Lymph-node metastases: efficacy for detection with helical CT in patients with gastric cancer

Comprehensive evaluation of tumor response better evaluates the efficacy of neoadjuvant chemotherapy and predicts the prognosis in gastric cancer - a post hoc analysis of a single-center randomized controlled trial

BACKGROUND

Perioperative chemotherapy combined with D2 radical gastrectomy has been proven to be the standard treatment for local advanced gastric cancer. However, tumor regression grading (TRG) is the only neoadjuvant chemotherapy (NACT) response evaluation criterion recommended by the NCCN guideline for gastric cancer (GC). Given TRG's limitations, we aim to explore a better comprehensive response evaluation method in this study.

METHODS

Clinical information of 96 GC patients who received NACT was collected prospectively. Clinicopathological variables predictive of the response to NACT were identified by comparing the pre- and post-NACT examination results. The correlations between the response mode and long-term survival rate were assessed.

RESULTS

Univariate Cox regression analysis showed that CT-based evaluation of the primary lesion thickness (CT-thickness) and tumor markers (TMs) were significantly associated with prognosis. The comprehensive evaluation method, including CT-thickness, TRG, and TMs, was constructed and proved to have a higher Harrell's C index. Significant differences in overall survival (OS) and recurrence-free survival (RFS) were observed between responders and non-responders distinguished by the comprehensive evaluation method.

CONCLUSIONS

The combination of CT-thickness, TRG, and TMs could be used to construct a pragmatic NACT efficacy evaluation method with both high sensitivity and specificity, which could facilitate clinical decision-making, NACT-related clinical research conduction, and efficacy predictive biomarker exploration.

Advanced Gastric Cancer: CT Radiomics Prediction of Lymph Modes Metastasis After Neoadjuvant Chemotherapy

This study aims to create and assess machine learning models for predicting lymph node metastases following neoadjuvant treatment in advanced gastric cancer (AGC) using baseline and restaging computed tomography (CT). We evaluated CT images and pathological data from 158 patients with resected stomach cancer from two institutions in this retrospective analysis. Patients were eligible for inclusion if they had histologically proven gastric cancer. They had received neoadjuvant chemotherapy, with at least 15 lymph nodes removed. All patients received baseline and preoperative abdominal CT and had complete clinicopathological reports. They were divided into two cohorts: (a) the primary cohort (n = 125) for model creation and (b) the testing cohort (n = 33) for evaluating models' capacity to predict the existence of lymph node metastases. The diagnostic ability of the radiomics-model for lymph node metastasis was compared to traditional CT morphological diagnosis by radiologist. The radiomics model based on the baseline and preoperative CT images produced encouraging results in the training group (AUC 0.846) and testing cohort (AUC 0.843). In the training cohort, the sensitivity and specificity were 81.3% and 77.8%, respectively, whereas in the testing cohort, they were 84% and 75%. The diagnostic sensitivity and specificity of the radiologist were 70% and 42.2% (using baseline CT) and 46.3% and 62.2% (using preoperative CT). In particular, the specificity of radiomics model was higher than that of conventional CT in diagnosing N0 cases (no lymph node metastasis). The CT-based radiomics model could assess lymph node metastasis more accurately than traditional CT imaging in AGC patients following neoadjuvant chemotherapy.

Synergizing traditional CT imaging with radiomics: a novel model for preoperative diagnosis of gastric neuroendocrine and mixed adenoneuroendocrine carcinoma

Objective

To develop diagnostic models for differentiating gastric neuroendocrine carcinoma (g-NEC) and gastric mixed adeno-neuroendocrine carcinoma (g-MANEC) from gastric adenocarcinoma (g-ADC) based on traditional contrast enhanced CT imaging features and radiomics features.

Methods

We retrospectively analyzed 90 g-(MA)NEC (g-MANEC and g-NEC) patients matched 1:1 by T-stage with 90 g-ADC patients. Traditional CT features were analyzed using univariable and multivariable logistic regression. Tumor segmentation and radiomics features extraction were performed with Slicer and PyRadiomics. Feature selection was conducted through univariable analysis, correlation analysis, LASSO, and multivariable stepwise logistic. The combined model incorporated clinical and radiomics predictors. Diagnostic performance was assessed with ROC curves and DeLong's test. The models' diagnostic efficacy was further validated in subgroup of g-NEC vs. g-ADC and g-MANEC vs. g-ADC cases.

Results

Tumor necrosis and lymph node metastasis were independent predictors for differentiating g-(MA)NEC from g-ADC (P < 0.05). The clinical model's AUC was 0.700 (training) and 0.667(validation). Five radiomics features were retained, with the radiomics model showing AUC of 0.809 (training) and 0.802 (validation). The combined model's AUCs were 0.853 (training) and 0.812 (validation), significantly outperforming the clinical model (P < 0.05). Subgroup analysis revealed that the combined model exhibited acceptable performance in differentiating g-NEC from g-ADC and g-MANEC from g-ADC, with AUC of 0.887 and 0.823 in the training cohort and 0.852 and 0.762 in the validation cohort.

Conclusion

A combined model based on traditional CT imaging and radiomic features provides a non-invasive and effective preoperative diagnostic method for differentiating g-(MA)NEC from g-ADC.

Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms

Background

Gastric cancer, a pervasive malignancy globally, often presents with regional lymph node metastasis (LNM), profoundly impacting prognosis and treatment options. Existing clinical methods for determining the presence of LNM are not precise enough, necessitating the development of an accurate risk prediction model.

Objective

Our primary objective was to employ machine learning algorithms to identify risk factors for LNM and establish a precise prediction model for stage II-III gastric cancer.

Methods

A study was conducted at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between May 2010 and December 2022. This retrospective study analyzed 1147 surgeries for gastric cancer and explored the clinicopathological differences between LNM and non-LNM cohorts. Utilizing univariate logistic regression and two machine learning methodologies-Least absolute shrinkage and selection operator (LASSO) and random forest (RF)-we identified vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit (HCT), and lymphocyte-monocyte ratio (LMR) as salient factors and consolidated them into a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves were used to evaluate the test efficacy of the nomogram. Shapley Additive Explanation (SHAP) values were utilized to illustrate the predictive impact of each feature on the model's output.

Results

Significant differences in tumor characteristics were discerned between LNM and non-LNM cohorts through appropriate statistical methods. A nomogram, incorporating vascular invasion, maximum tumor diameter, percentage of monocytes, HCT, and LMR, was developed and exhibited satisfactory predictive capabilities with an AUC of 0.787 (95% CI: 0.749-0.824) in the training set and 0.753 (95% CI: 0.694-0.812) in the validation set. Calibration curves and decision curves affirmed the nomogram's predictive accuracy.

Conclusion

In conclusion, leveraging machine learning algorithms, we devised a nomogram for precise LNM risk prognostication in stage II-III gastric cancer, offering a valuable tool for tailored risk assessment in clinical decision-making.

Deep Learning Prediction of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Clinical Implication-Applied Preprocessed CT Images

Background: Accurate detection of axillary lymph node (ALN) metastases in breast cancer is crucial for clinical staging and treatment planning. This study aims to develop a deep learning model using clinical implication-applied preprocessed computed tomography (CT) images to enhance the prediction of ALN metastasis in breast cancer patients. Methods: A total of 1128 axial CT images of ALN (538 malignant and 590 benign lymph nodes) were collected from 523 breast cancer patients who underwent preoperative CT scans between January 2012 and July 2022 at Hallym University Medical Center. To develop an optimal deep learning model for distinguishing metastatic ALN from benign ALN, a CT image preprocessing protocol with clinical implications and two different cropping methods (fixed size crop [FSC] method and adjustable square crop [ASC] method) were employed. The images were analyzed using three different convolutional neural network (CNN) architectures (ResNet, DenseNet, and EfficientNet). Ensemble methods involving and combining the selection of the two best-performing CNN architectures from each cropping method were applied to generate the final result. Results: For the two different cropping methods, DenseNet consistently outperformed ResNet and EfficientNet. The area under the receiver operating characteristic curve (AUROC) for DenseNet, using the FSC and ASC methods, was 0.934 and 0.939, respectively. The ensemble model, which combines the performance of the DenseNet121 architecture for both cropping methods, delivered outstanding results with an AUROC of 0.968, an accuracy of 0.938, a sensitivity of 0.980, and a specificity of 0.903. Furthermore, distinct trends observed in gradient-weighted class activation mapping images with the two cropping methods suggest that our deep learning model not only evaluates the lymph node itself, but also distinguishes subtler changes in lymph node margin and adjacent soft tissue, which often elude human interpretation. Conclusions: This research demonstrates the promising performance of a deep learning model in accurately detecting malignant ALNs in breast cancer patients using CT images. The integration of clinical considerations into image processing and the utilization of ensemble methods further improved diagnostic precision.

