Risk Of Survival In Patients Research Articles

Development and Validation of a Predictive Nomogram for Patients With Myxopapillary Ependymoma: A Surveillance, Epidemiology, and End Results (SEER) Retrospective Cohort Analysis.

Retrospective Study. Myxopapillary ependymomas (MPEs) are a unique subgroup of spinal ependymomas originating from the filum terminale's ependymal glia. The 2021 WHO classification reclassified all MPEs as grade 2, recognizing their higher recurrence risk. Due to their rarity, our objective with this study is to understand MPEs' clinical course and optimal management through a large retrospective cohort analysis. From the years 2000 to 2020, patients with MPEs were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariable Cox proportional hazard models were run to identify variables that had a significant impact on the primary endpoint of overall survival (OS). A predictive nomogram was built to predict 5-year and 10-year survival probability. This retrospective cohort includes 1373 patients. Patients 65 years or older at diagnosis had a poorer OS (P < 0.001). Most patients received subtotal resection. Only 320 patients (23%) received gross total resection (GTR). Patients that received GTR had the best OS when compared against all other modalities of treatment (P < 0.05). Receiving radiotherapy did not affect OS in patients with MPE (P = 0.2). Nomogram includes patient age and treatment modalities, demonstrating acceptable accuracy in estimating the survival probability at 5-year and 10-year intervals, with a C-index of 0.80 (95% CI of 0.71 to 0.90). This study highlights the survival benefit of GTR in the treatment of patients with MPE. The role of adjuvant radiotherapy remains unclear as it did not seem to improve OS. The nomogram stratifies the risk of survival in patients with MPE based on age and treatment modality.

STATUS AND SURVIVAL RISK OF PATIENTS WITH CHRONIC KIDNEY DISEASE AND HYPERTENSION

Objective: To provide nationwide data on status of control and survival risk of patients with hypertension and chronic kidney disease in China. Design and method: A total of 39,094 participants aged 35 years and above, without prior cardiovascular disease (CVD), were enrolled from a sub-study of the China Hypertension Survey. CKD defined as presence of impaired kidney function (eGFR of &lt;60 mL/min/1.73m2). Hypertension defined as 140/90 mmHg or higher. CVD events and all-cause mortality, with follow-up from 2018 to 2019. Multivariable Cox proportional hazards models were employed to estimate hazard ratios (HRs) of patients with CKD in Chinese adults with hypertension. Results: The mean age was 56.18 years, and the proportion of women was 54.4%. There were 16 276 adults with hypertension. The estimated proportion of CKD in patients with hypertension were 15.79% in China. The proportions of CKD were 13.22%, 17.26%, 21.64% at stage 1, 2, and 3 hypertension, respectively. Among the adults with hypertension, 21.07%, 63.14%, 13.07%, 2.32%, and 0.4% were at stage 1, 2, 3a, 3b, and 4 to 5 CKD, respectively. In patients with hypertension and CKD, the proportions of combined use of 2 or more antihypertensive drugs were 3.87%, 7.37%, 9.41%, 14.79%, and 10.80%, and the control rate of hypertension were 15.13%, 19.74%, 22.94%, 24.60%, and 21.54% at stage 1, 2, 3a, 3b, and 4 to 5 CKD, respectively. Among the hypertensive patients, after adjusting for age, sex, educational levels, alcohol drinking, smoking, and BMI, people with CKD had 58.8% higher risk of CVD (HR=1.588, 95%CI 1.341–1.881) and 34.3% higher risk in all-cause mortality (HR=1.343, 95%CI 1.138–1.584), compared with people without CKD. Significant higher risk of CVD and mortality at 3a, 3b, 4 to 5 CKD compared with stage 1 CKD. Conclusions: Among Chinese adults with hypertension, the proportion of CKD increased with higher blood pressure. Patients with hypertension and CKD had limited control rates of hypertension and high risks of CVD and all-cause mortality, which need more attention.

