuracy of deep learning algorithms or radiomics models for abdominopelvic malignancy by CT or MRI were selected. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed using the QUADAS-2 tool.

RESULTS

In total, 498 potentially eligible studies were identified, of which 21 were included and 17 offered enough information for a quantitative analysis. Studies were heterogeneous and substantial risk of bias was found in 18 studies. Almost all studies employed radiomics models (n = 20). The single published deep-learning model out-performed radiomics models with a higher AUROC (0.912 vs 0.895), but both radiomics and deep-learning models outperformed the radiologist's interpretation in isolation (0.774). Pooled results for radiomics nomograms amongst tumour subtypes demonstrated the highest AUC 0.895 (95 %CI, 0.810-0.980) for urological malignancy, and the lowest AUC 0.798 (95 %CI, 0.744-0.852) for colorectal malignancy.

CONCLUSION

Radiomics models improve the diagnostic accuracy of lymph node staging for abdominopelvic malignancies in comparison with radiologist's assessment. Deep learning models may further improve on this, but data remain limited.

Accuracy of 18F-FDG PET/CT and CECT for primary staging and diagnosis of recurrent gastric cancer: A meta-analysis

Contrast-enhanced computed tomography (CECT) is commonly used for staging and diagnosing recurrent gastric cancer. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)/CT gained popularity as a diagnostic tool owing to advantages including dual functional and anatomical imaging, which may facilitate early diagnosis. The diagnostic performance of 18F-FDG PET/CT and CECT has been assessed in several studies but with variable results. Therefore, the present meta-analysis aimed to evaluate the accuracy of 18F-FDG PET/CT and CECT for primary TNM staging and the diagnosis of recurrent gastric cancers. A systematic search of the PubMed Central, Medline, Scopus, Cochrane and Embase databases from inception until January 2020 was performed. The Quality Assessment of Diagnostic Accuracy Study-2 tool was used to determine the quality of the selected studies. Pooled estimates of sensitivity and specificity were calculated. A total of 58 studies comprising 9,997 patients were included. Most studies had a low risk of bias. The sensitivity and specificity for nodal staging of gastric cancer were 49% (95% CI, 37-61%) and 92% (95% CI, 86-96%) for 18F-FDG PET/CT, respectively, and 67% (95% CI, 57-76%) and 86% (95% CI, 81-89%) for CECT, respectively. For metastasis staging, the sensitivity and specificity were 56% (95% CI, 40-71%) and 97% (95% CI, 87-99%) for 18F-FDG PET/CT, respectively, and 59% (95% CI, 41-75%) and 96% (95% CI, 83-99%) for CECT, respectively. For diagnosing cancer recurrence, the pooled sensitivity and specificity were 81% (95% CI, 72-88%) and 83% (95% CI, 74-89%) for 18F-FDG PET/CT, respectively, and 59% (95% CI, 41-75%) and 96% (95% CI, 83-99%) for CECT, respectively. Both 18F-FDG PET/CT and CECT were deemed highly useful for diagnosing recurrent gastric cancer due to their high sensitivities and specificities. However, these techniques cannot be used to exclude or confirm the presence of lymph node metastases or recurrent gastric cancer tumors, but can be used for the confirmation of distal metastasis.

An Immune-Related Gene Panel for Preoperative Lymph Node Status Evaluation in Advanced Gastric Cancer

Background and Aim: Gastric cancer (GC) is the common leading cause of cancer-related death worldwide. Immune-related genes (IRGs) may potentially predict lymph node metastasis (LNM). We aimed to develop a preoperative model to predict LNM based on these IRGs. Methods: In this paper, we compared and evaluated three machine learning models to predict LNM based on publicly available gene expression data from TCGA-STAD. The Pearson correlation coefficient (PCC) method was utilized to feature selection according to its relationships with LN status. The performance of the model was assessed using the area under the curve (AUC) and F1 score. Results: The Naive Bayesian model showed better performance and was constructed based on 26 selected gene features, with AUCs of 0.741 in the training set and 0.688 in the test set. The F1 score in the training set and test set was 0.652 and 0.597, respectively. Furthermore, Naive Bayesian model based on 26 IRGs is the first diagnostic tool for the identification of LNM in advanced GC. Conclusion: These results indicate that our new methods have the value of auxiliary diagnosis with promising clinical potential.