Preoperative diagnosis of metastatic lymph nodes by CT-histopathologic matching analysis in gastric adenocarcinoma using dual-layer spectral detector CT

OBJECTIVES

There still remain challenges to accurate diagnosis of lymph node (LN) involvement in gastric cancer (GC) on conventional CT. This study evaluated the quantitative data derived from dual-layer spectral detector CT (DLCT) for preoperative diagnosis of metastatic LNs compared to conventional CT images.

METHODS

Patients with adenocarcinoma scheduled for gastrectomy were enrolled in this prospective study from July, 2021, to February, 2022. Regional LNs were labeled on preoperative DLCT. The LNs were located and matched using carbon nanoparticle solution during surgery according to their locations and anatomic landmarks on preoperative images. The matched LNs were randomly split into training and validation cohorts in a ratio of 2:1. The DLCT quantitative parameters in the training cohort were investigated using logistic regression models to identify independent predictors of metastatic LNs, and these predictors were subsequently applied to the validation cohort. Receiver operating characteristic curves were compared between the DLCT parameters and conventional CT images.

RESULTS

Fifty-five patients were included in the study, with 267 successfully matched LNs (90 metastatic, 177 nonmetastatic). Independent predictors included arterial phase CT attenuation on 70-keV images, venous phase electron density, and clustered feature. These combination predictors had areas under the curve (AUC) of 0.855 and 0.907 in the training and validation cohorts, respectively. Compared to conventional CT criteria alone, the model had higher AUC and accuracy (0.741 vs. 0.907, 75.28% vs. 87.64%; p < 0.01) for LN diagnosis.

CONCLUSION

Incorporating DLCT parameters improved preoperative diagnosis of LN metastasis in GC, increasing the accuracy of clinical N stage.

CLINICAL RELEVANCE STATEMENT

Compared to conventional CT criteria, quantitative parameters from dual-layer spectral detector CT showed higher diagnostic efficacy for the preoperative diagnosis of lymph node metastases in gastric cancer, increasing the accuracy of clinical N stage.

KEY POINTS

• Quantitative parameters from dual-layer spectral detector CT are useful for the preoperative diagnosis of lymph node metastases in gastric adenocarcinoma, increasing the accuracy of clinical N stage. • The values for metastatic lymph nodes are higher than those of nonmetastatic ones. The arterial phase of CT attenuation on 70-keV images, venous phase of electron density, and clustered feature independently predicted lymph node metastases. • Prediction model had area under the curve of 0.907, sensitivity of 81.82%, specificity of 91.07%, and accuracy of 87.64% for the preoperative diagnosis of lymph node metastasis.

Imaging evaluation of para-aortic lymph nodes in cervical cancer

BACKGROUND

In recent years, much literature has reported the diagnostic value of computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-CT in para-aortic lymph node metastasis of cervical cancer.

PURPOSE

To compare and analyze the para-aortic lymph node presentations found in cervical cancer on different images in order to determine the best precise imaging method for identifying metastatic lymph nodes.

MATERIAL AND METHODS

PubMed, Web of Science, MEDLINE, and other databases were searched for the non-invasive detection of metastatic lymph nodes for a comprehensive comparison.

RESULTS

Positive lymph nodes on CT are significantly related to the following factors: short axis ≥10 mm; and round or central necrosis. Positive lymph nodes on MRI are significantly related to the following factors: short axis ≥8 mm; inhomogeneous signal intensity; morphology: round, irregular edge, extracapsular invasion, central necrosis, loss of lymph node structure, burrs, or lobes; and ADC value decreases, combined with local actuality. On PET-CT examination, when the short axis of the lymph node is >5 mm, the SUV is >2.5, or the FDG uptake is greater than that of the surrounding tissue, it is a metastatic lymph node.

CONCLUSION

In conclusion, different imaging techniques show metastatic lymph nodes in different ways. Combining the patient's medical history with the symptoms of the aforementioned lymph nodes, together with one or more imaging techniques, is important to diagnose para-aortic lymph nodes in cervical cancer.

MR Imaging in Cervical Cancer

Cervical cancer remains a significant contributor to morbidity and mortality for women globally despite medical advances in preventative medicine and treatment. The 2018 Internal Federation of Gynecology and Obstetrics committee modified their original 2009 staging scheme to incorporate advanced imaging modalities, where available, to increase the accuracy of staging and to guide evolving treatments. Having a robust understanding of the newest staging iteration, its consequences on treatment pathways, and common imaging pitfalls will aid the radiologist in generating valuable and practical reports to optimize treatment strategies.

PET/CT for Predicting Occult Lymph Node Metastasis in Gastric Cancer

A portion of gastric cancer patients with negative lymph node metastasis at an early stage eventually die from tumor recurrence or advanced metastasis. Occult lymph node metastasis (OLNM] is a potential risk factor for the recurrence and metastasis in these patients, and it is highly important for clinical prognosis. Positron emission tomography (PET)/computed tomography (CT) is used to assess lymph node metastasis in gastric cancer due to its advantages in anatomical and functional imaging and non-invasive nature. Among the major metabolic parameters of PET, the maximum standardized uptake value (SUVmax) is commonly used for examining lymph node status. However, SUVmax is susceptible to interference by a variety of factors. In recent years, the exploration of new PET metabolic parameters, new PET imaging agents and radiomics, has become an active research topic. This paper aims to explore the feasibility and predict the effectiveness of using PET/CT to detect OLNM. The current landscape and future trends of primary metabolic parameters and new imaging agents of PET are reviewed. For gastric cancer patients, the possibility to detect OLNM non-invasively will help guide surgeons to choose the appropriate lymph node dissection area, thereby reducing unnecessary dissections and providing more reasonable, personalized and comprehensive treatments.

Optimization of Preoperative Lymph Node Staging in Patients with Muscle-Invasive Bladder Cancer Using Radiomics on Computed Tomography

Approximately 25% of the patients with muscle-invasive bladder cancer (MIBC) who are clinically node negative have occult lymph node metastases at radical cystectomy (RC) and pelvic lymph node dissection. The aim of this study was to evaluate preoperative CT-based radiomics to differentiate between pN+ and pN0 disease in patients with clinical stage cT2-T4aN0-N1M0 MIBC. Patients with cT2-T4aN0-N1M0 MIBC, of whom preoperative CT scans and pathology reports were available, were included from the prospective, multicenter CirGuidance trial. After manual segmentation of the lymph nodes, 564 radiomics features were extracted. A combination of different machine-learning methods was used to develop various decision models to differentiate between patients with pN+ and pN0 disease. A total of 209 patients (159 pN0; 50 pN+) were included, with a total of 3153 segmented lymph nodes. None of the individual radiomics features showed significant differences between pN+ and pN0 disease, and none of the radiomics models performed substantially better than random guessing. Hence, CT-based radiomics does not contribute to differentiation between pN+ and pN0 disease in patients with cT2-T4aN0-N1M0 MIBC.

