Predictor Of Survival In Patients Research Articles

Bioinformatic analysis constructs an optimal prognostic index for survival-related variables (OPISV) based on whole-genome expression data in Glioblastoma

PurposeUsing clinical information and transcriptomic sequencing data from glioblastoma (GBM) patients in the TCGA database to perform gene-by-gene analysis that is aligned with individual patient characteristics and develop an optimal prognostic index of survival-related variables (OPISV) through iterative machine learning techniques to predict the prognosis of GBM patients. Study designThe TCGA dataset was utilized as the training dataset, while two GEO datasets served as independent validation cohorts. Initially, survival analysis (p < 0.001***), differential gene expression analysis (p < 0.05*), and univariate Cox regression analysis (p < 0.05*) were employed to identify genes that are highly correlated with patient prognosis and exhibit significant differences in survival status. Subsequently, incorporating the non-excludable variable of age, a multivariate Cox regression analysis was performed in a stepwise manner to construct the OPISV. Finally, logistic and LASSO regressions were used to validate the association between the identified genes and patient survival. The OPISV performance is evaluated and its potential mechanisms are explored. ResultsAge, CTSD, PTPRN, PTPRN2, NSUN5, DNAJC30 and SOX21 emerged as the optimal variables through multivariate Cox regression iterations. Further analysis characterized Age, PTPRN and DNAJC30 as independent prognostic risk factors for constructing OPISV, which is validated with external GEO datasets and GEPIA database. In OPISV_high populations, significantly upregulated GABAergic synapse function was exposed. Differential genes identified from gene clustering of the GABAergic synapse pathway and gene module highly correlated with GABAergic synapse in the WGCNA analysis are pointing unequivocally to the glioma progress. ConclusionOPISV is feasible for predicting patient survival, as it may serve as a potential mechanism underlying the involvement of GABAergic synapses in the progression of GBM.

A Novel Prognostic Risk Score Model Based on RNA Editing Level in Lower-Grade Glioma

BackgroundLower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently. MethodsSurvival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) ResultsA risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing. ConclusionIn conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.

Development of a Combined Oxidative Stress and Endoplasmic Reticulum Stress-Related Prognostic Signature for Hepatocellular Carcinoma.

Oxidative stress and endoplasmic reticulum stress are important components of the cellular stress process, which plays a critical role in tumor initiation and progression. First, the correlation between oxidative stress and endoplasmic reticulum stress was detected in 68 human hepatocellular carcinoma (HCC) tissue microarray samples by immunohistochemistry. Differentially expressed oxidative stress- and endoplasmic reticulum stressrelated genes (OESGs) then were screened in HCC. Next, an OESGs prognostic signature was constructed for HCC in the training cohort (TCGA-LIHC from The Cancer Genome Atlas), by least absolute shrinkage and selection operator Cox and stepwise Cox regression analyses, and was verified in the external cohort (GSE14520 from the Gene Expression Omnibus). The MCP counter was employed to evaluate immune cell infiltration. The C-index was used to evaluate the predictive power of prognostic signature. Finally, a prognostic nomogram model was constructed to predict the survival probability of patients with HCC based on the results of Cox regression analysis. We demonstrated a positive correlation between oxidative stress and ER stress in human HCC samples. We then identified five OESGs as a prognostic signature consisting of IL18RAP, ECT2, PPARGC1A, STC2, and NQO1 for HCC. Related risk scores correlated with tumor stage, grade, and response to transcatheter arterial chemoembolization therapy, and the higher risk score group had less T cells, CD8+ T cells, cytotoxic lymphocytes and natural killer cell infiltration. The C-index of our OESGs prognostic signature was superior to four previously published signatures. Furthermore, we developed a nomogram based on the OESGs prognostic signature and clinical parameters for patients with HCC that is an effective quantitative analysis tool to predict patient survival. The OESGs signature showed excellent performance in predicting survival and therapeutic responses for patients with HCC.

Survival prediction in patients with head and neck squamous cell carcinoma and novel mechanistic insights of S100A8/A9.

