Double contrast-enhanced ultrasonography in preoperative Borrmann classification of advanced gastric carcinoma: comparison with histopathology.
ABSTRACT: The purpose of this study was to investigate the accuracy of double contrast-enhanced ultrasonography (DCEUS) for assessing the Borrmann classification of advanced gastric carcinoma (AGC) preoperatively. Three hundred twenty nine patients with proved AGC were enrolled. DCEUS (intravenous microbubbles combined with combined with oral contrast-enhanced ultrasound) was performed preoperatively. The diagnostic accuracy of DCEUS in determining Borrmann classification was compared with postoperative pathological findings. The reliability of DCEUS was analyzed. The accuracy of DCEUS in determining the Borrmann classification of AGC was 91.49%. The intra- and inter-observer reproducibility was both almost perfect for assessing the Borrmann classification of AGC by DCEUS. DCEUS could be considered as an accurate, non-invasive, and reliable diagnostic method for preoperative Borrmann classification of advanced gastric carcinoma.
Project description:INTRODUCTION:Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced computed tomography, to predict AHS and clinical outcomes of patients with GC. METHODS:Five hundred fifty-four patients with GC (370 training and 184 test) undergoing gastrectomy were retrospectively included. Six radiomic scores (R-scores) related to pT stage, pN stage, Lauren & Borrmann (L&B) classification, World Health Organization grade, lymphatic vascular infiltration, and an overall histopathologic score (H-score) were, respectively, built from 7,000+ radiomic features. R-scores and radiographic factors were then integrated into prediction models to assess AHS. The developed AHS-based Cox model was compared with the American Joint Committee on Cancer (AJCC) eighth stage model for predicting survival outcomes. RESULTS:Radiomics related to tumor gray-level intensity, size, and inhomogeneity were top-ranked features for AHS. R-scores constructed from those features reflected significant difference between AHS-absent and AHS-present groups (P < 0.001). Regression analysis identified 5 independent predictors for pT and pN stages, 2 predictors for Lauren & Borrmann classification, World Health Organization grade, and lymphatic vascular infiltration, and 3 predictors for H-score, respectively. Area under the curve of models using those predictors was training/test 0.93/0.94, 0.85/0.83, 0.63/0.59, 0.66/0.63, 0.71/0.69, and 0.84/0.77, respectively. The AHS-based Cox model produced higher area under the curve than the eighth AJCC staging model for predicting survival outcomes. Furthermore, adding AHS-based scores to the eighth AJCC staging model enabled better net benefits for disease outcome stratification. DISCUSSION:The developed computational approach demonstrates good performance for successfully decoding AHS of GC and preoperatively predicting disease clinical outcomes.
Project description:Gastric cancer is one of the leading causes of cancer-associated death; however, analysis of its molecular and clinical characteristics has been complicated by its histological and etiological heterogeneity. The present study aimed to estimate somatic mutation profiling in gastric cancer. To do so, targeted next-generation sequencing (NGS) was performed with the Oncomine Focus Assay to compare the clinicopathological characteristics with the mutation profiles in 50 patients with advanced gastric cancer (AGC). Among the 35 hotspot genes and 19 genes for copy number variations (CNVs), 18 single nucleotide variants (SNVs) or small insertions and deletions (14 missense and four frameshift mutations), and 10 amplifications were identified. To examine the association between mutation profiles and clinicopathological characteristics, each element of the clinicopathological characteristics was categorized into three groups: No alteration, PI3K catalytic subunit ? (PIK3CA) alterations and alterations other than PIK3CA. Fisher's exact test identified no statistical differences between the clinicopathological characteristics, with the exception of the Tumor-Node-Metastasis (TNM) T stage between the three groups. Cases of AGC with somatic alterations but no PIK3CA exhibited a significant difference in the TNM T stage compared with those with no alterations or PIK3CA alterations (P=0.044). In addition, AGC with PIK3CA alterations was categorized by Lauren's classification to the intestinal type only. The distribution of Lauren's classification in AGC with PIK3CA alterations was statistically different compared with AGC with alterations other than PIK3CA (P=0.028), but not compared with AGC with no alterations (P=0.076). In conclusion, the present study demonstrated a molecular profiling approach that identified potential molecular classifications for gastric cancer and suggested a framework for precision medicine in AGC.
