Nomogram Models for Predicting Risk and Prognosis of Newly Diagnosed Ovarian Cancer Patients with Liver Metastases - A Large Population-Based Real-World Study.
Ontology highlight
ABSTRACT:
SUBMITTER: Hou GM
PROVIDER: S-EPMC8734403 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Background: Population-based data on the prognosis of brain metastases at initial diagnosis of ovary cancer (OCBM) are currently lacking. Besides, the effective treatment for OCBM patients is still controversial now. The study aimed to explore the prognostic factors among OCBM. Methods: We retrospectively reviewed the OCBM patients from the Surveillance, Epidemiology, and End Result (SEER) database of the National Cancer Institute to investigate predictors of the presence of OCBM and its' prognostic factors related to all-cause mortality. We employed multivariable logistic and Cox regression analysis. Furthermore, to minimize the impact of potential confounding factors, we conducted a 1:1 propensity score matching (PSM) analysis. Results: A total of 29,512 cases of OC patients entered into the study, including 89 patients with brain metastases of ovarian cancer, which accounted for 0.30% of the entire cohort and 12.02% of the metastatic disease subset. We identified eight factors, including laterality, histology, surgery, radiotherapy, chemotherapy, and extracranial metastatic sites to bone, liver, and lung, as predictors of OCBM based on multivariable logistic regression among the entire cohort. The median survival time of OCBM was 2.0 months, and the interquartile range was 2.0-10.0 mo. The patients who received comprehensive treatment had better prognosis. Based on the multivariable Cox model, marital status, surgery, chemotherapy, and extensive therapy (including RSC, SC, and RC) were identified as predictors of OS. Besides, a new factor (brain metastasis) was identified by 1:1 PSM -based multiple Cox regression, apart from the above prognostic factors for OS. Conclusions: This study provided a population-based estimate of the proportion and prognosis for newly diagnosed ovary cancer with brain metastases. These findings may add materials to guidelines for preliminary screening and optimal treatment of OCBM patients.
| S-EPMC7255353 | biostudies-literature
Project description:Purpose: The purpose of this study was to analyze the frequency and prognosis of pulmonary metastases in newly diagnosed gastric cancer using population-based data from SEER. Methods: Patients with gastric cancer and pulmonary metastases (GCPM) at the time of diagnosis in advanced gastric cancer were identified using the Surveillance, Epidemiology and End Result (SEER) database of the National Cancer Institute from 2010 to 2014. Multivariable logistic regression was performed to identify predictors of the presence of GCPM at diagnosis. Receiver operator characteristics analysis was performed to significant predictors on multivariable logistic regression and was then assessed with Delong's test. Multivariable Cox regression was developed to identify factors associated with all-cause mortality and gastric cancer-specific mortality. Survival curves were obtained according to the Kaplan-Meier method and compared using the log-rank test. Results: We identified 1,104 patients with gastric cancer and pulmonary metastases at the time of diagnosis, representing 6.02% of the entire cohort and 15.19% of the subset with metastatic disease to any distant site. Among the entire cohort, multivariable logistic regression identified six factors (younger, upper 1/3 of stomach, intestinal-type, T4 staging, N1 staging, and presence of more extrapulmonary metastases to liver, bone, and brain) as positive predictors of the presence of pulmonary metastases at diagnosis. The value of AUC for the multivariable logistic regression model was 0.775. Median survival among the entire cohort with GCPM was 3.0 months (interquartile range: 1.0-9.0 mo). Multivariable Cox model in SEER cohort confirmed five factors (diagnosis at previous period, black race, adverse pathology grade, absence of chemotherapy, and presence of more extrapulmonary metastases to liver, bone, and brain) as negative predictors for overall survival. Conclusions: The findings of this study provided population-based estimates of the frequency and prognosis for GCPM at time of diagnosis. The multivariable logistic regression model had an acceptable performance to predict the presence of PM. These findings may provide preventive guidelines for the screening and treatment of PM in GC patients. Patients with high risk factors should be paid more attention before and after diagnosis.
