The risk and prognostic factors for brain metastases in esophageal cancer patients: an analysis of the SEER database

Incidence of brain metastasis according to patient race and primary cancer origin: a systematic review

PURPOSE

A systematic review was conducted to investigate differences in incidence and primary origin of synchronous brain metastasis (sBM) in varying racial groups with different primary cancers.

METHODS

Adhering to PRISMA 2020 guidelines a search was conducted using PubMed and Ovid databases for publications from January 2000 to January 2023, with search terms including combinations of "brain metastasis," "race," "ethnicity," and "incidence." Three independent reviewers screened for inclusion criteria encompassing studies clearly reporting primary cancer sites, patient demographics including race, and synchronous BM (sBM) incidence.

RESULTS

Of 806 articles, 10 studies comprised of mainly adult patients from the United States met final inclusion for data analysis. Higher sBM incidence proportions were observed in American Indian/Alaska native patients for primary breast (p < 0.001), colorectal (p = 0.015), and esophageal cancers (p = 0.024) as well as in Asian or Pacific islanders for primary stomach (p < 0.001), thyroid (p = 0.006), and lung/bronchus cancers (p < 0.001) yet higher proportions in White patients for malignant melanoma (p < 0.001). Compared to White patients, Black patients had higher sBM incidence likelihood in breast cancer (OR = 1.27, p = 0.01) but lower likelihood in renal (OR = 0.46, p < 0.001) and esophageal cancers (OR = 0.31, p = 0.005). American Indian/Alaska native patients had a higher sBM likelihood (OR = 3.78, p = 0.004) relative to White patients in esophageal cancer.

CONCLUSIONS

These findings reveal several comparative racial differences in sBM incidence arising from different primary cancer origins, underscoring a need for further research to explain these variations. Identifying the factors contributing to these disparities holds the potential to promote greater equity in oncological care according to cancer type.

Surgical Management of Brain Metastasis from Esophageal Cancer: A Systematic Review and Single-Center Experience

BACKGROUND

Brain metastases from esophageal cancer (BMEC) are rare and aggressive, with limited literature on optimal treatment modalities and a standard of care yet to be established. The objective of this study was to systematically review existing literature and perform a retrospective analysis of our institution's patients to evaluate the influence of different treatment modalities on patient outcomes.

METHODS

A systematic review of the literature following Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and a retrospective review of our institutional experience with BMEC were both conducted. Data based on mean survival,histology, metastasis location, and treatment modality were abstracted.

RESULTS

A total of 48 studies representing 136 patients with BMEC were identified, in addition to the 11 patients treated at our institution. There were a total of 100 males (12 unreported), with a median age of 62.2 at diagnosis in our systematic review, along with 8 males with a median age of 62 in our institutional review. Collectively, survival rates observed based on histology were not similar (squamous cell carcinoma: 9.2 months, adenocarcinoma: 13.4 months), however, based on treatment modalities (surgery: 11.6 months, radiation: 10.4 months, chemotherapy: 12.3 months), and metastasis location (supratentorial: 10.5 months, infratentorial: 9.9 months), the survival times were comparable.

CONCLUSIONS

Our review suggests that causes of death were often independent of brain metastases highlighting the need for further studies on early detection and prevention of primary esophageal cancer, as well as improved treatment modalities for BMECs.

A rare case of tumor-to-tumor metastasis of esophageal adenocarcinoma into meningioma

Tumor-to-tumor metastasis is a known but rare occurrence and is characterized as 2 distinct tumor types in same anatomic location. We present a rare case of intracranial tumor-to-tumor metastasis of esophageal adenocarcinoma into meningioma. Our case emphasizes the rare occurrence of intracranial tumor-to-tumor metastasis and importance of histology and immunohistochemical analysis in distinguishing between metastasis and meningioma, especially when faced with ambiguous demarcation. Awareness of this occurrence is crucial, given that metastases might be the initial indication of an underlying tumor and it can impact the clinical management decisions.

Prediction of hepatic metastasis in esophageal cancer based on machine learning

This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/ ) for predicting the risk of hepatic metastasis in esophageal cancer.

