Prognostic Model For Survival Research Articles

Blood laboratory parameters can predict relapse-free survival of patients with advanced squamous cell lung cancer and adenocarcinoma

The aim of the work was to study the relationship between the concentration of cells and proteins in the blood of patients with Stage III squamous cell lung cancer (SCLC) and adenocarcinoma (AC) before surgical treatment and the duration of the relapse-free period after tumor resection to develop prognostic models for relapse-free survival in these diseases. Using logistic regression equations, the models incorporated variables included cytokeratin 19 fragment antigen 21-1 (CYFRA 21-1) concentration, the proportion of lymphocytes expressing the C-X-C motif chemokine receptor 1 (CXCR1), and monocytes expressing the C-X-C motif chemokine receptor 2 for SCLC. For AC, the models included the CYFRA 21-1 concentration, lymphocytes expressing the CXCR1 receptor, and the eosinophil-to-monocyte ratio. These models can predict the probability of tumor recurrence based on measurements of blood parameters in the pre-operative period, with a prediction efficiency of 87.7% for SCLC and 89.0% for AC.

Construction and validation of a survival prognostic model for clear cell renal cell carcinoma.

Utilizing expression data of clear cell renal cell carcinoma (ccRCC) genes from the Cancer Genome Atlas (TCGA) database, this study employs weighted gene co-expression network analysis (WGCNA) and Cox regression analysis to identify genes associated with the occurrence and development of ccRCC, thereby providing a scientific basis for its treatment. Differentially expressed genes between tumor and control groups were identified by preprocessing and batch correction of ccRCC transcriptome data in the TCGA database using the Wilcoxon test. Prognostic prediction models were established through a combination of WGCNA analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. The reliability of these prognostic models was evaluated by plotting Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves and by further analyzing the relationship between model gene expression levels, tumor staging, and tumor grading. Post-batch correction, M2-type macrophage infiltration was pronounced in tumor tissue, and 13 out of 290 screened relevant differential genes were included in the prognostic model. The Kaplan-Meier survival curves indicated that the 3- and 5-year overall survival rates were significantly higher in the low-risk group compared with the high-risk group (83.7 vs. 69.1%; 75.7 vs. 52.6%, p = 1.169e-08). The area under the ROC curve was 0.732, signifying strong predictive power for the survival curve. In this model, the expression levels of 11 genes were positively correlated with tumor stage and pathological grade, whereas the remaining 2 genes were negatively correlated. This model can predict the overall survival of patients with ccRCC and has the potential to become an important therapeutic target.

Deciphering the impact of senescence in kidney transplant rejection: An integrative machine learning and multi-omics analysis via bulk and single-cell RNA sequencing.

The demographic shift towards an older population presents significant challenges for kidney transplantation (KTx), particularly due to the vulnerability of aged donor kidneys to ischemic damage, delayed graft function, and reduced graft survival. KTx rejection poses a significant threat to allograft function and longevity of the kidney graft. The relationship between senescence and rejection remains elusive and controversial. Gene Expression Omnibus (GEO) provided microarray and single-cell RNA sequencing datasets. After integrating Senescence-Related Genes (SRGs) from multiple established databases, differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were applied to identify predictive SRGs (pSRGs). A cluster analysis of rejection samples was conducted using the consensus clustering algorithm. Subsequently, we utilized multiple machine learning methods (RF, SVM, XGB, GLM and LASSO) based on pSRGs to develop the optimal Acute Rejection (AR) diagnostic model and long-term graft survival predictive signatures. Finally, we validated the role of pSRGs and senescence in kidney rejection through the single-cell landscape. Thirteen pSRGs were identified, correlating with rejection. Two rejection clusters were divided (Cluster C1 and C2). GSVA analysis of two clusters underscored a positive correlation between senescence, KTx rejection occurrence and worse graft survival. A non-invasive diagnostic model (AUC = 0.975) and a prognostic model (1- Year AUC = 0.881; 2- Year AUC = 0.880; 3- Year AUC = 0.883) for graft survival were developed, demonstrating significant predictive capabilities to early detect acute rejection and long-term graft outcomes. Single-cell sequencing analysis provided a detailed cellular-level landscape of rejection, supporting the conclusions drawn from above. Our comprehensive analysis underscores the pivotal role of senescence in KTx rejection, highlighting the potential of SRGs as biomarkers for diagnosing rejection and predicting graft survival, which may enhance personalized treatment strategies and improve transplant outcomes.