The Preoperative Enhanced Degree of Contrast-enhanced CT Images: A Potential Independent Predictor in Gastric Adenocarcinoma Patients After Radical Gastrectomy

Aim

To discover the value of contrast-enhanced CT parameters in predicting the prognosis of gastric adenocarcinoma (GAC) patients after radical gastrectomy.

Methods

The patients with a clinical diagnosis of GAC were retrospectively enrolled. Two radiologists drew the regions of interest (ROIs) in CT images and measured the CT attenuate value (CAV) in each phase and the corrected CAV (cCAV) in each contrast-enhanced phase. Patients were divided into two groups (high/low-enhancement) according to receiver operating characteristic (ROC) curve. Kaplan–Meier curve and Cox proportional hazards regression analysis were performed to evaluate correlation between prognosis and variables. Subgroup analysis was used to further analyze the prognostic value of variables.

Results

In total 435 patients were included. According to ROC curve, the cCAV in delayed phase (DP-cCAV) with maximum AUC and Youden index was chosen. A total of 312 patients (71.7%) entered DP-cCAV^low group and remaining 123 (28.3%) patients were in DP-cCAV^high group. According to univariate (high vs low, HR=2.120, p<0.001) and multivariate (high vs low, HR=1.623, p<0.001) Cox regression analysis, the low-enhancement state was considered as an independent protective factor. Subgroup analysis was based on age, maximum diameter of tumor, differentiation, vascular invasion status, and TNM staging. In most subgroups, the overall survival (OS) of DP-cCAV^low group was overwhelmingly satisfactory (all HR >1, expect TNM stage I, IV and differentiated type subgroups).

Conclusion

The prognostic effectiveness of CT parameters as biomarkers for OS in GAC patients treated with radical gastrectomy has potential value.

A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma

Purpose

To construct and verify a CT-based multidimensional nomogram for the evaluation of lymph node (LN) status in pancreatic ductal adenocarcinoma (PDAC).

Materials and Methods

We retrospectively assessed data from 172 patients with clinicopathologically confirmed PDAC surgically resected between February 2014 and November 2016. Patients were assigned to either a training cohort (n = 121) or a validation cohort (n = 51). We acquired radiomics features from the preoperative venous phase (VP) CT images. The maximum relevance–minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) methods were used to select the optimal features. We used multivariable logistic regression to construct a combined radiomics model for visualization in the form of a nomogram. Performance of the nomogram was evaluated by the receiver operating characteristic (ROC) curve approach, calibration testing, and analysis of clinical usefulness.

Results

A Rad score consisting of 10 LN status-related radiomics features was found to be significantly associated with the actual LN status (P < 0.01). A nomogram that consisted of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status performed well in terms of predictive power in the training cohort (area under the curve, 0.92), and this was confirmed in the validation cohort (area under the curve, 0.95). The nomogram also performed well in the calibration test and decision curve analysis, demonstrating its potential clinical value.

Conclusion

A multidimensional radiomics nomogram consisting of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status may contribute to the non-invasive evaluation of LN status in PDAC patients.

Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study

PURPOSE

Preoperative and noninvasive prognosis evaluation remains challenging for gastric cancer. Novel preoperative prognostic biomarkers should be investigated. This study aimed to develop multidetector-row computed tomography (MDCT)-guided prognostic models to direct follow-up strategy and improve prognosis.

METHODS

A retrospective dataset of 353 gastric cancer patients were enrolled from two centers and allocated to three cohorts: training cohort (n = 166), internal validation cohort (n = 83), and external validation cohort (n = 104). Quantitative radiomic features were extracted from MDCT images. The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature. A radiomic nomogram was established by integrating the radiomic signature and significant clinical risk factors. We also built a preoperative tumor-node-metastasis staging model for comparison. All models were evaluated considering the abilities of risk stratification, discrimination, calibration, and clinical use.

RESULTS

In the two validation cohorts, the established four-feature radiomic signature showed robust risk stratification power (P = 0.0260 and 0.0003, log-rank test). The radiomic nomogram incorporated radiomic signature, extramural vessel invasion, clinical T stage, and clinical N stage, outperforming all the other models (concordance index = 0.720 and 0.727) with good calibration and decision benefits. Also, the 2-yr disease-free survival (DFS) prediction was most effective (time-dependent area under curve = 0.771 and 0.765). Moreover, subgroup analysis indicated that the radiomic signature was more sensitive in risk stratifying patients with advanced clinical T/N stage.