Project description:BACKGROUND:Occult peritoneal metastasis (PM) in advanced gastric cancer (AGC) patients is highly possible to be missed on computed tomography (CT) images. Patients with occult PMs are subject to late detection or even improper surgical treatment. We therefore aimed to develop a radiomic nomogram to preoperatively identify occult PMs in AGC patients. PATIENTS AND METHODS:A total of 554 AGC patients from 4 centers were divided into 1 training, 1 internal validation, and 2 external validation cohorts. All patients' PM status was firstly diagnosed as negative by CT, but later confirmed by laparoscopy (PM-positive n = 122, PM-negative n = 432). Radiomic signatures reflecting phenotypes of the primary tumor (RS1) and peritoneum region (RS2) were built as predictors of PM from 266 quantitative image features. Individualized nomograms of PM status incorporating RS1, RS2, or clinical factors were developed and evaluated regarding prediction ability. RESULTS:RS1, RS2, and Lauren type were significant predictors of occult PM (all P < 0.05). A nomogram of these three factors demonstrated better diagnostic accuracy than the model with RS1, RS2, or clinical factors alone (all net reclassification improvement P < 0.05). The area under curve yielded was 0.958 [95% confidence interval (CI) 0.923-0.993], 0.941 (95% CI 0.904-0.977), 0.928 (95% CI 0.886-0.971), and 0.920 (95% CI 0.862-0.978) for the training, internal, and two external validation cohorts, respectively. Stratification analysis showed that this nomogram had potential generalization ability. CONCLUSION:CT phenotypes of both primary tumor and nearby peritoneum are significantly associated with occult PM status. A nomogram of these CT phenotypes and Lauren type has an excellent prediction ability of occult PM, and may have significant clinical implications on early detection of occult PM for AGC.
Project description:PURPOSE:Diagnosing early gastric cancer (EGC) or advanced gastric cancer (AGC) according to T-category is important for optimal GC treatment; however, the clinical and pathological diagnosis of tumor depths can sometimes vary. This study investigated the accuracy of clinical diagnosis of the tumor depth from the viewpoint of tumor localization and prognosis of patients with GC with discordance between clinical and pathological findings. METHODS:This study enrolled 741 patients with primary GC who underwent curative gastrectomy. Based on the clinical and pathological diagnosis of T-category, the patients were classified into four groups: Early-look EGC, Early-look AGC, Advanced-look EGC, and Advanced-look AGC. Tumor localization was classified longitudinally (the upper [U], middle [M], and lower [L] parts and cross-sectionally (the anterior [Ant] and posterior [Post] walls, and the lesser [Less] and greater [Gre] curvatures). RESULTS:Of the 462 clinical EGC cases, 52 were Early-look AGC cases that exhibited a significant association of tumor localization with the Post and Less in the U and M locations (UM-PL; p?=?0.037). An Advanced-look EGC (p?=?0.031) and Advanced-look AGC (p?=?0.025) were independent prognostic factors for relapse-free survival each in pathological EGC and AGC, respectively. CONCLUSIONS:Patients with clinically diagnosed EGC but with pathologically diagnosed AGC more frequently presented tumor in the UM-PL than in any other location. Selection of therapeutic strategy according to the clinical diagnosis might be critical; however, it should be also considered that the accuracy of preoperative assessments varies with tumor localization.