Correlating Quantitative Para-Aortic Lymph Node Computed Tomography Parameters With Fluorodeoxyglucose Positron Emission Tomography for Cervical Cancer Staging: Possible Solution for Resource Constrained Countries

OBJECTIVE

The aim of the study was to determine the most accurate quantitative morphological parameters on computed tomography (CT) that correlate with fluorodeoxyglucose (FDG)-avid para-aortic nodes (PANs) in patients with cervical cancer.

METHODS

A single-institution retrospective evaluation was performed of women with cervical cancer who underwent pretreatment positron emission tomography (PET)/CT and radiotherapy therapy planning CT between 2009 and 2020. A node-by-node correlation between pretreatment CT and PET/CT was performed for the reference standard of FDG avidity for short- and long-axis diameters, volume, and long-/short-axis ratio (L/S). The FDG-avid PANs were defined as PET-positive and non-FDG-avid PANs from patients without PET-determined PAN metastasis were defined as PET negative. Area under the receiver operator curve was calculated to access diagnostic accuracy of the different quantitative parameters.

RESULTS

A total of 94 women (mean age ± standard deviation, 52 ± 13 years) with cervical cancer were included. Forty-seven patients had PET-positive PANs (181 PET-positive PANs) and 47 patients had no PET-positive PANs (141 PET-negative PANs). The area under the receiver operator curve for volume (0.945) was greater (P < 0.001) than that of short axis (0.895), long axis (0.885), and L/S (0.583). At a specificity set point of 0.90 (127/141 PANs), the cutoff for volume was 0.443 cm3 or greater (0.85 sensitivity [154/181 PANs]; 95% confidence interval, 0.83-0.93) and for short-axis diameter was 5.9 mm or greater (0.75 sensitivity [135/181 PANs]; 95% confidence interval, 0.68-0.81).

CONCLUSIONS

Para-aortic lymph node volume demonstrated that improved node-by-node correlation between CT and PET/CT compared with short-axis diameter, long-axis diameter, and L/S and is an alternative to improve detection of PAN suspicious of metastatic diseases in locations without access to PET/CT.

Imaging findings of diseases affecting the gastrohepatic ligament: not as acquiscent as it seems

The gastrohepatic ligament, a major part of the lesser omentum with hepatoduodenal ligament, is an important anatomic structure connected to several other intraabdominal organs and ligaments. It is at the crossroads of several different anatomic structures and may be affected by different diseases. In this article, we aim to increase the awareness of imagers to this small anatomic structure and provide clues for correct diagnosis and assessment of diseases that may affect this area. We will examine various diseases involving the gastrohepatic ligament in detail and try to address its importance using representative cases.

The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2021

There exist differences in the epidemiological characteristics, clinicopathological features, tumor biological characteristics, treatment patterns, and drug selections between gastric cancer patients from the Eastern and Western countries. The Chinese Society of Clinical Oncology (CSCO) has organized a panel of senior experts specializing in all sub-specialties of gastric cancer to compile a clinical guideline for the diagnosis and treatment of gastric cancer since 2016 and renews it annually. Taking into account regional differences, giving full consideration to the accessibility of diagnosis and treatment resources, these experts have conducted expert consensus judgment on relevant evidence and made various grades of recommendations for the clinical diagnosis and treatment of gastric cancer to reflect the value of cancer treatment and meeting health economic indexes in China. The 2021 CSCO Clinical Practice Guidelines for Gastric Cancer covers the diagnosis, treatment, follow-up, and screening of gastric cancer. Based on the 2020 version of the CSCO Chinese Gastric Cancer guidelines, this updated guideline integrates the results of major clinical studies from China and overseas for the past year, focused on the inclusion of research data from the Chinese population for more personalized and clinically relevant recommendations. For the comprehensive treatment of non-metastatic gastric cancer, attentions were paid to neoadjuvant treatment. The value of perioperative chemotherapy is gradually becoming clearer and its recommendation level has been updated. For the comprehensive treatment of metastatic gastric cancer, recommendations for immunotherapy were included, and immune checkpoint inhibitors from third-line to the first-line of treatment for different patient groups with detailed notes are provided.

3D Multi-Attention Guided Multi-Task Learning Network for Automatic Gastric Tumor Segmentation and Lymph Node Classification

Automatic gastric tumor segmentation and lymph node (LN) classification not only can assist radiologists in reading images, but also provide image-guided clinical diagnosis and improve diagnosis accuracy. However, due to the inhomogeneous intensity distribution of gastric tumor and LN in CT scans, the ambiguous/missing boundaries, and highly variable shapes of gastric tumor, it is quite challenging to develop an automatic solution. To comprehensively address these challenges, we propose a novel 3D multi-attention guided multi-task learning network for simultaneous gastric tumor segmentation and LN classification, which makes full use of the complementary information extracted from different dimensions, scales, and tasks. Specifically, we tackle task correlation and heterogeneity with the convolutional neural network consisting of scale-aware attention-guided shared feature learning for refined and universal multi-scale features, and task-aware attention-guided feature learning for task-specific discriminative features. This shared feature learning is equipped with two types of scale-aware attention (visual attention and adaptive spatial attention) and two stage-wise deep supervision paths. The task-aware attention-guided feature learning comprises a segmentation-aware attention module and a classification-aware attention module. The proposed 3D multi-task learning network can balance all tasks by combining segmentation and classification loss functions with weight uncertainty. We evaluate our model on an in-house CT images dataset collected from three medical centers. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms, and obtains promising performance for tumor segmentation and LN classification. Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge. Our implementation is released at https://github.com/infinite-tao/MA-MTLN.

Computed Tomography Features of Gastric Cancer Patients With DNA Mismatch Repair Deficiency

Objective

To explore the computed tomography (CT) features of gastric cancer (GC) patients with DNA mismatch repair deficiency (dMMR).

Materials and Methods

This study reviewed the clinical and CT features of GC patients with dMMR, confirmed by the postoperative results, between September 2017 and December 2019. The expression pattern of MMR major proteins (MLH1, MSH2, MSH6, and PMS2) in immunohistochemistry was used to confirm the MMR status in GC tissues. The correlation between pre-treatment CT features and MMR status was statistically analyzed.

Results

A total of 28 patients with GC were diagnosed as dMMR in our study, and 49 patients were MMR-proficient (pMMR). The tumor locations were significantly different between the dMMR and pMMR groups (p = 0.006). The CT tumor thickness, CT long and short diameters of the largest lymph node, and the number of lymph nodes on CT of the dMMR group were significantly different from the pMMR group.

Conclusion

The dMMR GC exhibited a lower stomach location, smaller tumor thickness and lymph node diameter, and fewer lymph nodes on CT imaging.

Prognostic Implications of Tumor Differentiation in Clinical T1N0 Gastric Adenocarcinoma

Background

Current guidelines recommend neoadjuvant chemotherapy in patients with locoregional gastric adenocarcinoma. Patients diagnosed with early stage gastric adenocarcinoma are usually managed with upfront surgical intervention. However, pathologic staging in a subset of these clinically staged patients identifies more advanced locoregional disease requiring adjuvant treatment. Therefore, identifying these patients prior to surgical intervention is critical to ensure employment of the appropriate treatment paradigm. The aim of the current study was to define patient characteristics associated with clinical understaging in early gastric cancer.

Methods

Using the National Cancer Database (2004–2014) we identified 3,892 individuals with clinical T1N0 gastric adenocarcinoma who underwent upfront definitive surgery, had negative surgical margins, and did not receive preoperative chemotherapy or radiotherapy. Patient characteristics were compared between those with pathologic stage T1N0 disease and those who were upstaged upon surgery.

Results

Twenty‐seven percent of clinical T1N0 gastric adenocarcinomas had a change in stage because of pathologically defined ≥T2 disease or positive lymph nodes. Individuals who were upstaged had a higher tumor grade compared with those with pathologic stage T1N0 disease. Specifically, 41.9% (530/1,264) of individuals with a poorly differentiated tumor were upstaged, compared with only 10.7% (70/656) with a well‐differentiated tumor. Approximately 75% of cases involved upstaging because of T misclassification. The highest percentage of upstaging was shown for tumors located at the fundus and body of the stomach.