Association of retinopathy with risk of all-cause and specific-cause mortality in the National Health and Nutrition Examination Survey, 2005 to 2008.

This study aimed to elucidate the relationship between retinopathy status or severity and the all-cause and specific-cause mortality risk based on the updated National Health and Nutrition Examination Survey (NHANES) database and 2019 Public Access Link mortality file. In this prospective cohort study, a total of 6,797 participants aged over 40 years based on NHANES 2005-2008 were analyzed. The severity of retinopathy was classified into 4 grades-no retinopathy, mild non-proliferative retinopathy (NPR), moderate to severe NPR, and proliferative retinopathy (PR). Multiple covariate-adjusted Cox proportional hazards regression models and Fine and Gray competing risk regression models were used to assess the all-cause and cause-specific mortality risks, respectively. The propensity score matching (PSM) approach was also applied additionally to adequately balance between-group covariates to validate our findings. A final total of 4,808 participants representing 18,282,772 United States (US) non-hospitalized participants were included for analysis, 50.27% were male (n = 2,417), 55.32% were non-hispanic white (n = 2,660), and mean [SE] age, 56.10 [0.40] years. After a median follow-up of 12.24 years (interquartile range, 11.16-13.49 years), 1,164 participants died of all-cause mortality, of which 941 (80.84%) died without retinopathy and 223 (19.16%) died with retinopathy at baseline. The presence of retinopathy was associated with increased all-cause mortality, cardiovascular disease (CVD), and diabetes mellitus (DM)-specific mortality, and the results remain consistent after PSM. Severity analysis showed that only mild NPR was associated with an increased all-cause mortality risk (hazard ratio (HR) = 2.01; 95% confidence interval (CI), 1.00-4.03), while increased CVD and DM-specific mortality risk were associated with all grades of retinopathy and were exponentially greater with increasing retinopathy severity, and the trend test was also significant (P for trend 0.004 and 0.04, respectively). Our findings suggest that the diagnosis of retinopathy is an independent risk factor for all-cause mortality in people over 40 years old. Retinopathy grading is significantly associated with the survival risk of patients with CVD or DM, it can be a valuable predictor in the stratified management and risk warning of CVD or DM patients, as well as in the monitoring of systemic vasculopathy status.

Hybrid Graph Convolutional Network With Online Masked Autoencoder for Robust Multimodal Cancer Survival Prediction.

Cancer survival prediction requires exploiting related multimodal information (e.g., pathological, clinical and genomic features, etc.) and it is even more challenging in clinical practices due to the incompleteness of patient's multimodal data. Furthermore, existing methods lack sufficient intra- and inter-modal interactions, and suffer from significant performance degradation caused by missing modalities. This manuscript proposes a novel hybrid graph convolutional network, entitled HGCN, which is equipped with an online masked autoencoder paradigm for robust multimodal cancer survival prediction. Particularly, we pioneer modeling the patient's multimodal data into flexible and interpretable multimodal graphs with modality-specific preprocessing. HGCN integrates the advantages of graph convolutional networks (GCNs) and a hypergraph convolutional network (HCN) through node message passing and a hyperedge mixing mechanism to facilitate intra-modal and inter-modal interactions between multimodal graphs. With HGCN, the potential for multimodal data to create more reliable predictions of patient's survival risk is dramatically increased compared to prior methods. Most importantly, to compensate for missing patient modalities in clinical scenarios, we incorporated an online masked autoencoder paradigm into HGCN, which can effectively capture intrinsic dependence between modalities and seamlessly generate missing hyperedges for model inference. Extensive experiments and analysis on six cancer cohorts from TCGA show that our method significantly outperforms the state-of-the-arts in both complete and missing modal settings. Our codes are made available at https://github.com/lin-lcx/HGCN.

Incorporating Molecular Classification When Stratifying the Survival Risk of Patients with High-Grade Endometrial Carcinomas.

