Risk and prognostic nomograms for hepatocellular carcinoma with newly-diagnosed pulmonary metastasis using SEER data

Prevalence, management and outcomes of pulmonary metastases in hepatocellular carcinoma: a systematic review and meta-analysis

BACKGROUND

Hepatocellular carcinoma (HCC) presents a significant global health burden, with varying survival rates across regions. The presence of pulmonary metastases (PM) in HCC predicts a poorer prognosis, yet the global understanding of the progression and management is limited.

METHODS

This study aims to systematically review the burden of PM in HCC, document current treatment approaches, and evaluate treatment effectiveness through meta-analysis. A comprehensive literature search was conducted across multiple databases. Articles were screened, and data extraction was performed independently by two reviewers. Statistical analyses were conducted to synthesise data and assess treatment outcomes.

RESULTS

A total of 82 articles were included, comprising a population of 3241 participants with documented PM. Our analysis revealed a linear relationship between the HCC population size and the occurrence of PM (p < 0.005). Surgical intervention demonstrated the lowest hazard ratio (0.128) and significantly improved survival rates compared to other treatment modalities. However, data quality limitations underscore the need for further research to delineate patient subsets benefitting from surgical intervention for PM.

CONCLUSION

Our findings advocate for continued investigation into PM management strategies, notably the role of surgical resection alongside systemic therapies, to improve outcomes in HCC patients with PM.

Development and validation of a diagnostic and prognostic model for bone metastasis of intrahepatic cholangiocarcinoma: a population-based analysis

Background

Bone metastasis (BM) is a common site of metastasis in patients with intrahepatic cholangiocarcinoma (ICC), significantly impacting the quality of life and prognosis of affected individuals. This investigation aimed to assess the risk of BM development in ICC patients and to prognosticate for patients with ICC-associated BM (ICCBM) through the construction of two nomograms.

Methods

We conducted a retrospective analysis of data from 2,651 ICC patients, including 148 cases of BM, documented in the Surveillance, Epidemiology, and End Results (SEER) database spanning 2010 to 2017. Independent predictors for the occurrence of BM in ICC patients were identified via univariate and multivariate logistic regression analyses; simultaneously, independent prognostic indicators for ICCBM patients were ascertained through univariate and multivariate Cox regression analyses. The utility of the nomograms was evaluated through calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and Kaplan-Meier (KM) analysis.

Results

Independent risk factors for BM in ICC included sex, tumor size, lung metastasis, brain metastasis, and intrahepatic metastasis. For ICCBM patients, independent prognostic factors comprised age, chemotherapy, and radiotherapy. The prognostic nomogram exhibited C-indexes of 0.737 [95% confidential interval (CI): 0.682-0.792] for the training cohort and 0.696 (95% CI: 0.623-0.769) for the validation cohort. Calibration curves demonstrated strong concordance between predicted outcomes and observed events. The areas under the curve (AUC) for 3-, 6-, and 12-month cancer-specific survival (CSS) were 0.853, 0.781, and 0.739, respectively, in the training cohort, and 0.794, 0.822, and 0.780 in the validation cohort. DCA illustrated significant net benefits across a broad spectrum of threshold probabilities. KM analysis revealed 1-, 2-, and 3-year CSS rates of 23.91%, 7.55%, and 2.35%, respectively, with a median CSS of 6 months, underscoring the nomograms' capacity to distinctly stratify patients according to survival risk.

Conclusions

The development of these nomograms offers substantial clinical utility in forecasting BM risk among ICC patients and prognosticating for those with ICCBM, thereby facilitating the formulation of more efficacious treatment modalities.

Comparison of metastasis and prognosis between early-onset and late-onset hepatocellular carcinoma: A population-based study

Background

While hepatocellular carcinoma (HCC) represents a highly heterogeneous disease with variable oncogenesis mechanisms and biological features, little is understood about differences in distant metastasis (DM) and prognosis between early-onset and late-onset HCC. This study defined early-onset disease as cancer diagnosed at age younger than 50 years and aimed to present a comprehensive analysis to characterize these disparities based on age.