Individual survival prediction model for patients with leptomeningeal metastasis.

Survival prediction for patients with leptomeningeal metastasis (LM) is crucial for making proper management plans and counseling patients. Prognostic models in this patient domain have been limited, and existing models often include predictors that are not available in resource-limited settings. Our aim was to develop a practical, individualized survival prediction model for patients diagnosed with LM. We collected a retrospective cohort of patients diagnosed with LM from cerebrospinal fluid at Chiang Mai University Hospital from January 2015 to July 2021. Nine candidate predictors included male gender, age>60years, presence of extracranial involvement, types of primary cancer, the time between primary cancer and LM diagnosis, presence of cerebral symptoms, cranial symptoms, spinal symptoms, and abnormal CSF profiles. Flexible parametric survival analysis was used to develop the survival prognostic model for predicting survival at 3, 6, and 12months after diagnosis. The model was evaluated for discrimination and calibration. 161 patients with 133 events were included. The derived individual survival prediction model for patients with LM, or the LMsurv model, consists of three final predictors: types of primary cancer, presence of cerebral symptoms, and presence of spinal symptoms. The model showed acceptable discrimination (Harrell's C-statistics: 0.72; 95% confidence interval 0.68-0.76) and was well calibrated at 3, 6, and 12months. The LMsurv model, incorporating three practical predictors, demonstrated acceptable discrimination and calibration for predicting survival in LM patients. This model could serve as an assisting tool during clinical decision-making. External validation is suggested to confirm the generalizability of the model.

Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Non-Metastatic Paediatric Wilms' Tumour Patients Undergoing Upfront Radical Nephrectomy.

To analyse the relationship between the preoperative systemic immune-inflammation index (SII) and the relapse-free survival (RFS) of paediatric patients with Wilms' tumour (WT) after radical surgery, and to establish and validate a prognostic survival model. Observational study. Place and Duration of the Study: Department of Oncologic Surgery, Anhui Children's Hospital of Fudan University, Hefei, China, from January 2013 to August 2023. A retrospective analysis was conducted on 79 WT patients treated with radical resection, with their preoperative SII values computed. The best cut-off for SII was determined through the ROC curve, categorising patients into high and low SII groups. The Kaplan-Meier method and Cox-regression were used for survival analysis. A survival prognostic model was constructed and its predictive capability gauged (AUC of the ROC). The study included 79 WT patients with a median RFS of 65 months and an average of 75.5 ± 3.4 months. The optimal cut-off value for SII was 534.95. The low SII group had a higher RFS (Log-rank: χ2 = 9.380, p = 0.002). Preoperative SII (HR = 3.277, 95% CI: 1.167 - 9.200, p = 0.024), clinical staging (HR = 8.408, 95% CI: 2.604 - 27.147, p <0.001), and tissue differentiation (HR = 2.237, 95% CI: 1.043 - 5.828, p = 0.039) were independent risk factors for RFS. The model's diagnostic performance was 0.749 (95% CI: 0.636 - 0.861). Internal validation showed an AUC of 0.723 (95% CI: 0.608 - 0.838). Lower preoperative SII suggests a more favourable prognosis. The SII-based nomogram efficiently forecasts post-radical surgery prognosis for WT. Wilms' Tumour, Systemic immune-inflammation index, Relapse-free survival, Nomogram.

Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Non-Metastatic Paediatric Wilms' Tumour Patients Undergoing Upfront Radical Nephrectomy.

To analyse the relationship between the preoperative systemic immune-inflammation index (SII) and the relapse-free survival (RFS) of paediatric patients with Wilms' tumour (WT) after radical surgery, and to establish and validate a prognostic survival model. Observational study. Place and Duration of the Study: Department of Oncologic Surgery, Anhui Children's Hospital of Fudan University, Hefei, China, from January 2013 to August 2023. A retrospective analysis was conducted on 79 WT patients treated with radical resection, with their preoperative SII values computed. The best cut-off for SII was determined through the ROC curve, categorising patients into high and low SII groups. The Kaplan-Meier method and Cox-regression were used for survival analysis. A survival prognostic model was constructed and its predictive capability gauged (AUC of the ROC). The study included 79 WT patients with a median RFS of 65 months and an average of 75.5 ± 3.4 months. The optimal cut-off value for SII was 534.95. The low SII group had a higher RFS (Log-rank: χ2 = 9.380, p = 0.002). Preoperative SII (HR = 3.277, 95% CI: 1.167 - 9.200, p = 0.024), clinical staging (HR = 8.408, 95% CI: 2.604 - 27.147, p <0.001), and tissue differentiation (HR = 2.237, 95% CI: 1.043 - 5.828, p = 0.039) were independent risk factors for RFS. The model's diagnostic performance was 0.749 (95% CI: 0.636 - 0.861). Internal validation showed an AUC of 0.723 (95% CI: 0.608 - 0.838). Lower preoperative SII suggests a more favourable prognosis. The SII-based nomogram efficiently forecasts post-radical surgery prognosis for WT. Wilms' Tumour, Systemic immune-inflammation index, Relapse-free survival, Nomogram.