Project description:BACKGROUND:Due to the controversy over the prognostic significance of Borrmann type in patients with gastric cancer (GC), the present study was to investigate the clinical value of Borrmann type in advanced GC. METHODS:We retrospectively evaluated 2092 patients with advanced GC and subsequently examined the clinicopathological characteristics and prognosis of patients stratified by Borrmann type. RESULTS:Patients were divided into three groups according to Borrmann type (Borrmann types I+II, III, and IV). Patients with Borrmann types III and IV had larger size, more poorly differentiated tumor type, more advanced tumor stage, and higher chance of involving the entire stomach. The overall survival (OS) rates were significantly different among the three groups (p < 0.001). Stratification analysis revealed significant OS rates among the three groups in tumor-node-metastasis (TNM) stage III (p < 0.001) and TNM stage IV (p = 0.008). Multivariate analysis revealed that Borrmann types, adjuvant chemotherapy, curative resection, and TNM stage were all independent predictors of OS among GC patients. The subgroup analysis indicated that Borrmann type was an independent predictor of OS among GC patients who undergone curative resection and with TNM stage III cancer. However, curative resection and postoperative chemotherapy failed to prolong the survival of patients with Borrmann type IV. CONCLUSIONS:The clinicopathological characteristics and prognosis of patients with three Borrmann types of GC were different. Borrmann type can be simply used as a valuable factor to predict survival in advanced GC patients, especially in those TNM stage III undergoing curative resection. Additionally, more attention should be paid to the treatment for Borrmann type IV GC.
Project description:It is increasingly recognized that gastric cancer is a heterogeneous disease which may be divided into subgroups based on histological, anatomical, epidemiological and molecular classifications. Distinct molecular drivers and tumor biology, and thus different treatment targets and predictive biomarkers, may be implicated in each subtype. However, there is little evidence in the literature regarding the correlation among these different classifications, and particularly the molecular aberrations present in each subtype. In this review, we approach advanced gastric cancer (AGC) by presenting aberrant molecular pathways and their potential therapeutic targets in gastric cancer according to histological and anatomical classification, dividing gastric cancer into proximal nondiffuse, distal nondiffuse and diffuse disease. Several pathways are involved predominantly, although not exclusively, in different subtypes. This may help to explain the disappointing results of many published AGC trials in which study populations were heterogeneous regardless of clinicopathological characteristics of the primary tumor. Histological and anatomical classification may provide insights into tumor biology and facilitate selection of an enriched patient population for targeted agents in future studies and in the clinic. However, some molecular pathways implicated in gastric cancer have not been studied in correlation with histological or anatomical subtypes. Further studies are necessary to confirm the suggestion that such classification may predict tumor biology and facilitate selection of an enriched patient population for targeted agents in future studies and in the clinic.
Project description:Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, and assessing its histopathological grade requires visual inspection by an experienced pathologist. In this study, the histopathological H&E images from the Genomic Data Commons Databases were used to train a neural network (inception V3) for automatic classification. According to the evaluation of our model by the Matthews correlation coefficient, the performance level was close to the ability of a 5-year experience pathologist, with 96.0% accuracy for benign and malignant classification, and 89.6% accuracy for well, moderate, and poor tumor differentiation. Furthermore, the model was trained to predict the ten most common and prognostic mutated genes in HCC. We found that four of them, including CTNNB1, FMN2, TP53, and ZFX4, could be predicted from histopathology images, with external AUCs from 0.71 to 0.89. The findings demonstrated that convolutional neural networks could be used to assist pathologists in the classification and detection of gene mutation in liver cancer.
Project description:BACKGROUND:Although laparoscopic surgery has been recommended as an optional therapy for patients with early gastric cancer, whether patients with locally advanced gastric cancer (AGC) could benefit from laparoscopy-assisted distal gastrectomy (LADG) with D2 lymphadenectomy remains elusive due to a lack of comprehensive clinical data. To evaluate the efficacy of LADG, we conducted a multi-institutional randomized controlled trial to compare laparoscopy-assisted versus open distal gastrectomy (ODG) for AGC in North China. METHODS:In this RCT, after patients were enrolled according to the eligibility criteria, they were preoperatively assigned to LADG or ODG arm randomly with a 1:1 allocation ratio. The primary endpoint was the morbidity and mortality within 30 postoperative days to evaluate the surgical safety of LADG. The secondary endpoint was 3-year disease-free survival. This trial was registered at ClinicalTrial.gov as NCT02464215. RESULTS:Between March 2014 and August 2017, a total of 446 patients with cT2-4aN0-3M0 (AJCC 7th staging system) were enrolled. Of these, 222 patients underwent LADG and 220 patients underwent ODG were included in the modified intention-to-treat analysis. The compliance rate of D2 lymph node dissection was identical between the LADG and ODG arms (99.5%, P?=?1.000). No significant difference was observed regarding the overall postoperative complication rate in two groups (LADG 13.1%, ODG 17.7%, P?=?0.174). No operation-related death occurred in both arms. CONCLUSIONS:This trial confirmed that LADG performed by credentialed surgeons was safe and feasible for patients with AGC compared with conventional ODG.
