Actual Survival Rate Research Articles

Three-month life expectancy as inclusion criterion for clinical trials in advanced pancreatic cancer: is it really a valid tool for patient selection?

PurposeTo analyze the 3-month life expectancy rate in pancreatic cancer (PC) patients treated within prospective trials from the German AIO study group.Patients and methodsA pooled analysis was conducted for patients with advanced PC that were treated within five phase II/III studies conducted between 1997 and 2017 (Gem/Cis, Ro96, RC57, ACCEPT, RASH). The primary goal for the current report was to identify the actual 3-month survival rate, a standard inclusion criterion in oncology trials.ResultsOverall, 912 patients were included, 83% had metastatic and 17% locally advanced PC; the estimated median overall survival (OS) was 7.1 months. Twenty-one percent of the participants survived < 3 months, with a range from 26% in RC57 to 15% in RASH. Significant predictors for not reaching 3-month OS were > 1 previous treatment line (p < 0.001) and performance status (p < 0.001).ConclusionsDespite the definition of a life expectancy of > 3 months as a standard inclusion criterion in clinical trials for advanced PC, a significant proportion of study patients does not survive > 3 months.Trial registration numbersNCT00440167 (AIO-PK0104), NCT01729481 (RASH), NCT01728818 (ACCEPT).

스마트관광 시대의 관광숙박업 영업 예측 모형

The COVID-19 outbreak in 2020 caused continuous damage worldwide, especially the smart tourism industry was hit directly by the blockade of sky roads and restriction of going out. At a time when overseas travel and domestic travel have decreased significantly, the number of tourist hotels that are closed and closed due to the continued deficit is increasing. Therefore, in this study, licensing data from the Ministry of Public Administration and Security were collected and visualized to understand the operation status of the tourism and lodging industry. The machine learning classification algorithm was applied to implement the business status prediction model of the tourist hotel, the performance of the prediction model was optimized using the ensemble algorithm, and the performance of the model was evaluated through 5-Fold cross-validation. It was predicted that the survival rate of tourist hotels would decrease somewhat, but the actual survival rate was analyzed to be no different from before COVID-19. Through the prediction of the business status of the hotel industry in this paper, it can be used as a basis for grasping the operability and development trends of the entire tourism and lodging industry.

Online decision tools for personalized survival prediction and treatment optimization in elderly patients with lung squamous cell carcinoma: a retrospective cohort study

BackgroundDespite major advances in cancer therapeutics, the therapeutic options of Lung Squamous Cell Carcinoma (LSCC)-specific remain limited. Furthermore, the current staging system is imperfect for defining a prognosis and guiding treatment due to its simplicity and heterogeneity. We sought to develop prognostic decision tools for individualized survival prediction and treatment optimization in elderly patients with LSCC.MethodsClinical data of 4564 patients (stageIB-IIIB) diagnosed from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database for prognostic nomograms development. The proposed models were externally validated using a separate group consisting of 1299 patients (stage IB-IIIB) diagnosed from 2012–2015 in China. The prognostic performance was measured using the concordance index (C-index), calibration curves, the average time-dependent area under the receiver operator characteristic curves (AUC), and decision curve analysis.ResultsEleven candidate prognostic variables were identified by the univariable and multivariable Cox regression analysis. The calibration curves showed satisfactory agreement between the actual and nomogram-estimated Lung Cancer-Specific Survival (LCSS) rates. By calculating the c-indices and average AUC, our nomograms presented a higher prognostic accuracy than the current staging system. Clinical usefulness was revealed by the decision curve analysis. User-friendly online decision tools integrating proposed nomograms were created to estimate survival for patients with different treatment regimens.ConclusionsThe decision tools for individualized survival prediction and treatment optimization might facilitate clinicians with decision-making, medical teaching, and experimental design. Online tools are expected to be integrated into clinical practice by using the freely available website (https://loyal-brand-611803.framer.app/).