Risk, Predictive Factors, and Nomogram of Liver Metastatic Gastroesophageal Junction Cancer: A New Study Based on the Surveillance, Epidemiology, and End Results Database

BACKGROUND AND OBJECTIVES

 Liver metastases are associated with a poor prognosis in gastroesophageal junction (GEJ) cancer patients. The high rate of liver involvement is attributed to the unique anatomical location of the GEJ, which is close to the liver. Patients with liver metastasis typically have advanced, unresectable disease and limited treatment options. Therefore, early detection and prediction are crucial to guide appropriate treatment planning and improve the outcomes for patients with GEJ cancer at risk of liver metastases. Using data from the Surveillance, Epidemiology, and End Results (SEER) database, the present study aimed to elucidate the incidence and risk factors of liver metastases in GEJ cancer patients diagnosed between 2010 and 2019.

METHODS

This research employed univariable and multivariable logistic regression models to identify risk factors for the development of liver metastases. A predictive nomogram for liver metastases was developed and assessed. Patients' overall survival (OS) with liver metastases was analyzed using the Kaplan-Meier method.

RESULTS

The study included 1,322 eligible patients with GEJ cancer, 181 (13.6%) of whom were diagnosed with liver metastases. The median overall survival (mOS) for patients with liver metastasis was approximately eight months, compared to a shorter mOS for patients without liver metastasis (P < 0.001). Factors significantly associated with the occurrence of liver metastasis included N3 stage (OR: 1.84; 95% CI: (1.13-2.96); P < 0.001), surgery (OR: 0.09; 95% CI: (0.06-0.14); P < 0.001), lung metastasis (OR: 2.88; 95% CI: (1.78-4.63); P < 0.001), chemotherapy (OR: 0.54; 95% CI: (0.32-0.87); P < 0.001), and radiation therapy (OR: 0.33; 95% CI: (0.25-0.45); P < 0.001). The nomogram demonstrated good performance in predicting liver metastases in GEJ cancer patients (c-index: 0.820).

CONCLUSIONS

The study identified lymph node status, surgical, lung metastasis, chemotherapy, and radiation as important predictors of outcomes for patients with GEJ cancer. The developed nomogram might be a valuable tool for predicting the risk of liver metastases in GEJ cancer patients, potentially enhancing clinical decision-making processes. By predicting the risk of liver metastasis occurrence, clinicians might intervene in patients with GEJ cancers as early as possible.

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

Nomograms for Predicting Risk and Survival of Esophageal Cancer Lung Metastases: a SEER Analysis

Background: The overall survival rate is notably low for esophageal cancer patients with lung metastases (LM), presenting significant challenges in their treatment. Methods: Through the Surveillance, Epidemiology, and End Results (SEER) program, individuals diagnosed with esophageal cancer between 2010 and 2015 were enrolled. Based on whether esophageal cancer metastasized to the lungs, we used propensity score matching (PSM) to balance correlated variables. Propensity score matching was a critical step in our study that helped to minimize the impact of possible confounders on the study results. We balanced variables related to lung metastases using the PSM method to ensure more accurate comparisons between the study and control groups. Specifically, we performed PSM in the following steps. First, we performed a univariate logistic regression analysis to screen for variables associated with lung metastasis. For each patient, we calculated their propensity scores using a logistic regression model, taking into account several factors, including gender, T-stage, N-stage, surgical history, radiotherapy history, chemotherapy history, and bone/brain/liver metastases. We used a 1:1 matching ratio based on the propensity score to ensure more balanced baseline characteristics between the study and control groups after matching. After matching, we validated the balance of baseline characteristics to ensure that the effect of confounders was minimized. We used logistic regression to identify risk variables for LM, while Cox regression was used to find independent prognostic factors. We then created nomograms and assessed their accuracy using the calibration curve, receiver operating curves (ROC), and C index. Results: In the post-PSM cohort, individuals diagnosed with LM experienced a median overall survival (OS) of 5.0 months (95% confidence interval [CI] 4.3-5.7), which was significantly lower than those without LM (P<0.001). LM has been associated to sex, T stage, N stage, surgery, radiation, chemotherapy, and bone/brain/liver metastases. LM survival was affected by radiation, chemotherapy, and bone/liver metastases. The nomograms' predictive power was proved using the ROC curve, C-index, and validation curve. Conclusion: Patients with LM have a worse chance of surviving esophageal cancer. The nomograms can effectively predict the risk and prognosis of lung metastases from esophageal cancer.