S100A8/A9, an innate immune protein, significantly regulates inflammatory processes and immune responses. While S100A8/A9 has been linked to various diseases, its association with head and neck squamous cell carcinoma (HNSCC) remains unclear. Samples from the Cancer Genome Atlas (TCGA) were categorized into groups with low and high expression of S100A8/A9. R software, Sangerbox, UALCAN, GEPIA2, STRING, Cytoscape, TCGC Data Portal, miRcode, OncomiR and ENCORI databases were used to comprehensively study the expression, interactome and mutational profiles of S100A8/A9 and associated mechanism in HNSCC. The proteins S100A8/A9 were found to be associated with processes of 'epithelium development', 'regulating pluripotency of stem cells' and 'regulation of immune system'. The most frequent mutation observed in the S100A8 protein was E93K (3/37, MU4401889), while for the S100A9 protein, it was R10C (4/37, MU4633862). Furthermore, the group expressing high levels of S100A8/A9 showed increased infiltration by dendritic cells and neutrophils, but decreased infiltration by M2 macrophages, compared to the group with low S100A8/A9 expression. S100A8/A9 was also found to interact with a variety of mRNA transcripts, several of which were involved in initiation and progression of HNSCC. Through LASSO-Cox method, 20 genes (CALML5, MSX1, FZD3, STC2, SLC2A3, TMEM198B, DYNC1I1, SPHK2, ALMS1.IT1, SPPL2B, PDGFA, BCORL1, TEX101, SGCE, DNAJC12, RRN3P1, HOXB9, TMEM150C, METTL7B and PSPN) were screening to construct a prognostic model to distinguish favorable and poor prognosis of HNSCC patients. Besides, our prognostic model was also validated in GSE41613 cohort. S100A8/A9 may be a promising marker for the diagnostic and prognostic assessment of the HNSCC patients. Based on these insights, we have devised a new classification model for HNSCC, which has the potential to enhance the management and personalized treatment of HNSCC patients. The model should also be further optimized through the expansion of sample size and implemented experimental studies in future research.

Development and validation of a dynamic nomogram for individualized prediction of survival in patients with colon cancer.

Current tools for predicting survival outcomes in colon cancer patients predominantly rely on clinical and pathologic characteristics. However, accumulating evidence demonstrates a significant correlation between nutritional status and patient outcomes. This study aimed to establish a new dynamic nomogram for individualized prediction of postoperative overall survival (OS). The clinicopathological and nutritional data of colon cancer patients undergoing radical resection were retrospectively collected and randomly divided into the primary and validation cohorts. Risk factors on OS rates were investigated by Cox analyses and, the nomogram was constructed using significant predictors. Among 1,024 patients, 341 deaths were observed after median follow-up of 54months. Five independent prognostic factors, including nutritional status assessments, were incorporated into the nomogram. The C-index regarding 1-, 3-, and 5-year OS were 0.830, 0.859, and 0.757 in the primary cohort and 0.843, 0.870, and 0.773 in the validation cohort, respectively. Calibration curves for the probability of OS exhibited an optimal agreement. Decision curve analyses revealed the greater application value of the nomogram than the TNM staging system. Based on the nomogram, patients could be stratified into three scenarios with significant prognostic classification (P < 0.0001). In conclusion, we developed and validated an easy-to-use dynamic nomogram for predicting postoperative OS in colon cancer patients.