Conclusion

Upstaging of clinical T1N0 gastric adenocarcinoma is characterized by higher tumor grade and is mostly a result of a change in T stage. These findings mandate thorough workup in order to identify patients with clinically staged T1N0 disease requiring preoperative chemotherapy.

Implications for Practice

Upstaging of clinical T1N0 gastric adenocarcinoma is characterized by higher tumor grade and is mostly a result of a change in T stage. These findings mandate thorough workup in order to identify patients with clinically staged T1N0 disease requiring preoperative chemotherapy.

This article evaluates the frequency of upstaging following surgery among cT1N0 gastric cancer and defines the corresponding patient characteristics, with the goal of better identifying those patients who require preoperative chemotherapy.

[Comparative imaging study of mediastinal lymph node from pre-surgery dual energy CT versus post-surgeron verifications in non-small cell lung cancer patients]

OBJECTIVE

To validate the value of dual energy CT (DECT) in the differentiation of mediastinal metastatic lymph nodes from non-metastatic lymph nodes in non-small cell lung cancer (NSCLC).

METHODS

In the study, 57 surgically confirmed NSCLC patients who underwent enhanced DECT scan within 2 weeks before operation were enrolled. Two radiologists analyzed the CT images before operation. All mediastinal lymph nodes with short diameter≥5 mm on axial images were included in this study. The morphological parameters [long-axis diameter (L), short-axis diameter (S) and S/L of lymph nodes] and the DECT parameters [iodine concentration (IC), normalized iodine concentration (NIC), slope of spectral hounsfield unit curve (λHU) and effective atomic number (Zeff) in arterial and venous phase] were measured. The differences of morphological parameters and DECT parameters between metastatic and non-metastatic lymph nodes were compared. The parameters with significant difference were analyzed by the Logistic regression model, then a new predictive variable was established. Receiver operator characteristic (ROC) analyses were performed for S, NIC in venous phase and the new predictive variable.

RESULTS

In 57 patients, 49 metastatic lymph nodes and 938 non-metastatic lymph nodes were confirmed by surgical pathology. A total of 163 mediastinal lymph nodes (49 metastatic, 114 non-metastatic) with S≥5 mm were detected on axial CT images. The S, L and S/L of metastatic lymph nodes were significantly higher than those of non-metastatic lymph nodes (P < 0.05). The DECT parameters of metastatic lymph nodes were significantly lower than those of non-metastatic lymph nodes (P < 0.05). The best single morphological parameter for differentiation between metastatic and nonmetastatic lymph nodes was S (AUC, 0.752; threshold, 8.5 mm; sensitivity, 67.4%; specificity, 73.7%; accuracy, 71.8%). The best single DECT parameter for differentiation between metastatic and nonmetastatic lymph nodes was NIC in venous phase (AUC, 0.861; threshold, 0.53; sensitivity, 95.9%; specificity, 70.2%; accuracy, 77.9%). Multivariate analysis showed that S and NIC were independent predictors of lymph node metastasis. The AUC of combined S and NIC in the venous phase was 0.895(sensitivity, 79.6%; specificity, 87.7%; accuracy, 85.3%), which were significantly higher than that of S (P < 0.001) and NIC (P=0.037).

CONCLUSIONS

The ability of quantitative DECT parameters to distinguish mediastinal lymph node metastasis in NSCLC patients is better than that of morphological parameters. Combined S and NIC in venous phase can be used to improve preoperative diagnostic accuracy of metastatic lymph nodes.

Pelvic lymph nodes and pathways of disease spread in male pelvic malignancies

PURPOSE

Accurate nodal staging for male urogenital malignancies has important implications for therapy and prognosis. Male pelvic malignancies, including prostatic, penile, testicular, and bladder cancer, typically metastasize to regional lymph nodes first which is reported by the N-stage. Spread beyond these groups to non-regional nodes is regarded as M-stage disease.

METHODS

In this review, we discuss the typical patterns of male pelvic lymphatic drainage and the tumor-specific regional nodal chains.

RESULTS

The impact of tumor-specific imaging features and the implications of previous treatments on staging are discussed.

CONCLUSIONS

While anatomic imaging, including CT and MRI, is the most widely employed imaging modality at present, newer functional imaging techniques have demonstrated promise in the accurate identification and characterization of nodal metastases.

The value of multidetector-row computed tomography in lymph node staging of gastric cancer: a preliminary Vietnamese study

INTRODUCTION

Gastric cancer (GC) is the fourth most common malignant disease in the world, following breast cancer, colorectal cancer, and lung cancer. This study aimed to evaluate the usefulness of multidetector-row computed tomography (MDCT) in identifying the metastatic lymph node of GC.

MATERIAL AND METHODS

A cross-sectional study was performed after receiving approval by the institutional review board. A total of 88 patients with GC, who underwent radical gastrectomy, were examined by MDCT. Categorical variables were compared using Fisher's exact test. The discriminating ability of lymph node size was determined according to an area under the receiver operating curve(AUROC) analysis, and the optimal cut-off point was determined.

RESULTS

The proportion of metastatic lymph node patients in the proximal group (32.3%) was significantly higher than that in the distal group (18.4%). T categorisation and lymph node sizes were significantly different between the non-metastatic lymph node and metastatic lymph node groups. The AUROC for lymph node size was 0.738, with an optimal cut-off point of 7.5 mm,producing a sensitivity of 71.5% and a specificity of 70.5%.

CONCLUSIONS

MDCT displayed medium accuracy for the determination of metastatic lymph nodes and N categorisation. Based on our findings, although MDCT is generally the first choice for preoperative assessments in GC patients, other diagnostic modalities should supplement MDCT in order to achieve more precise N staging.

The Role of Hospital Transfer in Reexamination Computed Tomography Scans: A Nationwide Cohort Study of Gastric Cancer Patients Undergoing Surgery

Because the high-cost of medical imaging can cause a tremendous economic burden across the health care system, we investigated factors associated with taking additional computed tomography (CT) scans. Data of gastric cancer patients were eligible for analysis if the patient underwent a gastrectomy during the study period (2002–2013). We defined initial CT scans as those taken within 90 days from the surgery date. If there was an additional CT scan between the date of an initial CT scan and the surgery date, we regarded it as a reexamination. We used multivariate logistic regression analysis for reexamination CT scans. Among 3342 gastrectomy patients, 1165 participants underwent second CT scans. Transfer experience (adjusted odds ratio (OR) = 23.87, 95% confidence interval (CI) = 18.15–31.39) was associated with higher OR for reexamination. Among transferred patients, an increased number per 100 beds at the initial CT hospital was associated with a decreased OR for reexamination (OR = 0.88, 95% CI = 0.83–0.94), but increased beds in surgery hospitals was related to an increased OR for reexamination (OR = 1.29, 95% CI = 1.20–1.36). In our study, transfer experience, initial CT scan in a low-volume hospital, and surgical treatment in a high-volume hospital were associated with reexamination CT scans.

Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection

BACKGROUND

Controversy still exists on the optimal surgical resection for potentially curable gastric cancer (GC). Use of radiologic evaluation and machine learning algorithms might predict extent of lymphadenectomy to limit unnecessary surgical treatment. We purposed to design a machine learning-based clinical decision-support model for predicting extent of lymphadenectomy (D1 vs. D2) in local advanced GC.

METHODS

Clinicoradiologic features available from routine clinical assignments in 557 patients with GCs were retrospectively interpreted by an expert panel blinded to all histopathologic information. All patients underwent surgery using standard D2 resection. Decision models were developed with a logistic regression (LR), support vector machine (SVM) and auto-encoder (AE) algorithm in 371 training and tested in 186 test data, respectively. The primary end point was to measure diagnostic performance of decision model and a Japanese gastric cancer treatment guideline version 4th (JPN 4th) criteria for discriminate D1 (pT1 + pN0) versus D2 (≥ pT1 + ≥ pN1) lymphadenectomy.