Identification and validation of a novel NK cells-related signature to predict prognosis and immune microenvironment in LUAD

BackgroundThe prevalence and fatality rates of lung cancer are experiencing a rapid escalation. Natural Killer (NK) cells have been established to have a crucial role in both tumor initiation and progression. Nevertheless, uncertainties persist regarding their precise implications in the prognosis of LUAD. MethodsThe data were obtained from reputable sources, such as the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and our internally generated sequencing data. Utilizing the TCGA data as a background, we selected intersecting genes, validated by cluster analysis, to establish a Cox model and validated it using the GEO datasets. Furthermore, we conducted extensive analyses to investigate the significance of potential biomarkers in relation to immune cell infiltration, single-cell data, differential gene expression, and drug sensitivity. Results67 immune-related genes associated with NK cells (NK-IRGs) were identified in the TCGA datasets, whose research potential was demonstrated by cluster analysis. A prognostic signature was identified utilizing the univariate and multivariate Cox model, resulting in the identification of five genes, which was validated using GEO datasets. Additionally, the nomogram's calibration curve demonstrated exceptional concordance between the projected and actual survival rates. Subsequent investigations uncovered that this prognostic signature demonstrated its independence as a risk factor. Notably, in the low-risk group, NK cells exhibited elevated levels of immune checkpoint molecules, indicating heightened sensitivity to immune therapy. These findings highlight the potential of utilizing this signature as a valuable tool in the selection of patients who could benefit from targeted immune interventions.

Functional (Re)Development of SYNTAX Score II 2020: Predictive Performance and Risk Assessment.

The present study investigates the prognostic value of the Syntax Score II 2020 corrected for flow-limiting lesions and its ability to better address treatment by benefit prediction among patients with left main or multivessel disease. We analyzed 1274 patients from the HALE-BOPP cohort and integrated the Syntax Score II 2020 with the result of the fractional flow reserve (FFR) evaluation. Absolute risk difference (ARD) between surgical and percutaneous revascularization was calculated for anatomic and functional Syntax Score II 2020 predicted mortality. The ARD allowed to stratify the population into two large categories: "coronary artery bypass graft (CABG) better" with ARD ≥ 4.5% and "CABG-percutaneous coronary intervention (PCI) equipoise" with ARD < 4.5%. The mean global anatomical Syntax Score was 15.5 ± 9.2, whereas the functional one was 9.5 ± 10 (p < 0.01). Using the anatomic Syntax Score II 2020, 881 patients had a CABG-PCI equipoise. This number increased to 1041 after considering only flow-limiting lesions by FFR (p < 0.001); therefore, 40% of CABG better patients were reclassified within the CABG-PCI equipoise category. Kaplan-Maier curves showed similar actual survival rates for patients originally with CABG-PCI equipoise and those reclassified, in both cases higher than those from CABG better patients (p < 0.01). The integration between Syntax Score II 2020 and physiology is feasible, and merging clinical, anatomic and functional data allows for better risk prediction and therapeutic guidance.

Establishment and validation of a competitive risk model for predicting cancer-specific survival in patients with osteosarcoma: a population-based study.

Osteosarcoma is the most common primary bone tumor with a poor prognosis. The aim of this study was to establish a competitive risk model nomogram to predict cancer-specific survival in patients with osteosarcoma. Patient data was obtained from the Surveillance, Epidemiology, and End Results database in the United States. A sub-distribution proportional hazards model was used to analyze independent risk factors affecting cancer-specific mortality (CSM) in osteosarcoma patients. Based on these risk factors, a competitive risk model was constructed to predict 1-year, 3-year, and 5-year cancer-specific survival (CSS) in osteosarcoma patients. The reliability and accuracy of the nomogram were evaluated using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves. A total of 2900 osteosarcoma patients were included. The analysis showed that age, primary tumor site, M stage, surgery, chemotherapy, and median household income were independent risk factors influencing CSM in patients. The competitive risk model was constructed to predict CSS in osteosarcoma patients. In the training and validation sets, the C-index of the model was 0.756 (95% CI 0.725-0.787) and 0.737 (95% CI 0.717-0.757), respectively, and the AUC was greater than 0.7 for both. The calibration curves also demonstrated a high consistency between the predicted survival rates and the actual survival rates, confirming the accuracy and reliability of the model. We established a competitive risk model to predict 1-year, 3-year, and 5-year CSS in osteosarcoma patients. The model demonstrated good predictive performance and can assist clinicians and patients in making clinical decisions and formulating follow-up strategies.