CTNI-65. DETERMINANTS OF LONG-TERM SURVIVAL IN PATIENTS WITH IDH-MUTANT GLIOMAS

Abstract BACKGROUND Survival times of patients with IDH-mutant gliomas is variable and can extend to decades. Many studies only provide progression-free rather than overall survival times and prognostic factors remain ill-defined. Here we explored clinical disease characteristics of long-term survivors within a large cohort of patients with extended follow-up. METHODS This single-center, retrospective analysis included 114 patients with IDH-mutant glioma that either died due to tumor-related causes or survived at least 15 years after first diagnosis. Patient characteristics, outcomes, and prognostic factors were stratified by short- (&amp;lt; 15 years) versus long-term (≥ 15 years) survival. Initial diagnosis was (re-)classified according to the WHO 2016 classification. Uni- and multivariate analyses were performed. RESULTS Overall, 66 patients (58%) were diagnosed with astrocytoma and 48 patients (42%) with oligodendroglioma. Median follow-up of the survivors was 16.6 years (range 15-28.9). 62 tumor-related deaths (54%) had been reported at database closure. Patients that died before reaching 15 years survival time had been diagnosed with astrocytoma in 41 (72%) and oligodendroglioma in 16 cases (28%). The long-term survivor cohort comprised 25 patients (44%) with astrocytoma and 32 patients (56%) with oligodendroglioma. Long-term survival was associated with grade 2 histology (p &amp;lt; 0.01), smaller tumor volumes (p &amp;lt; 0.01), and lack of contrast enhancement (p = 0.02). 41 long-term survivors (72%) were initially monitored by a wait-and-scan strategy as opposed to 16 patients (28%) in the cohort of short-term survival (p&amp;lt;0.01). In patients with oligodendroglioma, a higher KPS was associated with longer survival (HR 0.25; 95% CI 0.02-0.67; p = 0.05). Tumor resection (HR 0.43; 95% CI 0.22-0.86; p = 0.02) and wait-and-scan strategies (HR 0.34; 95% CI 0.14-0.79; p = 0.01) were associated with longer survival in patients with astrocytoma. CONCLUSION Tumor resection followed by a wait-and-scan strategy may yield excellent survival times, especially in patients with IDH-mutant grade 2 astrocytoma. Age appears not be an important predictor for survival in patients with IDH-mutant gliomas.

Machine Learning-enhanced Signature of Metastasis-related T Cell Marker Genes for Predicting Overall Survival in Malignant Melanoma.

In this study, we aimed to investigate disparities in the tumor immune microenvironment (TME) between primary and metastatic malignant melanoma (MM) using single-cell RNA sequencing (scRNA-seq) and to identify metastasis-related T cell marker genes (MRTMGs) for predicting patient survival using machine learning techniques. We identified 6 distinct T cell clusters in 10×scRNA-seq data utilizing the Uniform Manifold Approximation and Projection (UMAP) algorithm. Four machine learning algorithms highlighted SRGN, PMEL, GPR143, EIF4A2, and DSP as pivotal MRTMGs, forming the foundation of the MRTMGs signature. A high MRTMGs signature was found to be correlated with poorer overall survival (OS) and suppression of antitumor immunity in MM patients. We developed a nomogram that combines the MRTMGs signature with the T stage and N stage, which accurately predicts 1-year, 3-year, and 5-year OS probabilities. Furthermore, in an immunotherapy cohort, a high MRTMG signature was associated with an unfavorable response to anti-programmed death 1 (PD-1) therapy. In conclusion, primary and metastatic MM display distinct TME landscapes with different T cell subsets playing crucial roles in metastasis. The MRTMGs signature, established through machine learning, holds potential as a valuable biomarker for predicting the survival of MM patients and their response to anti-PD-1 therapy.

Construction of a Prognostic Model for Mitochondria and Macrophage Polarization Correlation in Glioma Based on Single-Cell and Transcriptome Sequencing.

