The pelvis is the most common site of chondrosarcoma (CS), and the prognosis for patients with pelvic CS is worse than that for patients with CS in the extremities. However, clinicians have had few tools for estimating the likelihood of survival in patients with pelvic CS. Our aim was to develop nomograms to predict survival of patients with pelvic CS.

This retrospective study has been conducted to validate the performance of deep learning-based survival models in glioblastoma (GBM) patients alongside the Cox proportional hazards model (CoxPH) and the random survival forest (RSF). Furthermore, the effect of hyperparameters optimization methods on improving the prediction accuracy of deep learning-based survival models was investigated. Of the 305 cases, 260 GBM patients were included in our analysis based on the following criteria: demographic information (i.e., age, Karnofsky performance score, gender, and race), tumor characteristic (i.e., laterality and location), details of post-surgical treatment (i.e., time to initiate concurrent chemoradiation therapy, standard treatment, and radiotherapy techniques), and last follow-up time as well as the molecular markers (i.e., O-6-methylguanine methyltransferase and isocitrate dehydrogenase 1 status). Experimental results have demonstrated that age (Elderly > 65: hazard ratio [HR] = 1.63; 95% confidence interval [CI]: 1.213-2.18; p value = 0.001) and tumors located at multiple lobes ([HR] = 1.75; 95% [CI]: 1.177-2.61; p value = 0.006) were associated with poorer prognosis. In contrast, age (young < 40: [HR] = 0.57; 95% [CI]: 0.343-0.96; p value = 0.034) and type of radiotherapy (others include stereotactic and brachytherapy: [HR] = 0.5; 95%[CI]: 0.266-0.95; p value = 0.035) were significantly related to better prognosis. Furthermore, the proposed deep learning-based survival model (concordance index [c-index] = 0.823 configured by Bayesian hyperparameter optimization), outperformed the RSF (c-index = 0.728), and the CoxPH model (c-index = 0.713) in the training dataset. Our results show the ability of deep learning in learning a complex association of risk factors. Moreover, the remarkable performance of the deep-learning-based survival model could be promising to support decision-making systems in personalized medicine for patients with GBM.

The goal of this study is to establish a method for predicting overall survival (OS) and disease-free survival (DFS) in breast cancer patients after surgical operation. The gene expression profiles of cancer tissues from the patients, who underwent complete surgical resection of breast cancer and were subsequently monitored for postoperative survival, were analyzed using cDNA microarrays. We detected seven and three probes/genes associated with the postoperative OS and DFS, respectively, from our discovery cohort data. By incorporating these genes associated with the postoperative survival into MammaPrint genes, often used to predict prognosis of patients with early-stage breast cancer, we constructed postoperative OS and DFS prediction models from the discovery cohort data using a Cox proportional hazard model. The predictive ability of the models was evaluated in another independent cohort using Kaplan-Meier (KM) curves and the area under the receiver operating characteristic curve (AUC). The KM curves showed a statistically significant difference between the predicted high- and low-risk groups in both OS (log-rank trend test P = 0.0033) and DFS (log-rank trend test P = 0.00030). The models also achieved high AUC scores of 0.71 in OS and of 0.60 in DFS. Furthermore, our models had improved KM curves when compared to the models using MammaPrint genes (OS: P = 0.0058, DFS: P = 0.00054). Similar results were observed when our model was tested in publicly available datasets. These observations indicate that there is still room for improvement in the current methods of predicting postoperative OS and DFS in breast cancer.

This study aimed to predict the 5-year overall survival (OS) benefit of pola-R-CHP versus R-CHOP in the POLARIX trial based on the 2-year event-free survival (EFS) and progression-free survival (PFS) rates in diffuse large B-cell lymphoma (DLBCL). We identified randomized controlled trials (RCT) published before 31 May 2023. The correlation between the logarithmic (log) hazard ratio (HR) for EFS (HREFS ) or PFS (HRPFS ) and the HR for OS (HROS ) was estimated at the trial-level. Correlation analysis was performed between 2-year PFS or EFS and 5-year OS rates at the treatment arm-level. Linear regression models were used to calculate the 5-year OS of pola-R-CHP and R-CHOP. In the included 20 RCTs, a linear correlation between HREFS (r = 0.765) or HRPFS (r = 0.534) and HROS was observed at the trial- level. Two-year EFS (r = 0.918) or 2-year PFS (r = 0.865) correlated linearly with 5-year OS. Linear regression analysis between 2-year EFS/PFS and 5-year OS gave estimated 5-year OS rates between pola-R-CHP and R-CHOP of 6.4% and 6.3%, respectively. Two-year EFS and PFS are feasible early endpoints in patients with DLBCL treated primarily with immunochemotherapy. The pola-R-CHP regimen is expected to improve 5-year OS.

