Machine Learning Radiomics Liver Function Model for Prognostic Prediction After Radical Resection of Advanced Gastric Cancer: A Retrospective Study.

PURPOSE: We aimed to establish a machine learning radiomics liver function model to explore how liver function affects the prognosis of patients with gastric cancer (GC). METHODS: Patients with advanced GC were retrospectively enrolled in this study. Eight machine learning radiomic models were constructed by extracting radiomic features from portal-vein-phase contrast-enhanced computed tomography (CE-CT) images. Clinicopathological features were determined using univariate and multifactorial Cox regression analyses. These features were used to construct a GC survival nomogram. RESULTS: A total of 510 patients with GC were split into training and test cohorts in an 8:2 ratio. Kaplan-Meier analysis showed that patients with type I liver function had a better prognosis. Fifteen significant features were retained to establish the machine learning model. LightBGM showed the best predictive performance in the training (area under the receiver operating characteristic curve [AUC] 0.978) and test cohorts (AUC 0.714). Multivariate analysis revealed that gender, age, liver function, Nutritional Risk Screening 2002 (NRS-2002) score, tumor-lymph node-metastasis stage, tumor size, and tumor differentiation were independent risk factors for GC prognosis. The survival nomogram based on machine learning radiomics, instead of liver biochemical indicators, still had high accuracy (C-index of 0.771 vs. 0.773). CONCLUSION: The machine learning radiomics liver function model has high diagnostic value in predicting the influence of liver function on prognosis in patients with GC.

Serum creatinine/cystatin C ratio as a surrogate marker for sarcopenia in patients with gastric cancer.

BACKGROUND: Sarcopenia is an age-related syndrome that may have negative impact on surgical outcomes and long-term survival of patients with gastric cancer. Serum creatinine/cystatin C (Cr/CysC) ratio has attracted attention as a surrogate marker for sarcopenia but has not been adequately studied in patients with gastric cancer. The purpose of this study was to investigate the validity of serum Cr/CysC ratio as a predictor of sarcopenia, evaluate a statistical cut-off value, and assess the relationship between Cr/CysC ratio and prognosis of patients with gastric cancer. METHODS: We retrospectively studied 327 patients who underwent surgery for gastric cancer from June 2009 to October 2021. The skeletal muscle mass index was calculated using computed tomography (CT). We determined the relevance of serum Cr/CysC ratio as a surrogate maker for sarcopenia by comparing it with various biomarkers. The Concordance index (C-index) was calculted to measure whether the Cr/CysC ratio can prognosis of patients with gastric cancer. RESULTS: Serum Cr/CysC was significantly correlated with with Skeletal Muscle Index (SMI) (r = 0.221, p < 0.001) and Skeletal Muscle Area (SMA) (r = 0.258, p < 0.001). The area under the curve for sarcopenia was significantly larger for serum Cr/CysC ratio than for other biomarkers (Cr/CysC: 0.644, CysC: 0.535, Cr: 0.556). Patients in the high-Cr/CysC group have longer survival time than that in low-Cr/CysC group, defined by the cutoff value 0.67. The C-index of both Cr/CysC ratio and SMI with OS was 0.63. CONCLUSIONS: Serum Cr/CysC ratio can be used accurately, inexpensively, and easily to evaluate sarcopenia in male patients with gastric cancer. Our study shows that patients with Cr/CysC below 0.67 had possibility of sarcopenia and would be poor prognosis.

Muscle Mass, Density, and Strength Are Necessary to Diagnose Sarcopenia in Patients With Gastric Cancer.