Methods

Information of HCC patients was retrospectively collected from the SEER database and our hospital. Patient demographics, tumor characteristics, and survival were compared between the two groups. A 1:1 propensity score matching (PSM) was adopted to adjust confounding factors. Logistic and cox analysis were utilized to explore risk factors of DM and prognosis, respectively. Besides, the survival differences were assessed by the Kaplan-Meier curve and log-rank test.

Results

In total, 19187 HCC patients obtained from the SEER database and 129 HCC patients obtained from our own center were enrolled. Among 19187 patients with HCC, 3376 were identified in the matched cohort, including 1688 early-onset patients and 1688 late-onset patients. Compared with late-onset HCC, early-onset HCC was more likely to occur in female (25.2% vs. 22.9%, P = 0.030), have large tumors (>10.0 cm, 24.1% vs. 14.6%, P = 0.000), harbor poorly differentiated/undifferentiated cancers (17.0% vs. 14.0%, P = 0.003), present advanced clinical stage (T3+T4, 33.7% vs. 28.5%; N1, 9.2% vs. 6.7%; P = 0.000), and develop DM (13.0% vs. 9.5%, P = 0.000). After adjustment for confounders by PSM, we discovered that early-onset HCC remained an independent risk factor for DM. However, combined with Kaplan-Meier curve and cox analysis, early-onset HCC was an independent favorable predictor of survival. We validated these data on an independent cohort from our hospital.

Conclusion

In this population-based study, despite developing DM more frequently, early-onset HCC exhibited a superior prognosis than late-onset HCC. Nevertheless, further research is warranted to understand the underlying aetiologic basis for the disparities.

Machine learning was used to predict risk factors for distant metastasis of pancreatic cancer and prognosis analysis

BACKGROUND

The mechanisms of distant metastasis in pancreatic cancer (PC) have not been elucidated, and this study aimed to explore the risk factors affecting the metastasis and prognosis of metastatic patients and to develop a predictive model.

METHOD

Clinical data from patients meeting criteria from 1990 to 2019 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and two machine learning methods, random forest and support vector machine, combined with logistic regression, were used to explore risk factors influencing distant metastasis and to create nomograms. The performance of the model was validated using calibration curves and ROC curves based on the Shaanxi Provincial People's Hospital cohort. LASSO regression and Cox regression models were used to explore the independent risk factors affecting the prognosis of patients with distant PC metastases.

RESULTS

We found that independent risk factors affecting PC distant metastasis were: age, radiotherapy, chemotherapy, T and N; the independent risk factors for patient prognosis were: age, grade, bone metastasis, brain metastasis, lung metastasis, radiotherapy and chemotherapy.

CONCLUSION

Together, our study provides a method for risk factors and prognostic assessment for patients with distant PC metastases. The nomogram we developed can be used as a convenient individualized tool to facilitate aid in clinical decision making.

Development and validation of a diagnostic and prognostic model for lung metastasis of hepatocellular carcinoma: a study based on the SEER database

Background

Lung metastasis (LM) is a common occurrence in patients with hepatocellular carcinoma (HCC), and it is associated with a poorer prognosis compared to HCC patients without LM. This study aimed to identify predictors and prognostic factors for LM in HCC patients as well as develop diagnostic and prognostic nomograms specifically tailored for LM in HCC patients.

Methods

A retrospective analysis was conducted on HCC patients from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2015. The study employed multivariate logistic regression analysis to identify risk factors associated with LM in HCC patients. Additionally, multivariate Cox proportional hazards regression analysis was utilized to investigate prognostic factors for HCC patients with LM. Subsequently, two nomograms were developed to predict the risk and prognosis of LM in HCC patients. The performance of the nomograms was evaluated through calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).

Result

This retrospective study included a total of 5,934 patients diagnosed with HCC, out of which 174 patients were diagnosed with LM. Through multivariate logistic regression analysis, several independent risk factors for LM in HCC patients were identified, including tumor grade, tumor size, American Joint Committee for Cancer (AJCC) T stage, and AJCC N stage. Furthermore, multivariate Cox analysis revealed that tumor grade, delayed treatment, surgery, and radiation were independent prognostic factors for HCC patients with LM. To assess the predictive power of the developed nomograms, calibration curves, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) were employed. The findings demonstrated that the nomograms exhibited satisfactory performance in both the training and validation sets. Additionally, the prognostic nomogram effectively stratified HCC patients with LM into low- and high-risk groups for mortality.