No nomogram models addressing the personalized prognosis evaluation of patients with gingival squamous cell carcinoma (GSCC) have been documented. We sought to establish nomograms to forecast overall survival (OS) and cancer-specific survival (CSS) of patients with GSCC. We collected the detailed clinicopathological information of 2505 patients with GSCC from the Surveillance, Epidemiology and End Results (SEER) program. Afterward, we divided the 2505 cases into a modeling group (n = 1253) and an external validation cohort (n = 1252) via random split-sample method. We developed the nomograms on the basis of the Kaplan-Meier and multivariate Cox survival analysis of the modeling group and then split the modeling cohort into two parts based on cut-off values: high- and low-risk cohorts. An improved survival was shown in the low-risk group compared to their counterpart, with a significant difference after the log-rank test. The performance of the nomograms was evaluated via concordance-index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves. All the C-indexes and AUCs were greater than 0.7, showing high accuracy. Moreover, the calibrations showed that the actual observations were close to the 45° perfect reference line. In conclusion, we successfully developed two nomograms to provide individualized, patient-specific estimates of OS and CSS available for risk-stratification.

Currently, the prognosis of kidney cancer depends mainly on the pathological grade or tumor stage. Clinicians have few effective tools that can personalize and adequately evaluate the prognosis of kidney cancer patients.

The human CGB5 gene encodes chorionic gonadotropin (hCG)β 5, which is aberrantly expressed in trophoblastic neoplasm and in some non-trophoblastic neoplasms. Fucntional studies observed that it involved tumor initiation, growth, and metastatic outgrowth. In this study, using data from the International Cancer Genome Consortium (ICGC) and the Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD), we assessed the independent prognostic value of CGB5 expression in patients with primary gastric cancer (GC). Results showed that CGB5 expression was nearly not expressed in normal GC tissues. In comparison, its expression was detected in 214 of the 415 primary GC cases (51.6%) in TCGA-STAD and was associated with poor response to primary therapy and a higher risk of recurrence and death. In early stages, CGB5 expression was not a prognostic factor in terms of OS (HR: 1.448; 95% CI: 0.811-2.588, P = 0.211) or RFS (HR: 1.659; 95% CI: 0.778-3.540, P = 0.190). However, its expression was independently associated with unfavorable OS (HR: 1.719; 95% CI: 1.115-2.651, P = 0.014) and RFS (HR: 3.602; 95% CI: 1.708-7.598, P = 0.001) in advanced stages. Using deep sequencing data from TCGA-STAD, we found that CGB5 expression was not related to its genetic amplification or DNA methylation in GC. Based on these findings, we infer that CGB5 expression is common in GC patients and its expression might independently predict poor OS and RFS in advanced stages, but not in early stages of GC.

The aim of this study was to construct nomograms to predict long-term overall survival (OS) and tongue cancer-specific survival (TCSS) of tongue squamous cell carcinoma (TSCC) patients based on clinical and tumor characteristics. Clinical, tumor, and treatment characteristics of 12,674 patients diagnosed with TSCC between 2004 and 2013 were collected from the Surveillance, Epidemiology, and End Results database. These patients were then divided into surgery and nonsurgery cohorts, and nomograms were developed for each of these groups. The step-down method and cumulative incidence function were used for model selection to determine the significant prognostic factors associated with OS and TCSS. These prognostic variables were incorporated into nomograms. An external cohort was used to validate the surgery nomograms. Seven variables were used to create the surgery nomograms for OS and TCSS, which had c-indexes of 0.709 and 0.728, respectively; for the external validation cohort, the c-indexes were 0.691 and 0.711, respectively. Nine variables were used to create the nonsurgery nomograms for OS and TCSS, which had c-indexes of 0.750 and 0.754, respectively. The calibration curves of the 5- and 8-year surgery and nonsurgery nomograms showed excellent agreement between the probabilities and observed values. By incorporating clinicopathological and host characteristics in patients, we are the first to establish nomograms that accurately predict prognosis for individual patients with TSCC. These nomograms ought to provide more personalized and reliable prognostic information, and improve clinical decision-making for TSCC patients.