BACKGROUND: Accurate evaluation of muscle strength and function is difficult. Commonly known as sarcopenia, skeletal muscle loss is closely correlated with the prognosis of patients diagnosed with gastric cancer (GC). We wondered the correlation between skeletal muscle measures combined with computed tomography and grip strength (GS) and short-term outcomes after radical gastrectomy (RG) in patients with GC. MATERIALS AND METHODS: We analyzed 594 patients with GC who underwent RG. The skeletal muscle index (SMI), skeletal muscle density (Hounsfield unit average calculation [HUAC]), and GS were measured. The skeletal muscle gauge (SMG) was created by multiplying SMI and HUAC. Logistic regression modeling and multivariate analysis were used. RESULTS: Univariate analysis showed that low SMI, low HUAC, low GS, and low SMG were predictors of short-term complications after surgery. Compared with other muscle-related factors (AUCHUAC = 0.559, AUCSMI = 0.575, AUCGS = 0.580, AUCSMG = 0.598, AUCSMI+GS = 0.559), low GS plus SMG (AUCGS plus SMG = 0.614, P < 0.01) was a more accurate independent risk factor for postoperative complications. Moreover, the risk model with SMG plus GS for the diagnosis of skeletal muscle loss was superior to the other models (c-index = 0.729). CONCLUSIONS: Skeletal muscle measures using computed tomography and GS are closely correlated with short-term outcomes of patients diagnosed with GC after RG. The model contained SMG plus GS could effectively predict for patients with GC after RG at high risk of short-term outcomes. SMG plus GS has the highest accuracy for the several evaluation methods of sarcopenia.

A quantified risk-scoring system and rating model for postsurgical gastroparesis syndrome in gastric cancer patients.

BACKGROUND AND OBJECTIVES: The study aimed to investigate the relationship between obesity and postsurgical gastroparesis syndrome (PGS), and to construct a scoring system and a risk model to identify patients at high risk. METHODS: A total of 634 patients were retrospectively analyzed. Clinical characteristics were evaluated via receiver operating characteristic (ROC) curve analysis. Logistic analysis was performed to determine the independent predictive indicators of PGS. A scoring system consisting of these indicators and a risk-rating model were constructed and evaluated via ROC curve analysis. RESULTS: Based on the ROC curves, the visceral fat area (VFA) cutoff value for PGS was 94.00. Logistic analysis showed that visceral obesity (VFA ≥ 94.00 cm2 ), the reconstruction technique, and tumor size were independent prognostic factors for PGS. The scoring system could predict PGS reliably with a high area under the ROC curve ([AUC] = 0.769). A high-risk rating had a high AUC (AUC I = 0.56, AUC II = 0.65, and AUC III = 0.77), indicating that the risk-rating model could effectively screen patients at high risk of PGS. CONCLUSIONS: Visceral obesity defined by VFA effectively predicted PGS. Our scoring system may be a reliable instrument for identifying patients most at risk of PGS.

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.

Background: Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery. Methods: Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n = 128) and a validation cohort (n = 64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared. Results: Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates. Conclusion: The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

Computed tomography-based radiomics nomogram for predicting therapeutic response to neoadjuvant chemotherapy in locally advanced gastric cancer : A scale for treatment predicting.

BACKGROUND AND OBJECTIVE: Neoadjuvant chemotherapy results in various responses when used to treat locally advanced gastric cancer, we aimed to develop and validate a predictive model of the response to neoadjuvant chemotherapy in patients with gastric cancer. METHODS: A total of 128 patients with locally advanced gastric cancer who underwent pre-treatment computed tomography (CT) scanning followed by neoadjuvant chemoradiotherapy were included (training cohort: n = 64; validation cohort: n = 64). We built a radiomics score combined with laboratory parameters to create a nomogram for predicting the efficacy of neoadjuvant chemotherapy and calculating scores for risk factors. RESULTS: The radiomics score system demonstrated good stability and prediction performance for the response to neoadjuvant chemotherapy, with the area under the curve of the training and validation cohorts being 0.8 and 0.64, respectively. The radiomics score proved to be an independent risk factor affecting the efficacy of neoadjuvant chemotherapy. In addition to the radiomics score, four other risk factors were included in the nomogram, namely the platelet-to-lymphocyte ratio, total bilirubin, ALT/AST, and CA199. The model had a C-index of 0.8. CONCLUSIONS: Our results indicated that radiomics features could be potential biomarkers for the early prediction of the response to neoadjuvant treatment.

