A nomogram to predict overall survival and disease-free survival after curative-intent gastrectomy for gastric cancer

Using a nomogram based on the controlling nutritional status score to predict prognosis after surgery in patients with resectable gastric cancer

BACKGROUND

Various studies have shown that the controlling nutritional status (CONUT) score contributes to assessing the prognosis of cancer patients. This study aimed to establish a nomogram based on the CONUT score and several other important parameters based on patient age and tumor characteristics to accurately forecast the overall survival (OS) of patients with resectable gastric cancer (GC).

METHODS

This study retrospectively recruited 404 individuals who received a potentially curative radical gastrectomy performed by the same group of surgeons at our medical center from January 2019 to December 2021. We used Cox regression analysis to identify independent prognostic factors influencing patients' OS. We establish a nomogram based on the outcomes of the multivariate analysis to forecast the 1, 2, and 3-year OS of GC patients.

RESULTS

Univariate Cox regression analysis revealed that the age, body mass index (BMI), hemoglobin (HGB), serum albumin (ALB), Serum carcinoembryonic antigen (CEA), CONUT score, tumor size, pT stage, pN stage, nerve invasion, vascular invasion, tumor differentiation, and postoperative chemotherapy were prognostic indicators of postoperative OS in GC patients (all P < 0.05). Multivariate Cox regression analysis indicated that the age (P = 0.015), CONUT score (P = 0.002), pT stage (T3 vs T1: P = 0.011, T4 vs T1: P = 0.026), pN stage (N2 vs N0: P = 0.002, N3 vs N0: P < 0.001), nerve invasion (P = 0.021) were the independent risk factors. The nomogram based on the CONUT score, with a C-index of 0.792, enhanced the predictive ability of the TNM staging system alone, which had a C-index of 0.718 for OS.

CONCLUSION

The CONUT score can independently predict the OS for individuals with GC following surgery. The nomogram based on the CONUT score is a reliable tool for forecasting the postoperative survival of individuals with GC and may identify those patients wholesale benefit from a more aggressive treatment protocol.

Clinical Value of Nomograms Integrating Circulating Lipid and Inflammation Risk Score in Predicting Long-Term Outcomes After Radical Gastrectomy in Gastric Cancer: A Multicenter Real-World Study

BACKGROUND

The clinical value of incorporating lipid and inflammatory factors to predict long-term survival in patients with gastric cancer (GC) is unreported. This study aimed to investigate the clinical value of nomograms integrating the Circulating Lipid and Inflammation Risk Score (CLIRS) for predicting the long-term outcome of patients with GC.

METHODS

A retrospective analysis included patients with GC who underwent radical resection at four tertiary medical centers. Patients were divided into training and validation cohorts, with least absolute shrinkage and selection operator regression selecting optimal lipid and inflammatory indicators related to GC prognosis. The CLIRS was developed from six indicators: lymphocyte, triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and apolipoprotein B.

RESULTS

Overall, 2534 patients were studied, including 1910 in the training cohort and 624 in the validation cohort. The CLIRS was an independent risk factor for overall survival (OS; hazard ratio [HR] 1.529, 95% confidence interval [CI] 1.271-1.839; p < 0.001) and disease-free survival (DFS; HR 1.511, 95% CI 1.267-1.801; p < 0.001) in GC patients. The OS nomogram (area under the receiver operating characteristic curve 0.823 vs. 0.785; p < 0.05) and DFS nomogram (AUC 0.804 vs. 0.770; p < 0.05) based on the CLIRS outperformed pTNM stage. High-risk patients had earlier and more sustained recurrence, with higher rates of local, peritoneal, and distant recurrences (p < 0.05).

CONCLUSIONS

The CLIRS, combining circulating lipid and inflammatory factors, is an independent prognostic factor for patients with GC. Nomograms incorporating the CLIRS are superior to pTNM stage in predicting postoperative survival and recurrence in patients with GC.

Prognostic performance of examined lymph nodes, lymph node ratio, and positive lymph nodes in gastric cancer: a competing risk model study

Background

Previous research on the prognostic effectiveness of examined lymph nodes (ELN), lymph node ratio (LNR), and positive lymph nodes (pN) in postoperative gastric cancer (GC) has yielded inconsistent results despite their widespread use.