CONCLUSIONS

The proposed MDCT-guided radiomic signature was verified as a prognostic factor for gastric cancer. The radiomic nomogram was a noninvasive auxiliary model for preoperative individualized DFS prediction, holding potential in promoting treatment strategy and clinical prognosis.

Diagnostic ability of multi-detector spiral computed tomography for pathological lymph node metastasis of advanced gastric cancer

BACKGROUND

The reliability of preoperative nodal diagnosis of advanced gastric cancer by multi-detector spiral computed tomography (MDCT) is still unclear.

AIM

To examine the diagnostic ability of MDCT more precisely by using data on intranodal pathological metastatic patterns.

METHODS

A total of 108 patients with advanced gastric cancer who underwent MDCT and curative gastrectomy at Kanazawa Medical University Hospital were enrolled in this study. The nodal sizes measured on computed tomography (CT) images were compared with the pathology results. A receiver-operating characteristic curve was constructed, from which the critical value (CV) was calculated by using the data of the first 69 patients retrospectively. By using the CV, sensitivity and specificity were calculated with prospectively collected data from 39 consecutive patients. This enabled a more precise one-to-one correspondence of lymph nodes between CT and pathological examination by using the size data of lymph node mapping. The intranodal pathological metastatic patterns were classified into the following four types: Small nodular, peripheral, large nodular, and diffuse.

RESULTS

Although all the cases were clinically suspected as having metastasis, 81 had lymph node metastasis and 27 had no metastasis. The number of dissected, detected on CT, and metastatic nodes were, 4241, 897, and 801, respectively. The CV obtained from the receiver-operating characteristic was 7.6 mm for the long axis. The sensitivity was 91.4% and the specificity was 47.3% in the prospective phase. The large nodular and diffuse metastases were easy to diagnose because metastatic nodes with a large axis often exhibit these forms.

CONCLUSION

The ability of MDCT to contribute to a nodal diagnosis of advanced gastric cancer was examined prospectively with precise size data from node mapping, using a CV of 7.6 mm for the long axis that was calculated from the retrospectively collected data. The sensitivity was as high as 91%, and would be improved when referring to the enhanced patterns. However, its specificity was as low as 47%, because most of metastatic nodes in gastric cancer being small in size. The small nodular or peripheral type metastatic nodes were often small and considered difficult to diagnose.

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.

Contrast-Enhanced CT Parameters of Gastric Adenocarcinoma: Can Radiomic Features Be Surrogate Biomarkers for HER2 Over-Expression Status?

Objective

The aim of this study was to determine the role of contrast-enhanced computed tomography (CE-CT) parameters in predicting the expression status of HER2 in gastric adenocarcinoma (GAC) patients before radical gastrectomy.

Materials and Methods

A total of 460 GAC patients who underwent non-contrast CT (NC-CT) and CE-CT examinations before radical resection were enrolled in this retrospective study. The radiologists reviewed their CT scans and recorded parameters, including CT attenuate value (CAV) and corrected CAV (cCAV). The pathologist identified the postoperative HER2 expression status, and HER2 expression status was evaluated by immunohistochemical staining (IHC). The association between CE-CT parameters and HER2 expression status was analyzed.

Results

Among the 460 patients, 84 patients had HER2 over-expression status, at a prevalence of 18.3%. The CAVs were significantly different between the 2 different HER2 expression groups in the non-contrast and arterial phases (non-contrast phase: p = 0.005; arterial phase: p < 0.001). Besides, there was a significant difference in the cCAVs between the 2 groups in the arterial phase (arterial phase: p = 0.003). Univariate and multivariate logistic regression analyses identified that the maximum diameter of tumor, differentiation degree, CAV in non-contrast, arterial, and portal phases, and cCAV in the arterial phase were predictive factors of HER2 expression status.

Conclusion

Our analyses showed that the CE-CT parameters were significantly different between different HER2 expression groups. CE-CT parameters could serve as simple, objective predictive factors of HER2 expression status of GAC patients.

