Nomograms for predicting survival in patients with metastatic gastric adenocarcinoma who undergo palliative gastrectomy

A Systematic Review of Prognostic Factors in Patients with Cancer Receiving Palliative Radiotherapy: Evidence-Based Recommendations

(1) Background: Prognostication in patients with cancer receiving palliative radiotherapy remains a challenge. To improve the process, we aim to identify prognostic factors in this population from the literature and offer evidence-based recommendations on prognostication in patients undergoing palliative radiotherapy for non-curable or advanced cancers. (2) Methods: A systematic review was performed on the medical literature from 2005 to 2023 to extract papers on the prognosis of palliative radiotherapy patients with advanced cancer. The initial selection was performed by at least two authors to determine study relevance to the target area. Studies were then classified based on type and evidence quality to determine final recommendations. (3) Results: The literature search returned 57 papers to be evaluated. Clinical and biological prognostic factors were identified from these papers to improve clinical decision making or construct prognostic models. Twenty prognostic models were identified for clinical use. There is moderate evidence supporting (i) evidence-based factors (patient, clinical, disease, and lab) in guiding decision making around palliative radiation; (ii) that certain biological factors are of importance; (iii) prognostication models in patients with advanced cancer; and that (iv) SBRT or re-irradiation use can be guided by predictions of survival by prognostic scores or clinicians. Patients with more favorable prognoses are generally better suited to SBRT or re-irradiation, and the use of prognostic models can aid in this decision making. (4) Conclusions: This evaluation has identified several factors or tools to aid in prognosis and clinical decision making. Future studies should aim to further validate these tools and factors in a clinical setting, including the leveraging of electronic medical records for data availability. To increase our understanding of how causal factors interact with palliative radiotherapy, future studies should also examine and include prediction of response to radiation as an outcome.

Who are optimal candidates for primary tumor resection in patients with metastatic gastric adenocarcinoma? A population-based study

BACKGROUND

The research aimed to construct a novel predictive nomogram to identify specific metastatic gastric adenocarcinoma (mGAC) populations who could benefit from primary tumor resection (PTR).

METHOD

Patients with mGAC were included in the SEER database and divided into PTR and non-PTR groups. The Kaplan-Meier analysis, propensity score matching (PSM), least absolute shrink and selection operator (LASSO) regression, multivariable logistic regression, and multivariate Cox regression methods were then used. Finally, the prediction nomograms were built and tested.

RESULTS

3185 patients with mGAC were enrolled. Among the patients, 679 cases underwent PTR while the other 2506 patients didn't receive PTR. After PSM, the patients in the PTR group presented longer median overall survival (15.0 vs. 7.0 months, p < 0.001). Among the PTR group, 307 (72.9%) patients obtained longer overall survival than seven months (beneficial group). Then the LASSO logistic regression was performed, and gender, grade, T stage, N stage, pathology, and chemotherapy were included to construct the nomogram. In both the training and validation cohorts, the nomogram exhibited good discrimination (AUC: 0.761 and 0.753, respectively). Furthermore, the other nomogram was constructed to predict 3-, 6-, and 12-month cancer-specific survival based on the variables from the multivariate Cox analysis. The 3-, 6-, and 12-month AUC values were 0.794, 0.739, and 0.698 in the training cohort, and 0.805, 0.759, and 0.695 in the validation cohorts. The calibration curves demonstrated relatively good consistency between the predicted and observed probabilities of survival in two nomograms. The models' clinical utility was revealed through decision curve analysis.

CONCLUSION

The benefit nomogram could guide surgeons in decision-making and selecting optimal candidates for PTR among mGAC patients. And the prognostic nomogram presented great prediction ability for these patients.

Artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer

Background

Gastric cancer with synchronous distant metastases indicates a dismal prognosis. The success in survival improvement mainly relies on our ability to predict the potential benefit of a therapy. Our objective is to develop an artificial intelligence annotated clinical-pathologic risk model to predict its outcomes.

