Risk factors and prognosis of bone metastases in newly diagnosed gastric cancer

Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging

BACKGROUND

Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour.

AIM

To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data.

METHODS

Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared.

RESULTS

For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI.

CONCLUSION

The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

Development and validation of nomogram models to predict radiotherapy or chemotherapy benefit in stage III/IV gastric adenocarcinoma with surgery

Objectives

The advanced gastric adenocarcinoma (GAC) patients (stage III/IV) with surgery may have inconsistent prognoses due to different demographic and clinicopathological factors. In this retrospective study, we developed clinical prediction models for estimating the overall survival (OS) and cancer-specific survival (CSS) in advanced GAC patients with surgery.

Methods

A retrospective analysis was conducted using the Surveillance, Epidemiology, and End Results (SEER) database. The total population from 2004 to 2015 was divided into four levels according to age, of which 179 were younger than 45 years old, 695 were 45-59 years old, 1064 were 60-74 years old, and 708 were older than 75 years old. There were 1,712 men and 934 women. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for OS and CSS. Nomograms were constructed to predict the 1-, 3-, and 5-year OS and CSS. The models' calibration and discrimination efficiency were validated. Discrimination and accuracy were evaluated using the consistency index, area under the receiver operating characteristic curve, and calibration plots; and clinical usefulness was assessed using decision curve analysis. Cross-validation was also conducted to evaluate the accuracy and stability of the models. Prognostic factors identified by Cox regression were analyzed using Kaplan-Meier survival analysis.

Results

A total of 2,646 patients were included in our OS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as prognostic factors for OS in advanced GAC patients with surgery (P < 0.05). A total of 2,369 patients were included in our CSS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as risk factors for CSS in these patients (P < 0.05). These factors were used to construct the nomogram to predict the 1-, 3-, and 5-year OS and CSS of advanced GAC patients with surgery. The consistency index and area under the receiver operating characteristic curve demonstrated that the models effectively differentiated between events and nonevents. The calibration plots for 1-, 3-, and 5-year OS and CSS probability showed good consistence between the predicted and the actual events. The decision curve analysis indicated that the nomogram had higher clinical predictive value and more significant net gain than AJCC 6th_TNM stage in predicting OS and CSS of advanced GAC patients with surgery. Cross-validation also revealed good accuracy and stability of the models.

Conclusion

The developed predictive models provided available prognostic estimates for advanced GAC patients with surgery. Our findings suggested that both OS and CSS can benefit from chemotherapy or radiotherapy in these patients.

Two web-based dynamic prediction models for the diagnosis and prognosis of gastric cancer with bone metastases: evidence from the SEER database

Purpose

Our aim was to identify the clinical characteristics and develop and validate diagnostic and prognostic web-based dynamic prediction models for gastric cancer (GC) with bone metastasis (BM) using the SEER database.

Method

Our study retrospectively analyzed and extracted the clinical data of patients aged 18-85 years who were diagnosed with gastric cancer between 2010 and 2015 in the SEER database. We randomly divided all patients into a training set and a validation set according to the ratio of 7 to 3. Independent factors were identified using logistic regression and Cox regression analyses. Furthermore, we developed and validated two web-based clinical prediction models. We evaluated the prediction models using the C-index, ROC, calibration curve, and DCA.

Result

A total of 23,156 patients with gastric cancer were included in this study, of whom 975 developed bone metastases. Age, site, grade, T stage, N stage, brain metastasis, liver metastasis, and lung metastasis were identified as independent risk factors for the development of BM in GC patients. T stage, surgery, and chemotherapy were identified as independent prognostic factors for GC with BM. The AUCs of the diagnostic nomogram were 0.79 and 0.81 in the training and test sets, respectively. The AUCs of the prognostic nomogram at 6, 9, and 12 months were 0.93, 0.86, 0.78, and 0.65, 0.69, 0.70 in the training and test sets, respectively. The calibration curve and DCA showed good performance of the nomogram.

Conclusions

We established two web-based dynamic prediction models in our study. It could be used to predict the risk score and overall survival time of developing bone metastasis in patients with gastric cancer. In addition, we also hope that these two web-based applications will help physicians comprehensively manage gastric cancer patients with bone metastases.

Construction and validation of a novel nomogram to predict cancer-specific survival in patients with gastric adenocarcinoma

Background and aims

Adenocarcinoma is one of the most common pathological types of gastric cancer. The aims of this study were to develop and validate prognostic nomograms that could predict the probability of cancer-specific survival (CSS) for gastric adenocarcinoma (GAC) patients at 1, 3, and 5 years.

Methods

In total, 7747 patients with GAC diagnosed between 2010 and 2015, and 4591 patients diagnosed between 2004 and 2009 from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. The 7747 patients were used as a prognostic cohort to explore GAC-related prognostic risk factors. Moreover, the 4591 patients were used for external validation. The prognostic cohort was also divided into a training and internal validation sets for construction and internal validation of the nomogram. CSS predictors were screened using least absolute shrinkage and selection operator regression analysis. A prognostic model was built using Cox hazard regression analysis and provided as static and dynamic network-based nomograms.