An application study of CT perfusion imaging in assessing metastatic involvement of perigastric lymph nodes in patients with T1 gastric cancer

OBJECTIVE

To assess metastatic involvement of perigastric lymph nodes (PLNs) in patients with T1 gastric cancer by using CT perfusion imaging (CTPI).

METHODS

A total of 82 annotated PLNs of 33 patients with T1 gastric cancer confirmed by endoscopic ultrasonography underwent CTPI and portal phase CT scan before operation. The scan data were post-processed to acquire perfusion maps and calculate perfusion parameters including blood flow (BF) and permeability surface (PS). A radiologist measured the short axis diameters and perfusion parameters of PLNs. According to the post-operative pathology result, PLNs were divided into two groups: metastatic and inflammatory LNs. Perfusion parameters values and the size of PLNs between two groups were respectively compared statistically by t-test, and a receiver operating characteristic curve analysis was used to determine the optimal diagnostic cut-off value with sensitivity, specificity and area under the curve.

RESULTS

Examined 82 PLNs were metastatic in 45 (54.9%) and inflammatory in 37 (45.1%). The mean values of perfusion parameters and the short axis diameters in metastatic and inflammatory PLNs, respectively, were BF of 97.48 vs 81.21 ml/100 mg /min (p < 0.001), PS of 45.11 vs 36.80 ml/100 mg /min (p < 0.001), and the size of 1.51 cm vs 1.29 cm (p = 0.059). The sensitivity of 84.4%, specificity of 67.6% and area under the curve of 0.826 for BF with cut-off value of 88.89 ml/100 mg /min for differentiating metastatic from inflammatory nodes were higher than those of PS or the size of PLNs (p < 0.001).

CONCLUSION

CT perfusion parameters values were different between metastatic and inflammatory PLNs in T1 gastric cancer. BF value may be the most reliable diagnostic marker of metastatic PLNs, and it is helpful for clinicians to choose treatment modality or management plan in T1 gastric cancer patients.

ADVANCES IN KNOWLEDGE

CTPI gives information on vascularization of LNs.BF value might be a more effective marker than PS or the size of LNs for differentiating metastatic from inflammatory LNs in patients with T1 gastric cancer.

Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

OBJECTIVES

To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.

MATERIALS AND METHODS

Preoperative DECT images were retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28-81 years; 157 men [mean age, 60 years; range, 28-81 years] and 47 women [mean age, 54 years; range, 28-79 years]) between November 2011 and October 2018, They were divided into training (n = 136) and test (n = 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell's concordance index (C-index) based on patients' outcomes.

RESULTS

The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney U test, p < 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (p = 0.004) and 0.67 (p = 0.002).

CONCLUSION

The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients' prognosis.

KEY POINTS

• This study investigated the value of deep learning dual-energy CT-based radiomics in predicting lymph node metastasis in gastric cancer. • The dual-energy CT-based radiomics nomogram outweighed the single-energy model and the clinical model. • The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies.

Development and validation of a nomogram for preoperative prediction of lymph node metastasis in early gastric cancer

Background

The status of lymph nodes in early gastric cancer is critical to make further clinical treatment decision, but the prediction of lymph node metastasis remains difficult before operation. This study aimed to develop a nomogram that contained preoperative factors to predict lymph node metastasis in early gastric cancer patients.

Methods

This study analyzed the clinicopathologic features of 823 early gastric cancer patients who underwent gastrectomy retrospectively, among which 596 patients were recruited in the training cohort and 227 patients in the independent validation cohort. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated in and presented with a nomogram. And internal and external validation curves were plotted to evaluate the discrimination of the nomogram.

Results

Totally, six independent predictors, including the tumor size, macroscopic features, histology differentiation, P53, carbohydrate antigen 19-9, and computed tomography-reported lymph node status, were enrolled in the nomogram. Both the internal validation in the training cohort and the external validation in the validation cohort showed the nomogram had good discriminations, with a C-index of 0.82 (95%CI, 0.78 to 0.86) and 0.77 (95%CI, 0.60 to 0.94) respectively.

Conclusions

Our study developed a new nomogram which contained the most common and significant preoperative risk factors for lymph node metastasis in patients with early gastric cancer. The nomogram can identify early gastric cancer patients with the high probability of lymph node metastasis and help clinicians make more appropriate decisions in clinical practice.