Development and external validation of a nomogram predicting overall survival for Gastric adenocarcinoma patients with radical gastrectomy

Purpose The aim of this study was to develop and externally validate a nomogram to accurately predict the overall survival (OS) of patients with gastric adenocarcinoma who underwent radical gastrectomy. Materials and Methods A total of 3492 patients with gastric adenocarcinoma who underwent radical gastrectomy from 2012 to 2017 were included as the training cohort. Survival analysis was performed via Kaplan Meier method and log-rank test. Independent postoperative prognostic factors in patients with gastric adenocarcinoma were analyzed using univariate and multifactorial COX analysis methods. The prognosis nomogram was established in the training cohort and verified externally in the Surveillance, Epidemiology and End Results (SEER) database. Results According to the univariate and multifactorial COX analyses, metastatic lymph node ratio (MLNR) and five other independent prognostic factors (age at surgery, type of gastrectomy, tumor size, T stage, and pathological grade) were included in the prognostic nomogram. The nomogram had better prognostic predictive ability than the American Joint Committee on Cancer (AJCC) TNM staging in both the training (C-index: 0.736 VS. 0.668) and external validation cohort (C-index: 0.712 VS. 0.627). The calibration plots showed that the predicted survival rate was in good agreement with the actual survival rate. And the decision curve analysis (DCA) curves revealed that nomogram showed stronger ability in predicting 1-year, 3-year, and 5-year OS. Conclusion This study estimated the excellent prognostic predictive power and clinical application potential of the MLNR-based nomogram, which may be used to facilitate postoperative clinical treatment decisions and potentially improve patient survival outcomes.

Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool.

A complex relationship between donor and recipient characteristics influences kidney transplant (KT) success. A tool developed by Bae S. et al. (Survival Benefit Estimator, SBE) helps estimate post-KT survival. We aim to evaluate the predictive performance of the SBE tool in terms of 5-year patient survival after a kidney transplant. A retrospective cohort study of all deceased-donor KT recipients between January 2009 to December 2021. A descriptive analysis of clinical and sociodemographic characteristics was performed. The SBE online tool was used to calculate the predicted patient survival (PPS) and the survival benefit at five years post-KT. Comparisons between predictive vs. actual patient survival were made using quintile subgroups. Three Cox regression models were built using PPS, EPTS, and KDPI. A total of 1145 recipients were evaluated. Mortality occurred in 157 patients. Patient survival was 86.2%. Predictive survival for patients if they remained on the waiting list was 70.6%. The PPS was 89.3%, which results in a survival benefit (SB) of 18.7% for our population. Actual survival rates were lower than the predicted ones across all the quintiles. In unadjusted analysis, PPS was a significant protective factor for mortality (HR 0.66), whereas EPTS (HR 8.9) and KDPI (HR 3.25) scores were significant risk factors. The discrimination of KDPI, PPS, and EPTS scores models were 0.59, 0.65, and 0.66, respectively. SBE score overestimated actual survival rates in our sample. The discrimination power of the score was moderate, although the utility of this tool may be limited in this specific population.

Nomogram for prognosis of elderly patients with cervical cancer who receive combined radiotherapy

This retrospective study identified prognostic factors to help guide the clinical treatment of elderly patients (≥ 65 years) with cervical cancer who had undergone radiotherapy. A personalized model to predict 3- and 5-years survival was developed. A review was conducted of 367 elderly women with cervical cancer (staged II–III) who had undergone radiotherapy in our hospital between January 2012 and December 2016. The Cox proportional hazards regression model was used for survival analysis that considered age, hemoglobin, squamous cell carcinoma antigen, pathologic type, stage, pelvic lymph node metastasis status, and others. A nomogram was constructed to predict the survival rates. The median follow-up time was 71 months (4–118 months). The 3- (5-) years overall, progression-free, local recurrence-free, and distant metastasis-free survival rates were, respectively, 91.0% (84.4%), 92.3% (85.9%), 99.18% (99.01%), and 99.18% (97.82%). The following were significant independent prognostic factors for overall survival: tumor size, pre-treatment hemoglobin, chemotherapy, and pelvic lymph node metastasis. The C-index of the line chart was 0.699 (95% CI 0.652–0.746). The areas under the receiver operating characteristic curves for 3- and 5-years survival were 0.751 and 0.724. The nomogram was in good concordance with the actual survival rates. The independent prognostic factors for overall survival in elderly patients with cervical cancer after radiotherapy were: tumor size, pre-treatment hemoglobin, chemotherapy, and pelvic lymph node metastasis. The novel prognostic nomogram based on these factors showed good concordance with the actual survival rates and can be used to guide personalized clinical treatment.