Numerous diseases are associated with the interplay of mitochondrial and macrophage polarization. However, the correlation of mitochondria-related genes (MRGs) and macrophage polarization-related genes (MPRGs) with the prognosis of glioma remains unclear. This study aimed to examine this relationship based on bioinformatic analysis. Glioma-related datasets (TCGA-GBMLGG, mRNA-seq-325, mRNA-seq-693, GSE16011, GSE4290, and GSE138794) were included in this study. The intersection genes were obtained by overlapping differentially expressed genes (DEGs) from differential expression analysis in GSE16011, key module genes from WGCNA, and MRGs. Subsequently, the intersection genes were further screened to obtain prognostic genes. Following this, a risk model was developed and verified. After that, independent prognostic factors were identified, followed by the construction of a nomogram and subsequent evaluation of its predictive ability. Furthermore, immune microenvironment analysis and expression validation were implemented. The GSE138794 dataset was utilized to evaluate the expression of prognostic genes at a cellular level, followed by conducting an analysis on cell-to-cell communication. Finally, the results were validated in different datasets and tissue samples from patients. ECI2, MCCC2, OXCT1, SUCLG2, and CPT2 were identified as prognostic genes for glioma. The risk model constructed based on these genes in TCGA-GBMLGG demonstrated certain accuracy in predicting the occurrence of glioma. Additionally, the nomogram constructed based on risk score and grade exhibited strong performance in predicting patient survival. Significant differences were observed in the proportion of 27 immune cell types (e.g., activated B cells and macrophages) and the expression of 32 immune checkpoints (e.g., CD70, CD200, and CD48) between the two risk groups. Single-cell RNA sequencing showed that CPT2, ECI2, and SUCLG2 were highly expressed in oligodendrocytes, neural progenitor cells, and BMDMs, respectively. The results of cell-cell communication analysis revealed that both oligodendrocytes and BMDMs exhibited a substantial number of interactions with high strength. This study revealed five genes associated with the prognosis of glioma (ECI2, MCCC2, OXCT1, SUCLG2, and CPT2), providing novel insights into individualized treatment and prognosis.

Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis

Background and objectivesThe aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer. MethodsFour-hundred twenty-two patients from “Lung1” dataset were included in the study. A 3D convolutional autoencoder (AE) was built and features from the latent space extracted for further analysis. Radiomic features were derived from the 3D volume of the tumor region using PyRadiomics. Both radiomic and AE-based features underwent feature selection, by removing: i) highly correlated and ii) constant features. The selected variables were then used to derive both mono-domain (radiomics, AE and clinic) and multi-domain signatures fitting a Cox Proportional Hazard model with LASSO penalization and evaluated considering the concordance (C)-index as performance metric. ResultsBoth mono-domain and multi-domain signatures could significantly differentiate high risk from low risk patients. Among the mono-domain signatures, the highest hazard ratio (HR) in the test set was obtained using radiomics (HR = 1.5428) followed by the AE-based signature (HR = 1.5012) and the clinical signature (HR = 1.4770). The best overall performance was achieved by combining all three signatures, resulting in the highest HR (HR = 1.7383), while the combination of AE-based and clinical signatures yielded the highest C-index (C-index = 0.6309). ConclusionsThese preliminary results show that combining information carried by AE, radiomic and clinical domain shows potential for improving the prediction of overall survival in NSCLC patients.

Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.

Glioblastoma (GBM), one of the most common brain tumors, is known for its low survival rates and poor treatment responses. This study aims to create a robust predictive model that integrates multiple feature types, including clinical data, RNA expression, and tumor microenvironment data, using fusion techniques to enhance model performance. We obtained data from the SEER database to assess the impact of nine demographic and clinical features on the survival of 58,495 GBM patients and built predictive machine learning models. Additionally, mRNA expression data from 600 GBM patients from TCGA, CGGA, and GEO were analyzed. We used Cox regression and LASSO to create a gene signature, which was compared against 13 published signatures for accuracy. Twenty-one machine learning models were applied to predict survival at multiple time points. Finally, we integrated multiple feature types using fusion techniques and developed a Shiny app to provide survival predictions for GBM patients. Using the SEER database, we constructed machine learning models based on nine clinical variables: age, gender, marital status, race, tumor site, laterality, surgery, chemotherapy, and radiation therapy. The best-performing model achieved AUC values of 0.775, 0.728, 0.692, and 0.683 for predicting survival at 6, 12, 18, and 24months in the testing cohort. In the merged TCGA, CGGA, and GEO cohorts, we identified 11 genes to develop predictive models. These 11 genes outperformed 13 other published gene signatures in predicting the prognosis of GBM. When incorporating mRNA features, tumor microenvironment features, and clinical variables into the fusion models, the AUC values for predicting survival at 6, 12, 18, and 24months were 0.641, 0.624, 0.655, and 0.637, respectively. A user-friendly tool for predicting the survival curve of individual GBM patients is available at https://zzubioinfo.shinyapps.io/mlGBM/ . Our study provides a web-based tool that includes two modules: one for predicting survival curves using only clinical variables, and another that integrates multiple feature types for more comprehensive predictions.