Value of computed tomography evaluation in pathologic classification and prognosis prediction of gastric neuroendocrine tumors

Background

The study aims to investigate the correlation of CT characteristics with pathological classifications and the prognostic value of CT features in patients with gastric neuroendocrine neoplasms (g-NENs).

Methods

Ninety-one cases of pathologically diagnosed g-NENs, including 15 cases of well-differentiated neuroendocrine tumors (NETs) (G1 and G2) and 76 cases of poor-differentiated neuroendocrine carcinomas (NECs) (G3 and MANEC) were retrospectively studied. All cases were included in correlation analysis of CT characteristics with pathologic grades. Among them, 76 patients who had fulfilled follow-up data were included for overall survival (OS) and disease-free survival (DFS) analysis.

Results

CT characteristics that favor poor differentiation include tumor location (fundus and cardia), larger tumor size (>3.0 cm), infiltrative growth, unclear tumor margin, serosa involvement, ulceration and lymph node metastasis (P<0.05). Most variables had sensitivities >80% and specificities >60% to distinguish NECs from NETs. Through log-rank analysis, it was revealed that serosa involvement, cystic degeneration, necrosis, heterogeneous enhancement and lymph node metastasis led to worse DFS and OS for patients with g-NENs (P<0.05). COX regression analysis showed that serosa involvement and lymph node metastasis were independent risk factor for DFS and OS, respectively, despite of grading, staging and therapeutic choices (P<0.05). Moreover, high Ki-67 index (>55%) in G3 g-NENs is in correlation with serosa involvement and lymph node metastasis; accordingly, patients with higher Ki-67 index had worse 1-year DFS (61.7% vs. 92.3%; P<0.05).

Conclusions

CT characteristics can be useful discriminators and prognostic factors for g-NENs and may help identify G3 g-NEC from G3 g-NEN by revealing its poor differentiation and high invasive potential.

Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method

The development of an objective and rapid method that can be used for the early diagnosis of gastric cancer has important clinical application value. In this study, the fluorescence hyperspectral imaging technique was used to acquire fluorescence spectral images. Deep learning combined with spectral-spatial classification methods based on 120 fresh tissues samples that had a confirmed diagnosis by histopathological examinations was used to automatically identify and extract the "spectral + spatial" features to construct an early diagnosis model of gastric cancer. The model results showed that the overall accuracy for the nonprecancerous lesion, precancerous lesion, and gastric cancer groups was 96.5% with specificities of 96.0%, 97.3%, and 96.7% and sensitivities of 97.0%, 96.3%, and 96.6%, respectively. Therefore, the proposed method can increase the diagnostic accuracy and is expected to be a new method for the early diagnosis of gastric cancer.

High expression of APC is an unfavorable prognostic biomarker in T4 gastric cancer patients

BACKGROUND

Adenoma polyposis coli (APC) mutation is associated with tumorigenesis via the Wnt signaling pathway.

AIM

To investigate the clinical features and mechanism of APC expression in gastric cancer (GC).

METHODS

Based on APC expression profile, the related genome-wide mRNA expression, microRNA (miRNA) expression, and methylation profile in GC, the relationship between APC and GC, as well as the prognostic significance of APC were systematically analyzed by multi-dimensional methods.

RESULTS

We found that high expression of APC (APC high) was significantly associated with adverse outcomes of T4 GC patients. Genome-wide gene expression analysis revealed that varying APC expression levels in GC were associated with some important oncogenes, and corresponding cellular functional pathways. Genome-wide miRNA expression analysis indicated that most of miRNAs associated with high APC expression were downregulated. The mRNA-miRNA regulatory network analysis revealed that down-regulated miRNAs affected their inhibitory effect on tumor genes. Genome-wide methylation profiles associated with APC expression showed that there was differential methylation between the APC high and APC low groups. The number of hypermethylation sites was larger than that of hypomethylation sites, and most of hypermethylation sites were enriched in CpG islands.

CONCLUSION

Our research demonstrated that high APC expression is an unfavorable prognostic factor for T4 GC patients and may be used as a novel biomarker for pathogenesis research, diagnosis, and treatment of GC.