Assessing survival risk in patients with high-grade endometrial carcinomas has remained challenging. We aimed to investigate the distribution of molecular subtypes and assess their prognostic role in a large cohort of 355 patients with high-grade endometrial carcinoma. Molecular classification was determined using DNA polymerase epsilon (POLE) sequencing as well as immunohistochemical staining for p53 and mismatch repair (MMR) proteins. Endometrial carcinomas were stratified into four subtypes: POLE ultramutated, MMR-deficient, non-specific molecular profile (NSMP), and p53-mutant. This study included 177 and 178 patients with endometrioid and non-endometrioid carcinomas, respectively. Forty-two patients (11.8%) were categorized as POLE ultramutated, 106 (29.9%) as MMR-deficient, 128 (36.1%) as p53-mutant, and 79 (22.2%) as NSMP. Patients of different molecular subtypes had distinct survival times; molecular classification, but not histotype, was significantly associated with survival outcomes. When incorporating molecular classification into the stratification model, 52 patients (15.5%) switched risk groups, with 40 (11.9%) shifting to a lower risk for having a POLE mutation and 12 (3.6%) shifting to a higher risk owing to p53-mutant status. Molecular classification may provide more accurate prognostic information among patients with high-grade endometrial carcinomas and improve their stratification for purposes of clinical management.

CNSC-07. MECHANISMS OF GLIOBLASTOMA-INDUCED CORTICAL REMODELING IDENTIFY THERAPEUTIC VULNERABILITIES

Abstract Prior work demonstrated synaptic integration of malignant gliomas into neural circuits induces local hyperexcitability and tumor proliferation. However, prognostication and therapeutic vulnerabilities are lacking from preclinical models. Here, we integrate in vivo and in vitro neurophysiology spatially matched with gene expression programs and protein signaling mechanisms across 66 IDH WT glioblastoma patients to identify thrombospondin-1 (TSP-1) as a molecular driver of glioma-induced network remodeling. Bulk and single cell RNA-sequencing of 11 intratumoral regions maintaining functional connectivity (13,730 cells analyzed) revealed a distinct neurogenic signature enriched for the synaptogenic factor TSP-1. Mechanistic and functional studies validating therapeutic vulnerabilities to TSP-1 silencing by shRNA knockdown and FDA-approved inhibitors (Gabapentin and LSKL) of excitatory synapse formation through the gabapentin a2d-1 receptor was performed in vitro and in vivo. Glioma-neuron co-culture of TSP-1 overexpressing cells demonstrated increased Ki67 proliferation and tumor microtube (TMT) formation when cultured in the presence of neurons. Pharmacological inhibition of TSP-1 using gabapentin or TSP-1 shRNA inhibited the proliferation and TMT-mediated expansion. Hippocampal xenografted mice with TSP-1 over expressing primary patient cultures demonstrated shorter survival and gabapentin treatment of xenografted mice significantly reduced the proliferation of TSP-1 overexpressing cells in vivo. Electrophysiological properties of glioma-neuron co-cultures analyzed using multi-electrode array (MEA) demonstrated increased neuronal spiking activity and network burst synchrony in the presence of TSP-1 over expressing cells. Strikingly, these increases were eliminated in the presence of gabapentin. We modeled survival risk in patients incorporating the effects of glioma intrinsic neuronal activity, molecular, therapeutic, and clinical factors on overall survival by recursive partitioning. Three risk groups were identified based on tumor intrinsic neuronal activity, pre- and post-operative tumor volume with shortest overall survival in patients with glioma intrinsic neuronal activity. These data identify glioma-induced secretion of TSP-1 as a key contributor of tumor proliferation shedding light on new therapies.

Prognostic Relevance of Lymphatic Vessel Density in Squamous Cell Carcinomas of the Oral Cavity: A Systematic Review and Meta-Analysis.