The apparent diffusion coefficient can serve as a predictor of survival in patients with gliomas

Background and purposeMagnetic resonance imaging is indispensable for the preoperative diagnosis of glioma. This study aimed to investigate the role of the apparent diffusion coefficient values as predictors of survival in patients with gliomas.Methods and materialsA retrospective analysis was conducted on 101 patients with gliomas who underwent surgery between 2015 and 2020. Diffusion-weighted MRI was performed before the surgery. The regions of interest were categorized into parenchymal area, non-enhancing peritumoral area, and necrotic or cystic area. All the patients were divided into three subgroups: the parenchyma group, the non-enhancing peritumoral signal abnormality group, and the necrosis or cyst group. Univariate and multivariate analyses were performed using COX regression.ResultsIn the parenchymal group, Ki67, P53, IDH, and the high or low ADC values were identified as independent prognosticators for disease-free survival, while Ki67, IDH, and the high or low ADC values for overall survival. In the non-enhancing peritumoral signal abnormality group, Ki67, P53, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio were identified as independent prognostic factors for disease-free survival, while Ki67, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio for overall survival. In the necrosis or cyst group, Ki67 was significantly associated with disease-free survival, while Ki67 and the ADC value of the necrotic or cystic area for overall survival.ConclusionsThe ADC values, including the ADC value in the parenchymal area, the ADC parenchymal area/ADC non−enhancing peritumoral area ratio, and the ADC value in the necrotic or cystic area, can serve as an efficient and potential index for predicting the survival of patients with glioma.

Electrocardiographic markers of depolarization and repolarization in lead V1 predict right ventricular dysfunction in patients with heart failure

Abstract Background Right ventricular (RV) dysfunction is an important predictor of survival in patients with heart failure (HF) with both preserved (HFpEF) and reduced ejection fraction (HFrEF). To date there is limited data on the value of the electrocardiogram in predicting RV function. Purpose Given these considerations the aim of our study was to assess the possible relationship between several electrocardiographic (ECG) parameters and right ventricular function evaluated by echocardiography. Methods We included 109 patients (age 61±14 years, 68men) of whom 89 had HF (HFpEF N=62 and HFrEF N=27) and 20 patients without HF. Twelve-lead ECG and echocardiograms were performed in all patients. We assessed echocardiographic parameters of global and regional RV function. Results Time to Intrinsicoid deflection in lead V1 (TID-V1) and the QTc interval measured in lead V1 (QTc-V1) were significantly more prolonged in patients with HF vs. patients with no HF (36±24ms vs. 23±15ms; p=0.03, and 450±40ms vs. 427±20ms; p=0.047 respectively). TID-V1 correlated significantly with the tricuspid annulus systolic excursion (TAPSE) and RV fractional area change (FAC; r=-0.31; p=0.02, and r=-0.35; p=0.029 respectively). QTc-V1 correlated with parameters of global and regional RV function including FAC, RV apex longitudinal strain, RV free wall strain, and global RV strain (r=-0.42; p=0.0004, r=0.35; p=0.0032, r=0.21; p=0.045, and r=0.41; p=0.0005). RV dysfunction as defined by RV-FAC&amp;lt;35% was predicted by TID-V1 &amp;gt; 30ms with a sensitivity of 61% and specificity of 75%; AUC=0.752 and by QTc-V1&amp;gt;440ms with a sensitivity of 80% and specificity of 57%; AUC=0.888, and by a multivariate logistic regression model that also included TID-V1 and QTcV1, age, TAPSE, and LV ejection fraction with an AUC=0.967 (Figure). Conclusions Lead V1 time to intrinsic deflection and QTc-V1 predict RV dysfunction independent of echocardiographic parameters of ventricular function in patients with HF.