A prognostic model based on regulatory T-cell-related genes in gastric cancer: Systematic construction and validation.

Human gastrointestinal tumours have been shown to contain massive numbers of tumour infiltrating regulatory T cells (Tregs), the presence of which are closely related to tumour immunity. This study was designed to develop new Treg-related prognostic biomarkers to monitor the prognosis of patients with gastric cancer (GC). Treg-related prognostic genes were screened from Treg-related differentially expressed genes in GC patients by using Cox regression analysis, based on which a prognostic model was constructed. Then, combined with RiskScore, survival curve, survival status assessment and ROC analysis, these genes were used to verify the accuracy of the model, whose independent prognostic ability was also evaluated. Six Treg-related prognostic genes (CHRDL1, APOC3, NPTX1, TREML4, MCEMP1, GH2) in GC were identified, and a 6-gene Treg-related prognostic model was constructed. Survival analysis revealed that patients had a higher survival rate in the low-risk group. Combining clinicopathological features, we performed univariate and multivariate regression analyses, with results establishing that the RiskScore was an independent prognostic factor. Predicted 1-, 3- and 5-year survival rates of GC patients had a good fit with the actual survival rates according to nomogram results. In addition patients in the low-risk group had higher tumour mutational burden (TMB) values. Gene Set Enrichment Analysis (GSEA) demonstrated that genes in the high-risk group were significantly enriched in pathways related to immune inflammation, tumour proliferation and migration. In general, we constructed a 6-gene Treg-associated GC prognostic model with good prediction accuracy, where RiskScore could act as an independent prognostic factor. This model is expected to provide a reference for clinicians to estimate the prognosis of GC patients.

A risk score model based on endoplasmic reticulum stress related genes for predicting prognostic value of osteosarcoma

BackgroundWe aimed to establish an osteosarcoma prognosis prediction model based on a signature of endoplasmic reticulum stress-related genes.MethodsDifferentially expressed genes (DEGs) between osteosarcoma with and without metastasis from The Cancer Genome Atlas (TCGA) database were mapped to ERS genes retrieved from Gene Set Enrichment Analysis to select endoplasmic reticulum stress-related DEGs. Subsequently, we constructed a risk score model based on survival-related endoplasmic reticulum stress DEGs and a nomogram of independent survival prognostic factors. Based on the median risk score, we stratified the samples into high- and low-risk groups. The ability of the model was assessed by Kaplan–Meier, receiver operating characteristic curve, and functional analyses. Additionally, the expression of the identified prognostic endoplasmic reticulum stress-related DEGs was verified using real-time quantitative PCR (RT-qPCR).ResultsIn total, 41 endoplasmic reticulum stress-related DEGs were identified in patients with osteosarcoma with metastasis. A risk score model consisting of six prognostic endoplasmic reticulum stress-related DEGs (ATP2A3, ERMP1, FBXO6, ITPR1, NFE2L2, and USP13) was established, and the Kaplan–Meier and receiver operating characteristic curves validated their performance in the training and validation datasets. Age, tumor metastasis, and the risk score model were demonstrated to be independent prognostic clinical factors for osteosarcoma and were used to establish a nomogram survival model. The nomogram model showed similar performance of one, three, and five year-survival rate to the actual survival rates. Nine immune cell types in the high-risk group were found to be significantly different from those in the low-risk group. These survival-related genes were significantly enriched in nine Kyoto Encyclopedia of Genes and Genomes pathways, including cell adhesion molecule cascades, and chemokine signaling pathways. Further, RT-qPCR results demonstrated that the consistency rate of bioinformatics analysis was approximately 83.33%, suggesting the relatively high reliability of the bioinformatics analysis.ConclusionWe established an osteosarcoma prediction model based on six prognostic endoplasmic reticulum stress-related DEGs that could be helpful in directing personalized treatment.