Prognostic value of the preoperative albumin-bilirubin score among patients with stages I-III gastric cancer undergoing radical resection: A retrospective study.

The albumin-bilirubin (ALBI) score was originally used to accurately assess liver dysfunction and predict the prognoses of patients with hepatocellular carcinoma. Following its more recent application to patients with gastrointestinal tumors, this study analyzed the prognostic value of the ALBI score in Chinese patients with advanced resectable (tumor-node-metastasis [TNM] stages I-III) gastric cancer (GC). This study investigated 1185 patients with advanced GC, including 429 with TNM stage I. The patients were divided into training and verifications groups (593 and 592 patients, respectively) in which these patients were classified as high risk (ALBI score ≥ -2.65) or low risk (ALBI score < -2.65). Univariate and multivariate Cox regression analyses were performed, and a visual survival prediction model (nomogram) was created. On Kaplan-Meier analysis, patients who were low-risk and high-risk according to their ALBI scores had significantly different survival rates in both the training and verification groups (p < 0.01). The difference was also significant when analyzing only patients with TNM stage I GC (p = 0.031). Univariate and multivariate analyses showed that the ALBI score (p = 0.014), age (p < 0.001), Nutritional Risk Screening 2002 score (p = 0.024), sarcopenia (p = 0.049), tumor differentiation (p = 0.027), and TNM stage (p < 0.001) were independent risk factors for survival. Our survival prediction model that incorporated the ALBI score accurately predicted the 5-year survival rate of Chinese patients with GC. Therefore, the ALBI score is a valid clinical indicator and good predictor of survival after surgery for progressive GC. Moreover, this score is simple to derive and does not burden patients with additional costs.

The relationship between GLIM-malnutrition, post-operative complications and long-term prognosis in elderly patients undergoing colorectal cancer surgery.

Background: Elderly people and patients with colorectal cancer (CRC) are both at high risk of malnutrition. Therefore, it is of great significance to explore suitable malnutrition screening and diagnostic indicators for elderly patients with CRC. Recently, the Global Leadership Initiative on Malnutrition (GLIM) proposed new diagnostic criteria for malnutrition. The aim of this article was to evaluate the diagnostic value of GLIM criteria for malnutrition in elderly colorectal patients. We explored the relationship between GLIM-malnutrition, post-operative complications and the long-term prognosis of elderly colorectal patients. Methods: Elderly patients (aged ≥65 years) who underwent CRC surgery from January 2015 to December 2018 were included. Malnutrition was diagnosed based on the GLIM criteria. The relationships between GLIM-malnutrition and clinical characteristics were analyzed by t-tests, Mann-Whitney U tests, and chi-squared tests. The relationships between GLIM-malnutrition and post-operative complications were analyzed by chi-squared tests, and logistic regression analyses. The relationships between GLIM-malnutrition and the long-term prognosis were analyzed by Kaplan-Meier analyses and logistic and Cox regression analyses. Results: A total of 385 elderly patients were included in this study, and 118 patients (30.65%) were diagnosed with malnutrition according to the GLIM criteria. GLIM-malnutrition was significantly associated with older age, lower body mass index (BMI), lower grip strength, tumor location, higher Nutrition Risk Screening 2002 (NRS-2002), and lower levels of albumin and hemoglobin. GLIM-malnutrition was an independent risk factor [odds ratio (OR): 1.753, 95% confidence interval (CI): 1.100-2.795, P=0.018] for post-operative complications. Cox regression analysis showed that GLIM-malnutrition was an independent risk factor for overall survival in elderly patients with CRC. Conclusions: The GLIM criteria are feasible diagnostic criteria for malnutrition of elderly patients with CRC. GLIM-malnutrition is significantly associated with post-operative complications and overall survival in elderly patients with CRC.

