Comparison between CT Net enhancement and PET/CT SUV for N staging of gastric cancer: A case series

A comprehensive update on early gastric cancer: defining terms, etiology, and alarming risk factors

INTRODUCTION

Early gastric cancer (EGC) is a well-defined gastric malignancy that is limited to the mucosa or submucosa, irrespective of lymph node metastasis. At an early stage, gastric cancer often does not cause symptoms until it becomes advanced, and it is a heterogeneous disease and usually encountered in its late stages.

AREA COVERED

This comprehensive review will provide a novel insight into the evaluation of EGC epidemiology, defining terms, extensive etiology and risk factors, and timely diagnosis since prevention is an essential approach for controlling this cancer and reducing its morbidity and mortality.

EXPERT OPINION

The causative manner of EGC is complex and multifactorial. In recent years, researchers have made significant contributions to understanding the etiology and pathogenesis of EGC, and standardization in the evaluation of disease activity. Though the incidence of this cancer is steadily declining in some advanced societies owing to appropriate interventions, there remains a serious threat to health in developing nations. Early detection of resectable gastric cancer is crucial for better patient outcomes.

Cancer Risk Following Bariatric Surgery-Systematic Review and Meta-analysis of National Population-Based Cohort Studies

This study has analyzed results from registry-based population studies to assess the effect of bariatric surgery upon cancer incidence at a population level. Relevant studies were identified and meta-analysis was used to calculate pooled odds ratios (POR) for the incidence of cancer after bariatric surgery compared to controls. Eight population-based studies were included with 635,642 total patients. Bariatric surgery was associated with a significant reduction in overall cancer incidence (POR = 0.72; 95% CI 0.59 to 0.87; p = 0.0007) and incidence of obesity-related cancer (POR = 0.55; 95% CI 0.31 to 0.96; p = 0.04). Bariatric surgery was also protective for breast cancer development (POR = 0.50; 95% CI 0.25 to 0.99; p = 0.045). Bariatric surgery appears to be associated with a reduction in cancer incidence at a population-based level.

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.

Is Accurate N - Staging for Gastric Cancer Possible?

Node stage (N stage) is of paramount importance for gastric cancer staging. Radiologically node status implies detection and characterization of suspect malignant lymph nodes. Clinically it might determine survival and alter therapeutic plans. A number of modalities, including computerized tomography, MRI, PET and endoscopic ultrasound are currently available. Using a multimodality strategy, accuracy ranges between 50-90% across various studies. Specificity and sensitivity varies with respect to method, number of positive lymph nodes, their location and other characteristics. Restaging after neoadjuvant therapy and staging of recurrence presents its own, particular challenges. Each method has its advantages and limitations and none of them alone is adequate enough for staging. While most of them are clinically well established, they are also active research topics. To overcome the aforementioned limitations a multidisciplinary, multimodality approach with emphasis on clinical staging and treatment plans is proposed.