Epidemiology and nomogram for predicting the cancer-specific survival of ovarian granulosa cell tumor: A seer database study.

ovarian granulosa cell tumor (OGCT) is a kind of infrequent ovarian malignant tumor with limited epidemiological data available. we established a predictive nomograph to verify the clinical prognosis. 1005 diagnosed with ovarian granulosa cell tumor (OGCT) were extracted from Surveillance, Epidemiology, and End Results (SEER) public database from 2000-2018. Kaplan-Meier analysis was applied to distinguish risk factors, univariate and multivariate Cox analyses were used to determine the independent prognostic factors for cancer-specific survival (CSS) of OGCT patients. The obtained prognostic variables were combined to construct a nomogram model for predicting CSS in OGCT patients. Model performance was detected and evaluated with ROC curves and calibration plots. Data collected from 1005 patients were divided into two groups: training cohort(n=703,70%) and validation cohort(n=302,30%). The multivariate Cox model identified five covariates including age, marital status, AJCC stages, surgery and chemotherapy as independent interfering factors of CSS. The nomogram has shown a promising and excellent accuracy in evaluating 3 -, 5 -, 8-year CSS in OGCT patients. In terms of the CSS of the training cohort, the AUC values of the 3 -, 5 -, 8-year ROC curves were 0.819,0.8,0.819, while in terms of the CSS of the validation cohort, the AUC values of the validation cohort were 0.822,0.84,0.823, respectively. All the calibration curves showed pleasant consistency between predicted and actual survival rates. The nomogram model established in the study can improve the veracity of prognosis prediction, thereby improving the accuracy of individualized survival risk assessment, and providing targeted and constructive recommendations for specific treatment options. Age, advanced clinical stage, widower and without surgery therapy are independent risk factors for poor prognosis and the nomogram we constructed can help clinicians efficiently recognize high-risk OGCT patients to guide targeted therapies and improve their outcomes.

Overall survival prediction models for gynecological endometrioid adenocarcinoma with squamous differentiation (GE-ASqD) using machine-learning algorithms

The actual 5-year survival rates for Gynecological Endometrioid Adenocarcinoma with Squamous Differentiation (GE-ASqD) are rarely reported. The purpose of this study was to evaluate how histological subtypes affected long-term survivors of GE-ASqD (> 5 years). We conducted a retrospective analysis of patients diagnosed GE-ASqD from the Surveillance, Epidemiology, and End Results database (2004–2015). In order to conduct the studies, we employed the chi-square test, univariate cox regression, and multivariate cox proportional hazards model. A total of 1131 patients with GE-ASqD were included in the survival study from 2004 to 2015 after applying the inclusion and exclusion criteria and the sample randomly split into a training set and a test set at a ratio of 7:3. Five machine learning algorithms were trained based on nine clinical variables to predict the 5-year overall survival. The AUC of the training group for the LR, Decision Tree, forest, Gbdt, and gbm algorithms were 0.809, 0.336, 0.841, 0.823, and 0.856 respectively. The AUC of the testing group was 0.779, 0.738, 0.753, 0.767 and 0.734, respectively. The calibration curves confirmed good performance of the five machine learning algorithms. Finally, five algorithms were combined to create a machine learning model that forecasts the 5-year overall survival rate of patients with GE-ASqD.

Pretransplant C-peptide Levels and Pancreas Transplant Outcomes: Risk and Reward.