Clinical application of molecular residual disease detection by circulation tumor DNA in solid cancers and a comparison of technologies: review article.

Molecular residual disease (MRD), detected by circulating tumor DNA (ctDNA) can be involved in the entire process of solid tumor management, including recurrence prediction, efficacy evaluation, and risk stratification. Currently, the detection technologies are divided into two main categories, as follows: tumor-agnostic and tumor informed. Tumor-informed assay obtains mutation information by sequencing tumor tissue samples before blood MRD monitoring, followed by formulation of a personalized MRD panel. Tumor-agnostic assays are carried out using a fixed panel without the mutation information from primary tumor tissue. The choice of testing strategy may depend on the level of evidence from ongoing randomized clinical trials, investigator preference, cost-effectiveness, patient economics, and availability of tumor tissue. The review describes the difference between tumor informed and tumor agnostic detection. In addition, the clinical application of ctDNA MRD in solid tumors was introduced, with emphasis on lung cancer, colorectal cancer, Urinary system cancer, and breast cancer.

Effect of low-level creatinine clearance on short-term postoperative complications in patients with colorectal cancer.

Background: Renal function is closely related to cancer prognosis. Since preoperative renal insufficiency has been identified as a risk factor for postoperative complications, this study aimed to investigate the effect of preoperative creatinine clearance rate (CrCl) on short-term prognosis of patients undergoing colorectal surgery. Methods: A retrospective analysis was conducted of the electronic health records of 526 adult patients who underwent elective colorectal cancer (CRC) surgery from September 2014 to February 2019 at the First Affiliated Hospital of Wenzhou Medical University. Cases were divided into two groups according to CrCl level and clinical variables were compared. Risk factors associated with postoperative complications were evaluated through univariate and multivariate logistic regression analyses. Results: A total of 526 patients met the inclusion criteria. The overall rate of postoperative complications was 28.14%. Overall, the incidence of postoperative complications was significantly higher in the low CrCl patients. A low-level CrCl, multi-organ combined resection, and Charlson comorbidity index (CCI) were independent risk factors for short-term complications in patients with CRC. However, a low CrCl was identified as an independent risk factor for short-term postoperative complications in elderly, but not young patients in a subgroup analysis. Conclusions: Preoperative low-level CrCl, multi-organ combined resection, and CCI were significant risk factors of postoperative complications in CRC patients. Preoperative low-level CrCl and multi-organ combined resection has a poor prognostic impact for elderly patients with CRC. These findings should have important implications for health care decision-making among patients with CRC who are at higher risk for post-operative complications.

Global leaders malnutrition initiative-defined malnutrition affects long-term survival of different subgroups of patients with gastric cancer: A propensity score-matched analysis.

As defined by the Global Leaders Malnutrition Initiative (GLIM), malnutrition is strongly associated with a lower quality of life and poor prognosis in gastric cancer patients. However, few studies have precisely explored the predictors of malnutrition, as defined by the GLIM, for overall survival (OS) after gastric cancer surgery in subgroups of patients stratified according to population characteristics. Our research aimed to analyze whether the predictors of malnutrition defined by the GLIM for postoperative OS in gastric cancer patients differ across subgroups. Patients who underwent radical gastric cancer surgery at our center between July 2014 and February 2019 were included in the study. Propensity score matching (PSM) was used to minimize bias. The study population was divided into malnourished and normal groups based on whether they were malnourished as defined by the GLIM. Univariate and multivariate analyses were performed to identify the risk factors affecting OS. The Kaplan-Meier curve and log-rank test were performed to determine the survival rate difference between subgroups. Overall, 1,007 patients were enrolled in the research. Multivariate analysis showed that malnutrition among the patients was 33.47%. Additionally, GLIM-defined malnutrition was an independent risk factor [hazard ratio (HR): 1.429, P = 0.001] for a shorter OS in gastric cancer patients. Furthermore, subgroup analysis showed that the GLIM was more appropriate for predicting OS in older aged patients (≥65 years), females, those with comorbidities (Charlson comorbidity index ≥ 2), and those with advanced gastric cancer (TNM stage = 3). GLIM-defined malnutrition affects the long-term survival of gastric cancer patients, especially older patients, females, patients with comorbidities, and patients with advanced gastric cancer.