Leptomeningeal carcinomatosis and brain metastases in gastroesophageal carcinoma: a real-world analysis of clinical and pathologic characteristics and outcomes

BACKGROUND

Brain metastasis (BrM) and Leptomeningeal Carcinomatosis (LMC) are uncommon complications in gastroesophageal carcinoma (GEC) patients. These patients have a poor prognosis and are challenging to treat. We described the clinicopathologic features and outcomes in the largest cohort of Central Nervous System (CNS) metastasis in GEC patients.

METHODS

single-center retrospective study of GEC treated from 2007 to 2021. Clinicopathologic characteristics and treatment modalities were reviewed. Survival was calculated from the date of CNS diagnosis until date of death/last follow-up using the Kaplan-Meier method. A multivariable Cox proportional hazards regression model was used.

RESULTS

Of 3283 GEC patients, 100 (3.04%) were diagnosed with BrM and 20 with LMC (0.61%). Patients with known human epidermal growth factor receptor 2 (HER2) status (N = 48), 60% were HER2 positive (defined as IHC 3 + or IHC 2+/FISH+). Among LMC patients most were signet-ring subtype (85%), and only 15% (2/13) were HER2 positive. Median survival was 0.7; 3.8; and 7.7 months in BrM patients treated with best supportive care, radiation, and surgery, respectively (p < 0.001). In LMC, median survival was 0.7 month in patients who had best supportive care (7/19) and 2.8 months for those who had whole brain radiation therapy (p = 0.015). Multivariate analysis showed worse outcomes in ECOG ≥ 2 (p = 0.002), number of BrM ≥ 4 (p < 0.001) and number of metastatic sites (p = 0.009).

CONCLUSION

HER2 expression were enriched in patients with BrM, while it is uncommon in LMC. Patients treated with surgery followed by radiation had an improved OS in BrM and WBRT benefited patients with LMC.

Clinical features and outcomes of advanced HER2+ esophageal/GEJ cancer with brain metastasis

BACKGROUND

Brain metastasis (BRM) is uncommon in gastroesophageal cancer. As such, clinicopathologic and molecular determinants of BRM and impact on clinical outcome remain incompletely understood.

METHODS

We retrospectively analyzed clinicopathologic data from advanced esophageal/gastroesophageal junction (E/GEJ) patients at Johns Hopkins from 2003 to 2021. We investigated the association between several clinical and molecular features and the occurrence of BRM, with particular focus on human epidermal growth factor receptor 2 (HER2) overexpression. Survival outcomes and time to BRM onset were also evaluated.

RESULTS

We included 515 patients with advanced E/GEJ cancer. Tumors were 78.3% esophageal primary, 82.9% adenocarcinoma, 31.0% HER2 positive. Cumulative incidence of BRM in the overall cohort and within HER2+ subgroup was 13.8% and 24.3%, respectively. HER2 overexpression was associated with increased risk of BRM [odds ratio 2.45; 95% confidence interval (CI) 1.10-5.46]. On initial presentation with BRM, 50.7% had a solitary brain lesion and 11.3% were asymptomatic. HER2+ status was associated with longer median time to onset of BRM (14.0 versus 6.3 months, P < 0.01), improved median progression free survival on first-line systemic therapy (hazard ratio 0.35, 95% CI 0.16-0.80), and improved median overall survival (hazard ratio 0.20, 95% CI 0.08-0.54) in patients with BRM.

CONCLUSION

HER2 overexpression identifies a gastroesophageal cancer molecular subtype that is significantly associated with increased risk of BRM, though with later onset of BRM and improved survival likely reflecting the impact of central nervous system-penetrant HER2-directed therapy. The prevalence of asymptomatic and solitary brain lesions suggests that brain surveillance for HER2+ patients warrants prospective investigation.

Prevalence and risk factors of bone metastasis and the development of bone metastatic prognostic classification system: a pan-cancer population study

BACKGROUND

The prevalence of bone metastasis (BM) varies among primary cancer patients, and it has a significant impact on prognosis. However, there is a lack of research in this area. This study aims to explore the clinical characteristics, prevalence, and risk factors, and to establish a prognostic classification system for pan-cancer patients with BM.

METHODS

The data obtained from the Surveillance, Epidemiology and End Results database were investigated. The prevalence and prognosis of patients with BM were analyzed. Hierarchical clustering was used to develop a prognostic classification system.