Methods

In participants (n=47553) with gastric cancer of the surveillance, epidemiology, and end results program, we selected patients with distant metastases at first diagnosis, complete clinical-pathologic data and follow-up information. Patients were randomly divided into the training and test cohort at 7:3 ratio. 93 patients with advanced gastric cancer from six other cancer centers were collected as the external validation cohort. Multivariable analysis was used to identify the prognosis-related clinical-pathologic features. Then a survival prediction model was established and validated. Importantly, we provided explanations to the prediction with artificial intelligence SHAP (Shapley additive explanations) method. We also provide novel insights into treatment options.

Results

Data from a total 2549 patients were included in model development and internal test (median age, 61 years [range, 53-69 years]; 1725 [67.7%] male). Data from an additional 93 patients were collected as the external validation cohort (median age, 59 years [range, 48-66 years]; 51 [54.8%] male). The clinical-pathologic model achieved a consistently high accuracy for predicting prognosis in the training (C-index: 0.705 [range, 0.690-0.720]), test (C-index: 0.737 [range, 0.717-0.757]), and external validation (C-index: 0.694 [range, 0.562-0.826]) cohorts. Shapley values indicated that undergoing surgery, chemotherapy, young, absence of lung metastases and well differentiated were the top 5 contributors to the high likelihood of survival. A combination of surgery and chemotherapy had the greatest benefit. However, aggressive treatment did not equate to a survival benefit. SHAP dependence plots demonstrated insightful nonlinear interactive associations among predictors in survival benefit prediction. For example, patients who were elderly, or poor differentiated, or presence of lung or bone metastases had a worse prognosis if they undergo surgery or chemotherapy, while patients with metastases to liver alone seemed to gain benefit from surgery and chemotherapy.

Conclusion

In this large multicenter cohort study, we developed an artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer. It could be used to discuss treatment options.

Primary Tumor Resection for Metastatic Colorectal, Gastric and Pancreatic Cancer Patients: In Search of Scientific Evidence to Inform Clinical Practice

The management of the primary tumor in metastatic colorectal, gastric and pancreatic cancer patients may be challenging. Indeed, primary tumor progression could be associated with severe symptoms, compromising the quality of life and the feasibility of effective systemic therapy, and might result in life-threatening complications. While retrospective series have suggested that surgery on the primary tumor may confer a survival advantage even in asymptomatic patients, randomized trials seem not to definitively support this hypothesis. We discuss the evidence for and against primary tumor resection for patients with metastatic gastrointestinal (colorectal, gastric and pancreatic) cancers treated with systemic therapies and put in context the pros and cons of the onco-surgical approach in the time of precision oncology. We also evaluate current ongoing trials on this topic, anticipating how these will influence both research and everyday practice.

A nomogram model based on the number of examined lymph nodes-related signature to predict prognosis and guide clinical therapy in gastric cancer

BACKGROUND

Increasing evidence suggests that the number of examined lymph nodes (ELNs) is strongly linked to the survivorship of gastric cancer (GC). The goal of this study was to assess the prognostic implications of the ELNs number and to construct an ELNs-based risk signature and nomogram model to predict overall survival (OS) characteristics in GC patients.

METHODS

This inception cohort study included 19,317 GC patients from the U.S. Surveillance, Epidemiology, and End Results (SEER) database, who were separated into a training group and an internal validation group. The nomogram was built with the training set, then internally verified with SEER data, and externally validated with two different data sets. Based on the RNA-seq data, ELNs-related DERNAs (DElncRNAs, DEmiRNAs, andDEmRNAs) and immune cells were identified. The LASSO-Cox regression analysis was utilized to construct ELNs-related DERNAs and immune cell prognostic signature in The Cancer Genome Atlas (TCGA) cohort. The OS of subgroups with high- and low-ELN signature was compared using the Kaplan-Meier (K-M) analysis. A nomogram was successfully constructed based on the ELNs signature and other clinical characteristics. The concordance index (C-index), calibration plot, receiver operating characteristic curve, and decision curve analysis (DCA) were all used to evaluate the nomogram model. The meta-analysis, the Gene Expression Profiling Interactive Analysis database, and reverse transcription-quantitative PCR (RT-qPCR) were utilized to validate the RNA expression or abundance of prognostic genes and immune cells between GC tissues and normal gastric tissues, respectively. Finally, we analyzed the correlations between immune checkpoints, chemotherapy drug sensitivity, and risk score.