Impact of malnutrition diagnosed using Global Leadership Initiative on Malnutrition criteria on clinical outcomes of patients with gastric cancer.

OBJECTIVE: Our objective is to validate the effectiveness of the Global Leadership Initiative on Malnutrition (GLIM) criteria in malnutrition diagnosis compared with Patient-Generated Subjective Global Assessment (PG-SGA) and assess the impact of malnutrition diagnosed using GLIM criteria on the clinical outcomes of patients with GC. METHODS: We retrospectively analyzed the data of 895 patients who underwent radical gastrectomy at the First Affiliated Hospital of Wenzhou Medical University. Nutrition assessment was performed on all patients according to the GLIM criteria and PG-SGA. The κ statistic was used to evaluate the agreement between two methods. Multivariate logistic regression and Cox regression based on single-factor analysis were used to predict postoperative complications and overall survival rates. RESULTS: Based on the GLIM criteria, 38.3% of the patients were diagnosed as malnourished, including 21.7% Stage I (moderate malnutrition) and 16.6% Stage II (severe malnutrition). GLIM criteria had a moderate agreement with PG-SGA (κ = 0.548). Patients in the Stage II malnutrition group had a higher incidence of complications, a longer postoperative length of stay, and higher hospitalization costs. Logistic regression showed that Stage II malnutrition was an independent risk predictor of postoperative complications (odds ratio, 3.28; 95% confidence interval [CI], 2.18-4.94). Furthermore, Cox regression analysis showed that both Stage I (hazard ratio [HR], 1.52; 95% CI, 1.11-2.07; P = .009) and Stage II (HR, 1.85; 95% CI, 1.34-2.53; P < .001) malnutrition were independent risk predictors of overall survival. CONCLUSION: Diagnosis of malnutrition according to the GLIM criteria is useful in predicting the adverse postoperative clinical outcomes of patients with gastric cancer.

Effect of Short-Term Preoperative Parenteral Nutrition Support for Gastric Cancer Patients with Sarcopenia: a Propensity Score Matching Analysis.

INTRODUCTION: Sarcopenia is well recognized as an unfavorable prognostic marker for gastric cancer (GC) patients. Currently, few nutritional interventions-such as parenteral nutrition-exist for the treatment of patients with sarcopenia. This study aimed to estimate the effectiveness of short-term preoperative parenteral nutrition (PN) in GC patients with sarcopenia. MATERIALS AND METHODS: We collected data on GC patients with sarcopenia who underwent radical gastrectomy at our hospital from 2010 to 2018. A 1:1 ratio propensity score matching (PSM) was applied to establish the PN and control groups. Data were analyzed using the chi-squared, Mann-Whitney U, and Fisher's exact tests. RESULTS: In total, 428 patients met the inclusion criteria, and the propensity scores identified 166 matched pairs of patients with sarcopenia. The overall incidence of postoperative complications between both groups was not significantly different (P = 0.728). The PN group had a lower rate of intra-abdominal infection (P = 0.032) and higher hospitalization costs (P < 0.001) than the control group. Multivariate analysis demonstrated that age, Charlson score, and TNM stage were independent risk factors for postoperative complications. Additionally, subgroup analysis revealed that short-term preoperative PN support is associated with decreased postoperative surgical complications in patients with albumin levels < 35 g/L (P = 0.025). CONCLUSION: Short-term preoperative PN support is not associated with reduction of overall complication rate in patients with GC and sarcopenia. However, those with sarcopenia and hypoalbuminemia benefited from preoperative PN support.

Establish a New Diagnosis of Sarcopenia Based on Extracted Radiomic Features to Predict Prognosis of Patients With Gastric Cancer.

