Usefulness of histologic differences and perivascular infiltration for preoperative T staging of advanced gastric cancer using computed tomography. Jpn J Radiol. 2019;37:817-825. [PMID: 31625013 DOI: 10.1007/s11604-019-00887-3]

Prospective Comparison of Oral Contrast-Enhanced Transabdominal Ultrasound Imaging With Contrast-Enhanced Computed Tomography in Pre-operative Tumor Staging of Gastric Cancer

The aim of this prospective study was to compare the diagnostic accuracy of oral contrast-enhanced transabdominal ultrasound imaging (OCTU) with that of contrast-enhanced computed tomography (CT) for the pre-operative tumor staging of gastric cancer, with post-operative pathology as the standard. We included 108 cases of gastric cancer with simultaneous OCTU and enhanced CT pre-operative tumor staging diagnoses. Results were compared with post-operative pathology based on the eighth edition of the American Joint Committee on Cancer tumor-node-metastasis staging guidelines for gastric cancer. The accuracy of each tumor stage was obtained by comparing OCTU and enhanced CT diagnoses with post-operative pathology. The McNemar test was used to compare the overall accuracy of the two methods. There was no statistical difference in accuracy between OCTU (72.2%) and enhanced CT (75.9%, p = 0.644) for overall pre-operative tumor staging diagnosis. For stages T1 to T4, the accuracy rates of OCTU were 84.2%, 81.8%, 69.4% and 65.5%, respectively, and those for enhanced CT were 52.6%, 72.7%, 87.8% and 72.4%, respectively. OCTU is comparable to enhanced CT in the preoperative overall T-stage diagnosis of gastric cancer.

Effect of visceral adipose tissue on the accuracy of preoperative T-staging of gastric cancer

BACKGROUND

Due to the anatomical characteristics of the tumor and the specific variables of the patients, the accuracy of preoperative T-staging of gastric cancer needs to be further improved. This study investigated the effect of visceral adipose tissue (VAT) on the accuracy of clinical T-staging of gastric cancer.

METHODS

The clinical data of 455 patients who underwent gastrectomy from January 2013 to December 2018 were analyzed retrospectively. Taking the postoperative pathological results as the reference standard, the patients were divided into accurate staging group and mistaken staging group according to the comparison of clinical T stage (cT) and pathological T stage (pT). The individual characteristics of the two groups were compared, including visceral fat content at L2/L3 level calculated on computed tomography, age, sex, tumor size, tumor location (cardia, stomach body, stomach antrum), and degree of differentiation. Multivariate logistic regression was used to determine the independent factors affecting the accuracy of cT staging.

RESULTS

Among the 455 patients, 355 patients (78.0 %) had accurate preoperative cT staging and 100 patients (22.0 %) had inaccurate preoperative cT staging. The average area of VAT in the accurate staging group was (129.8 ± 72.6) cm² and that in the mistaken staging group was (74.6 ± 61.6) cm² (P < 0.001). The optimal cut-off value of VAT was 97.8 cm² calculated according to the Yoden index. Multivariate logistic regression analysis showed that VAT, tumor location and tumor size were independent predictors of cT accuracy.

CONCLUSIONS

Patients with lower visceral fat content (<97.8 cm²) based on L2/L3 level had a higher risk of false staging in preoperative clinical T staging.

Artificial intelligence: A new budding star in gastric cancer

The pursuit of health has always been the driving force for the advancement of human society, and social development will be profoundly affected by every breakthrough in the medical industry. With the arrival of the information technology revolution era, artificial intelligence (AI) technology has been rapidly developed. AI has been combined with medicine but it has been less studied with gastric cancer (GC). AI is a new budding star in GC, and its contribution to GC is mainly focused on diagnosis and treatment. For early GC, AI’s impact is not only reflected in its high accuracy but also its ability to quickly train primary doctors, improve the diagnosis rate of early GC, and reduce missed cases. At the same time, it will also reduce the possibility of missed diagnosis of advanced GC in cardia. Furthermore, it is used to assist imaging doctors to determine the location of lymph nodes and, more importantly, it can more effectively judge the lymph node metastasis of GC, which is conducive to the prognosis of patients. In surgical treatment of GC, it also has great potential. Robotic surgery is the latest technology in GC surgery. It is a bright star for minimally invasive treatment of GC, and together with laparoscopic surgery, it has become a common treatment for GC. Through machine learning, robotic systems can reduce operator errors and trauma of patients, and can predict the prognosis of GC patients. Throughout the centuries of development of surgery, the history gradually changes from traumatic to minimally invasive. In the future, AI will help GC patients reduce surgical trauma and further improve the efficiency of minimally invasive treatment of GC.