Conclusion

These two nomograms optimally predicted the risk and prognosis of LM in HCC patients. Both nomograms have satisfactory performance. This would help clinicians to make accurate clinical decisions.

Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database

Our study aimed to develop a prediction model to predict the short-term mortality of hepatocellular carcinoma (HCC) patients with lung metastasis. The retrospective data of HCC patients with lung metastasis was from the Surveillance, Epidemiology, and End Results registration database between 2010 and 2015. 1905 patients were randomly divided into training set (n = 1333) and validation set (n = 572). There were 1092 patients extracted from the Surveillance, Epidemiology, and End Results database 2015 to 2019 as the validation set. The variable importance was calculated to screen predictors. The constructed prediction models of logistic regression, random forest, broad learning system, deep neural network, support vector machine, and naïve Bayes were compared through the predictive performance. The mortality of HCC patients with lung metastasis was 51.65% within 1 month. The screened prognostic factors (age, N stage, T stage, tumor size, surgery, grade, radiation, and chemotherapy) and gender were used to construct prediction models. The area under curve (0.853 vs. 0.771) of random forest model was more optimized than that of logistic regression model in the training set. But, there were no significant differences in testing and validation sets between random forest and logistic regression models. The value of area under curve in the logistic regression model was significantly higher than that of the broad learning system model (0.763 vs. 0.745), support vector machine model (0.763 vs. 0.689) in the validation set, and higher than that of the naïve Bayes model (0.775 vs. 0.744) in the testing model. We further chose the logistic regression prediction model and built the prognostic nomogram. We have developed a prediction model for predicting short-term mortality with 9 easily acquired predictors of HCC patients with lung metastasis, which performed well in the internal and external validation. It could assist clinicians to adjust treatment strategies in time to improve the prognosis.

Pan-cancer analysis of the prevalence and associated factors of lung metastasis and the construction of the lung metastatic classification system

This study first presents an analysis of the prevalence and associated factors of the lung metastasis (LM) database and then uses this analysis to construct an LM classification system. Using cancer patient data gathered from the surveillance, epidemiology, and end results (SEER) database, this study shows that the prevalence of LM is not consistent among different cancers; that is, the prevalence of LM ranges from 0.0013 [brain; 95% confidence interval (95% CI); 0.0010–0.0018] to 0.234 (“other digestive organs”; 95% CI; 0.221–0.249). This study finds that advanced age, poor grade, higher tumor or node stage, and metastases including bone, brain, and liver are positively related to LM occurrence, while female gender, income, marital status, and insured status are negatively related. Then, this study generates four categories from 58 cancer types based on prevalence and influence factors and satisfactorily validates these. This classification system reflects the LM risk of different cancers. It can guide individualized treatment and the management of these synchronous metastatic cancer patients and help clinicians better distribute medical resources.

Risk factors and predictive nomograms for early death of patients with advanced hepatocellular carcinoma: a large retrospective study based on the SEER database

BACKGROUND

Hepatocellular carcinoma (HCC) is a kind of tumor with high invasiveness, and patients with advanced HCC have a higher risk of early death. The aim of the present study was to identify the risk factors of early death in patients with advanced HCC and establish predictive nomograms.

METHODS

Death that occurred within 3 months of initial diagnosis is defined as early death. Patients diagnosed with stage IV HCC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and verification. Univariable and multivariable logistic regression analyses were used to identify the risk factors. Predictive nomograms were constructed and an internal validation was performed. Decision curve analysis (DCA) was used to verify the true clinical application value of the models.