RESULTS

The decision model with AE analysis produced highest area under ROC curve (train: 0.965, 95% confidence interval (CI) 0.948-0.978; test: 0.946, 95% CI 0.925-0.978), followed by SVM (train: 0.925, 95% CI 0.902-0.944; test: 0.942, 95% CI 0.922-0.973) and LR (train: 0.886, 95% CI 0.858-0.910; test: 0.891, 95% CI 0.891-0.952). By this improvement, overtreatment was reduced from 21.7% (121/557) by treat-all pattern, to 15.1% (84/557) by JPN 4th criteria, and to 0.7-0.9% (4-5/557) by the new approach.

CONCLUSIONS

The decision model with machine learning analysis demonstrates high accuracy for identifying patients who are candidates for D1 versus D2 resection. Its approximate 14-20% improvements in overtreatment compared to treat-all pattern and JPN 4th criteria potentially increase the number of patients with local advanced GCs who can safely avoid unnecessary lymphadenectomy.

Role of D2 gastrectomy in gastric cancer with clinical para-aortic lymph node metastasis

BACKGROUND

Owing to the technical difficulty of pathological diagnosis, imaging is still the most commonly used method for clinical diagnosis of para-aortic lymph node metastasis (PALM) and evaluation of therapeutic effects in gastric cancer, which leads to inevitable false-positive findings in imaging. Patients with clinical PALM may have entirely different pathological stages (stage IV or not), which require completely different treatment strategies. There is no consensus on whether surgical intervention should be implemented for this group of patients. In particular, the value of D2 gastrectomy in a multidisciplinary treatment (MDT) approach for advanced gastric cancer with clinical PALM remains unknown.

AIM

To investigate the value of D2 gastrectomy in a MDT approach for gastric cancer patients with clinical PALM.

METHODS

In this real-world study, clinico-pathological data of all gastric cancer patients treated at the Cancer Hospital, Chinese Academy of Medical Sciences between 2011 and 2016 were reviewed to identify those with clinically enlarged PALM. All the clinico-pathological data were prospectively documented in the patient medical record. For all the gastric cancer patients with advanced stage disease, especially those with suspicious distant metastasis, the treatment methods were determined by a multidisciplinary team.

RESULTS

In total, 48 of 7077 primary gastric cancer patients were diagnosed as having clinical PALM without other distant metastases. All 48 patients received chemotherapy as the initial treatment. Complete or partial response was observed in 39.6% (19/48) of patients in overall and 52.1% (25/48) of patients in the primary tumor. Complete response of PALM was observed in 50.0% (24/48) of patients. After chemotherapy, 45.8% (22/48) of patients received D2 gastrectomy, and 12.5% (6/48) of patients received additional radiotherapy. The postoperative major complication rate and mortality were 27.3% (6/22) and 4.5% (1/22), respectively. The median overall survival and progression-free survival of all the patients were 18.9 and 12.1 mo, respectively. The median overall survival of patients who underwent surgical resection or not was 50.7 and 12.8 mo, respectively. The 3-year and 5-year survival rates were 56.8% and 47.3%, respectively, for patients who underwent D2 resection. Limited PALM and complete response of PALM after chemotherapy were identified as favorable factors for D2 gastrectomy.

CONCLUSION

For gastric cancer patients with radiologically suspicious PALM that responds well to chemotherapy, D2 gastrectomy could be a safe and effective treatment and should be adopted in a MDT approach for gastric cancer with clinical PALM.

Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior

Cervical tumor segmentation on 3D ¹⁸FDG PET images is a challenging task because of the proximity between cervix and bladder, both of which can uptake ¹⁸FDG tracers. This problem makes traditional segmentation based on intensity variation methods ineffective and reduces overall accuracy. Based on anatomy knowledge, including 'roundness' of the cervical tumor and relative positioning between the bladder and cervix, we propose a supervised machine learning method that integrates convolutional neural network (CNN) with this prior information to segment cervical tumors. First, we constructed a spatial information embedded CNN model (S-CNN) that maps the PET image to its corresponding label map, in which bladder, other normal tissue, and cervical tumor pixels are labeled as  -1, 0, and 1, respectively. Then, we obtained the final segmentation from the output of the network by a prior information constrained (PIC) thresholding method. We evaluated the performance of the PIC-S-CNN method on PET images from 50 cervical cancer patients. The PIC-S-CNN method achieved a mean Dice similarity coefficient (DSC) of 0.84 while region-growing, Chan-Vese, graph-cut, fully convolutional neural networks (FCN) based FCN-8 stride, and FCN-2 stride, and U-net achieved 0.55, 0.64, 0.67, 0.71, 0.77, and 0.80 mean DSC, respectively. The proposed PIC-S-CNN provides a more accurate way for segmenting cervical tumors on 3D PET images. Our results suggest that combining deep learning and anatomic prior information may improve segmentation accuracy for cervical tumors.

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

PURPOSE

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

METHODS

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

RESULTS

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

Urinary kallikrein 10 predicts the incurability of gastric cancer

The current imaging modalities are not sufficient to identify inoperable tumor factors, including distant metastasis and local invasion. Hence, we conducted this study using urine samples to discover non-invasive biomarkers for the incurability of gastric cancer (GC). Urine samples from 111 GC patients were analyzed in this study. The GC cohort was categorized and analyzed according to disease stage and operability. In the discovery phase, protease protein array analysis identified 3 potential candidate proteins that were elevated in the urine of advanced GC patients compared to early GC patients. Among them, urinary kallikrein 10 (KLK10) was positively associated with tumor stage progression. Moreover, the urinary level of KLK10 (uKLK10) was significantly elevated in the urine of patients with inoperable GC compared to operable GC patients (median, 118 vs. 229; P=0.014). The combination of uKLK10, tumor location and tumor size distinguished operability of GC with an area under the curve of 0.859, 82.4% sensitivity and 86.2% specificity. Disease-free survival (DFS) was significantly shorter in GC patients with high uKLK10 compared to those with low uKLK10 (hazard ratio: 3.30 [95% confidence interval, 1.58-6.90] P<0.001). Immunohistochemical analyses also demonstrated a positive correlation between tumor stage and KLK10 expression in GC tissues (r=0.426, P<0.001). In addition, GC patients with high expression of pathological KLK10 (pKLK10) showed a significantly shorter DFS compared to those with low pKLK10 (hazard ratio: 3.79 [95% confidence interval, 1.27-11.24] P=0.010). uKLK10 is a promising non-invasive biomarker for the inoperability and incurability of GC.

Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer?

Purpose

To retrospectively investigate whether texture features obtained from preoperative CT images of advanced gastric cancer (AGC) patients could be used for the prediction of occult peritoneal carcinomatosis (PC) detected during operation.

Materials and methods

51 AGC patients with occult PC detected during operation from January 2009 to December 2012 were included as occult PC group. For the control group, other 51 AGC patients without evidence of distant metastasis including PC, and whose clinical T and N stage could be matched to those of the patients of the occult PC group, were selected from the period of January 2011 to July 2012. Each group was divided into test (n = 41) and validation cohort (n = 10). Demographic and clinical data of these patients were acquired from the hospital database. Texture features including average, standard deviation, kurtosis, skewness, entropy, correlation, and contrast were obtained from manually drawn region of interest (ROI) over the omentum on the axial CT image showing the omentum at its largest cross sectional area. After using Fisher's exact and Wilcoxon signed-rank test for comparison of the clinical and texture features between the two groups of the test cohort, conditional logistic regression analysis was performed to determine significant independent predictor for occult PC. Using the optimal cut-off value from receiver operating characteristic (ROC) analysis for the significant variables, diagnostic sensitivity and specificity were determined in the test cohort. The cut-off value of the significant variables obtained from the test cohort was then applied to the validation cohort. Bonferroni correction was used to adjust P value for multiple comparisons.