We read with interest the report by Parajuli et al1, chronicling the University of Wisconsin experience in simultaneous pancreas-kidney transplantation (SPKT) according to the pretransplant fasting serum C-peptide levels. The authors demonstrated that higher pretransplant C-peptide levels predicted the risk of a composite outcome (combination of pancreas graft failure according to the United Network for Organ Sharing definition and pancreas graft dysfunction defined as initiation of any antidiabetic agent) posttransplant in patients with a type 2 diabetes phenotype.1,2 Recipients were categorized into 3 groups based on pretransplant fasting serum C-peptide levels: low (≤2 ng/mL, n = 14), medium (>2–8 ng/mL, n = 47), and high (>8 ng/mL, n = 15). The uncensored composite outcome of pancreas graft failure or dysfunction occurred in 0% in the low, 23% in the medium, and 33% of patients in the high C-peptide groups, respectively, suggesting that higher levels of pretransplant C-peptide are associated with inferior posttransplant outcomes. These findings confirm our recent report based on a case-controlled, single-center, retrospective study of 46 SPKT patients with pretransplant fasting serum C-peptide levels ≥2.0 ng/mL compared with 46 control patients (C-peptide level <0.5 ng/mL) matched for recipient age, gender, race, and transplant date.3 In our study, we likewise reported inferior mid-term outcomes in the group with higher pretransplant C-peptide levels. In our overall SPKT experience, we have 205 patients with a minimum of 5-y follow-up. Using the above C-peptide categories as per the Wisconsin study, we identified 166 patients in the low, 32 patients in the medium, and 7 patients in the high C-peptide groups. Five-y actual (uncensored) patient and graft survival rates were as follows: patient (91% low versus 93.8% medium versus 85.7% high) and kidney (80.7% versus 71.9% versus 71.4%), respectively. Using the composite end point of pancreas graft failure or dysfunction, pancreas outcomes were 25.3% low versus 34.4% medium versus 71.4% high C-peptide (P = 0.10). Similar to the Wisconsin study, we are limited by the small sample size in the high pretransplant C-peptide group but the findings indicate a trend toward a greater need for supplemental antidiabetic therapy in patients with higher pretransplant C-peptide levels. Unfortunately, what is missing from these studies is a better characterization of patients with partial pancreas allograft function (dysfunction) who continue to be free from wide glycemic excursions and do not experience hypoglycemia (despite the need for antidiabetic therapy) because of preserved counterregulatory function of the pancreas allograft.4 In our experience, we identified posttransplant weight gain, insulin resistance, and rejection as potential mitigating factors to explain these inferior outcomes. Consequently, we recommend greater emphasis on weight management strategies, behavioral modification, nutritional counseling, structured physical training programs, timely adjustments in (and use of less diabetogenic) immunosuppression, more intense long-term monitoring by the transplant center, and increased use of novel antidiabetic agents such as Glucagon-like peptide 1 receptor agonists and Sodium-glucose cotransporter-2 inhibitors when indicated in this unique population. We congratulate the authors on this excellent study and agree that these findings should not be an impetus to limit access to pancreas transplantation but rather help guide clinical decision-making and optimize posttransplant management.

Percepción de los estudiantes de pregrado de medicina frente a la tasa de éxito de la reanimación cardiopulmonar:

Introduction: According to the American Heart Association, the survival rate after cardiopulmonary resuscitation (CPR) performed at a hospital is 25.8%. In both medical staff and the general population, multiple factors cause an overestimation of the effectiveness of CPR maneuvers. Methods: Observational cross-sectional study. Through an online survey, the perception of medical students about the survival rate after receiving CPR in an in-hospital setting was inquired. Additionally, it was asked if they would accept CPR in case of presenting a cardiac arrest. Participants were then informed of the actual survival rates and asked again if they would accept receiving CPR. Results: 692 students were surveyed (78.9% of the target population). 61% of the participants stated that the survival rate after CPR was greater than 30%. Before knowing the actual survival rates, 95.5% would accept CPR maneuvers, and after knowing the actual survival rates, only 75.3% would accept them. Discussion: The results confirmed that the perception of the survival rate after in-hospital CPR maneuvers is overestimated by medical students. This could affect their medical criteria and shared decision-making process with patients. Additionally, having adequate information about the result of an intervention changes the decision towards receiving it or not.

Nomogram to Predict Long-Term Overall Survival and Cancer-Specific Survival of Radiotherapy Patients with Nasopharyngeal Carcinoma.