RESULTS

From 2010 to 2019, the prevalence of BM has increased by 41.43%. BM most commonly occurs in cancers that originate in the adrenal gland, lung and bronchus and overlapping lesion of digestive systems. Negative prognostic factors included older age, male sex, poorer grade, unmarried status, low income, non-metropolitan living, advanced tumor stages, previous chemotherapy, and synchronous liver, lung, and brain metastasis. Three categories with significantly different survival time were identified in the classification system.

CONCLUSIONS

The clinical features, prevalence, risk factors, and prognostic factors in pan-cancer patients with BM were investigated. A prognostic classification system was developed to provide survival information and aid physicians in selecting personalized treatment plans for patients with BM.

Local Control and Survival Outcomes After Stereotactic Radiosurgery for Brain Metastases From Gastrointestinal Primaries: An International Multicenter Analysis

BACKGROUND

There are limited data regarding outcomes for patients with gastrointestinal (GI) primaries and brain metastases treated with stereotactic radiosurgery (SRS).

OBJECTIVE

To examine clinical outcomes after SRS for patients with brain metastases from GI primaries and evaluate potential prognostic factors.

METHODS

The International Radiosurgery Research Foundation centers were queried for patients with brain metastases from GI primaries managed with SRS. Primary outcomes were local control (LC) and overall survival (OS). Kaplan-Meier analysis was used for univariate analysis (UVA) of prognostic factors. Factors significant on UVA were evaluated with a Cox multivariate analysis proportional hazards model. Logistic regressions were used to examine correlations with RN.

RESULTS

We identified 263 eligible patients with 543 brain metastases. Common primary sites were rectal (31.2%), colon (31.2%), and esophagus (25.5%) with a median age of 61.6 years (range: 37-91.4 years) and a median Karnofsky performance status (KPS) of 90% (range: 40%-100%). One-year and 2-year LC rates were 83.5% (95% CI: 78.9%-87.1%) and 73.0% (95% CI: 66.4%-78.5%), respectively. On UVA, age >65 years ( P = .001), dose <20 Gy ( P = .006) for single-fraction plans, KPS <90% ( P < .001), and planning target volume ≥2cc ( P = .007) were associated with inferior LC. All factors other than dose were significant on multivariate analysis ( P ≤ .002). One-year and 2-year OS rates were 68.0% (95% CI: 61.5%-73.6%) and 31.2% (95% CI: 24.6%-37.9%), respectively. Age > 65 years ( P = .006), KPS <90% ( P = .005), and extracranial metastases ( P = .05) were associated with inferior OS.

CONCLUSION

SRS resulted in comparable LC with common primaries. Age and KPS were associated with both LC and OS with planning target volume and extracranial metastases correlating with LC and OS, respectively. These factors should be considered in GI cancer patient selection for SRS.

Nomogram for predicting survival after lymphatic metastasis in esophageal cancer: A SEER analysis

Lymphatic metastasis (LM) is a significant mechanism for the spread of esophageal cancer (EC) and predicts the poor prognosis of EC patients. This research aimed to assess the survival of patients with LM from EC by developing a nomogram. In this retrospective study, EC patients with LM from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were divided by year of diagnosis into a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were employed to determine the prognostic factors of LM, and a nomogram was constructed. The discrimination and calibration of the nomogram were compared by the C-index, area under the curve value, and calibration plots. The survival time difference was compared using Kaplan-Meier curves. A total of 11,695 patients with EC were included in this analysis. LM occurred in 56.5% (n = 6614) of EC patients. In the post-propensity score matching (PSM) cohort, patients with LM had significantly lower median overall survival (OS) than those without LM. Multivariate Cox regression was used to identify the eleven independent prognostic factors. The C-index was 0.709 in both the training and test sets, revealing the good predictive performance of the nomogram. Based on the results of calibration plots and the receiver operating characteristic (ROC) curve, we demonstrate the great performance of the prognostic model. The survival time of EC patients with LM was remarkably lower than that of EC patients without LM. The nomogram model established in this study can precisely predict the survival of EC patients with LM.

Stereotactic radiosurgery for patients with brain metastases from gastroesophageal cancers

BACKGROUND

The prognosis of patients with brain metastases from gastroesophageal (GE) cancers remains unclear despite recent advances in systemic therapies. The authors present a large single-institution experience in the use of stereotactic radiosurgery (SRS).

METHODS

A retrospective review of 71 GE cancer patients (64 male, 90.14%) who underwent Gamma Knife SRS was conducted. Overall, 243 brain metastases were treated and the median number of metastases per patient was 2 (range:1-21). The median age at SRS was 66 years (range: 26-85) and the median treatment day KPS was 80 (range: 50-100). The median cumulative tumor volume was 6.7 cc (range: 0.27-104.76) and the median single-session margin dose was 18 Gy (range: 12-20).