Results

Between the two groups, there was no significant difference in the clinical features. Regarding the texture features, the occult PC group showed significantly higher average, entropy, standard deviation, and significantly lower correlation (P value < 0.004 for all). Conditional logistic regression analysis demonstrated that entropy was significant independent predictor for occult PC. When the cut-off value of entropy (> 7.141) was applied to the validation cohort, sensitivity and specificity for the prediction of occult PC were 80% and 90%, respectively.

Conclusion

For AGC patients whose PC cannot be detected with routine imaging such as CT, texture analysis may be a useful adjunct for the prediction of occult PC.

The overexpression of MDM4: an effective and novel predictor of gastric adenocarcinoma lymph node metastasis

Background

MDM4 is the important negative regulator of the tumor suppressor protein p53, which is overexpressed in various human cancers. This study evaluates the MDM4 expression in patients with gastric adenocarcinoma (GTAC) at the mRNA and protein levels and examines relationships among MDM4 expression, clinicopathological features, and prognosis.

Results

The qRT-PCR and the Western blot analysis showed that the MDM4 expression level was high in GTACN⁺ but not in GTACN⁻. The high expression level of MDM4 was significantly associated with age (P = 0.047), lymph node metastasis (LNM) (P < 0.001), pathological stage (P < 0.001), differentiation status (P = 0.001), and preoperative serum CA19-9 level (P < 0.001). Moreover, the survival analysis showed that Borrmann type, depth of invasion, LNM, and preoperative serum CA19-9 level were independent prognostic factors. The univariate analysis revealed that MDM4 expression influenced GTAC prognosis. Furthermore, the influence of overall prognosis relies on whether or not the high MDM4 expression level could lead to LNM.

Materials and Methods

We investigated MDM4 expression in primary GTAC and paired normal gastric tissues (30 pairs) through qRT-PCR and Western blot analyses. We also performed immunohistochemistry analysis on 336 paraffin-embedded GTAC specimens and 33 matched normal specimens.

Conclusions

MDM4 expression may result in LMN of GTAC. High MDM4 expression levels are associated with LMN of GTAC and influence the prognosis of patients with GTAC.

Does Computed Tomography Have the Ability to Differentiate Aggressive From Nonaggressive Solid Pseudopapillary Neoplasm?

OBJECTIVE

The aim of the study was to assess the ability of contrast-enhanced computed tomography (CECT) to differentiate aggressive from nonaggressive solid pseudopapillary neoplasms (SPNs).

MATERIALS AND METHODS

Forty treatment-naive patients with pathologically proven pancreatic SPNs were included. Imaging characteristics were determined by consensus of 3 radiologists blinded to histopathologic aggressiveness. All patients underwent 4-phase CECT using a pancreatic protocol. The regions of interest of the tumor and the normal pancreas were documented on all phases. Lymph nodes were considered metastatic if greater than 1.0 cm in short-axis diameter.Fisher exact and Wilcoxon rank-sum tests were used to compare between aggressive and nonaggressive tumors.

RESULTS

No significant difference was noted between imaging covariates, such as internal hemorrhage, calcification, wall thickness perceptibility, vascular invasion, margins, cystic component, and pancreatic and biliary ductal dilation. Tumors with greater than 62.5 Hounsfield units and progressive enhancement during the delayed phase had aggressive characteristics (P = 0.03).

CONCLUSIONS

On delayed phase CECT, pathologically aggressive SPNs may show greater enhancement than nonaggressive SPNs.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Accuracy of Multi-Slice Spiral Computed Tomography for Preoperative Tumor Node Metastasis (TNM) Staging of Colorectal Carcinoma

Background

With the advances in imaging technologies, multi-slice spiral computed tomography (MSCT) has demonstrated superiority in the diagnosis and staging of colorectal carcinoma. In the current study, preoperative TNM staging of colorectal carcinoma by using MSCT was conducted and compared with the corresponding postoperative pathological examination findings, in order to evaluate the accuracy of preoperative MSCT for TNM staging.

Material/Methods

Combinations of biphasic or triphasic enhanced-phase MSCT scans were obtained for 76 patients with colorectal carcinoma, and the TNM stage was determined based on imaging reconstruction from various angles and perspectives to display the size, location, and affected range of tumors. The preoperative TNM stage was compared with the postoperative pathological stage, and the consistency between the 2 methods was tested by the κ test using SPSS 17.0 software.

Results

Among the different combinations of enhanced-phase MSCT scanning, triphasic MSCT imaging, comprising the arterial, portal venous, and delayed phases, showed the highest accuracy rates, at 81.6% (62/76), 82.89% (63/76), and 96.1% (73/76) for T, N, and M staging, respectively, with κ values of 0.72, 0.65, and 0.56, respectively, indicating consistency with the postoperative pathological staging.

Conclusions

Combined MSCT scanning comprising the arterial phase, portal venous phase, and delayed phase showed satisfying consistency with the postoperative pathological analysis results for TNM staging of colorectal carcinoma. Thus, MSCT is an important clinical value for improving the accuracy of TNM staging and for planning the appropriate colorectal cancer treatment.

Consistency mapping of 16 lymph node stations in gastric cancer by CT-based vessel-guided delineation of 255 patients

In order to refine the location and metastasis-risk density of 16 lymph node stations of gastric cancer for neoadjuvant radiotherapy, we retrospectively reviewed the initial images and pathological reports of 255 gastric cancer patients with lymphatic metastasis. Metastatic lymph nodes identified in the initial computed tomography images were investigated by two radiologists with gastrointestinal specialty. A circle with a diameter of 5 mm was used to identify the central position of each metastatic lymph node, defined as the LNc (the central position of the lymph node). The LNc was drawn at the equivalent location on the reference images of a standard patient based on the relative distances to the same reference vessels and the gastric wall using a Monaco® version 5.0 workstation. The image manipulation software Medi-capture was programmed for image analysis to produce a contour and density atlas of 16 lymph node stations. Based on a total of 2846 LNcs contoured (31-599 per lymph node station), we created a density distribution map of 16 lymph node drainage stations of the stomach on computed tomography images, showing the detailed radiographic delineation of each lymph node station as well as high-risk areas for lymph node metastasis. Our mapping can serve as a template for the delineation of gastric lymph node stations when defining clinical target volume in pre-operative radiotherapy for gastric cancer.

Lymph nodes metastasis of gastric cancer: Measurement with multidetector CT oblique multiplanar reformation-correlation with histopathologic results

The aim of this study was to retrospectively evaluate the ability of multidetector computed tomography (MDCT) oblique multiplanar reformation (MPR) for differentiating metastatic lymph nodes (LNs) in patients with gastric cancer.Seventy-nine patients with gastric cancer underwent preoperative computed tomography (CT). One-to-one correlation of LN was made between CT oblique multiplanar reformation and histopathologic slides. Long diameters, short diameters, and short-to-long axis ratios of LNs were evaluated to differentiate metastasis.Short diameters of nodes performed better for diagnosing metastasis than long diameters and short-to-long ratios. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve of short diameter were 57.8%, 74.7%, 68.2%, and 0.713, respectively. With different thresholds of short diameters of nodes (No. 8 group >6 mm and other groups >4 mm), total sensitivity, specificity, and accuracy can reach 57.2%, 79.0%, and 70.3%, respectively.MDCT oblique MPR images have certain reference value to distinguish metastasis of LNs in gastric cancer. The diagnostic power for LN metastasis of gastric cancer can be improved by using different threshold for No. 8 group LNs and other groups.

Topographic distribution and characteristics of normal gastric regional lymph nodes on diffusion-weighted magnetic resonance imaging

BACKGROUND

Current lack of recognition of normal gastric regional lymph nodes (GRLNs) and inherent defect of morphological imaging limit the accuracy of preoperative nodal (N) staging of gastric cancer.

PURPOSE

To map the distribution of normal GRLNs and evaluating the characteristics of GRLNs with diffusion-weighted imaging (DWI) in healthy population.

MATERIAL AND METHODS

Forty-nine enrolled healthy volunteers were divided into two age groups and underwent conventional magnetic resonance imaging (MRI) and DWI examinations. The characteristics of GRLNs in 14 regional stations, including short axis diameter (SD), short-to-long axis diameter ratio (SLR), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC), were recorded and compared between age groups and among different stations.