Methods

This study used a competing risk model (CRM) to evaluate the prognostic efficacy of these markers in patients with GC. Data from 337 patients with lymph node (LN)-positive stage II GC undergoing resection and chemotherapy between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Optimal cutoff values for ELN and LNR were determined using restricted cubic splines, and pN was divided into three groups based on the AJCC staging system. The survival analyses were conducted using Kaplan-Meier curves, Cox proportional hazards analysis, cumulative incidence curves, and CRM. Subgroup analysis and interaction tests were performed to evaluate the correlation between LN status and survival within subgroups.

Results

The results indicated that the optimal cutoff values for ELN, LNR, and pN were 16, 0.1, and 2. Multivariate Cox analysis showed that ELN (hazard ratio [HR] = 0.67), LNR (HR = 2.23), and pN (HR = 2.80) were independent predictors of overall survival, whereas only LNR (HR = 2.08) was independently associated with disease-specific survival. The CRM revealed that LNR (sub-distribution hazard ratio [SHR] = 1.89) and pN (SHR = 2.80) were independently associated with disease-specific survival.

Conclusion

In conclusion, ELN, LNR, and pN are all significant predictors of overall survival for GC. However, LNR demonstrates stronger robustness in predicting DSS than ELN and pN. The LNR may supplement the TNM staging system in identifying prognostic discrepancies.

Immune infiltration and prognosis in gastric cancer: role of NAD+ metabolism-related markers

Background

This study endeavored to develop a nicotinamide adenine dinucleotide (NAD+) metabolism-related biomarkers in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients.

Methods

In this study, differentially expressed genes (DEGs1) between GC and paraneoplastic tissues were overlapped with NAD+ metabolism-related genes (NMRGs) to identify differentially expressed NMRGs (DE-NMRGs). Then, GC patients were divided into high and low score groups by gene set variation analysis (GSVA) algorithm for differential expression analysis to obtain DEGs2, which was overlapped with DEGs1 for identification of intersection genes. These genes were further analyzed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to obtain prognostic genes for constructing a risk model. Enrichment and immune infiltration analyses further investigated investigate the different risk groups, and qRT-PCR validated the prognostic genes.

Results

Initially, we identified DE-NMRGs involved in NAD biosynthesis, with seven (DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B and SNCG) showing prognostic significance in GC. Subsequent, a prognostic model was constructed in which the risk score, derived from the expression profiles of these genes, along with gender, emerged as robust independent predictors of patient outcomes in GC. Enrichment analysis linked high-risk patients to synaptic membrane pathways and low-risk to the CMG complex pathway. Tumor immune infiltration analysis revealed correlations between risk scores and immune cell abundance, suggesting a relationship between NAD+ metabolism and immune response in GC. The prognostic significance of our identified genes was validated by qRT-PCR, which confirmed their upregulated expression in GC tissue samples.

Conclusion

In this study, seven NAD+ metabolism-related markers were established, which is of great significance for the development of prognostic molecular biomarkers and clinical prognosis prediction for gastric cancer patients.

Current use of artificial intelligence in the diagnosis and management of acute appendicitis

INTRODUCTION

Acute appendicitis is a common and time-sensitive surgical emergency, requiring rapid and accurate diagnosis and management to prevent complications. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant potential to improve the diagnosis and management of acute appendicitis. This review provides an overview of the evolving role of AI in the diagnosis and management of acute appendicitis, highlighting its benefits, challenges, and future perspectives.

EVIDENCE ACQUISITION

We performed a literature search on articles published from 2018 to September 2023. We included only original articles.

EVIDENCE SYNTHESIS

Overall, 121 studies were examined. We included 32 studies: 23 studies addressed the diagnosis, five the differentiation between complicated and uncomplicated appendicitis, and 4 studies the management of acute appendicitis.

CONCLUSIONS

AI is poised to revolutionize the diagnosis and management of acute appendicitis by improving accuracy, speed and consistency. It could potentially reduce healthcare costs. As AI technologies continue to evolve, further research and collaboration are needed to fully realize their potential in the diagnosis and management of acute appendicitis.