RESULTS

The median overall survival after SRS was 7 months (range: 1-64). At last follow up, 54 (76.06%) patients were deceased, 8 of whom (14.81%) expired secondary to their intracranial metastases. Four patients (5.63%) experienced local tumor progression at a median time of 8 months (range: 2-13) after SRS. Ten patients (14%) experienced new remote tumor development at a median time of 4 months (range: 0-14) after SRS. Whole-brain radiation therapy (2 patients, 20%) and repeat SRS (8 patients, 80%) were used for newly developed tumors. The incidence of transient adverse radiation effects was 8.45%.

CONCLUSIONS

In this study, the 12-month local tumor control rate was 90%. Incidences of adverse radiation effect rates were rare. The median overall survival of 7 months indicates the poor prognosis of patients with brain spread of their GE cancer.

Prediction of distant organ metastasis and overall survival of lung cancer patients: a SEER population-based cohort study

Background

Distant organ metastasis is a common event in lung cancer (LC). However, the preferential metastatic pattern of different pathological types of LC and its effect on prognosis have not been comprehensively elucidated. This study aimed to explore the distant metastasis pattern and construct nomograms predicting the metastasis and survival of LC patients using the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

LC data were downloaded from the SEER database to conduct logistic regression and investigate risk factors for developing organ metastasis. A Cox regression analysis was conducted to investigate prognostic factors of LC. A Kaplan-Meier analysis was used to estimate overall survival outcomes. Nomograms were constructed to predict the probability of organ metastasis and the 1-, 3- and 5-year survival probability of LC patients. Receiver operating characteristic curves were used to evaluate the diagnostic accuracy of the nomograms. All statistical analyses were conducted within R software.

Results

The liver is the most common metastatic organ of small cell carcinoma. The brain is the most likely metastasis site of large cell carcinoma, and bone is the most likely metastasis site for squamous cell carcinoma and adenocarcinoma. Patients with triple metastases (brain-bone-liver) have the worst prognosis, and for nonsquamous carcinoma with single organ metastasis, liver metastases conferred the worst prognosis. Our nomograms based on clinical factors could predict the metastasis and prognosis of LC patients.

Conclusion

Different pathological types of LC have different preferential metastatic sites. Our nomograms showed good performance in predicting distant metastasis and overall survival. These results will provide a reference for clinicians and contribute to clinical evaluations and individualized therapeutic strategies.

Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study

Background

Metastasis in the lungs is common in patients with rectal cancer, and it can have severe consequences on their survival and quality of life. Therefore, it is essential to identify patients who may be at risk of developing lung metastasis from rectal cancer.

Methods

In this study, we utilized eight machine-learning methods to create a model for predicting the risk of lung metastasis in patients with rectal cancer. Our cohort consisted of 27,180 rectal cancer patients selected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017 for model development. Additionally, we validated our models using 1118 rectal cancer patients from a Chinese hospital to evaluate model performance and generalizability. We assessed our models' performance using various metrics, including the area under the curve (AUC), the area under the precision-recall curve (AUPR), the Matthews Correlation Coefficient (MCC), decision curve analysis (DCA), and calibration curves. Finally, we applied the best model to develop a web-based calculator for predicting the risk of lung metastasis in patients with rectal cancer.

Result

Our study employed tenfold cross-validation to assess the performance of eight machine-learning models for predicting the risk of lung metastasis in patients with rectal cancer. The AUC values ranged from 0.73 to 0.96 in the training set, with the extreme gradient boosting (XGB) model achieving the highest AUC value of 0.96. Moreover, the XGB model obtained the best AUPR and MCC in the training set, reaching 0.98 and 0.88, respectively. We found that the XGB model demonstrated the best predictive power, achieving an AUC of 0.87, an AUPR of 0.60, an accuracy of 0.92, and a sensitivity of 0.93 in the internal test set. Furthermore, the XGB model was evaluated in the external test set and achieved an AUC of 0.91, an AUPR of 0.63, an accuracy of 0.93, a sensitivity of 0.92, and a specificity of 0.93. The XGB model obtained the highest MCC in the internal test set and external validation set, with 0.61 and 0.68, respectively. Based on the DCA and calibration curve analysis, the XGB model had better clinical decision-making ability and predictive power than the other seven models. Lastly, we developed an online web calculator using the XGB model to assist doctors in making informed decisions and to facilitate the model's wider adoption (https://share.streamlit.io/woshiwz/rectal_cancer/main/lung.py).