Clinical characteristics and development of a prognostic model for overall survival in pediatric and adolescent liposarcoma: a SEER database analysis.

Clinical characteristics and development of a prognostic model for overall survival in pediatric and adolescent liposarcoma: a SEER database analysis.

Unlocking the Potential of Disulfidptosis-Related LncRNAs in Lung Adenocarcinoma: A Promising Prognostic LncRNA Model for Survival and Immunotherapy Prediction.

Disulfidptosis was stimulated in high SLC7A11 expression cells starving to glucose. We attempted to identify disulfidptosis-related lncRNAs (DRLs), built a prognostic model to predict survival, and analyzed the tumor microenvironment. The TCGA database was utilized to procure the pertinent data. By utilizing both Cox regression and the least absolute shrinkage and selection operator (LASSO) method, a risk model based on DRLs was formulated for prognostic evaluation. The ability of survival prediction was validated by multiple approaches. The biological functions were screened through GO, KEGG, and GSEA. Various methods were employed to evaluate the tumor immune environment, which included ESTIMATE, tumor mutation burden (TMB) score, CIBERSORT algorithm, and tumor immune dysfunction and exclusion (TIDE) score. Ninety-one DRLs were recognized, and lncRNA AC092718.4, AL365181.2, AL606489.1, EMSLR, and ENTPD3-AS1 were involved in the risk model. The GEO database was used to verify the influence of these lncRNAs on survival. The following analyses showed that survival could be predicted excellently by the DRLs risk model. The results of enrichment analyses pointed toward the involvement of the cell cycle and IgA production pathways. In the low-risk patient group, there was a notable surge in stromal, immune, and ESTIMATE scores, while the TMB scores took a tumble. Conversely, the high-risk patient group displayed a converse trend. Notably, the group of patients with lower risk scores and higher TMB scores showed the most favorable survival outcomes, underscoring the importance of considering both risk score and TMB in predicting the response to immune checkpoint blockade therapy. Furthermore, patients classified as high-risk might display resistance to both chemotherapy and targeted therapy. Cellular biological experiments proved that lncRNA AC092718.4 promoted invasion, migration, and proliferation abilities invitro. These results provided valuable insights into the role of DRLs in LUAD and presented a possible effective treatment approach for LUAD. We developed a disulfidptosis-related risk model with 5 lncRNAs that enables survival prediciton for LUAD patients and aids cilinical decisions by forecasting the TME, TMB, and drug sensitivity, making it a valuable tool for outcomes prediction.

Establishment and verification of a prognostic immune cell signature-based model for breast cancer overall survival.