Oral Squamous Cell Carcinoma (OSCC), a major debilitating illness demands focus in recent times due to a constant upsurge in cases and poor prognostic implications. An urgent mandate upon finding evidence of relevant prognostic markers is the need of the hour. This systematic review and meta-analysis, therefore, elect an objective assessment of Lymphatic Vessel Density (LVD) as a pertinent parameter governing OSCC prognosis. The study protocol was registered at the International Prospective Register Of Systematic Reviews (PROSPERO). Databases were searched using the MeSH keywords for all study types following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The exposure under consideration was the evaluation of LVD in patients of OSCC. The outcome was measured as pooled Hazard/Odd's/Risk ratios in survived versus non-survived OSCC population. The risk of bias assessment was performed using the QUIPS tool. Heterogeneity was assessed by Chi-square and I2 statistics whereas publication bias was investigated using Egger's test of significance. All the statistical analysis was conducted using STATA version 13.0. The initial search of 226 records were screened and filtered through the inclusion and exclusion criteria to achieve an outcome of 15 studies for qualitative synthesis out of which seven studies were eligible for meta-analysis. Pooled Hazard of enhanced Lymphatic Vessel Density was not found to be statistically significant (HR = 1.98, p = 0.553); contrary to the pooled Odd's/Risk for patient survival which was statistically significant (RR = 1.33, p = 0.046). The I2 test of heterogeneity was also significant (58.8%, p = 0.046). This meta-analysis helps to generate pathfinding evidence for a noteworthy role of Lymphatic Vessel Density evaluation in suggesting OSCC prognosis.

Development and Verify of Survival Analysis Models for Chinese Patients With Systemic Lupus Erythematosus

BackgroundThe aim of this study is to develop survival analysis models of hospitalized systemic lupus erythematosus (h-SLE) patients in Jiangsu province using data mining techniques to predict patient survival outcomes and survival status.MethodsIn this study, based on 1999–2009 survival data of 2453 hospitalized SLE (h-SLE) patients in Jiangsu Province, we not only used the Cox proportional hazards model to analyze patients’ survival factors, but also used neural network models to predict survival outcomes. We used semi-supervised learning to label the censored data and introduced cost-sensitivity to achieve data augmentation, addressing category imbalance and pseudo label credibility. In addition, the risk score model was developed by logistic regression.ResultsThe overall accuracy of the survival outcome prediction model exceeded 0.7, and the sensitivity was close to 0.8, and through the comparative analysis of multiple indicators, our model outperformed traditional classifiers. The developed survival risk assessment model based on logistic regression found that there was a clear threshold, i.e., a survival threshold indicating the survival risk of patients, and cardiopulmonary and neuropsychiatric involvement, abnormal blood urea nitrogen levels and alanine aminotransferase level had the greatest impact on patient survival time. In addition, the study developed a graphical user interface (GUI) integrating survival analysis models to assist physicians in diagnosis and treatment.ConclusionsThe proposed survival analysis scheme identifies disease-related pathogenic and prognosis factors, and has the potential to improve the effectiveness of clinical interventions.

A Novel 16-Genes Signature Scoring System as Prognostic Model to Evaluate Survival Risk in Patients with Glioblastoma.

Previous studies have found that gene expression levels are associated with prognosis and some genes can be used to predict the survival risk of glioblastoma (GBM) patients. However, most of them just built the survival-related gene signature, and personal survival risk can be evaluated only in group. This study aimed to find the prognostic survival related genes of GBM, and construct survival risk prediction model, which can be used to evaluate survival risk by individual. We collected gene expression data and clinical information from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analysis and LASSO-cox regression analysis were performed to get survival-related genes and establish the overall survival prediction model. The ROC curve and Kaplan Meier analysis were used to evaluate the prediction ability of the model in training set and two independent cohorts. We also analyzed the biological functions of survival-related genes by GO and KEGG enrichment analysis. We identified 99 genes associated with overall survival and selected 16 genes (IGFBP2, GPRASP1, C1R, CHRM3, CLSTN2, NELL1, SEZ6L2, NMB, ICAM5, HPCAL4, SNAP91, PCSK1N, PGBD5, INA, UCHL1 and LHX6) to establish the survival risk prediction model. Multivariate Cox regression analysis indicted that the risk score could predict overall survival independent of age and gender. ROC analyses showed that our model was more robust than four existing signatures. The sixteen genes can also be potential transcriptional biomarkers and the model can assist doctors on clinical decision-making and personalized treatment of GBM patients.