Application of Survival Quilts for prognosis prediction of gastrectomy patients based on the Surveillance, Epidemiology, and End Results database and China National Cancer Center Gastric Cancer database

Objective

Accurate prognosis prediction is critical for individualized-therapy making of gastric cancer patients. We aimed to develop and test 6-month, 1-, 2-, 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS) prediction models for gastric cancer patients following gastrectomy.

Methods

We derived and tested Survival Quilts, a machine learning-based model, to develop 6-month, 1-, 2-, 3-, 5-, and 10-year OS and CSS prediction models. Gastrectomy patients in the development set (n = 20,583) and the internal validation set (n = 5,106) were recruited from the Surveillance, Epidemiology, and End Results (SEER) database, while those in the external validation set (n = 6,352) were recruited from the China National Cancer Center Gastric Cancer (NCCGC) database. Furthermore, we selected gastrectomy patients without neoadjuvant therapy as a subgroup to train and test the prognostic models in order to keep the accuracy of tumor-node-metastasis (TNM) stage. Prognostic performances of these OS and CSS models were assessed using the Concordance Index (C-index) and area under the curve (AUC) values.

Results

The machine learning model had a consistently high accuracy in predicting 6-month, 1-, 2-, 3-, 5-, and 10-year OS in the SEER development set (C-index = 0.861, 0.832, 0.789, 0.766, 0.740, and 0.709; AUC = 0.784, 0.828, 0.840, 0.849, 0.869, and 0.902, respectively), SEER validation set (C-index = 0.782, 0.739, 0.712, 0.698, 0.681, and 0.660; AUC = 0.751, 0.772, 0.767, 0.762, 0.766, and 0.787, respectively), and NCCGC set (C-index = 0.691, 0.756, 0.751, 0.737, 0.722, and 0.701; AUC = 0.769, 0.788, 0.790, 0.790, 0.787, and 0.788, respectively). The model was able to predict 6-month, 1-, 2-, 3-, 5-, and 10-year CSS in the SEER development set (C-index = 0.879, 0.858, 0.820, 0.802, 0.784, and 0.774; AUC = 0.756, 0.827, 0.852, 0.863, 0.874, and 0.884, respectively) and SEER validation set (C-index = 0.790, 0.763, 0.741, 0.729, 0.718, and 0.708; AUC = 0.706, 0.758, 0.767, 0.766, 0.766, and 0.764, respectively). In multivariate analysis, the high-risk group with risk score output by 5-year OS model was proved to be a strong survival predictor both in the SEER development set (hazard ratio [HR] = 14.59, 95% confidence interval [CI]: 1.872-2.774, P < 0.001), SEER validation set (HR = 2.28, 95% CI: 13.089-16.293, P < 0.001), and NCCGC set (HR = 1.98, 95% CI: 1.617-2.437, P < 0.001). We further explored the prognostic value of risk score resulted 5-year CSS model of gastrectomy patients, and found that high-risk group remained as an independent CSS factor in the SEER development set (HR = 12.81, 95% CI: 11.568-14.194, P < 0.001) and SEER validation set (HR = 1.61, 95% CI: 1.338-1.935, P < 0.001).

Conclusion

Survival Quilts could allow accurate prediction of 6-month, 1-, 2-, 3-, 5-, and 10-year OS and CSS in gastric cancer patients following gastrectomy.

Development and validation of nomogram for predicting early recurrence after radical gastrectomy of gastric cancer

BACKGROUND

After radical surgery, early detection of recurrence and metastasis is a crucial factor in enhancing the prognosis and survival of patients with gastric cancer (GC). Therefore, assessing the risk of recurrence in gastric cancer patients and determining the timing for postoperative recurrence is crucial.

METHODS

The clinicopathological data of 521 patients with recurrent gastric cancer, who underwent radical gastrectomy at Zhejiang Cancer Hospital between January 2010 and January 2017, were retrospectively analyzed. These patients were randomly divided into two groups: a training group (n = 365) and a validation group (n = 156). In the training set, patients were further categorized into early recurrence (n = 263) and late recurrence (n = 102) groups based on a 2-year boundary. Comparative analyses of clinicopathological features and prognoses were conducted between these two groups. Subsequently, a nomogram for predicting early recurrence was developed and validated.