Background: Preoperative sarcopenia is a prognostic risk factor for gastric cancer (GC). This study aimed to determine whether radiomic sarcopenia features on computed tomography (CT) could be used to diagnose sarcopenia preoperatively, and whether they could be used to accurately predict the postoperative survival and complication prognosis of patients with GC. Methods: We retrospectively analyzed data of 550 patients with GC who underwent radical gastrectomy. The patients were divided into training (2014-2016) and validation (2017-2019) cohorts. We established a radiomics-based diagnosis tool for sarcopenia. Thereafter, univariate and multivariate analyses of diagnostic factors were carried out. Receiver operator characteristic (ROC) curves and area under the curve (AUC) were used to compare different diagnostic models. The Kaplan-Meier method was used to estimate the survival curve. Results: Radiomic sarcopenia correlated with complications and long-term survival. Skeletal muscle index, grip strength, and walking speed were correlated with postoperative complications in both cohorts (AUCs: 0.632, 0.577, and 0.614, respectively in the training cohort; 0.570, 0.605, 0.546, respectively, in the validation cohort), and original sarcopenia was more accurate than any of these indicators. However, radiomic sarcopenia has a higher AUC in predicting short-term complications than original sarcopenia in both groups (AUCs: 0.646 vs. 0.635 in the training cohort; 0.641 vs. 0.625 in the validation cohort). In the training cohort, the overall survival time of patients with original sarcopenia was shorter than normal patients (hazard ratio, HR = 1.741; 95% confidence interval [CI], 1.044-2.903; p = 0.031). While radiomic sarcopenia had a greater prognostic significance, the overall survival time of patients with radiomic sarcopenia was significantly worse than normal patients (HR, 1.880; 95% CI, 1.225-2.885, p = 0.003). Conclusion: Extracted sarcopenia features based on CT can predict long-term survival and short-term complications of GC patients after surgery, and its accuracy has been verified by training and validation groups. Compared with original sarcopenia, radiomic sarcopenia can effectively improve the accuracy of survival and complication prediction and also shorten the time and steps of traditional screening, thereby reducing the subjectivity effects of sarcopenia assessment.

Preoperative Fibrosis-4 (FIB-4) Evaluation May Be Helpful to Evaluate Prognosis of Gastric Cancer Patients Undergoing Operation: A Retrospective Study.

BACKGROUND: Liver dysfunction and chronic inflammation influence the prognosis of many tumors and surgical outcomes. This study was performed to determine whether the Fibrosis-4 (FIB-4) index, originally defined as a noninvasive fibrosis marker, can predict the prognosis of patients with gastric cancer undergoing radical gastric cancer surgery. METHODS: We have retrospectively analyzed 594 consecutive patients with gastric cancer who underwent gastrectomy in our database. The FIB-4 index was calculated using laboratory data and age before gastrectomy. The clinical utility of FIB-4 was evaluated by X-tile. Patients were divided into two groups (high and low FIB-4 index groups), and their overall survival (OS) was investigated. Cox regression analysis was used to identify the independent parameters associated with prognosis. Finally, we developed a prognostic prediction model by using R statistical software. RESULTS: A total of 556 patients, including 422 men and 134 women, were enrolled. Of these, 61 (11.0%) and 495 (89.0%) patients had low and FIB-4 indexes, respectively. In addition to the indicators of FIB-4, preoperative age, tumor site, surgical procedure, TNM stage, and postoperative complications were found to be independent predictors of prognosis (P < 0.05). Among patients, the FIB-4 index group had significantly shorter OS (log-rank P = 0.01) than the low FIB-4 index group. This association was also confirmed in the multivariate analysis (hazard ratio, 4.65; 95% confidence interval, 1.07-4.29; P = 0.031). CONCLUSIONS: Preoperative FIB-4 index can predict long-term outcomes of gastric cancer patients who had undergone gastrectomy.