RESULTS

Of 6603 patients (57% age > 60, 81% male, 70% white, 46% married), 21% and 79% had stage IVA and IVB, respectively. On the multivariable analyses, risk factors for early deaths in patients with stage IVA were age, tumor size, histological grade, alpha-fetoprotein (AFP), fibrosis score, tumor stage (T stage), surgery, radiotherapy, and chemotherapy, and that in stage IVB were age, histological grade, AFP, T stage, node stage (N stage), bone metastasis, lung metastasis, surgery, radiotherapy, and chemotherapy. The areas under the curves (AUCs) were 0.830 (95% CI 0.809-0.851) and 0.789 (95% CI 0.768-0.810) in stage IVA and IVB, respectively. Nomograms comprising risk factors with the concordance indexes (C-indexes) were 0.820 (95% CI 0.799-0.841) in stage IVA and 0.785 (95% CI 0.764-0.0.806) in stage IVB for internal validation (Bootstrapping, 1000re-samplings). The calibration plots of the nomograms show that the predicted early death was consistent with the actual value. The results of the DCA analysis show that the nomograms had a good clinical application.

CONCLUSION

The nomograms can be beneficial for clinicians in identifying the risk factors for early death of patients with advanced HCC and predicting the probability of early death, so as to allow for individualized treatment plans to be accurately selected.

Risk Factors for Brain Metastasis of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignancy with high mortality, especially in HCC patients with brain metastases (BMS). However, few studies have investigated the risk factors for BMS among HCC patients based on large-scale population. The study involved clinical data of 36,091 patients who met the inclusion criteria from the SEER database, from 2004 to 2016. Univariate analysis and multifactor logistics regression analysis was used to analyze risk factors affecting BMS among HCC patients. This study revealed that BMS occurred in 108 of 36,091 patients, with an incidence of 0.33%. Median survival was 7 months for patients with BMS, but 12 months for patients without BMS. Univariate analysis showed that pathological low differentiation and undifferentiation, lymph node metastasis, no surgical treatment, and no chemotherapy and radiotherapy increased risk of BMS (P < 0.05). Multivariate analysis suggested that no surgical treatment and no chemotherapy or radiotherapy were independent risk factors for BMS (P < 0.001). Our findings highlighted that the independent risk factors for BMS were no surgical treatment, no chemotherapy, and no radiotherapy.

Patient stratification in hepatocellular carcinoma: impact on choice of therapy

INTRODUCTION

HCC comprises around 60 to 80% of all primary liver cancers and exhibits wide geographical variability. Appropriate treatment allocation needs to include both patient and tumor characteristics.

AREAS COVERED

Current HCC classification systems to guide therapy are either liver function-centric and evaluate physiologic liver function to guide therapy or prognostic stratification classification systems broadly based on tumor morphologic parameters, patient performance status, and liver reserve assessment. This review focuses on different classification systems for HCC, their strengths, and weaknesses as well as the use of artificial intelligence in improving prognostication in HCC.

EXPERT OPINION

Future HCC classification systems will need to incorporate clinic-pathologic data from a multitude of sources and emerging therapies to develop patient-specific treatment plans targeting a patient's unique tumor profile.

Development and validation of a nomogram for predicting the overall survival of patients with gastroenteropancreatic neuroendocrine neoplasms

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are increasing in incidence. Clinicians urgently need a method that can effectively predict the prognosis of GEP-NENs.A total of 14770 GEP-NENs patients with pathologically confirmed between 1975 and 2016 were obtained from the surveillance, epidemiology, and end results database. All the patients were divided into primary (n = 10377) and validation (n = 4393) cohorts based on the principle of random grouping. Multivariate Cox proportional hazards proportional hazards regression analysis was performed to evaluate predictors associated with overall survival, and a nomogram was constructed based on the primary cohort. An independent external validation cohort and comparison with the eighth edition American Joint Committee on Cancer TNM staging system were subsequently used to assess the predictive performance of the nomogram.The multivariate Cox model indicated that age, tumour differentiation, and distant metastases were independent predictors associated with overall survival. With respect to the primary cohort, the nomogram exhibited better discriminatory power than the TNM classification (C-index: 0.821 vs 0.738). Discrimination was also superior to that of TNM classification for the validation cohort (C-index: 0.823 vs 0.738). The calibrated nomogram predicted 3- and 5-years survival rate that closely corresponded to the actual survival rate.This study developed and validated a prognostic nomogram applied to patients with GEP-NENs, which may help clinicians make reasonable prognostic judgments and treatment plans to a certain extent.