RESULTS

In this study, the developed nomogram incorporated age, serous infiltration, lymph node metastasis, recurrence mode, and the tumour marker CA19-9. In the training cohort, the area under the curve (AUC value) was 0.739 (95% CI, 0.682-0.798), with a corresponding C-index of 0.739. This nomogram was subsequently validated in an independent validation cohort, yielding an AUC of 0.743 (95% CI, 0.652-0.833) and a C-index of 0.743. Furthermore, independent risk factors for prognosis were identified, including age, absence of postoperative chemotherapy, early recurrence, lymph node metastasis, abdominal metastasis, and vascular cancer embolus.

CONCLUSION

Independent risk factors for gastric cancer recurrence following radical surgery were utilized to construct a nomogram for predicting early relapse. This nomogram effectively assesses the risk of recurrence, aids in treatment decision-making and follow-up planning in clinical settings, and demonstrated strong performance in the validation cohort.

Development and validation of prognostic nomogram for patients with metastatic gastric adenocarcinoma based on the SEER database

The aim of this study was to investigate the prognostic factors affecting overall survival in patients with metastatic gastric adenocarcinoma and to establish a nomogram prediction model for comprehensive clinical application. Data from 2370 patients with metastatic gastric adenocarcinoma between 2010 and 2017 were retrieved from the surveillance, epidemiology, and end results database. They were randomly divided into a training set (70%) and a validation set (30%), univariate and multivariate Cox proportional hazards regressions were used to screen important variables that may affect overall survival and to establish the nomogram. The nomogram model was evaluated using a receiver operating characteristic curve, calibration plot, and decision curve analysis. Internal validation was performed to test the accuracy and validity of the nomogram. Univariate and multivariate Cox regression analyses revealed that, age, primary site, grade, and American joint committee on cancer. T, bone metastasis, liver metastasis, lung metastasis, tumor Size, and chemotherapy were identified as independent prognostic factors for overall survival and were included in the prognostic model to construct a nomogram. The prognostic nomogram showed good overall survival risk stratification ability for the area under the curve, calibration plots, and decision curve analysis in both the training and validation sets. Kaplan-Meier curves further showed that patients in the low-risk group had better overall survival. This study synthesizes the clinical, pathological, therapeutic characteristics of patients with metastatic gastric adenocarcinoma, establishes a clinically effective prognostic model, and that can help clinicians to better evaluate the patient's condition and provide accurate treatment.

Construction and validation of a glycolysis-related lncRNA signature for prognosis prediction in Stomach Adenocarcinoma

Background: Glycolysis is closely related to the occurrence and progression of gastric cancer (GC). Currently, there is no systematic study on using the glycolysis-related long non-coding RNA (lncRNA) as a model for predicting the survival time in patients with GC. Therefore, it was essential to develop a signature for predicting the survival based on glycolysis-related lncRNA in patients with GC. Materials and methods: LncRNA expression profiles, containing 375 stomach adenocarcinoma (STAD) samples, were obtained from The Cancer Genome Atlas (TCGA) database. The co-expression network of lncRNA and glycolysis-related genes was used to identify the glycolysis-related lncRNAs. The Kaplan-Meier survival analysis and univariate Cox regression analysis were used to detect the glycolysis-related lncRNA with prognostic significance. Then, Bayesian Lasso-logistic and multivariate Cox regression analyses were performed to screen the glycolysis-related lncRNA with independent prognostic significance and to develop the risk model. Patients were assigned into the low- and high-risk cohorts according to their risk scores. A nomogram model was constructed based on clinical information and risk scores. Gene Set Enrichment Analysis (GSEA) was performed to visualize the functional and pathway enrichment analyses of the glycolysis-related lncRNA. Finally, the robustness of the results obtained was verified in an internal validation data set. Results: Seven glycolysis-related lncRNAs (AL353804.1, AC010719.1, TNFRSF10A-AS1, AC005586.1, AL355574.1, AC009948.1, and AL161785.1) were obtained to construct a risk model for prognosis prediction in the STAD patients using Lasso regression and multivariate Cox regression analyses. The risk score was identified as an independent prognostic factor for the patients with STAD [HR = 1.315, 95% CI (1.056-1.130), p < 0.001] via multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were drawn and the area under curve (AUC) values of 1-, 3-, and 5-year overall survival (OS) were calculated to be 0.691, 0.717, and 0.723 respectively. Similar results were obtained in the validation data set. In addition, seven glycolysis-related lncRNAs were significantly enriched in the classical tumor processes and pathways including cell adhesion, positive regulation of vascular endothelial growth factor, leukocyte transendothelial migration, and JAK_STAT signaling pathway. Conclusion: The prognostic prediction model constructed using seven glycolysis-related lncRNA could be used to predict the prognosis in patients with STAD, which might help clinicians in the clinical treatment for STAD.