Conclusion

In this study, we developed an XGB model based on clinicopathological information to predict the risk of lung metastasis in patients with rectal cancer, which may help physicians make clinical decisions.

Survival Prediction Models for Ovarian Cancer Patients with Lung Metastasis: A Retrospective Cohort Study Based on SEER Database

To develop a random forest prediction model for the and short- and long-term survival of ovarian cancer patients with lung metastasis. This retrospective cohort study enrolled primary ovarian cancer patients with lung metastasis from the surveillance, epidemiology and end results (SEER) database (2010–2015). All eligible women were randomly divided into the training (n = 1357) and testing set (n = 582). The outcomes were 1-, 3- and 5-year survival. Predictive factors were screened by random forest analysis. The prediction models for predicting the 1-, 3- and 5-year survival were conducted using the training set, and the internal validation was carried out by the testing set. The performance of the models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). The subgroups based on the pathological classification further assessed the model’s performance. Totally 1345 patients suffered from death within 5 years. The median follow-up was 7.00 (1.00, 21.00) months. Age at diagnosis, race, marital status, tumor size, tumor grade, TNM stage, brain metastasis, liver metastasis, bone metastasis, etc. were predictors. The AUCs of the prediction model for the 1-, 3-, 5-year survival in the testing set were 0.849 [95% confidence interval (CI): 0.820–0.884], 0.789 (95% CI 0.753–0.826) and 0.763 (95% CI 0.723–0.802), respectively. The results of subgroups on different pathological classifications showed that the AUCs of the model were over 0.7. This random forest model performed well predictive ability for the short- and long-term survival of ovarian cancer patients with lung metastasis, which may be beneficial to identify high-risk individuals for intelligent medical services.

Risk and prognostic factors of brain metastasis in lung cancer patients: a Surveillance, Epidemiology, and End Results population‑based cohort study

BACKGROUND

Brain is a common metastasis site in lung cancer patients. However, homogeneous and heterogeneous risk/prognostic factors of brain metastasis for lung cancer patients have not been comprehensively elucidated. This study aimed to explore the brain metastasis risk and prognostic factors in lung cancer patients using the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

Lung cancer data were downloaded from SEER database to investigate risk factors for developing brain metastasis using logistic regression analysis. Univariate and multivariate Cox analyses were used to identify potential prognostic factors. Kaplan-Meier analysis was conducted to evaluate the survival. Propensity score matching was conducted to eliminate baseline differences between two groups.

RESULTS

A total of 10 818 (14.1%) patients with brain metastasis were diagnosed among 76 483 lung cancer patients. For non-small-cell lung carcinoma (NSCLC), distant liver/bone/lymph node metastases, higher T, N stages were risk factors. Black race, bone metastases and distant lymph node metastases and T4 were brain metastasis risk factors for SCLC patients. Cox analysis suggested that older age, male, primary lesion at main bronchus, liver/ bone/distant lymph node metastases, T2-4, N1-3, no surgery/chemotherapy/radiotherapy were associated with worse prognosis of NSCLC-brain metastasis patients. Age older than 80, liver/bone metastases, without radiotherapy and chemotherapy were associated with worse prognosis of SCLC-brain metastasis patients. Surgery of primary site could prolong the overall survival (OS) of NSCLC patients with brain metastasis, but not SCLC.

CONCLUSION

In this study, we analyzed the homogeneous and heterogeneous risk/prognostic factors of brain metastasis in lung cancer patients. What is more, our results showed that surgery of primary site was associated with longer OS of NSCLC patients with brain metastasis.

A population-based investigation: How to identify high-risk T1-2N0 esophageal cancer patients?

Purpose

Newly diagnosed T1-2N0 esophageal cancer (EC) is generally deemed as early local disease, with distant metastases (DM) easily overlooked. This retrospective study aimed to describe the metastatic patterns, identify risk factors and established a risk prediction model for DM in T1-2N0 EC patients.

Methods

A total of 4623 T1-2N0 EC patients were identified in the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2018. Multivariable logistic regression was used to identify risk factors for DM. A nomogram was developed for presentation of the final model.