Project description:Objective:Peritoneal dissemination is difficult to diagnose by conventional imaging technologies. We aimed to construct a nomogram to predict peritoneal dissemination in gastric cancer (GC) patients. Methods:We retrospectively analyzed 1,112 GC patients in Sun Yat-sen University Cancer Center between 2001 and 2010 as the development set and 474 patients from The Sixth Affiliated Hospital, Sun Yat-sen University between 2010 and 2016 as the validation set. The clinicopathological variables associated with gastric cancer with peritoneal dissemination (GCPD) were analyzed. We used logistic regression analysis to identify independent risk factors for peritoneal dissemination. Then, we constructed a nomogram for the prediction of GCPD and defined its predictive value with a receiver operating characteristic (ROC) curve. External validation was performed to validate the applicability of the nomogram. Results:In total, 250 patients were histologically identified as having peritoneal dissemination. Logistic regression analysis demonstrated that age, sex, tumor location, tumor size, signet-ring cell carcinoma (SRCC), T stage, N stage and Borrmann classification IV (Borrmann IV) were independent risk factors for peritoneal dissemination. We constructed a nomogram consisting of these eight factors to predict GCPD and found an optimistic predictive capability, with a C-index of 0.791, an area under the curve (AUC) of 0.791, and a 95% confidence interval (95% CI) of 0.762-0.820. The results found in the external validation set were also promising. Conclusions:We constructed a highly sensitive nomogram that can assist clinicians in the early diagnosis of GCPD and serve as a reference for optimizing clinical management strategies.
Project description:Gastric cancer (GC) remains the second tumor caused death threat worldwide, and personalized medicine for GC is far from expectation. Finding novel, recurrently mutated genes through next-generation sequencing (NGS) is a powerful and productive approach. However, previous genomic data for GC are based on surgical resected samples while a large proportion of advanced gastric cancer (AGC) patients have already missed the chance for operation. The aim of this study is to assess frequent genomic alteration in AGC via biopsy samples. Here we performed targeted genomic sequencing of 78 AGC patients' tumor biopsies along with matched lymphocyte samples based on a 118 cancer related gene panel. In total, we observed 301 somatic nonsynonymous genomic alterations in 92 different genes, as well as 37 copy number gain events among 15 different genes (fold change 2-12), and validated the fold changes of ERBB2 copy number gains with IHC and FISH test showed an accuracy of 81.8%. Previously reported driver genes for gastric cancer (TP53, KMT2D, KMT2B, EGFR, PIK3CA, GNAQ, and ARID1A), and several unreported mutations (TGFBR2, RNF213, NF1, NSD1, and LRP2) showed high non-silent mutation prevalence (7.7%-34.6%). When comparing intestinal-type gastric cancer (IGC) with diffuse-type gastric cancer (DGC), TP53 and GNAQ appear to be more frequently mutated in IGC (P=0.028 and P=0.023, respectively), whereas LRP2, BRCA2 and FGFR3 mutations are not observed in IGC, but have 12.8%, 7.7% and 7.7% mutation rates, respectively, in DGC patients. Patients with one or more mutations in adherens junction pathway (CREBBP, EP300, CDH1, CTNNB1, EGFR, MET, TGFBR2 and ERBB2) or TGF-β signaling pathway (CREBBP, EP300, MYST4, KRAS and TGFBR2) showed significantly better overall survival (P=0.007 and P=0.014, respectively), consistent with The Cancer Genome Atlas (TCGA) cohort data. Importantly, 57 (73.1%) patients harbored at least one genomic alteration with potential treatments, making NGS-based drug target screening a viable option for AGC patients. Our study established a comprehensive genomic portrait of AGC, and identified several mutation signatures highly associated with clinical features, survival outcomes, which may be used to design future personalized treatments.