RESULTS

The multivariate analysis revealed that the high ELNs improved OS compared with low ELNs (hazard ratio [HR] = 0.659, 95% confidence interval [CI]: 0.626-0.694, p < 0.0001). Using the training set, a nomogram incorporating ELNs was built and proven to have good calibration and discrimination (C-index [95% CI], 0.714 [0.710-0.718]), which was validated in the internal validation set (C-index [95% CI], 0.720 [0.714-0.726]), the TCGA set (C-index [95% CI], 0.693 [0.662-0.724]), and the Chinese set (C-index [95% CI], 0.750 [0.720-0.782]). An ELNs-related signature model based on ELNs group, regulatory T cells (Tregs), neutrophils, CDKN2B-AS1, H19, HOTTIP, LINC00643, MIR663AHG, TMEM236, ZNF705A, and hsa-miR-135a-5p was constructed by the LASSO-Cox regression analysis. The result showed that OS was remarkably lower in patients with high-ELNs signature compared with those with low-ELN signature (HR = 2.418, 95% CI: 1.804-3.241, p < 0.001). This signature performed well in predicting 1-, 3-, and 5-year survival (AUC [95% CI] = 0.688 [0.612-0.763], 0.744 [0.659-0.830], and 0.778 [0.647-0.909], respectively). The multivariate Cox analysis illustrated that the risk score was an independent predictor of survival for patients with GC. Moreover, the expression of prognostic genes (LINC00643, TMEM236, and hsa-miR-135a-5p) displayed differences between GC tissues and adjacent non-tumor tissues. The C-index of the nomogram that can be used to predict the OS of GC patients was 0.710 (95% CI: 0.663-0.753). Both the calibration plots and DCA showed that the nomogram has good predictive performance. Moreover, the signature was significantly correlated with the N stage and T stage. According to our analysis, GC patients in the low-ELN signature group may have a better immunotherapy response and OS outcome.

CONCLUSIONS

We explored the prognostic role of ELNs in GC and successfully constructed an ELNs signature linked to the GC prognosis in TCGA. The findings manifested that the signature is a powerful predictive indicator for patients with GC. The signature might contain potential biomarkers for treatment response prediction for GC patients. Additionally, we identified a novel and robust nomogram combining the characteristics of ELNs and clinical factors for predicting 1-, 3-, and 5-year OS in GC patients, which will facilitate personalized survival prediction and aid clinical decision-making in GC patients.

Construction of a nomogram to predict the survival of metastatic gastric cancer patients that received immunotherapy

Background

Immunotherapy has shown promising results for metastatic gastric cancer (MGC) patients. Nevertheless, not all patients can benefit from anti-PD-1 treatment. Thus, this study aimed to develop and validate a prognostic nomogram for MGC patients that received immunotherapy.

Methods

Herein, MGC patients treated with anti-PD-1 between 1 October 2016 and 1 June 2022 at two separate Chinese PLA General Hospital centers were enrolled and randomly divided into training and validation sets (186 and 80 patients, respectively). The nomogram was constructed based on a multivariable Cox model using baseline variables from the training cohort. Its predictive accuracy was validated by the validation set. The consistency index (C-index) and calibration plots were used to evaluate the discriminative ability and accuracy of the nomogram. The net benefit of the nomogram was evaluated using decision curve analysis (DCA). Finally, we stratified patients by median total nomogram scores and performed Kaplan–Meier survival analyses.

Results

We developed the nomogram based on the multivariate analysis of the training cohort, including four parameters: surgery history, treatment line, lung immune prognostic index (LIPI), and platelet-to-lymphocyte ratio (PLR). The C-index of the nomogram was 0.745 in the training set. The calibration curve for 1- and 2-year survival showed good agreement between nomogram predictions and actual observations. In the validation group, the calibration curves demonstrated good performance of the nomogram, with a C-index for overall survival (OS) prediction of 0.713. The OS of patients with a score greater than the median nomogram score was significantly longer than patients with a score lower or equal to the median (p < 0.001).