Developmental-status-aware transcriptional decomposition establishes a cell state panorama of human cancers

BackgroundCancer cells evolve under unique functional adaptations that unlock transcriptional programs embedded in adult stem and progenitor-like cells for progression, metastasis, and therapeutic resistance. However, it remains challenging to quantify the stemness-aware cell state of a tumor based on its gene expression profile.MethodsWe develop a developmental-status-aware transcriptional decomposition strategy using single-cell RNA-sequencing-derived tissue-specific fetal and adult cell signatures as anchors. We apply our method to various biological contexts, including developing human organs, adult human tissues, experimentally induced differentiation cultures, and bulk human tumors, to benchmark its performance and to reveal novel biology of entangled developmental signaling in oncogenic processes.ResultsOur strategy successfully captures complex dynamics in developmental tissue bulks, reveals remarkable cellular heterogeneity in adult tissues, and resolves the ambiguity of cell identities in in vitro transformations. Applying it to large patient cohorts of bulk RNA-seq, we identify clinically relevant cell-of-origin patterns and observe that decomposed fetal cell signals significantly increase in tumors versus normal tissues and metastases versus primary tumors. Across cancer types, the inferred fetal-state strength outperforms published stemness indices in predicting patient survival and confers substantially improved predictive power for therapeutic responses.ConclusionsOur study not only provides a general approach to quantifying developmental-status-aware cell states of bulk samples but also constructs an information-rich, biologically interpretable, cell-state panorama of human cancers, enabling diverse translational applications.

Prognostic significance of Standard Uptake Value (SUVmax) and primary tumor size predicting patient survival in vulvar tumors.

Prognostic significance of Standard Uptake Value (SUVmax) and primary tumor size predicting patient survival in vulvar tumors.

18F]F-AraG Uptake in Vertebral Bone Marrow May Predict Survival in Patients with Non-Small Cell Lung Cancer Treated with Anti-PD-(L)1 Immunotherapy.

Despite the systemic impact of both cancer and the associated immune response, immuno-PET is predominantly centered on assessment of the immune milieu within the tumor microenvironment. The aim of this study was to assess the value of [18F]F-AraG PET imaging as a noninvasive method for evaluation of system-wide immune status of patients with non-small cell lung cancer before starting immunotherapy. Methods: Eleven patients with advanced non-small cell lung cancer were imaged with [18F]F-AraG before starting immunotherapy. Diagnostic [18F]FDG PET/CT scans were analyzed to assess differences in the extent of disease among patients. SUVmax, SUVmean, and total SUV (SUVtotal) from all tumor lesions, active lymph nodes, spleen, vertebral bone marrow, liver, thyroid, heart, and bowel were extracted from the baseline [18F]F-AraG scans, and discriminant and Kaplan-Meier analyses were performed to test their ability to predict patient response and overall survival. Results: The extent of the disease was variable in the patient cohort, but none of the [18F]FDG biomarkers associated with tumor burden (SUVmax, total metabolic tumor volume, and total lesion glycolysis) was predictive of patient survival. The differences in the [18F]F-AraG and [18F]FDG distribution were observed both within and between lesions, confirming that they capture distinct aspects of the tumor microenvironment. Of the 3 SUV parameters studied, [18F]F-AraG SUVtotal provided a dynamic range suitable for stratifying tumors or patients according to their immune activity. [18F]F-AraG SUVtotal measured in the lumbar and sacral vertebrae differentiated between patients who progressed on therapy and those who did not with 90.9% and 81.8% accuracy, respectively. The Kaplan-Meier analysis revealed that patients with high [18F]F-AraG SUVtotal in the lumbar bone marrow had significantly lower probability of survival than those with a low signal (P = 0.0003). Conclusion: This study highlights the significance of assessing systemic immunity and indicates the potential of the [18F]F-AraG bone marrow signal as a predictive imaging biomarker for patient stratification and treatment guidance.