Very Early Recurrence After Curative-Intent Surgery for Gastric Adenocarcinoma

BACKGROUND

Recurrence after curative-intent surgery can occur in more than 50% of gastric cancer (GC) patients. We sought to identify predictors of very early recurrence (VER) among GC patients who underwent curative-intent surgery.

METHODS

A multi-institutional database of GC patients undergoing curative-intent surgery between 2000 and 2020 at 8 major institutions was queried. VER was defined as local or distant tumor recurrence within 6 months from surgery. Univariable Cox proportional hazard models were used to evaluate the predictive value of clinical-pathological features on VER. A regularized Cox regression model was employed to build a predictive model of VER and recurrence within 12 months. The discriminant ability of the Cox regularized models was evaluated by reporting a ROC curve together with the calibration plot, considering 200 runs.

RESULTS

Among 1133 patients, 65 (16.0%) patients experienced a VER. Preoperative symptoms (HR 1.198), comorbidities (HR 1.289), tumor grade (HR 1.043), LNR (HR 4.339) and T stage (HR 1.639) were associated with an increased likelihood of VER. Model performance was very good at predicting VER at 6 months (AUC of 0.722) and 12 months (AUC 0.733). Two nomograms to predict 6-month and 12-month VER were built based on the predictive model. A higher nomogram score was associated with worse prognosis. There was good prediction between observed and estimated VER with minimal evidence of overfitting and good performance on internal bootstrapping validation.

CONCLUSION

One in 6 patients experienced VER following curative-intent surgery for GC. Nomograms to predict risk of VER performed well on internal validation, and stratified patients into distinct prognostic groups relative to 6- and 12-months recurrence.

Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma

Background. The aim of this study was to identify the most accurate computed-tomography (CT) dimensional criteria of loco-regional lymph nodes (LNs) for detecting nodal metastases in gastric cancer (GC) patients. Methods. Staging CTs of surgically resected GC were jointly reviewed by two radiologists, considering only loco-regional LNs with a long axis (LA) ≥ 5 mm. For each nodal group, the short axis (SA), volume and SA/LA ratio of the largest LN, the sum of the SAs of all LNs, and the mean of the SA/LA ratios were plotted in ROC curves, taking the presence/absence of metastases at histopathology for reference. On a per-patient basis, the sums of the SAs of all LNs, and the sums of the SAs, volumes, and SA/LA ratios of the largest LNs in all nodal groups were also plotted, taking the presence/absence of metastatic LNs in each patient for reference. Results. Four hundred and forty-three nodal groups were harvested during surgery from 107 patients with GC, and 173 (39.1%) were metastatic at histopathology. By nodal group, the sum of the SAs showed the best Area Under the Curve (AUC), with a sensitivity/specificity of 62.4/72.6% using Youden’s index with a >8 mm cutoff. In the per-patient analysis, the sum of the SAs of all LNs in the loco-regional nodal groups showed the best AUC with a sensitivity/specificity of 65.6%/83.7%, using Youden’s index with a >39 mm cutoff. Conclusion. In patients with GC, the sum of the SAs of all the LNs at staging CT is the best predictor among dimensional LNs criteria of both metastatic invasion of the nodal group and the presence of metastatic LNs.