Establishment of the Radiologic Tumor Invasion Index Based on Radiomics Splenic Features and Clinical Factors to Predict Serous Invasion of Gastric Cancer.

BACKGROUND: Currently, there are shortcomings in diagnosing gastric cancer with or without serous invasion, making it difficult for patients to receive appropriate treatment. Therefore, we aimed to develop a radiomic nomogram for preoperative identification of serosal invasion. METHODS: We selected 315 patients with gastric cancer, confirmed by pathology, and randomly divided them into two groups: the training group (189 patients) and the verification group (126 patients). We obtained patient splenic imaging data for the training group. A p-value of <0.05 was considered significant for features that were selected for lasso regression. Eight features were chosen to construct a serous invasion prediction model. Patients were divided into high- and low-risk groups according to the radiologic tumor invasion risk score. Subsequently, univariate and multivariate regression analyses were performed with other invasion-related factors to establish a visual combined prediction model. RESULTS: The diagnostic accuracy of the radiologic tumor invasion score was consistent in the training and verification groups (p<0.001 and p=0.009, respectively). Univariate and multivariate analyses of invasion risk factors revealed that the radiologic tumor invasion index (p=0.002), preoperative hemoglobin <100 (p=0.042), and the platelet and lymphocyte ratio <92.8 (p=0.031) were independent risk factors for serosal invasion in the training cohort. The prediction model based on the three indexes accurately predicted the serosal invasion risk with an area under the curve of 0.884 in the training cohort and 0.837 in the testing cohort. CONCLUSIONS: Radiological tumor invasion index based on splenic imaging combined with other factors accurately predicts serosal invasion of gastric cancer, increases diagnostic precision for the most effective treatment, and is time-efficient.

Sarcopenia in Patients With Normal Body Mass Index Is an Independent Predictor for Postoperative Complication and Long-Term Survival in Gastric Cancer.

Malnutrition in patients with gastric cancer (GC) with normal body mass index (BMI) is often ignored. This study aimed to explore the role of sarcopenia in predicting postoperative complication and long-term survival in patients with GC with normal BMI. We included patients with normal BMI (18.5 kg/m2  ≤ BMI < 23 kg/m2 ) who underwent radical gastrectomy between July 2014 and December 2016. Sarcopenia was assessed by muscle mass, handgrip strength, and gait speed. Kaplan-Meier survival analysis was used to analyze the association between sarcopenia and the prognosis of patients with GC. Univariate and multivariate analyses were used to identify risk factors contributing to postoperative complications and long-term survival. Overall, 267 patients with GC with normal BMI were included in this study; of which 49 (18.35%) patients were diagnosed with sarcopenia. Patients with sarcopenia had higher incidence of a major postoperative complication, longer postoperative hospital stays, and greater hospital costs. The Kaplan-Meier survival analysis showed that patients with sarcopenia had poorer overall survival than non-sarcopenia patients. Univariate and multivariate analyses showed that sarcopenia was an independent predictor for postoperative complication and long-term survival in such patients. Sarcopenia is an independent predictor for postoperative complications and long-term survival in patients with normal BMI after radical gastrectomy for GC. We recommend that patients with normal BMI should perform nutritional risk screening by sarcopenia.

Impact of Preoperative Short-Term Parenteral Nutrition Support on the Clinical Outcome of Gastric Cancer Patients: A Propensity Score Matching Analysis.