Conclusion

We constructed a nomogram to predict the outcomes of MGC patients that received immunotherapy. This nomogram might facilitate individualized survival predictions and be helpful during clinical decision-making for MGC patients under anti-PD-1 therapy.

Clinicopathological Characteristics and Response to Chemotherapy in Treatment-Naive Epstein-Barr Virus Associated Gastric Cancer: A Retrospective Study

Background

Epstein–Barr virus associated gastric cancer (EBVaGC) is a special subtype of gastric cancer. However, the perioperative treatment plan and the response to chemotherapy are still uncertain.

Methods

We retrospectively enrolled patients diagnosed with EBVaGC from March 2013 to July 2020 in Beijing Cancer Hospital. Clinicopathological characteristics were recorded. Disease-free survival (DFS) were then calculated, and variants affecting DFS were tested in a Cox proportional regression model.

Results

One hundred sixty consecutive patients were finally included in our study. Of the patients, 96.9% had adenocarcinoma, while five had squamous cell carcinoma component. Most (70.9%) of them were poorly differentiated. Prevalent programmed death-ligand 1 (PD-L1) (69%) and minor HER-2 (3.8%) expression were noticed; all of the patients were MMR proficient (pMMR) or microsatellite stable (MSS). Among 33 patients who experienced neoadjuvant therapy, the number of tumor regression grade (TRG) 1, TRG 2, and TRG 3 was 5, 16, and 12, respectively. Patients with advanced tumor stage and T stage showed poorer response. Thirty-one patients experienced first-line chemotherapy; ORR was 33.3%, and DCR was 61.9%. One hundred forty-seven patients underwent surgery, and 27 of them showed disease recurrence; the 3-year DFS rate was 71.0%. Tumor stage, neoadjuvant chemotherapy, vascular invasion, and negative PD-L1 expression were associated with poorer DFS. Vascular invasion was the independent risk factor of DFS. Only seven patients reached OS with median follow-up time of 14 months.

Conclusion

EBVaGC exhibits unique clinicopathological characteristics. Neoadjuvant chemotherapy may not be suitable for EBVaGC, and EBVaGC exhibited relatively poor response to chemotherapy.

Development and validation of a Surveillance, Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in elderly patients with gastric cancer after surgery

Background

Elderly gastric cancer (ELGC) remains one of the intensively investigated topics during the last decades. To establish a comprehensive nomogram for effective clinical practice and assessment is of significance. This study is designed to develop a prognostic nomogram for ELGC both in overall survival (OS) and cancer-specific survival (CSS).

Methods

The recruited cases were from the Surveillance, Epidemiology, and End Results (SEER) database and input for the construction of nomogram.

Results

A total of 4,414 individuals were recruited for this study, of which 2,208 were randomly in training group and 2,206 were in validation group. In univariate analysis of OS, significant variables (P<0.05) included age, marital status, grade, American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage, bone/brain/liver/lung metastasis and tumor size. In univariate analysis of CSS, significant variables (P<0.05) included age, grade, AJCC TNM stage, bone/brain/liver/lung metastasis and tumor size. In multivariate analysis of OS, sex, age, race, grade, TNM stage, lung metastasis and tumor size were considered as the significant variables and subjected to the establishment of nomogram. In multivariable analysis of CSS, age, grade, TNM, tumor size were considered as the significant variables and input to the establishment of nomogram. Sex, age, race, grade, TNM stage, lung metastasis and tumor size were included for the establishment of nomogram in OS while age, grade, TNM, tumor size were included to the establishment of nomogram in CSS. C-index, decision curve analysis (DCA) and the area under the curve (AUC) showed distinct value of newly established nomogram models. Both OS and CSS nomograms showed higher statistic power over the AJCC stage.

Conclusions

This study established and validated novel nomogram models of OS and CSS for ELGC based on population dataset.