| S-EPMC6683847 | biostudies-literature
Project description:Background/aimPatients with large (>5 cm) hepatocellular carcinoma (HCC) have limited treatment options, thus necessitating the identification of prognostic factors and the development of predictive tools. This study aimed to identify prognostic factors and to construct a nomogram to predict survival outcomes in patients with large HCC.MethodsA cohort of 438 patients, who were diagnosed with large HCC at a tertiary hospital between 2015 and 2018, was analyzed. Cox proportional hazards models were used to identify key prognosticators of overall survival (OS), and an independent set of prognostic factors was used to develop a nomogram. The discrimination and calibration abilities of the nomogram were assessed and internal validation was performed using cross-validation and bootstrapping methods.ResultsDuring a median follow-up of 9.3 months, the median OS was 9.9 months, and the 1-year OS rate was 43.9%. Multivariable Cox regression analysis revealed that performance status, modified albumin-bilirubin grade, tumor size, extent of portal vein tumor thrombosis, and initial treatment significantly affected OS. The newly developed nomogram incorporating these variables demonstrated favorable accuracy (Harrell's concordance index, 0.807).ConclusionsThe newly developed nomogram facilitated the estimation of individual survival outcomes in patients with large HCC, providing an acceptable level of accuracy.
| S-EPMC10565549 | biostudies-literature
Project description:BackgroundThe optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial.MethodsWe selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. After the selection of inclusion criteria and exclusion criterion, the rest of the patients were incorporated into model analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to select the most important features for construction of a nomogram predicting cancer-specific survival. A calibration plot and the concordance index (C-index) were used to estimate nomogram efficacy in a validation cohort. The association between important factors selected by LASSO regression, and prognosis was assessed by the Kaplan-Meier (KM) survival curve. The receiver operating characteristic (ROC) curves were drawn to compare sensitivity and specificity between the nomogram we built and the TNM stage-based model.ResultsA total of 1,369 patients met the inclusion criteria, but not the exclusion criteria. The LASSO regression model reduced 15 features to seven potential predictors of survival, including tumor grade, the extent of surgery, N and T status, histological profile, and brain and bone metastasis status. Such features had good discrimination in the KM survival curves. The nomogram showed excellent discriminatory power (C-index, 0.71; 95% confidence interval: 0.70 to 0.72) and good calibration in terms of both 1- and 2-year cancer-specific survival. The nomogram showed great discriminatory power (C-index 0.68) and adequate calibration when applied to the validation cohort. The areas under the curve (AUCs) of nomogram were 0.767 and 0.780, respectively, and the AUCs of TNM stage were 0.617 and 0.618 at 1 and 2 years, respectively.ConclusionsOur nomogram might play a major role in predicting the cancer-specific survival of RCC patients with lung metastases.
| S-EPMC7997741 | biostudies-literature
Project description:BackgroundPopulation-based analysis for the liver metastases of small bowel cancer is currently lacking. This study aimed to analyze the frequency, prognosis and treatment modalities for newly diagnosed small bowel cancer patients with liver metastases.MethodsPatients with small bowel cancer diagnosed from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Binary logistic regression analysis was performed to determine predictors for the presence of liver metastases at diagnosis. Kaplan-Meier method and Cox regression analyses were performed for survival analyses.ResultsA total of 1461 small bowel cancer patients with liver metastases at initial diagnosis were identified, representing 16.5% of the entire set and 63.9% of the subset with metastatic disease to any distant site. Primary tumor with poorer histological type, larger tumor size, later N staging, more extrahepatic metastatic sites, and tumor on lower part of small intestine had increased propensity of developing liver metastases. The combined diagnostic model exhibited acceptable diagnostic efficiency with AUC value equal to 0.749. Patients with liver metastases had significant poorer survival (P < 0.001) than those without liver metastases. In addition, combination of surgery and chemotherapy (HR = 0.27, P < 0.001) conferred the optimal survival for patients with adenocarcinoma, while the optimal treatment options for NEC and GIST seemed to be surgery alone (HR = 0.24, P < 0.001) and chemotherapy alone (HR = 0.08, P = 0.022), respectively.ConclusionsThe combined predictor had a good ability to predict the presence of liver metastases. In addition, those patients with different histologic types should be treated with distinct therapeutic strategy for obtaining optimal survival.