Results

Of 4623 T1-2N0 patients, 4062 (87.9%) had M0 disease and 561 (12.1%) had M1 disease. The most common metastatic site was liver (n = 156, 47.3%), followed by lung (n = 89, 27.0%), bone (n = 70, 21.2%) and brain (n = 15, 4.5%). Variables independently associated with DM included age at diagnosis, gender, tumor grade, primary site, tumor size and T stage. A nomogram based on the variables had a good predictive accuracy (area under the curve: 0.750). Independent risk factors for bone metastases (BoM), brain metastases (BrM), liver metastases (LiM) and lung metastases (LuM) were identified, respectively.

Conclusions

We identified independent predictive factors for DM, as well as for BoM, BrM, LiM and LuM. Above all, a practical and convenient nomogram with a great accuracy to predict DM probability for T1-2N0 EC patients was established.

Construction and validation of a nomogram model to predict the overall survival rate of esophageal cancer patients receiving neoadjuvant chemotherapy: A population-based study

Introduction

The development of neoadjuvant chemotherapy(nCT) improves the overall survival (OS) of patients with esophageal cancer(EC). The aim of this study was to determine the independent prognostic factors of EC patients receiving nCT, and to construct a nomogram model for predicting OS.

Method

This retrospective analysis was conducted from the National Cancer Institute's Surveillance Epidemiology and End Results, Clinicopathological data of patients with EC who received nCT from 2004 to 2015. The included patients were randomly divided into the training cohort and the validation cohort. Univariate and multivariate Cox proportional hazards models were used to analyze the patients in the training cohort to determine the independent prognostic factors. Based on the independent prognostic variables, nomogram models for 1-year, 2-year and 3-year OS were constructed. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the discriminative ability. The calibration curves, decision curve analysis (DCA) and Kaplan-Meier (K-M) survival analysis were used to evaluate the predictive accuracy and clinical application value.

Results

A total of 2,493 patients were enrolled, with 1,748 patients in the training cohort and 745 patients in the validation cohort. Gender, marital status, tumor pathological grade, T stage, N stage, and M stage were identified as independent prognostic factor (P < 0.05). A novel nomogram model was constructed. ROC curve analysis revealed that the model had moderate predictive performance, which was better than that of the AJCC TNM staging system.The calibration curves showed a high agreement between the actual observed values and the predicted values. The DCA suggested that the newly constructed prediction model had good clinical application value. K-M survival analysis showed that the model was helpful to accurately distinguish the prognosis of patients with different risk levels.

Conclusions

Gender, tumor pathological grade, marital status, T stage, N stage and M stage were identified as independent prognostic factors for overall survival of patients with esophageal cancer who received neoadjuvant chemotherapy. A nomogram prediction model was established, which was helpful to accurately and reliably predict the overall survival rate of patients with esophageal cancer who received neoadjuvant chemotherapy at 1, 2 and 3 years.

Is it necessary for patients with potentially resectable esophageal squamous cell cancer to receive routine preoperative brain MRI/CT?

BACKGROUND

This study aimed to investigate the value and efficiency of routine brain MRI or CT in the preoperative workup for patients with potentially resectable (cT_1-4a N_0-3 ) thoracic esophageal squamous cell cancer (ESCC).

METHODS

This was a prospective cross-sectional clinical trial (ChiCTR1800020304). A total of 385 patients with potentially resectable (cT_1-4a N_0-3 ) thoracic ESCC diagnosed from October 2018 to August 2020 were included. Plain brain MRI or CT was performed preoperatively to detect brain metastases (BrM). The primary endpoint was BrM detected by imaging.

RESULTS

Of all 385 patients, the rate of positive brain MRI/CT findings was 1% (n = 4). BrM Patients received chemoradiotherapy, and the median OS was 6 months (95% CI: 4.303-7.697). All 381 remaining patients with initial negative brain MRI/CT diagnosis revealed no brain-associated symptoms within 6 months. The median follow-up for patients without BrM was 20 months (range, from 6 to 32). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of plain MRI or CT to detect BrM were all 100%.

CONCLUSIONS

Preoperative plain MRI or CT is an effective method to detect BrM for potentially resectable (cT_1-4a N_0-3 ) thoracic ESCC. However, due to the low incidence, the value of brain MRI/CT as a routinely preoperational examination in potentially resectable esophageal squamous cell cancer is rather limited. Therefore, preoperative brain MRI/CT should not be recommended as a routine preoperative examination for ESCC.