RESULTS

The normal GRLNs were mainly distributed in station 7 in both age groups, followed by stations 3, 8, and 9. The SLR was lower in the young group than in the old group (P = 0.034) while SD, SNR, CNR, and ADC were significantly higher in the young group compared to the old group, P = 0.045, 0.041, 0.037, and 0.042, respectively. SD was different among stations in both age groups (P = 0.002, 0.001), especially bigger in station 8, and the SNRs and CNRs of stations 8 and 9 were relatively high in the old group (P = 0.031, 0.035), while there was no difference in ADC value.

CONCLUSION

Better understanding of the appearances of normal GRLNs on conventional MRI and DWI may help to build more appropriate imaging criteria for GRLN assessment in gastric cancer.

Clinical significance and prognostic impact of the total diameter of enlarged lymph nodes on preoperative multidetector computed tomography in patients with gastric cancer

BACKGROUND AND AIM

This study was designed to evaluate the clinical significance and prognostic impact of the total diameter of enlarged lymph nodes (TDL) on preoperative multidetector computed tomography (MDCT) in gastric cancer (GC).

METHODS

Of a total of 480 GC patients between 2005 and 2009, 70 patients with a preoperative diagnosis of nodal metastasis on MDCT were included in this study. All regional lymph nodes showing metastatic involvement were preoperatively counted and measured.

RESULTS

The TDL was calculated, and using a receiver operating characteristic curve, a cutoff value of 45 mm in the two groups of large TDL (LTDL) and small TDL was found to be appropriate for TDL. No significant differences were observed in clinicopathological features, except for tumor recurrence, between the two groups. Univariate survival analysis revealed that patients with LTDL had a worse prognosis as well as an upper tumor location, deeper tumor depth, and further advanced pathological stage. Multivariable prognostic analysis identified LTDL as an independent worse prognostic factor (P = 0.0128).

CONCLUSIONS

GC patients with the total nodal diameter measuring 45 mm or more on MDCT have a worse prognosis. GC patients with the novel surrogate indicator of worse prognosis for a preoperative imaging diagnosis may have need of multimodal treatment to improve the survival.

The Role of 18F-FDG PET/CT in the Primary Staging of Gastric Cancer

Objective: The aim of this study is to explore the role of 18F-FDG PET/CT in the primary staging of gastric cancer in the comparison of ceCT as routine staging method and evaluate influencing parameters of 18F-FDG uptake.

Methods: Thirty-one patients (mean age: 58.9±12.6) who underwent 18F-FDG PET/CT for primary staging of gastric cancer between June 2011 and June 2012 were included to the study. 18F-FDG PET/CT findings were compared with pathological reports in patients who underwent surgery following PET/CT. 18F-FDG PET/CT findings of primary lesions, lymph nodes and adjacent organs were compared with ceCT findings and pathological reports. Since 6 patients were accepted as inoperable according to 18F-FDG PET/CT and/or ceCT and/or laparotomy and/or laparoscopy findings, pathological confirmation could not be possible.

Results: In the postoperative TNM staging of patients, while 1 (4%), 1 (4%), 4 (16%), 2 (8%), 12 (48%) and 5 (20%) patients were staged as T0, Tis, T1, T2, T3 and T4, respectively, 8 (32%), 6 (24%), 6 (24%) and 5 (20%) patients were N0, N1, N2 and N3 respectively. 18F-FDG PET/CT was totally normal in 2 patients. While primary tumors were FDG avid in 27 patients, in 17 and 6 patients FDG uptake was observed in perigastric lymph nodes and distant organs, respectively. Mean SUVmax of FDG avid tumors was calculated as 13.49±9.29 (3.00-44.60). However, SUVmax of lymph nodes was computed as 9.28±6.92 (2.80-29.10). According to sub-analysis of histopathological subtypes of primary tumors, SUVmax of adenocarsinomas was calculated as 15.16 (3.00-44.60), of signet ring cells as 9.90 (5.50-17.70), of adenocarcinomas with signet ring cell component as 11.27 (6.20-13.90) (p=0.721). In the comparison with histopathological examination while ceCT was TP, TN, FN in 23, 1 and 1 patients, 18F-FDG PET/CT was TP, FP, FN in 20, 1 and 4 patients, respectively. Sensitivity, specificity, accuracy, PPD and NPV of ceCT in the detection of lymph node metastasis was calculated as 83.3%, 75%, 80%, 87.5% and 66.6%, respectively. These parameters for 18F-FDG PET/CT were 64.7%, 100%, 76%, 100% and 57.1%.

Conclusion: Despite lower sensitivity than ceCT, diagnostic power of 18F-FDG PET/CT in the preoperative staging of gastric cancer is acceptable. Because of its high PPV, it might be beneficial in the evaluation of patients with suspected lymph nodes. The role of 18F-FDG PET/CT seems to be limited in the early stage and signet ring cell carcinomas due to lower 18F-FDG uptake.

Laparoscopy, computerised tomography and fluorodeoxyglucose positron emission tomography in the management of gastric and gastro-oesophageal junction cancers

BACKGROUND

The staging laparoscopy has been used in the management of gastrointestinal cancers. The aim of this study was to evaluate the role of staging laparoscopy, in comparison with computed tomography (CT) and fluorodeoxyglucose positron emission tomography (FDG-PET) imaging in staging patients with gastro-oesophageal junction (GOJ) and gastric cancers.

METHODS

The data were collected for patients between 1996 and 2013 undergoing investigation and treatment for GOJ and gastric cancers at a single institute. The pre-operative data (staging data), intraoperative details, post-operative course and follow-up were analysed for individual cases.

RESULTS

Staging laparoscopy altered management plan in 64 (17 %) of 387 patients with negative staging CT and FDG-PET scan. Twenty-seven (7 %) patients with GOJ cancer (types I, II and III) were identified with pathological intraperitoneal nodes, 15 (4 %) gastric cancer with metastatic intraperitoneal deposits and liver metastases and 3 % gastric cancers with positive ascitic fluid for cancer cells. Ten (3 %) of patients were downstaged and were offered curative resection. Patients with metastatic disease were referred for palliative chemotherapy. The overall sensitivity of staging laparoscopy in diagnosing intraabdominal pathology was 86 % in comparison with CT (81 %) and FDG-PET (78 %).

CONCLUSIONS

The diagnostic laparoscopy is useful for detecting and confirming nodal involvement and distant metastatic disease not evident on the staging CT scan and FDG-PET. This could potentially alter treatment and prognosis in patients with upper gastrointestinal cancer. The diagnostic laparoscopy should be performed as part of investigation and treatment planning for patients suffering from GOJ and gastric cancers. This can help to avoid surgery in patients with advanced disease.

Enhanced intensity on preoperative double contrast-enhanced sonography as a useful indicator of lymph node metastasis in patients with gastric cancer

OBJECTIVES

The aim of this study was to investigate the predictive value of enhanced intensity on double contrast-enhanced sonography in assessing lymph node metastasis of gastric cancer.

METHODS

A total of 357 patients with gastric cancer were enrolled in this study. Double contrast-enhanced sonography, in which an oral ultrasound contrast agent is combined with an intravenous contrast agent, was performed preoperatively, and the data were analyzed quantitatively. The predictive ability of enhanced intensity, a quantitative double contrast-enhanced sonographic measure, for lymph node metastasis was evaluated retrospectively.

RESULTS

Compared to negative lymph node metastasis cases, the presence of thicker lesions, deeper invasion, poorer differentiation, and higher enhanced intensity were found in positive cases (P< .05). An enhanced intensity cutoff value of 16.91 dB was the best point for balancing the sensitivity and specificity (71.50% and 79.30%, respectively) for prediction of lymph node metastasis, with the highest Youden index of 0.508. The area under the receiver operating characteristic curve was 0.828 (P < .001; 95% confidence interval, 0.786-0.870). In cases in which the lesions were hyperenhanced (enhanced intensity >16.91 dB), the lesions were significantly thicker and had deeper invasion, poorer differentiation, and more positive metastasis findings compared to non-hyperenhanced cases (enhanced intensity ≤16.91 dB; P < .05). On logistic regression analysis, the enhanced intensity of primary tumors and the invasion depth were significantly associated with lymph node metastasis.