| S-EPMC7558693 | biostudies-literature
Project description:BackgroundSystemic inflammation measured by the neutrophil-to-lymphocyte ratio (NLR), leucocyte-to-lymphocyte ratio (LLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR) and CRP/albumin ratio (CRP/Alb) was shown to impact the survival prognosis in patients with extracranial solid cancer.MethodsOne thousand two hundred and fifty patients with newly diagnosed brain metastases (BM) were identified from the Vienna Brain Metastasis Registry.ResultsPLR and CRP/Alb were higher in patients with progressive extracranial disease and lower in patients with no evidence of extracranial disease. Lower NLR (cut-off = 5.07; 9.3 vs. 5.0 months), LLR (cut-off = 5.76; 10.0 vs. 5.3 months), PLR (cut-off = 335; 8.0 vs. 3.8 months), MLR (cut-off = 0.53; 6.0 vs. 3.5 months) and CRP/Alb (cut-off = 2.93; 8.5 vs. 3.7 months; padj < 0.05) were associated with longer overall survival (OS). In multivariate analysis with graded prognostic assessment (hazard ratio (HR) 1.45; 95% confidence interval (CI): 1.32-1.59; padj = 1.62e - 13), NLR (HR 1.55; 95% CI: 1.38-1.75; padj = 1.92e - 11), LLR (HR 1.57; 95% CI: 1.39-1.77; padj = 1.96e - 11), PLR (HR 1.60; 95% CI: 1.39-1.85; padj = 2.87955e - 9), MLR (HR 1.41; 95% CI: 1.14-1.75; padj = 0.027) and CRP/Alb (HR 1.83; 95% CI: 1.54-2.18; padj = 2.73e - 10) remained independent factors associated with OS at BM diagnosis.ConclusionsSystemic inflammation, measured by NLR, LLR, PLR, MLR and CRP/Alb, was associated with OS in patients with BM. Further exploration of immune modulating therapies is warranted in the setting of BM.
| S-EPMC8007827 | biostudies-literature
Project description:Aim: The purpose of this study was to analyze the incidence, clinical characteristics, prognostic factors and survival of ovarian cancer patients with liver metastases upon initial diagnosis. Methods: Patients with ovarian cancer liver metastases upon initial diagnosis between 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression was performed to identify the predictors of the presence of liver metastases in newly diagnosed ovarian cancer patients. Overall survival (OS) was assessed using the Kaplan-Meier method and log-rank test. Univariate and multivariate Cox regression was conducted to determine the independent prognostic factors for OS. Results: A total of 1,744 ovarian cancer patients with liver metastases was identified from the SEER database, accounting for 6.7% of the entire ovarian cancer patients. As to the unique distant organ provided by SEER, liver was the most common metastatic site of ovarian cancer (4.65%). Age, race, laterality, histology, pathological grade, extrahepatic sites, stage of tumor were the predictors of the presence with liver metastases revealed by multivariable logistic regression model. Median OS for the patients with liver metastases at initial diagnosis of ovarian cancer was 16.0 months. Multivariate Cox regression model confirmed race, histology, extrahepatic metastatic sites, surgery and marital status were independent prognostic factors for OS. Conclusion: The study provided population-based estimates of the incidence and prognosis of newly diagnosed ovary cancer patients with liver metastases, which could be potentially used for the risk assessment and individualized treatment.