OBJECTIVES: Nutrition status is of great significance to the clinical outcome after major abdominal surgery. However, the effect of preoperative short-term parenteral nutrition (PN) support among gastric cancer (GC) patients remains unknown and was evaluated in the current study. METHODS: We retrospectively analyzed 455 nutritionally at-risk GC patients after radical resection from 2010 to 2016. We matched patients with 3-7 days of PN support to those without PN support. χ2 And Mann-Whitney U tests were used to compare differences between the PN and control groups. RESULTS: The propensity-matched sample included 368 GC patients (PN group, n = 184; control group, n = 184). The PN and control groups did not differ regarding postoperative complications (P = .528). The incidence of anastomotic leakage in the PN group was lower than in the control group (P = .011), whereas other complications were not found to differ between the groups. The hospitalization cost of the PN group was significantly higher than that of the control group (P < .001), whereas other outcome indicators were similar. Subgroup analysis showed that short-term PN support may have an improved benefit for patients with serum albumin level <35 g/L, but not at the level of statistical significance (P = .17). CONCLUSION: Short-term PN support did not significantly improve the short-term clinical outcomes of nutritionally at-risk GC patients, with the exception of a lower incidence of anastomotic leakage. Considering that short-term PN support increases economic burden, PN should not be the preferred method among these patients.

Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer.

INTRODUCTION: Radiomics allows for mining of imaging data to examine tissue characteristics non-invasively, which can be used to predict the prognosis of a patient. This study explored the use of imaging techniques to evaluate splenic tissue characteristics to predict the prognosis of patients with gastric cancer. MATERIALS AND METHODS: Computed tomography images from patients with gastric cancer were collected retrospectively. Splenic image characteristics, extracted with pyradiomics, of patients in the training group were randomly divided. Characteristics with a P value < 0.1 were selected for lasso regression to construct a survival risk model. Models for high-and low-risk groups were established. Patients were divided into the high- and low-risk groups for univariate and multivariate regression analysis of survival-related factors, and a visual prognostic prediction model was established. RESULTS: The splenic characteristic prognostic model was consistent in the training and verification groups (p < 0.001 and p = 0.016, respectively). The two groups that displayed different splenic characteristics showed no statistical difference in other basic data except the tumour-node-metastasis (pTNM) stage (p = 0.007). Univariate and multivariate analysis of survival risk factors showed that splenic characteristics (p = 0.042), age (p < 0.001), tumor location (p = 0.002), and pTNM stage (p < 0.001) were independent risk factors for survival. The prognostic prediction model combined with splenic characteristics significantly improved the accuracy of prognosis, predicting one-and three-year survival rates. CONCLUSION: Splenic features extracted from imaging technology can accurately predict the long-term survival of patients with gastric cancer. Splenic characteristic grouping can effectively improve the accuracy of survival prediction and gastric cancer prognosis.

Preoperative Splenic Density for the Prediction of Survival and Adjuvant Chemotherapy Benefits in Gastric Cancer.

Background: We aimed to determine whether splenic features change during tumor progression by evaluating the clinicopathological characteristics relevant to splenic density in patients with gastric cancer (GC) and identify a new predictive indicator of prognosis and chemotherapy benefits. Methods: In the present analysis, 408 patients who underwent gastrectomy were included. Density was expressed in mean spleen Hounsfield units on computed tomography. Other clinical characteristics and detailed follow-up data were collected. The cutoff splenic density was 47.8 by the Xtile software. The R software was used for characteristic differential analysis in patients with different splenic densities. The Cox proportional hazards model and forest plot were used for prognosis and chemotherapy benefit analyses. Results: Patients with low splenic density had significantly worse 3-year disease-free survival (DFS) and overall survival (OS) rates (high vs low splenic density: DFS, 63.4% vs 44.6%, p<0.001; OS, 69.8% vs 52.4%, p<0.001). Splenic density showed strong negative correlations with age, number of metastasized lymph nodes, tumor size, and depth of tumor invasion. The benefits of adjuvant chemotherapy were better in the low splenic density group (hazard ratio of OS, 0.546; p=0.001) than in the low-density group (hazard ratio of OS, 0.701; p=0.106). Conclusions: Patients with low splenic density tended to have more advanced tumors and poor prognosis, but better chemotherapy benefits. Splenic density can be regarded as a new indicator of chemotherapy benefits and increase the accuracy of preoperative staging evaluation. Moreover, preoperative evaluation of splenic density may help establish individualized treatment strategies.