Frequency and Prognosis of Pulmonary Metastases in Newly Diagnosed Gastric Cancer

Purpose: The purpose of this study was to analyze the frequency and prognosis of pulmonary metastases in newly diagnosed gastric cancer using population-based data from SEER. Methods: Patients with gastric cancer and pulmonary metastases (GCPM) at the time of diagnosis in advanced gastric cancer were identified using the Surveillance, Epidemiology and End Result (SEER) database of the National Cancer Institute from 2010 to 2014. Multivariable logistic regression was performed to identify predictors of the presence of GCPM at diagnosis. Receiver operator characteristics analysis was performed to significant predictors on multivariable logistic regression and was then assessed with Delong's test. Multivariable Cox regression was developed to identify factors associated with all-cause mortality and gastric cancer-specific mortality. Survival curves were obtained according to the Kaplan-Meier method and compared using the log-rank test. Results: We identified 1,104 patients with gastric cancer and pulmonary metastases at the time of diagnosis, representing 6.02% of the entire cohort and 15.19% of the subset with metastatic disease to any distant site. Among the entire cohort, multivariable logistic regression identified six factors (younger, upper 1/3 of stomach, intestinal-type, T4 staging, N1 staging, and presence of more extrapulmonary metastases to liver, bone, and brain) as positive predictors of the presence of pulmonary metastases at diagnosis. The value of AUC for the multivariable logistic regression model was 0.775. Median survival among the entire cohort with GCPM was 3.0 months (interquartile range: 1.0-9.0 mo). Multivariable Cox model in SEER cohort confirmed five factors (diagnosis at previous period, black race, adverse pathology grade, absence of chemotherapy, and presence of more extrapulmonary metastases to liver, bone, and brain) as negative predictors for overall survival. Conclusions: The findings of this study provided population-based estimates of the frequency and prognosis for GCPM at time of diagnosis. The multivariable logistic regression model had an acceptable performance to predict the presence of PM. These findings may provide preventive guidelines for the screening and treatment of PM in GC patients. Patients with high risk factors should be paid more attention before and after diagnosis.

CT-Detected Extramural Vessel Invasion and Regional Lymph Node Involvement in Stage T4a Gastric Cancer for Predicting Progression-Free Survival

OBJECTIVE. This study aimed to investigate the 3-year progression-free survival (PFS) of patients with stage T4a gastric cancer with extramural vessel invasion (EMVI) detected with contrast-enhanced (CE) MDCT. In addition, we investigated the possibility that CT of EMVI could improve clinical nodal (N) staging. MATERIALS AND METHODS. This retrospective study included 143 patients with T4a gastric cancer. Clinical staging was performed with CE-MDCT. All patients underwent radical gastrectomy with extended lymphadenectomy, adjuvant chemotherapy, and conventional follow-up visits. Potential prognostic factors, including CE-MDCT-detected N status, pathologic N status, EMVI detected at CT, tumor location or growth pattern, histologic type or tumor differentiation, and tumor size, were recorded. Survival estimates for PFS were obtained using the Kaplan-Meier product limit for the following patient subgroups: EMVI positive-N positive, EMVI positive-N negative, EMVI negative-N positive, and EMVI negative-N negative. Hazard ratios for 3-year PFS were generated using a Cox proportional hazard regression analysis. RESULTS. The frequency of EMVI detected at CT was 55.9% (80/143). The 3-year PFS rates were 25.0% for the EMVI positive-N positive group, 53.1% for the EMVI positive-N negative group, 75.6% for the EMVI negative-N positive group, and 64.7% for the EMVI negative-N negative group. The EMVI positive-N positive subgroup 3-year PFS rate was significantly lower than that of the other three groups (p < 0.05, log-rank test). Using Cox proportional hazards regression analysis, EMVI positive-N positive status was found to be an independent factor for reduced 3-year PFS, with a hazard ratio of 2.169 (95% CI, 1.300-3.618; p = 0.003). CONCLUSION. EMVI detected at CT, combined with N status detected with CE-MDCT, could be used as a valuable preoperative prognostic factor in patients with T4a gastric cancer.

Radiomics Signature on Computed Tomography Imaging: Association With Lymph Node Metastasis in Patients With Gastric Cancer

Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status.