Patients with early-stage breast cancer (BC) live long but have competing comorbidities. This study aimed to estimate the effect of cancer and other causes of death in patients with early-stage BC and further quantify the survival differences.

Previous studies found that uninsured and Medicaid insured cancer patients have poorer outcomes than cancer patients with private insurance. We examined the association between health insurance status and survival of New Jersey patients 18-64 diagnosed with seven common cancers during 1999-2004. Hazard ratios (HRs) with 95% confidence intervals for 5-year cause-specific survival were calculated from Cox proportional hazards regression models; health insurance status was the primary predictor with adjustment for other significant factors in univariate chi-square or Kaplan-Meier survival log-rank tests. Two diagnosis periods by health insurance status were compared using Kaplan-Meier survival log-rank tests. For breast, colorectal, lung, non-Hodgkin lymphoma (NHL), and prostate cancer, uninsured and Medicaid insured patients had significantly higher risks of death than privately insured patients. For bladder cancer, uninsured patients had a significantly higher risk of death than privately insured patients. Survival improved between the two diagnosis periods for privately insured patients with breast, colorectal, or lung cancer and NHL, for Medicaid insured patients with NHL, and not at all for uninsured patients. Survival from cancer appears to be related to a complex set of demographic and clinical factors of which insurance status is a part. While ensuring that everyone has adequate health insurance is an important step, additional measures must be taken to address cancer survival disparities.

Although the latest Gleason grading system in 2014 has distinguished between Gleason 3 + 4 and 4 + 3, Gleason 8 and Gleason 9-10 are remained systemically classified.

The goal of this study was to establish and validate two nomograms for predicting the long-term overall survival (OS) and cancer-specific survival (CSS) in lip squamous cell carcinoma (LSCC).

Polymorphisms in miRNA and miRNA pathway genes have been previously associated with cancer risk and outcome, but have not been studied in esophageal adenocarcinoma outcomes. Here, we evaluate candidate miRNA pathway polymorphisms in esophageal adenocarcinoma prognosis and attempt to validate them in an independent cohort of esophageal adenocarcinoma patients. Among 231 esophageal adenocarcinoma patients of all stages/treatment plans, 38 candidate genetic polymorphisms (17 biogenesis, 9 miRNA targets, 5 pri-miRNA, 7 pre-miRNA) were genotyped and analyzed. Cox proportional hazard models adjusted for sociodemographic and clinicopathological covariates helped assess the association of genetic polymorphisms with overall survival (OS) and progression-free survival (PFS). Significantly associated polymorphisms were then evaluated in an independent cohort of 137 esophageal adenocarcinoma patients. Among the 231 discovery cohort patients, 86% were male, median diagnosis age was 64 years, 34% were metastatic at diagnosis, and median OS and PFS were 20 and 12 months, respectively. GEMIN3 rs197412 (aHR = 1.37, 95%CI: [1.04-1.80]; P = 0.02), hsa-mir-124-1 rs531564 (aHR = 0.60, 95% CI: [0.53-0.90]; P = 0.05), and KIAA0423 rs1053667 (aHR = 0.51, 95% CI: [0.28-0.96]; P = 0.04) were found associated with OS. Furthermore, GEMIN3 rs197412 (aHR = 1.33, 95% CI: [1.03-1.74]; P = 0.03) and KRT81 rs3660 (aHR = 1.29, 95% CI: [1.01-1.64]; P = 0.04) were found associated with PFS. Although none of these polymorphisms were significant in the second cohort, hsa-mir-124-1 rs531564 and KIAA0423 rs1053667 had trends in the same direction; when both cohorts were combined together, GEMIN3 rs197412, hsa-mir-124-1 rs531564, and KIAA0423 rs1053667 remained significantly associated with OS. We demonstrate the association of multiple miRNA pathway polymorphisms with esophageal adenocarcinoma prognosis in a discovery cohort of patients, which did not validate in a separate cohort but had consistent associations in the pooled cohort. Larger studies are required to confirm/validate the prognostic value of these polymorphisms in esophageal adenocarcinoma.