Pulmonary metastases in newly diagnosed hepatocellular carcinoma: a population-based retrospective study

BACKGROUND

Hepatocellular carcinoma (HCC) is a major form of primary liver cancer with steadily increasing incidence for the decades, and has propensity to have extrahepatic metastases, especially pulmonary metastases (PM). This study aimed to investigate temporal incidence trends, treatment, and survival of patients with HCCPM.

METHODS

Patients with HCCPM were retrospectively reviewed from 2010 to 2016 in US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results registry (SEER).

RESULTS

2242 patients with HCCPM were identified. Overall HCCPM incidence did not change from 2010 to 2016, with an annual percent change (APC) of 0.87% (95% CI = -2.50%-4.35%, P = 0.542). Similar incidence trends patterns were found in subgroup analyses of sex, age, and race. 1-year observed survival for HCCPM was 10.8% (95%CI = 8.9%-12.8%) and relative survival was 11.0% (95%CI = 9.1%-13.1%). Better outcomes were noted among patients who underwent liver-directed surgery, those who treated with chemotherapy, and those who received radiation.

CONCLUSIONS

The incidence of HCCPM does not increase with the increasing incidence of HCC. Patients with HCCPM have a dismal prognosis with low survival rates. Liver-directed surgery, use of chemotherapy, and radiation may be associated with improved outcomes.

The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database

Background: Patients with glioblastoma have a poor prognosis. We want to develop and validate nomograms for predicting overall survival in patients with glioblastoma. Methods: Data of patients with glioblastoma diagnosed pathologically in the SEER database from 2007 to 2016 were collected by SEER*Stat software. After eliminating invalid and missing clinical information, 3,635 patients (total group) were finally identified and randomly divided into the training group (2,183 cases) and the verification group (1,452 cases). Cox proportional risk regression model was used in the training group, the verification group and the total group to analyze the prognostic factors of patients in the training group, and then the nomogram was constructed. C-indexes and calibration curves were used to evaluate the predictive value of nomogram by internal (training group data) and external validation (verification group data). Results: Cox proportional risk regression model in the training group showed that age, year of diagnosis, laterality, radiation, chemotherapy were all influential factors for prognosis of patients with glioblastoma (P < 0.05) and were all used to construct nomogram as well. The internal and external validation results of nomogram showed that the C-index of the training group was 0.729 [95% CI was (0.715, 0.743)], and the verification group was 0.734 [95% CI was (0.718, 0.750)]. The calibration curves of both groups showed good consistency. Conclusions: The proposed nomogram resulted in accurate prognostic prediction for patients with glioblastoma.

Prognostic factors and survival outcomes according to tumor subtype in patients with breast cancer lung metastases

BACKGROUND

Reports on the incidence and prognoses of lung metastases when diagnosing breast cancer patients with different subtypes are limited. Our study investigated the effect of molecular sub-typing stratification on the prognoses of lung metastatic breast caner patients.

METHODS

Patients with breast cancer and lung metastases were identified from Surveillance, Epidemiology and End Results population-based data between 2010 and 2015. Univariate and multivariate Cox regression analyses were performed to identify risk factors and prognoses, overall survival (OS) and breast cancer-specific survival for patients with breast cancer lung metastases.

RESULTS

We identified 6,516 patients with lung metastatic breast cancer, representing 1.7% of the entire cohort and 30.4% of the subset with metastatic disease. This included 2,940 hormone receptor (HR)+/HER2- patients, 852 HR+/HER2+ patients, 547 HR-/HER2+ patients and 983 triple-negative patients. The median OS for all lung metastatic patients was 13 months. Multivariate analysis revealed that those lung metastatic breast cancer patients of older age (>80), black race, with poorly differentiated tumors, carcinoma histology, triple-negative subtype, more metastatic sites and no surgery, and no chemotherapy showed significantly poor survival, both overall and breast cancer-specific.

CONCLUSIONS

Our findings show that molecular sub-type and more metastatic sites might have significant influence on the incidence and prognosis of breast cancer lung metastases. We also identified several prognostic factors that could guide therapy selection in the treatment of lung metastatic patients.