Methods: We retrospectively analyzed radiomics features of CT images in 1,689 consecutive patients from three cancer centers. The prediction model was developed in the training cohort and validated in internal and external validation cohorts. Lasso regression model was utilized to select features and build radiomics signature. Multivariable logistic regression analysis was utilized to develop the model. We integrated the radiomics signature, clinical T and N stage, and other independent clinicopathologic variables, and this was presented as a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness.

Results: The radiomics signature was significantly associated with pathological LN stage in training and validation cohorts. Multivariable logistic analysis found the radiomics signature was an independent predictor of LN metastasis. The nomogram showed good discrimination and calibration.

Conclusions: The newly developed radiomic signature was a powerful predictor of LN metastasis and the radiomics nomogram could facilitate the preoperative individualized prediction of LN status.

Application of spectral CT imaging in evaluating lymph node metastasis in patients with gastric cancers: initial findings

BACKGROUND

Traditional computed tomography (CT) can predict the lymph node metastasis of gastric cancers with moderate accuracy; however, investigation of spectral CT imaging in this field is still limited.

PURPOSE

To explore the application of spectral CT imaging in evaluating lymph node metastasis in patients with gastric cancers.

MATERIAL AND METHODS

Twenty-four patients with gastric cancers prospectively underwent spectral CT imaging in the arterial phase. The short and long diameters, material concentrations, and CT values were measured and compared between lymph nodes with and without metastasis. The diagnostic performance of the CT index in identifying metastatic lymph nodes was analyzed with receiver operating characteristic (ROC) analysis.

RESULTS

A total of 102 lymph nodes (77 metastatic, 25 non-metastatic) were detected on spectral CT imaging with the reference of postoperative pathologic exanimation. The short and long diameters, water/fat concentrations, CT value, and ratio between lymph nodes vs. tumors of metastatic lymph nodes were significantly higher than those of non-metastatic ones (all P < 0.05). With a cut-off of 0.785, the CT ratio of lymph node/tumor on 70-keV monochromatic images yielded an accuracy of 81.4% in differentiating lymph nodes with and without metastasis.

CONCLUSION

Spectral CT imaging detects lymph nodes more clearly, and the CT ratio of lymph node/tumor on 70-keV monochromatic images holds great potential in differentiating lymph nodes with and without metastasis, which is more accurate than size measurement.

A genomewide transcriptomic approach identifies a novel gene expression signature for the detection of lymph node metastasis in patients with early stage gastric cancer

BACKGROUND

Although identification of lymph node (LN) metastasis is a well-recognized strategy for improving outcomes in patients with gastric cancer (GC), currently there is lack of availability of adequate molecular biomarkers that can identify such metastasis. Herein we have developed a robust gene-expression signature for detecting LN metastasis in early stage GC by using a transcriptome-wide biomarker discovery and subsequent validation in multiple clinical cohorts.

METHODS

A total of 532 patients with pathological T1 and T2 GC from 4 different cohorts were analyzed. Two independent datasets (n = 96, and n = 188) were used to establish a gene signature for the identification of LN metastasis in GC patients. The diagnostic performance of our gene-expression signature was subsequently assessed in two independent clinical cohorts using qRT-PCR assays (n = 101, and n = 147), and subsequently compared against conventional tumor markers and image-based diagnostics.

FINDINGS

We established a 15-gene signature by analyzing multiple high throughput datasets, which robustly distinguished LN status in both training (AUC = 0.765, 95% CI 0.667-0.863) and validation cohorts (AUC = 0.742, 95% CI 0.630-0.852). Notably, the 15-gene signature was significantly superior compared to the conventional tumor markers, CEA (P = .04) and CA19-9 (P = .005), as well as computed tomography-based imaging (P = .04).

INTERPRETATION

We have established and validated a 15-gene signature for detecting LN metastasis in GC patients, which offers a robust diagnostic tool for potentially improving treatment outcomes in gastric cancer patients. FUND: NIH: CA72851, CA181572, CA14792, CA202797, CA187956; CPRIT: RP140784: Baylor Sammons Cancer Center polot grants (AG), VPRT: 9610337, CityU 21101115, 11102317, 11103718; JCYJ20170307091256048 (XW).

Predictive Value of Preoperative Multidetector-Row Computed Tomography for Axillary Lymph Nodes Metastasis in Patients With Breast Cancer

Introduction: Axillary lymph nodes (ALN) status is an essential component in tumor staging and treatment planning for patients with breast cancer. The aim of present study was to evaluate the predictive value of preoperative multidetector-row computed tomography (MDCT) for ALN metastasis in breast cancer patients.