Results

The primary site, tumor grade, surgery of the primary site, T stage, N stage, and M stage were determined to be independent prognostic factors for CSS and were subsequently included in construction of the nomogram. CSS was accurately estimated using the nomogram at 1, 3, and 5 years. The areas under the curve (AUCs) for the training group at 1, 3, and 5 years were 0.816, 0.853, and 0.863, respectively. Following internal validation, these values were 0.817, 0.851, and 0.861. Further, the AUC of the nomogram was much greater than that of American Joint Committee on Cancer (AJCC) or SEER staging. Moreover, the anticipated and actual CSS values were in good agreement based on decision curves and time-calibrated plots. Then, patients from the two subgroups were divided into high- and low-risk groups based on this nomogram. The survival rate of high-risk patients was considerably lower than that of low-risk patients, according to Kaplan-Meier (K-M) curves (p<0.0001).

Conclusions

A reliable and convenient nomogram in the form of a static nomogram or an online calculator was constructed and validated to assist physicians in quantifying the probability of CSS in GAC patients.

Tumor Microenvironment, Clinical Features, and Advances in Therapy for Bone Metastasis in Gastric Cancer

Gastric cancer (GC) is one of the most malignant neoplasms worldwide, accounting for about 770,000 deaths in 2020. The incidence of gastric cancer bone metastasis (GC-BM) is low, about 0.9–13.4%, and GC patients develop GC-BM because of a suitable bone microenvironment. Osteoblasts, osteoclasts, and tumor cells interact with each other, secreting cytokines such as PTHrP, RANK-L, IL-6, and other growth factors that disrupt the normal bone balance and promote tumor growth. The functions and numbers of immune cells in the bone microenvironment are continuously inhibited, resulting in bone balance disorder due to the cytokines released from destroyed bone and growing tumor cells. Patients with GC-BM are generally younger than 65 years old and they often present with a later stage of the disease, as well as more aggressive tumors. They usually have shorter overall survival (OS) because of the occurrence of skeletal-related events (SREs) and undetected bone destruction due to the untimely bone inspection. Current treatments of GC-BM focus mainly on gastric cancer and SRE-related treatment. This article reviews the clinical features, possible molecular pathogeneses, and the most commonly used diagnostic methods and treatments of bone metastasis in gastric cancer.

Combination treatment with trastuzumab and crizotinib in metastatic gastric cancer harboring Her-2 amplification and c-MET amplification: A case report

RATIONALE

Metastatic gastric cancer patients with poor eastern cooperative oncology group performance status (PS) (≥3) were lack of effective anti-tumor strategies. They always lived with poor PS, severe and multiple symptoms, and usually resulted in extremely limited survival time. Herein, we reported a patient diagnosed with gastric cancer metastasized to multiple bones, along with lymphangitis carcinomatosa in lungs, harboring Her-2 and c-MET amplification with poor PS, positively responded to combinational therapy with trastuzumab and crizotinib.

PATIENT CONCERNS

The patient complained of persistent cough and fatigue for 2 months, otherwise, she denied smoking, alcohol history, or any other medical or family history.

DIAGNOSIS

With the biopsy results from gastroscopy, as well as computer tomography for chest and abdomen, the patient was diagnosed as gastric adenocarcinoma, with metastasis on lungs, left adrenal gland, retroperitoneal lymph nodes, and multiple bones.

INTERVENTIONS

Because of the poor PS (PS = 3), as well as Her-2 and c-MET amplification, the patient received combination treatment with trastuzumab and crizotinib as salvage strategy.

OUTCOMES

After 2 months' exposure of trastuzumab and crizotinib, symptoms including persistent cough, and chest distress were alleviated significantly. Simultaneously, chest computer tomography showed significant dissipation of lymphangitis carcinomatosa, as well as apparent reduction of pleural effusion. No adverse reactions including nausea, vomiting, diarrhea, or hypertension was observed during the following 2 months.

LESSONS

The present case suggested that combinational therapy with trastuzumab and crizotinib might be effective in metastatic gastric cancer patients harboring Her-2 and c-MET amplification, even with a poor PS. It was also implied that gene sequencing might be valuable, especially in patients with limited treatment strategies.

Bone Metastases from Gastric Cancer: What We Know and How to Deal with Them

Gastric cancer (GC) is the third cause of cancer-related death worldwide; the prognosis is poor especially in the case of metastatic disease. Liver, lymph nodes, peritoneum, and lung are the most frequent sites of metastases from GC; however, bone metastases from GC have been reported in the literature. Nevertheless, it is unclear how the metastatic sites may affect the prognosis. In particular, knowledge about the impact of bone metastases on GC patients’ outcome is scant, and this may be related to the rarity of bone lesions and/or their underestimation at the time of diagnosis. In fact, there is still a lack of specific recommendation for their detection at the diagnosis. Then, the majority of the evidences in this field came from retrospective analysis on very heterogeneous study populations. In this context, the aim of this narrative review is to delineate an overview about the evidences existing about bone metastases in GC patients, focusing on their incidence and biology, the prognostic role of bone involvement, and their possible implication in the treatment choice.

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.