Magnetic Resonance for Differential Diagnosis of Left Ventricular Hypertrophy: Diagnostic and Prognostic Implications.

Background: Left ventricular hypertrophy (LVH) may be due to different causes, ranging from benign secondary forms to severe cardiomyopathies. Transthoracic Echocardiography (TTE) and ECG are the first-level examinations for LVH diagnosis. Cardiac magnetic resonance (CMR) accurately defines LVH type, extent and severity. Objectives: to evaluate the diagnostic and prognostic role of CMR in patients with TTE and/or ECG evidence of LVH. Methods: We performed CMR in 300 consecutive patients with echocardiographic and/or ECG signs of LVH. Results: Overall, 275 patients had TTE evidence of LVH, with initial suspicion of hypertrophic cardiomyopathy (HCM) in 132 (44%), cardiac amyloidosis in 41 (14%), hypertensive LVH in 48 (16%), aortic stenosis in 4 (1%), and undetermined LVH in 50 (16%). The initial echocardiographic diagnostic suspicion of LVH was confirmed in 172 patients (57.3%) and changed in 128 patients (42.7%, p < 0.0001): the diagnosis of HCM increased from 44% to 71% of patients; hypertensive and undetermined LVH decreased significantly (respectively to 4% and 5%). CMR allowed for a diagnosis in 41 out of 50 (82%) patients with undetermined LVH at TTE. CMR also identified HCM in 17 out of 25 patients with apparently normal echocardiography but with ECG criteria for LVH. Finally, the reclassification of the diagnosis by CMR was associated with a change in survival risk of patients: after CMR reclassification, no events occurred in patients with undetermined or hypertensive LVH. Conclusions: CMR changed echocardiographic suspicion in almost half of patients with LVH. In the subgroup of patients with abnormal ECG, CMR identified LVH (particularly HCM) in 80% of patients. This study highlights the indication of CMR to better characterize the type, extent and severity of LVH detected at echocardiography and suspected with ECG.

Identification of an independent immune-genes prognostic index for renal cell carcinoma

BackgroundConsiderable evidence has indicated an association between the immune microenvironment and clinical outcome in ccRCC. The purpose of this study is to extensively figure out the influence of immune-related genes of tumors on the prognosis of patients with ccRCC.MethodsFiles containing 2498 immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort), and the transcriptome data and clinical information relevant to patients with ccRCC were identified and downloaded from the TCGA data-base. Univariate and multivariate Cox regression analyses were used to screen out prognostic immune genes. The immune risk score model was established in light of the regression coefficient between survival and hub immune-related genes. We eventually set up a nomogram for the prediction of the overall survival for ccRCC. Kaplan-Meier (K-M) and ROC curve was used in evaluating the value of the predictive risk model. A P value of < 0.05 indicated statistically significant differences throughout data analysis.ResultsVia differential analysis, we found that 556 immune-related genes were expressed differentially between tumor and normal tissues (p < 0. 05). The analysis of univariate Cox regression exhibited that there was a statistical correlation between 43 immune genes and survival risk in patients with ccRCC (p < 0.05). Through Lasso-Cox regression analysis, we established an immune genetic risk scoring model based on 18 immune-related genes. The high-risk group showed a bad prognosis in K-M analysis. (p < 0.001). ROC curve showed that it was reliable of the immune risk score model to predict survival risk (5 year over survival, AUC = 0.802). The model indicated satisfactory AUC and survival correlation in the validation data set (5 year OS, Area Under Curve = 0.705, p < 0.05). From Multivariate regression analysis, the immune-risk score model plays an isolated role in the prediction of the prognosis of ccRCC. Under multivariate-Cox regression analysis, we set up a nomogram for comprehensive prediction of ccRCC patients’ survival rate. At last, it was identified that 18 immune-related genes and risk scores were not only tremendously related to clinical prognosis but also contained in a variety of carcinogenic pathways.ConclusionIn general, tumor immune-related genes play essential roles in ccRCC development and progression. Our research established an unequal 18-immune gene risk index to predict the prognosis of ccRCC visually. This index was found to be an independent predictive factor for ccRCC.

Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis.

Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of prostate cancer (PCa). Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis. Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa based on TCGA data. Then we used the data of TCGA and ICGC to verify the performance of our prediction model. The receiver operating characteristic curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, Cox regression analysis was conducted to examine whether the signature's prediction ability was independent of additional clinicopathological variables. Possible biological functions for those prognostic lncRNAs were predicted on those 4 protein-coding genes (PCGs) related to lncRNAs. Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane. Our current study successfully identifies a novel candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.

Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19).

In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses.

Statin use and the overall survival of renal cell carcinoma: A meta-analysis.

Statins are commonly prescribed drugs that reduce cholesterol levels and the risk of cardiovascular and cerebrovascular events. Clinical studies have shown that statins also possess cancer-preventive properties. Two studies have reported that statins also possess cancer-preventive properties; however, whether statins improve the prognosis of patients with renal cell carcinoma is still unclear. In this study, we used meta-analysis to evaluate the association between statin use and overall survival risk in patients with renal cell carcinoma. Published studies on statin-treated renal cell carcinoma were retrieved from PubMed, Embase, The Cochrane Library, China National Knowledge Infrastructure and Wanfang databases from inception to July 2019. The relevant data were extracted and a meta-analysis was performed using Cochrane Review Manager (RevMan 5.3) software. Data from five studies, which reported on 5,299 patients, were analysed. The application of statins showed no effects on the overall survival of patients with renal cell carcinoma compared with the control group (OR = 1.07, 95% CI:0.77 to 1.49, P = 0.68). The findings of this meta-analysis suggest that statin application does not affect the overall survival of patients with renal cell carcinoma.

Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy.

BackgroundTo explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal carcinoma (NPC) using the method of deep learning.MethodsThe pathological microscopic features were extracted using the software QuPath (version 0.1.3. Queen's University) in the training cohort (Guangzhou training cohort, n = 843). We used the neural network DeepSurv to analyze the pathological microscopic features (DSPMF) and then classified patients into high‐risk and low‐risk groups through the time‐dependent receiver operating characteristic (ROC). The prognosis accuracy of the pathological feature was validated in a validation cohort (n = 212). The primary endpoint was progression‐free survival (PFS).ResultsWe found 429 pathological microscopic features in the H&E image. Patients with high‐risk scores in the training cohort had shorter 5‐year PFS (HR 10.03, 6.06‐16.61; P < .0001). The DSPMF (C‐index: 0.723) had the higher C‐index than the EBV DNA (C‐index: 0.612) copies and the N stage (C‐index: 0.593). Furthermore, induction chemotherapy (ICT) plus concomitant chemoradiotherapy (CCRT) had better 5‐year PFS to those received CCRT (P < .0001) in the high‐risk group.ConclusionThe DSPMF is a reliable prognostic tool for survival risk in patients with NPC and might be able to guide the treatment decision.

Identification of metabolism-associated pathways and genes involved in male and female liver cancer patients

Hepatocellular carcinoma (HCC) is a major type of primary liver cancer. HCC is influenced by sex and multiple metabolic abnormalities. The present study aimed to compare the overall metabolic changes between male and female HCC patients and identify key metabolic genes. Metabolic genes and pathways were identified based on analyses of publicly available data. Differential expression analysis, gene set enrichment analysis, survival analysis and transcriptional regulation analysis were employed to explore sex differences and identify key metabolic genes in HCC. The results suggested that female patients had more severe metabolic gene expression abnormalities and pathway deregulation than male patients. This study identified 9 key metabolic genes, and only upregulated ALDH1A2 independently increased overall survival risk in patients. Bioinformatic analyses suggest that upregulated GATA3 and TAL1 activate ALDH1A2 and then disrupt amino acid and carbohydrate metabolism, which may increase the risk of HCC. This study identified a novel contribution of upregulated ALDH1A2 to HCC. Future studies are needed to elucidate the potential metabolic mechanism of the role of ALDH1A2 in HCC.

Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy

BackgroundEvaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. MethodsIndependent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. FindingsThe overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. InterpretationThe proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. FundThis work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.

En bloc spondylectomy in patients older than 60years: indications, results and complications in a series of 37 patients.

To review a series of patients > 60years affected by primary spine bone tumors, who have undergone surgery, and to describe their clinical presentation, results and complications associated with surgical treatment. A review of all patients > 60years affected by primary spine bone tumor surgically treated with en bloc spondylectomy from 1993 to 2015 was performed. Thirty-seven cases were identified, and clinical and radiological characteristics, therapy, complications and survival were evaluated. Only 14/37 cases were not previously treated. Complications were quite frequent: 64% of patients experienced almost one early complication; 48% and 27% experienced 2 and ≥ 3 early complications, respectively; 37% of patients experienced almost one late complication; and 10% and 8% experienced 2 and ≥ 3 late complications, respectively. Massive blood loss and dural tear were the more frequent surgical complications; no deaths were reported during surgeries; one patient died during the first postoperative day due to hemorrhage and cardiac complications, one during the third postoperative day for the same cause despite of a savage surgery, and another one died at 7days from index surgery due to myocardial infarction. The 5-year disease-related survival and global survival were 62.8% and 52.1%, respectively. Nineteen patients are still alive, 15 of whom without any evidence of disease. Primary malignant or locally aggressive bone tumors of the spine should be treated with wide surgery also in the older age, although the complications rate and the risk of patient survival can be considered high. These slides can be retrieved under Electronic Supplementary Material.

Hepatic and cardiac and iron overload detected by T2* magnetic resonance (MRI) in patients with myelodisplastic syndrome: A cross-sectional study

IntroductionTransfusion-dependent anemia and iron overload are associatedwith reduced survival in myelodysplastic syndrome (MDS). This cross-sectional study aimed to evaluate the prevalence of hepatic and cardiac overload in patients with MDS as measured by T2* magnetic resonance imaging (MRI), and its correlation with survival. MethodsMDS or chronic myelomonocytic leukemia patients had iron overload evaluated by T2* MRI. HIO was considered when hepatic iron concentration ≥ 2 g/mg. Cardiac iron overload was considered with a T2*-value < 20 ms. ResultsAmong 71 patients analyzed, median hepatic iron concentration was 3.9 g/mg (range 0.9–16 g/mg), and 68%of patients had hepatic iron overload. Patients with hepatic iron overload had higher mean ferritin levels (1182 ng/mL versus 185 ng/mL, p < 0.0001), transferrin saturation (76% versus 34%, p < 0.0001) and lower survival rates. Median cardiac T2*value was 42 ms (range 19.7–70.1 ms), and only one patienthad a T2* value indicative of cardiac iron overload. ConclusionsHepatic iron overload is found in two thirds of patients, even in cases without laboratory signs of iron overload. Hepatic iron overload by T2* MRI is associated with a decreased risk of survival in patients with MDS.

The Value of Risk Stratification Tools in Multiple Myeloma (MM) in the Real-World: Validation of the Revised- International Staging System (R-ISS) at Initiation of Treatment and Relevance of the ISS and the R-ISS for Risk Stratification in the Relapsed Setting Using Data from the Czech Registry of Monoclonal Gammopathies (RMG)

The Value of Risk Stratification Tools in Multiple Myeloma (MM) in the Real-World: Validation of the Revised- International Staging System (R-ISS) at Initiation of Treatment and Relevance of the ISS and the R-ISS for Risk Stratification in the Relapsed Setting Using Data from the Czech Registry of Monoclonal Gammopathies (RMG)