The objective of this study is to examine the impact of marital status on incidence of metastasis at diagnosis, receipt of surgery, and cause-specific survival (CSS) in patients with gastric cancer (GC). Research data is extracted from The Surveillance, Epidemiology, and End Results (SEER) database, and 18,196 patients diagnosed with GC from 2004 to 2010 are involved. Effects of marital status on incidence of metastasis at diagnosis, receipt of surgery, and CSS are determined using multivariable logistic regression and multivariable Cox regression models, as appropriate. Single GC patients have a higher incidence of metastasis at diagnosis than married patients, while the differences between divorced/separated patients or widowed patients and married patients are not significant. Among those without distant metastasis, single patients, divorced/separated patients, and widowed patients are much less likely to accept surgery compared with married patients. Finally, in the whole group of 18,196 GC patients, single patients, divorced/separated patients, and widowed patients have shorter CSS compared with married patients, even in each of the TNM stage. Marriage had a protective effect against undertreatment and cause-specific mortality (CSM) in GC. Spousal support may contribute to higher rate of surgery receipt and better survival in patients with GC.

Pancreatic neuroendocrine tumor (pancreatic NETs), is an important cause of cancer-related death worldwide. No study has rigorously explored the impact of ethnicity on pancreatic NETs. We aimed to demonstrate the relationship between ethnicity and the survival of patients with pancreatic NETs. We used the SEER database to identify patients with pancreatic NETs from 2004 to 2013. Kaplan-Meier methods and Cox proportional hazard models were used to evaluate the impact of race on survival in pancreatic NETs patients. A total of 3850 patients were included: 3357 Non-Blacks, 493 Blacks. We stratified races as "Black" and "White/Other." Blacks were more likely to be diagnosed with later stages of tumors (P = 0.021). As for the treatment, the access to surgery seemed to be more limited in Blacks than non-Black patients (P = 0.012). Compared with non-Black patients, Black patients have worse overall survival (OS) (HR = 1.17, 95% CI: 1.00-1.37, P = 0.046) and pancreatic neuroendocrine tumors specific survival (PNSS) (HR = 1.22, 95% CI: 1.01-1.48, P = 0.044). Multivariate Cox analysis identified that disease extension at the time of diagnosis and surgical status contributed to the ethnical survival disparity. Black patients whose stages at diagnosis were localized had significantly worse OS (HR = 2.09, 95% CI: 1.18-3.71, P = 0.011) and PNSS (HR = 3.79, 95% CI: 1.62-8.82, P = 0.002). As for the patients who did not receive surgery, Blacks also have a worse OS (HR = 1.18, 95% CI: 1.00-1.41, P = 0.045). The Black patients had both worse OS and PNSS compared to non-Black patients. The restricted utilization of surgery, and the advanced disease extension at the time of diagnosis are the possible contributors to poorer survival of Blacks with pancreatic NETs.

Despite 3-year survival being used as a primary endpoint in some randomized controlled trials (RCTs), limited evidence supports the use of intermediate endpoints to evaluate the effect of new therapies in esophageal squamous cell cancer (ESCC). This study aimed to systematically evaluate progression-free survival at 3 years (3-year PFS) and overall survival (OS) among patients with ESCC.

Persons newly diagnosed with pancreas cancer and who have survived a previous cancer are often excluded from clinical trials, despite limited evidence about their prognosis. We examined the association between previous cancer and overall survival.

This study was designed to establish and validate promising and reliable nomograms for predicting the survival of angiosarcoma (AS) patients.

The effect of socioeconomic status (SES) on hepatocellular carcinoma (HCC) is still unclear, and there is no nomogram integrated SES and clinicopathological factors to predict the prognosis of HCC. This research aims to confirm the effects of SES on predicting patients' survival and to establish a nomogram to predict the prognosis of HCC.

Malignant hemangioendothelioma is an endothelial cancer with heterogeneous clinical behavior that can range from indolent to aggressive, of which the majority are epithelioid (EHE). Its incidence and demographics have not been previously well defined in a large cohort.