Methods: A total of 148 cases underwent preoperative MDCT examination and ALN surgery were eligible for the study. Logistic regression analysis of MDCT variates was used to estimate independent predictive factors for ALN metastasis. The prediction of ALN metastasis was determined with MDCT variates through receiver operating characteristic (ROC) analysis.

Results: Among the 148 cases, 61 (41.2%) cases had ALN metastasis. The cortical thickness in metastatic ALN was significantly thicker than that in non-metastatic ALN (7.5 ± 5.0 mm vs. 2.6 ± 2.8 mm, P < 0.001). Multi-logistic regression analysis indicated that cortical thickness of >3 mm (OR: 12.32, 95% CI: 4.50–33.75, P < 0.001) and non-fatty hilum (OR: 5.38, 95% CI: 1.51–19.19, P = 0.009) were independent predictors for ALN metastasis. The sensitivity, specificity and AUC of MDCT for ALN metastasis prediction based on combined-variated analysis were 85.3%, 87.4%, and 0.893 (95% CI: 0.832–0.938, P < 0.001), respectively.

Conclusions: Cortical thickness (>3 mm) and non-fatty hilum of MDCT were independent predictors for ALN metastasis. MDCT is a potent imaging tool for predicting ALN metastasis in breast cancer. Future prospective study on the value of contrast enhanced MDCT in preoperative ALN evaluation is warranted.

Predictive values of multidetector-row computed tomography combined with serum tumor biomarkers in preoperative lymph node metastasis of gastric cancer

Objective

Multidetector-row computed tomography (MDCT) and serum tumor biomarkers are commonly used to evaluate the preoperative lymph node metastasis and the clinical staging of gastric cancer (GC). This study intends to evaluate the clinical predictive value of MDCT and serum tumor biomarkers in lymph node metastasis of GC.

Methods

The clinicopathologic data of 445 GC patients who underwent radical gastrectomy were retrospectively analyzed to evaluate the diagnostic value of MDCT and serum tumor biomarkers in lymph node metastatic staging of GC before surgery.

Results

With the multinomial logistic regression analysis, the independent relative factors of lymph node metastasis of GC were identified as tumor size, depth of tumor invasion, vessel invasion, vascular embolus, and soft tissue invasion. The optimal critical value of the short diameter of lymph nodes detected by MDCT scanning for evaluation of preoperative lymph node metastasis was 6.0 mm, with 75.7% as predictive accuracy of lymph node metastasis compared to the postoperative pathological results of GC patients. In addition, the critical value of the short diameter of lymph nodes combined with serum tumor biomarkers [including carbohydrate antigen (CA)-724 and CA-199] could show an enhancement of predictive sensitivity of lymph node metastasis (up to 89.3%) before surgery.

Conclusions

MDCT combined with serum tumor biomarkers should be adopted to improve preoperative sensitivity and accuracy of lymph node metastasis for GC patients.

Usefulness of diffusion-weighted magnetic resonance imaging for evaluating the effect of hemostatic radiotherapy for unresectable gastric cancer

There are several reports that vouch for the usefulness of diffusion-weighted image (DWI) in making a diagnosis before treatment. However, no study has evaluated the effect of radiotherapy (RT) for unresectable gastric cancer. In the present case report, we evaluated the effectiveness of RT using DWI. An 81-year-old man was hospitalized with a broken bone and then diagnosed with advanced gastric cancer with breeding. He had chorionic renal failure and surgery was impossible. Further, contrast-enhanced computed tomography and magnetic resonance imaging (MRI) were not performed due to renal failure, whereas palliative RT was performed. We followed up the patient using blood test and MRI (DWI) to estimate whether bleeding had stopped or not after radiotherapy. Hemostasis effect was found after 2 weeks of RT. In DWI examination, there was a decrease in the tumor signal intensity 30 days after RT. Similarly, at day 60, the tumor signal intensity further decreased on DWI and the blood test results indicated no progression of anemia. At 4 months after the RT, the patient died because of respiratory failure without any bleeding. DWI is useful not only for the initial diagnosis but also for evaluating the effectiveness of RT.Trial registration: National clinical study registered number: UMIN000026362.