ANGIOGENIC FACTORS AS PREDICTORS OF SURVIVAL IN PATIENTS WITH BONE SARCOMAS

Introduction. Primary bone sarcomas are relatively rare neoplasms. They occur predominantly in adolescents and young adults, are characterized by an aggressive clinical course and a high metastatic potential, have a poor prognosis. In this regard, the problem of stratifying the risk of metastasis and death in order to select the optimal treatment tactics always remains relevant. The study of such markers as endostatin, vascular endothelial growth factor (VEGF), insulin-like growth factors IGF-1, IGF-2 as potentially informative predictors of survival in patients with bone sarcomas is pathogenetically determined by their involvement in the processes of tumor growth and metastasis. Aim – to analyze the relationship between pre-treatment serum levels of endostatin, VEGF, IGF-1 and IGF-2 with overall survival rates in patients with bone sarcomas. Material and methods. An analysis of overall survival rates was carried out in 134 patients with malignant bone tumors aged from 1 to 73 years (Me=27.0 years; Q1–Q3: 18.0 – 43.0 years), among them 87 men (64.9%) and 47 women (35.1%). All patients comprised 4 groups: osteosarcoma (n=58), undifferentiated pleomorphic sarcoma (n=5), Ewing sarcoma (n=29), chondrosarcoma (n=42). In all of them, before the start of specific antitumor treatment, the levels of endostatin, VEGF, IGF-1, and IGF-2 in the blood serum were determined using enzyme immunoassay. The follow-up period ranged from 1 to 229 months (Me=23.0 months; Q1–Q3: 10.0–121.0 months). To analyze overall survival, life tables and Kaplan–Meier curves were constructed. The effect of the studied serum markers on survival rates was assessed using the logrank test and Cox proportional hazards regression model. Results. During observation, death occurred in 51 patients (38.1%). In the group of deceased patients, serum levels of endostatin and IGF-2 were statistically significantly increased compared to the group of surviving patients (p 0.001 and p=0.011, respectively). Kaplan-Meier survival analysis using the logrank test revealed statistically significant differences in overall survival between the four groups of patients with different serum levels of endostatin, IGF-1 and IGF-2. The Cox regression method established the prognostic significance of endostatin as a predictor of overall survival (HR=1.012; p=0.013). Conclusion. Endostatin, IGF-1, IGF-2 can serve as predictors of overall survival of patients with bone sarcomas.

Dynamic prediction of kidney allograft and patient survival using post-transplant estimated glomerular filtration rate trajectory.

Allograft loss is the most feared outcome of kidney transplant recipients. We aimed to develop a dynamic Bayesian model using estimated glomerular filtration rate (eGFR) trajectories to predict long-term allograft and patient survivals. We used data from the Australian and New Zealand Dialysis and Transplant registry and included all adult kidney transplant recipients (1980-2017) in Australia (derivation cohort) and New Zealand (NZ, validation cohort). Using a joint model, the temporal changes of eGFR trajectories were used to predict patient and allograft survivals. The cohort composed of 14 915 kidney transplant recipients [12 777 (86%) from Australia and 2138 (14%) from NZ] who were followed for a median of 8.9 years. In the derivation cohort, eGFR trajectory was inversely associated with allograft loss [every 10ml/min/1.73 m2 reduction in eGFR, adjusted hazard ratio [HR, 95% credible intervals (95%CI) 1.31 (1.23-1.39)] and death [1.12 (1.10-1.14)]. Similar estimates were observed in the validation cohort. The respective dynamic area under curve (AUC) (95%CI) estimates for predicting allograft loss at 5-years post-transplantation were 0.83 (0.75-0.91) and 0.81 (0.68-0.93) for the derivation and validation cohorts. This straightforward model, using a single metric of eGFR trajectory, shows good model performance, and effectively distinguish transplant recipients who are at risk of death and allograft loss from those who are not. This simple bedside tool may facilitate early identification of individuals at risk of allograft loss and death.