Nomograms Predict Overall Survival and Cancer-Specific Survival in Patients with Fibrosarcoma: A SEER-Based Study

Purpose

Due to the rarity, it is difficult to predict the survival of patients with fibrosarcoma. This study aimed to apply a nomogram to predict survival outcomes in patients with fibrosarcoma.

Methods

A total of 2235 patients with diagnoses of fibrosarcoma were registered in the Surveillance, Epidemiology, and End Results database, of whom 663 patients were eventually enrolled. Univariate and multivariate Cox analyses were used to identify independent prognostic factors. Nomograms were constructed to predict 3-year and 5‐year overall survival and cancer‐specific survival of patients with fibrosarcoma.

Results

In univariate and multivariate analyses of OS, age, sex, race, tumor stage, pathologic grade, use of surgery, and tumor size were identified as independent prognostic factors. Age, sex, tumor stage, pathologic grade, use of surgery, and tumor size were significantly associated with CSS. These characteristics were further included to establish the nomogram for predicting 3-year and 5-year OS and CSS. For the internal validation of the nomogram predictions of OS and CSS, the C-indices were 0.784 and 0.801.

Conclusion

We developed the nomograms that estimated 3-year and 5-year OS and CSS. These nomograms not only have good discrimination performance and calibration but also provide patients with better clinical benefits.

The incidence, risk factors and predictive nomograms for early death among patients with stage IV gastric cancer: a population-based study

Background

Although advances in the treatment of stage IV gastric cancer (GC) patients, some patients were observed to die within 3 months of initial diagnosis. The present study aimed to explore the early mortality and risk factors for stage IV GC and further develop nomograms.

Methods

A total of 2,174 eligible stage IV GC patients were selected from the Surveillance, Epidemiology, and End Results database. Logistic regression analyses were used to determine the risk factors and develop the nomograms to predict all-cause early death and cancer-specific early death. The predictive performance of the nomograms was assessed by receiver operating characteristic curves (ROC), calibration plots and decision curve analyses (DCA) in both training and validation cohorts.

Results

Of 2,174 patients enrolled, 708 died within 3 months of initial diagnosis (n=668 for cancer-specific early death). Early mortality remained stable from 2010-2015. Non-Asian or Pacific Islander (API) race, poorer differentiation, middle sites of the stomach, no surgery, no radiotherapy, no chemotherapy, lung metastases and liver metastases were associated with high risk of both all-causes early death and cancer-specific early death. The nomograms constructed based on these factors showed favorable sensitivity, with the area under the ROC range of 0.816-0.847. The calibration curves and DCAs also exhibited adequate fit and ideal net benefit in prediction and clinical application.

Conclusions

Approximately one-third of stage IV GC patients experienced early death. These associated risk factors and predictive nomograms may help clinicians identify the patients at high risk of early death and be the reference for treatment choices.

A Population-Based Analysis of Distant Metastasis in Stage IV Gastric Cancer

BACKGROUND Distant metastasis (DM) is a crucial problem in management of patients with gastric cancer. Identification of the risk factors for development of DM and the prognostic factors for patients with DM is essential in development of individualized treatment of patients at the advanced stage with specific metastasis. MATERIAL AND METHODS Records of patients with gastric cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Survival duration of patients with specific DM was estimated, and the prognostic factors were investigated using the Cox proportional hazard regression model. The logistic regression model was used to reveal the inherent risk factors for development of DM. RESULTS Eventually, 32.6% (11,918 out of 36,588) of gastric cancer patients were diagnosed with DM between 2010 and 2015, among whom 5,361, 1,778, 1,495, and 231 patients were diagnosed with liver, lung, bone, and brain metastasis, respectively. The median overall survival for patients with DM was 5.0 (95% CI: 4.8-5.2) months, with a 5-year survival rate of 3.9%. Primary tumor site, histology types, tumor grade, T stage, N stage, surgery, chemotherapy, and the number of metastases were associated with worse survival. Younger age and higher tumor grade were positively associated with the development of DM. CONCLUSIONS Initial DM was found in 32.6% of patients with gastric cancer. Homogenous and heterogenous predictive factors were identified for patients with a specific metastatic site, which can be used in targeted screening and individualized treatment.