| S-EPMC7545579 | biostudies-literature
Project description:New treatment modalities have been recently introduced in the management of ovarian cancer (OC). Herein, we sought to investigate their implementation in routine clinical practice and examine the real-world management of OC in Greece. EpOCa was a non-interventional, multicenter, retrospective study in patients with advanced epithelial OC. The primary outcome was to estimate the proportions of the different treatment regimens used per line of therapy, while progression-free survival (PFS) and overall survival (OS) were the key secondary endpoints. A total of 154 patients were enrolled in the study, among whom, 40% were tested for BRCA mutations and 30% were found to be positive. Nearly 90% of patients underwent debulking surgery at diagnosis, with few operations being also recorded upon relapse. Platinum-based chemotherapy (CT) was predominantly used in the first line with half of patients also receiving angiogenesis inhibitor (AI), while non-platinum-based CT was preferred in later lines. The median PFS was 18.2 and 8.8 months in the first- and second-line setting, respectively, whereas the median OS was approximately 50 months. Our study adds to the available, but limited, real world data on the management of ovarian cancer providing evidence regarding the applied treatment strategies and outcomes of patients in Greece.
| S-EPMC8699844 | biostudies-literature
Project description:Background/aimsWaldenström macroglobulinemia (WM) is a rare lymphoproliferative disorder that usually follows an indolent clinical course. However, some patients show an aggressive clinical course leading to death. We explored the risk factors predicting poor prognosis in WM patients.MethodsWe retrospectively analyzed 47 patients diagnosed with WM between 2000 and 2018 to explore risk factors predicting poor prognosis using various clinical and laboratory parameters and risk models including the International Prognostic Staging System for WM (IPSS-WM).ResultsOver a median follow-up duration of 80.4 months, 29 patients died. The main causes of death were disease progression, organ failure related to amyloidosis, and infection. The median overall survival (OS) was 55.1 months, and 14 patients, including three with amyloidosis, died within 2 years. Serum β2-microglobulin level higher than 4 mg/dL was significantly associated with poor OS. Accordingly, the IPSS-WM showed a significant association with poor prognosis compared with other risk models, and the low-risk group had better OS than intermediate- and high-risk groups. In the retrospective analysis using the results of targeted sequencing in two cases representing good and bad prognosis, different patterns of mutation profiles were observed, including mutations of MYD88, TP53, ARID1A, and JAK2 in a refractory case.ConclusionSerum β2-microglobulin could be a single biomarker strongly predictive of poor survival of WM patients, and the low-risk group of the IPSS-WM risk model including serum β2-microglobulin has better prognostic value than other risk models. Mutation analysis also might provide additional information to predict high-risk patients.
| S-EPMC8137404 | biostudies-literature
Project description:ObjectivesThe present study aims to discover the risk factors of multiple metastases and develop a functional nomogram to forecast multiple metastases in metastatic colorectal cancer (mCRC) patients.MethodsmCRC cases were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016. Survival times between multiple metastases and single metastasis were compared using Kaplan-Meier analysis and log-rank tests. Risk factors for multiple metastases were determined by univariate and multivariate logistic regression analyses, and a nomogram was developed to forecast the probability of multiple metastases in mCRC patients. We assessed the nomogram performance in terms of discrimination and calibration, including concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). Bootstrap resampling was used as an internal verification method, and at the same time we select external data from Renmin Hospital of Wuhan University as independent validation sets.ResultsA total of 5,302 cases were included in this study as training group, while 120 cases were as validation group. The patients with single metastasis and multiple metastases were 3,531 and 1,771, respectively. The median overall survival (OS) and cancer-specific survival (CSS) for patients with multiple metastases or single metastasis were 19 vs. 31 months, and 20 vs. 33 months, respectively. Based on the univariate and multivariate analyses, clinicopathological characteristics were associated with number of metastasis and were used to establish nomograms to predict the risk of multiple metastases. The C-indexes and AUC for the forecast of multiple metastases were 0.715 (95% confidence interval (CI), 0.707-0.723), which showed the nomogram had good discrimination and calibration curves of the nomogram showed no significant bias from the reference line, indicating a good degree of calibration. In the validation group, the AUC was 0.734 (95% CI, 0.653-0.834), and calibration curve also showed no significant bias, indicating the favorable effects of our nomogram.ConclusionsWe developed a new nomogram to predict the risk of multiple metastases. The nomogram shows the good prediction effect and can provide assistance for clinical diagnosis and treatment.
| S-EPMC8155489 | biostudies-literature