To establish and validate a nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with nasopharyngeal carcinoma (NPC) receiving radiotherapy by integrating multiple independent prognostic factors. Data from 5663 patients with NPC who received definite radiotherapy between 2004 and 2018 were included and divided into training and validation cohorts. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors of patients with NPC after radiotherapy. Thereafter, the predictive accuracy of the nomogram model was evaluated. Age, race, marital status, pathological type, tumor size, T stage, N stage, M stage, American Joint Committee on Cancer stage, and chemotherapy were independent factors affecting the prognosis of patients with NPC receiving radiotherapy. Nomograms with a concordance index of 0.726 (95% confidence interval (CI): 0.675-0.777) and 0.732 (95% CI: 0.680-0.785) were able to predict OS and CSS, respectively. The area under the curve showed excellent predictive performance. Additionally, the calibration curve indicated that the predicted survival rate was consistent with the actual survival rate, and the decision curve indicated its clinical value. The established risk stratification system was able to accurately stratify patients receiving radiotherapy for NPC into three risk subgroups with significant differences in prognosis (P < 0.05). The constructed nomogram had good prognostic performance and could be used as an effective tool to evaluate the prognosis of patients with NPC after radiotherapy. This nomogram could be further used to guide clinical decisions and personalized treatment plans.

Construction and validation of a protein-associated prognostic model for gastrointestinal cancer.

This paper aims to identify the hub prognosis-related proteins (PAPs) and construct a prognosis model to GIC patients for clinical application. Protein expression data of GIC were obtained from The Cancer Proteome Atlas (TCPA) and downloaded the clinicopathological data from The Cancer Genome Atlas database (TCGA). Besides, hub proteins were filtrated out via univariate and multivariate Cox regression analysis. Moreover, survival analysis and nomogram were used to predict overall survival (OS). We used the calibration curves to assess the predictive and actual survival rate consistency. The consistency index (C-index) was used to evaluate the prognostic ability of the predictive model. Furthermore, functional enrichment analysis and protein co-expression of PAPs were used to explore their roles in GIC. Finally, a prognosis model was conducted based on ten PAPs (CYCLIND1, DVL3, NCADHERIN, SYK, ANNEXIN VII, CD20, CMET, RB, TFRC, and PREX1). The risk score calculated by the model was an independent prognostic predictor. Compared with the high-risk subgroup, the low-risk subgroup had better OS. In the TCGA cohort, the area under the curve value of the receiver operating characteristic curve of the prognostic model was 0.692. The expression of proteins and risk score had a significant association with the clinicopathological characteristics of GIC. Besides, nomogram based on GIC clinicopathological feature and risk score could properly predict the OS of individual GIC patients. The C-index is 0.71 in the TCGA cohort and 0.73 in the GEO cohort, respectively. The results indicate that the risk score is an independent prognostic biomarker and is related to the malignant clinical features of GIC patients. Besides, several PAPs, associated with the survival and clinicopathological characteristics of GIC might be potential biomarkers for GIC diagnosis and treatment.

Nomogram Model for Predicting the Prognosis of High-Grade Glioma in Adults Receiving Standard Treatment: A Retrospective Cohort Study

To identify the critical factors associated with the progression-free survival (PFS) and overall survival (OS) of high-grade glioma (HGG) in adults who have received standard treatment and establish a novel graphical nomogram and an online dynamic nomogram. This is a retrospective study of adult HGG patients receiving standard treatment (surgery, postoperative radiotherapy, and temozolomide (TMZ) chemotherapy) at Huashan Hospital, Fudan University between January 2017 and December 2019. We used uni- and multi-variable COX models to identify the significant prognostic factors for PFS and OS. Based on the significant predictors, graphical and online nomograms were established. A total of 246 patients were enrolled in the study based on the inclusion criteria. The average PFS and OS were 22.99 ± 11.43 and 30.51 ± 13.73 months, respectively. According to the multi-variable COX model, age, extent of resection (EOR), and IDH mutation were associated with PFS and OS, while edema index (EI) was relevant to PFS. In addition, patients with IDH and TERT promoter co-mutations had longer PFSs and OSs, and no apparent survival benefit was found in the long-cycle TMZ adjuvant chemotherapy compared with the standard Stupp protocol. Based on these critical factors, a graphical nomogram and online nomogram were developed for predicting PFS and OS, respectively. The calibration curve showed favorable consistency between the predicted and actual survival rates. C-index and time-dependent AUC showed good discrimination abilities. We identified the significant predictors for the PFS and OS of HGG adults receiving standard treatment and established user-friendly nomogram models to assist neurosurgeons in optimizing clinical management and treatment strategies.