CONCLUSIONS

Double contrast-enhanced sonography with quantitative analysis may be considered a novel alternative imaging modality for noninvasive preoperative evaluation of lymph node metastasis with good reliability.

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

OBJECTIVES

The purpose of this study was to determine the optimal cut-off value of lymph node size for diagnosing metastasis in gastric cancer with multidetector-row computed tomography (MDCT) after categorizing perigastric lymph nodes into three regions.

METHODS

The study included 90 gastric cancer patients who underwent gastrectomy. The long-axis diameter (LAD) and short-axis diameter (SAD) of all visualized lymph nodes were measured with transverse MDCT images. The locations of lymph nodes were categorized into three regions: lesser curvature, greater curvature, and suprapancreatic. The diagnostic value of lymph node metastasis was assessed with receiver operating characteristic (ROC) analysis.

RESULTS

The area under the curve was larger for SAD than LAD in all groups. The optimal cut-off values of SAD were determined as follows: overall, 9 mm; differentiated type, 9 mm; undifferentiated type, 8 mm; lesser curvature region, 7 mm; greater curvature region, 6 mm; and suprapancreatic region, 9 mm. The diagnostic accuracies for lymph node metastasis using individual cut-off values were 71.1% based on histological type and 76.6% based on region of lymph node location.

CONCLUSIONS

The diagnostic accuracy of lymph node metastasis in gastric cancer was improved by using individual cut-off values for each lymph node region.

KEY POINTS

• Multidetector-row computed tomography is widely used to predict pathological nodal status. • An optimal cut-off value of lymph node size has not been determined. • Cut-off values were assessed according to histology and nodal location. • The optimal cut-off values differed based on histology and nodal location. • Diagnostic accuracy was improved by using individual cut-off values for each region.

Upper abdominal gadoxetic acid-enhanced and diffusion-weighted MRI for the detection of gastric cancer: Comparison with two-dimensional multidetector row CT

AIM

To evaluate the diagnostic performance of abdominal magnetic resonance imaging (MRI) for the detection of gastric cancer in comparison with that of two-dimensional (2D) multidetector row computed tomography (CT).

MATERIALS AND METHODS

The study included 189 patients with 170 surgically confirmed gastric cancers and 19 patients without gastric cancer, all of whom underwent gadoxetic acid-enhanced MRI with diffusion-weighted (DW) imaging, and multidetector contrast-enhanced abdominal CT imaging. Two observers independently analysed three sets of images (CT set, conventional MRI set, and combined conventional and DW MRI set). A five-point scale for likelihood of gastric cancer was used. Diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were evaluated. Quantitative [apparent diffusion coefficient (ADC) analyses with Mann-Whitney U-test were conducted for gastric cancers and the nearby normal gastric wall.

RESULTS

The diagnostic accuracy and sensitivity for detection of gastric cancer were significantly higher on combined conventional and DW MRI set (77.8-78.3%; 75.3-75.9%) than the CT imaging set (67.7-71.4%; 64.1-68.2%) or the conventional MRI set (72-73%; 68.8-70%; p < 0.01). In particular, for gastric cancers with pT2 and pT3, the combined conventional and DW MRI set (91.6-92.6%) yielded significantly higher sensitivity for detection of gastric cancer than did the CT imaging set (76.8-81.1%) by both observers (p < 0.01). The mean ADC of gastric cancer lesions (1 ± 0.23 × 10(-3) mm(2)/s) differed significantly from that of normal gastric wall (1.77 ± 0.25 × 10(-3) mm(2)/s; p < 0.01).

CONCLUSION

Abdominal MRI with DW imaging was more sensitive for the detection of gastric cancer than 2D-multidetector row CT or conventional MRI alone.

Contrast enhanced computed tomography characterization of fluorodeoxygluocose-avid regional and non-regional lymph nodes in patients with suspicion of metastatic bladder cancer

Objective:

The objective of this study is to assess if size alone can predict the presence of metastatic disease within lymph nodes seen on contrast enhanced-computed tomography (CE-CT) in patients with suspicion of metastatic bladder cancer and also to evaluate the nodal distribution and morphological characteristics of fluorodeoxygluocose (FDG) avid lymph nodes on CE-CT.

Materials and Methods:

A retrospective analysis from 2002 to 2009 was performed on patients with suspicion of recurrent disease undergoing restaging FDG-positron emission tomography (PET)/CT. Standardized uptake value (SUVmax) adjusted for lean body mass was recorded in abnormal lymph nodes in the abdominopelvic region. Distribution, size, shape, presence of necrosis and clustering of the FDG-avid lymph nodes was assessed on CE-CT obtained within 4 weeks of the PET/CT. The abnormal nodes were then compared with non-FDG avid lymph nodes on the contralateral side serving as control.

Results:

A total of 103 lymph nodes were found to be FDG-avid in 14 patients on 17 PET/CT examinations. Overall, mean SULmax was 4.7 (range: 1.6-10.7), which is significantly higher than background of 1.5 (P < 0.05). Regional pelvic lymph nodes were FDG-avid in 93% of patients and metastatic extra-pelvic in 100% of patients. The overall average size of the FDG avid lymph nodes on CE-CT was 11 mm with a third of these measuring 3-8 mm. The average size of FDG-avid lymph nodes was 11 mm in the paraaortic region 13 mm in the common iliac 9 mm in the internal iliac and 13 mm in the external iliac regions. Nearly 88.4% of lymph nodes were round in shape, clustering was present in 68% and necrosis in 7% and average size of lymph nodes that served as controls was 6 mm with reniform morphology in 92% and absence of clustering and necrosis.

Conclusion:

Overlap in size exists between FDG-avid pathological and non-pathological lymph nodes seen on CE-CT in patients with metastatic bladder cancer. Other characteristic such as abnormal morphology and clustering are useful adjuncts in the evaluation of nodal metastatic disease.

An evidential reasoning based model for diagnosis of lymph node metastasis in gastric cancer

Background

Lymph node metastasis (LNM) in gastric cancer is a very important prognostic factor affecting long-term survival. Currently, several common imaging techniques are used to evaluate the lymph node status. However, they are incapable of achieving both high sensitivity and specificity simultaneously. In order to deal with this complex issue, a new evidential reasoning (ER) based model is proposed to support diagnosis of LNM in gastric cancer.

Methods

There are 175 consecutive patients who went through multidetector computed tomography (MDCT) consecutively before the surgery. Eight indicators, which are serosal invasion, tumor classification, tumor enhancement pattern, tumor thickness, number of lymph nodes, maximum lymph node size, lymph node station and lymph node enhancement are utilized to evaluate the tumor and lymph node through CT images. All of the above indicators reflect the biological behavior of gastric cancer. An ER based model is constructed by taking the above indicators as input index. The output index determines whether LNM occurs for the patients, which is decided by the surgery and histopathology. A technique called k-fold cross-validation is used for training and testing the new model. The diagnostic capability of LNM is evaluated by receiver operating characteristic (ROC) curves. A Radiologist classifies LNM by adopting lymph node size for comparison.

Results

134 out of 175 cases are cases of LNM, and the remains are not. Eight indicators have statistically significant difference between the positive and negative groups. The sensitivity, specificity and AUC of the ER based model are 88.41%, 77.57% and 0.813, respectively. However, for the radiologist evaluating LNM by maximum lymph node size, the corresponding values are only 63.4%, 75.6% and 0.757. Therefore, the proposed model can obtain better performance than the radiologist. Besides, the proposed model also outperforms other machine learning methods.

Conclusions

According to the biological behavior information of gastric cancer, the ER based model can diagnose LNM effectively and preoperatively.