The risk and prognostic factors for liver metastases in esophageal cancer patients: A large-cohort based study

BACKGROUND

This retrospective study aimed to explore risk factors for liver metastases (LiM) in patients with esophageal cancer (EC) and to identify prognostic factors in patients initially diagnosed with LiM.

METHODS

A total of 28 654 EC patients were retrieved from the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2018. A multivariate logistic regression model was utilized to identify risk factors for LiM. A Cox regression model was used to identify prognostic factors for patients with LiM.

RESULTS

Of 28 654 EC patients, 4062 (14.2%) had LiM at diagnosis. The median overall survival (OS) for patients with and without LiM was 6.00 (95% CI: 5.70-6.30) months and 15.00 (95% CI: 14.64-15.36) months, respectively. Variables significantly associated with LiM included gender, age, tumor site, histology, tumor grade, tumor size, clinical T stage, clinical N stage, bone metastases (BoM), brain metastases (BrM) and lung metastases (LuM). Variables independently predicting survival for EC patients with LiM were age, histology, tumor grade, BoM, BrM, LuM, and chemotherapy. A risk prediction model and two survival prediction models were then constructed revealing satisfactory predictive accuracy.

CONCLUSIONS

Based on the largest known cohort of EC, independent predictors of LiM and prognostic indicators of survival for patients with LiM were identified. Two models for predicting survival as well as a risk prediction model were developed with robust predictive accuracy.

Outcomes and Molecular Features of Brain Metastasis in Gastroesophageal Adenocarcinoma

IMPORTANCE

Brain metastasis (BrM) in gastroesophageal adenocarcinoma (GEA) is a rare and poorly understood phenomenon associated with poor prognosis.

OBJECTIVES

To examine the clinical and genomic features of patients with BrM from GEA and evaluate factors associated with survival.

DESIGN, SETTING, AND PARTICIPANTS

In this single-institution retrospective cohort study, 68 patients with BrM from GEA diagnosed between January 1, 2008, and December 31, 2020, were identified via review of billing codes and imaging reports from the electronic medical record with follow-up through November 3, 2021. Genomic data were derived from the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets clinical sequencing platform.

EXPOSURES

Treatment with BrM resection and/or radiotherapy.

MAIN OUTCOMES AND MEASURES

Overall survival after BrM diagnosis.

RESULTS

Sixty-eight patients (median age at diagnosis, 57.4 years [IQR, 49.8-66.4 years]; 59 [86.8%] male; 55 [85.9%] White) participated in the study. A total of 57 (83.8%) had primary tumors in the distal esophagus or gastroesophageal junction. Median time from initial diagnosis to BrM diagnosis was 16.9 months (IQR, 8.5-27.7 months). Median survival from BrM diagnosis was 8.7 months (95% CI, 5.5-11.5 months). Overall survival was 35% (95% CI, 25%-48%) at 1 year and 24% (95% CI, 16%-37%) at 2 years. In a multivariable analysis, an Eastern Cooperative Oncology Group performance status of 2 or greater (hazard ratio [HR], 4.66; 95% CI, 1.47-14.70; P = .009) and lack of surgical or radiotherapeutic intervention (HR, 7.71; 95% CI, 2.01-29.60; P = .003) were associated with increased risk of all-cause mortality, whereas 3 or more extracranial sites of disease (HR, 1.85; 95% CI, 0.64-5.29; P = .25) and 4 or more BrMs (HR, 2.15; 95% CI, 0.93-4.98; P = .07) were not statistically significant. A total of 31 patients (45.6%) had ERBB2 (formerly HER2 or HER2/neu)-positive tumors, and alterations in ERBB2 were enriched in BrM relative to primary tumors (8 [47.1%] vs 7 [20.6%], P = .05), as were alterations in PTPRT (7 [41.2%] vs 4 [11.8%], P = .03).

CONCLUSIONS AND RELEVANCE

This study suggests that that a notable proportion of patients with BrM from GEA achieve survival exceeding 1 and 2 years from BrM diagnosis, a more favorable prognosis than previously reported. Good performance status and treatment with combination surgery and radiotherapy were associated with the best outcomes. ERBB2 positivity and amplification as well as PTPRT alterations were enriched in BrM tissue compared with primary tumors; therefore, further study should be pursued to identify whether these variables represent genomic risk factors for BrM development.