Nomogram Model for Predicting the Overall Survival of Patients With Meningiomas: a Retrospective Cohort Study

To identify the significant prognostic factors of overall survival (OS) for patients living with meningiomas (MMs), and establish a novel graphical nomogram and an online dynamic nomogram. Patients diagnosed with MMs were identified retrospectively from the SEER database. The cohort was split into training (70%) and test (30%) groups randomly. Univariable and multivariable Cox models were successively used to screen the significant prognostic factors. Subsequently, the independent predictors were used as items to establish the graphic and dynamic nomogram model. To assess the accuracy of the model, a calibration curve was plotted. To assess the discrimination performance, C-index and time-dependent area under the receiver operator characteristic curve (AUC) were selected. Additionally, the decision curve was generated to evaluate the clinical net benefit of the model. A total of 899 patients were involved, of which 629 and 270 were split into training group and test group, respectively. Age, sex, radiotherapy, tumor size, and tumor histology were identified as the significant prognostic factors. Based on these factors, a graphical nomogram and online nomogram (Web site: https://helloshinyweb.shinyapps.io/dynamic_nomogram/) were developed. The calibration curve showed favorable consistence between predicted and actual survival rate. C-index and time-dependent AUC showed good discrimination ability, and the decision curve analysis showed positive net benefit of the model in clinical practice. Age of diagnosis, sex, tumor size, tumor histology, and radiotherapy were independent predictors for OS, while extent of resection had a borderline significant. A nomogram model was successfully developed and validated to dynamically predict the long-term OS for MM patients, expecting to help neurosurgeons optimize clinical management and treatment strategies.

Actual over 3-year survival after stereotactic body radiation therapy in patients with unresectable intrahepatic cholangiocarcinoma.

As a non-invasive treatment, stereotactic body radiation therapy (SBRT) has been an emerging and effective option for patients with unresectable intrahepatic cholangiocarcinoma (ICC). The Cyber Knife has an SBRT system, which can realize real-time tracking of tumors during treatment. It can protect the surrounding normal liver tissue while the tumor gets the therapeutic dose. The purpose of this study was to evaluate the factors affecting the local control rate for patients after SBRT treatment, and to predict the factors affecting survival rates, then to report the 3-year actual survival rates after treatment and identify the influencing factors of 3-year survival rate. We conducted a long-term follow-up of 43 patients with unresectable intrahepatic cholangiocarcinoma who underwent Cyber Knife in our hospital from January 2016 to December 2018. Regular medical check-ups were performed every 2-3 months after SBRT to evaluated the effect of treatment. The median follow-up time was 15 months (4-78 months), and the median progression-free survival (PFS) was 6 months (95% CI, 2.788-9.212) and the median overall survival (OS) was 12 months (95% CI, 3.434-20.566), respectively. Based on modified Response Evaluation and Criteria in Solid Tumor (mRECIST), response rate (RR) and disease control rate (DCR) of SBRT in unresectable ICC were 55.2% and 86%. The 1-, 2- and 3-years OS rate were 51.2%, 32.6% and 23.3%. Multivariate analysis based on competing risk survival analysis identified that patients with multiple nodules, large diameter, high level of CA199 and CEA, poor ECOG performance status had worse overall survival (p<0.05). Patients who survived ≥3 years had significantly lower levels of CEA, CA199, smaller tumor diameters and lower number of lesions (p<0.05). The SBRT might be a candidate option for patients who unable to perform surgery. The rate of 3-year survival after SBRT for unresectable ICC can be expected with 23.3%.