VASARI-AUTO: PERFORMANT, EQUITABLE, EffiCIENT, ECONOMIC, AND SURVIVAL PREDICTIVE FEATURISATION OF GLIOMA MRI

Abstract AIMS The VASARI (Visually AcceSAble Rembrandt Images) MRI feature set is a quantitative system designed to standardize glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and therefore seldom used in clinical practice. This is however a problem that performant machine-learning software could plausibly automate. METHOD In 100 patients, two consultant neuroradiologists independently quantified VASARI features. In parallel, we developed VASARI-auto, an automated VASARI-labelling software applied to both open-source hand-segmented lesion masks and our openly available tumour segmentation model. We quantified: 1) agreement across neuroradiologists and VASARI-auto; 2) calibration of patient equity; 3) an economic workforce analysis; and 4) fidelity in predicting patient overall survival. Neuroradiologists were blinded to VASARI-auto software development and evaluations, and software developers were blinded to neuroradiologist labelling. RESULTS Segmentation and VASARI-auto were equally performant regardless of age or sex (mean segmentation Dice coefficient 0.947). A modest inter-rater variability between neuroradiologists (mean Cohen’s Kappa 0.49) was comparable between neuroradiologists and VASARI-auto (mean Cohen’s Kappa 0.41), with higher agreement across VASARI-auto methods (mean Cohen’s Kappa 0.94). The time taken for neuroradiologists to derive VASARI features was substantially higher than VASARI-auto (mean time per case 317 vs. 3 seconds, p&amp;lt;0.0001). A hospital workforce analysis of our centre forecast that three years of VASARI glioma featurisation would demand 771 consultant neuroradiologist workforce hours (£40,789) or 8.6 hours of computing time (£3.76 of power) with VASARI-auto. The best-performing survival model utilised VASARI-auto features (R2 0.25), opposed to those derived by consultant neuroradiologists (R2 0.21). CONCLUSION VASARI-auto is a highly efficient automated labelling system for VASARI featurisation with equitable performance regardless of patient age or sex, a favourable economic profile if used as a decision support tool, and with non-inferior fidelity in downstream patient survival prediction. Future work should integrate such tools to enhance patient care.

Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning

PurposeWe sought to develop an effective combined model for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL) based on the multimodal PET-CT deep features radiomics signature (DFR-signature).Methods369 DLBCL patients from two medical centers were included in this study. Their PET and CT images were fused to construct the multimodal PET-CT images using a deep learning fusion network. Then the deep features were extracted from those fused PET-CT images, and the DFR-signature was constructed through an Automated machine learning (AutoML) model. Combined with clinical indexes from the Cox regression analysis, we constructed a combined model to predict the progression-free survival (PFS) and the overall survival (OS) of patients. In addition, the combined model was evaluated in the concordance index (C-index) and the time-dependent area under the ROC curve (tdAUC).ResultsA total of 1000 deep features were extracted to build a DFR-signature. Besides the DFR-signature, the combined model integrating metabolic and clinical factors performed best in terms of PFS and OS. For PFS, the C-indices are 0.784 and 0.739 in the training cohort and internal validation cohort, respectively. For OS, the C-indices are 0.831 and 0.782 in the training cohort and internal validation cohort.ConclusionsDFR-signature constructed from multimodal images improved the classification accuracy of prognosis for DLBCL patients. Moreover, the constructed DFR-signature combined with NCCN-IPI exhibited excellent potential for risk stratification of DLBCL patients.

New model to predict survival in advanced pancreatic ductal adenocarcinoma patients by measuring GGT and LDH levels and monocyte count.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with a poor survival outcome. Predicting patient survival allows physicians to tailor treatments to specific individuals. Thus, a simple and cost-effective prognosis model is sorely needed. This retrospective study assesses the prognostic value of blood biomarkers in advanced and metastatic PDAC patients (n=96) from Spain. Cut-off points for hematological parameters were calculated and correlated with overall survival (OS) using Kaplan-Meier, log-rank test, robust Cox proportional hazards and logistic regressions. In univariate analysis, individuals with low levels of GGT, LDH, ALP, leukocyte-, neutrophil- and monocyte counts showed significantly longer survival than patients with higher levels. In multivariate analysis, lower levels of GGT (HR (95%CI), 2.734 (1.223-6.111); p=0.014), LDH (HR (95%CI), 1.876 (1.035-3.400); p=0.038) and monocyte count (HR (95%CI), 1.657 (1.095-2.506); p = 0.017) remained significantly beneficial. In consequence, we propose a prognostic model based on logistic regression (AUC=0.741) of these three biomarkers as a pioneer tool to estimate OS in PDAC. This study has demonstrated that the joint use of GGT (<92.00), LDH (<220.00) and monocyte count (<800) are independent positive prognostic factors in PDAC that can predict one-year survival in a novel prognostic logistic model.