Breast cancer (BRCA) is a prevalent and aggressive disease. Despite various treatments being applied, a significant number of patients continue to experience unfavorable prognoses. Accurate prognosis prediction in BRCA is crucial for tailoring individualized treatment plans and improving patient outcomes. Recent studies have highlighted the significance of immune cell infiltration in the tumor microenvironment (TME), but predicting survival remains challenging due to the heterogeneity of BRCA. The aim of this study was thus to produce an immune cell signature-based framework capable of predicting the prognosis of patients with BRCA. The GSE169246 dataset was from the Gene Expression Omnibus (GEO) database, comprising single-cell RNA sequencing (scRNA-seq) data from 95 individuals with BRCA. Seurat, principal component analysis (PCA), the unified matrix polynomial approach (UMAP) algorithm, and linear dimensionality reduction were used to determine the heterogeneity of T cells. Overlapping analysis of differentially expressed genes (DEGs), genes associated with prognosis, and T-cell pharmacodynamics-related genes were used to obtain the T-cell core pharmacodynamics-related genes. The dimensionality of the T-cell core pharmacodynamics-related genes was reduced employing the least absolute shrinkage and selection operator (LASSO) Cox regression model and the LASSO model. The prognostic model was built via a Cox analysis of the overall survival (OS) information. The clinical sample included 95 patients with BRCA who underwent surgical treatment from October 2018 to October 2021 at the Second Affiliated Hospital of Qiqihar Medical University. Patients were divided into a good prognosis group and a poor prognosis group based on their prognostic outcomes. The predictive value of tumor characteristics and immune responses was validated through correlation analysis, logistic regression analysis, and receiver operating characteristic (ROC) analysis. A group of 95 genes was used to establish a prognostic model. In the GEO clinical sample, with a high-risk group demonstrating shorter median survival times (2,447 vs. 6,498 days, P=4.733e-12). Area under the curve (AUC) values of 0.75, 0.75, and 0.72 were obtained for 2-, 4-, and 6-year OS predictions, respectively. Clinical validation found that the 6-year OS of the favorable prognosis group was significantly higher than that of the unfavorable prognosis group (92.06% vs. 65.62%; P=0.005). Poor prognosis was positively correlated with age, tumor size, B-cell level, and CTLA4 level and negatively correlated with tumor stage (T1/T2), lymph node metastasis stage (N0), clinical stage I-II, CD3+T-cell, CD4+T-cell, CD8+T-cell, neutrophil, lymphocyte, natural kill cell, TIGIT expression and OS. The combined model of clinical parameters had an AUC value of 0.898. This study established a prognostic model that demonstrated excellent predictive value for OS of BRCA. The predictive model developed offers valuable insights into prognosis and treatment planning, emphasizing the importance of tumor characteristics and immune cell infiltration.

Prognostic model for second progression-free survival and overall survival in patients with high-risk metastatic hormone-sensitive prostate cancer treated with abiraterone acetate and androgen deprivation therapy.

To develop and validate a prognostic risk model for high-risk metastatic hormone-sensitive prostate cancer (mHSPC) patients treated with upfront abiraterone acetate (ABI). This retrospective multicenter study involved 233 high-risk mHSPC patients who received upfront ABI, developed by three academic centers. The model was externally validated with an independent cohort of 282 patients. To identify independent prognostic factors for second progression-free survival (PFS2) and develop the best-fitted model, Cox proportional hazards regression, followed by the Akaike information criterion, was used. Patients were categorized into three groups based on their risk scores. PFS2 and overall survival (OS) were evaluated according to the risk groups in the discovery and validation cohorts. The median age was 72 (range 51-89) years, with a median follow-up duration of 27 months. Independent factors linked to PFS2 included an Eastern Cooperative Oncology Group performance status ≥2, a primary Gleason score of 5, an extent of disease score of ≥3 or liver metastasis, and lactate dehydrogenase >220 U/L. Median PFS2 for favorable-, intermediate-, and poor-risk groups were not reached, 43 months, and 16 months, respectively. The median OS was 29 months in the poor-risk group, whereas it was not reached in the favorable- and intermediate-risk groups. The 2-year OS rates in the favorable-, intermediate- and poor-risk groups were 94.5%, 80.1%, and 60.3%, respectively. The validation cohort confirmed the risk model's relationship with PFS2 and OS. The median PFS2 and OS in the high-risk group were 21 months and 32 months, respectively. Our prognostic model, including five clinical factors, is useful for patient care and treatment selection in high-risk mHSPC patients treated with ADT plus ABI. The developed model could provide more accurate information, guide treatment decisions, or classify patients in future clinical trials.

Identification and Validation of an Invasion-Related Disease-Free Survival Prognostic Model for Tongue Squamous Cell Carcinoma

Introduction: Tongue squamous cell carcinoma (TSCC) is a common malignant tumour type with aggressive invasion and a poor prognosis. To date, invasion-related gene expression signatures for the prognostic stratification of TSCC patients are unavailable in clinical practice. This study aimed to assess the impact of invasion-related genes on the prognosis of TSCC patients. Methods: We obtained mRNA profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (TCGA-TSCC and GSE41116, respectively). The TSCC samples from the TCGA-TSCC cohort were randomly divided into TCGA training and TCGA test datasets at a 7:3 ratio. Next, a disease-free survival (DFS) prognostic risk model was established on the basis of univariate and stepwise multivariate Cox regression analyses of the TCGA training cohort. Moreover, prognostic genes were screened. The model was subsequently evaluated and validated using the TCGA test and GSE41116 datasets. In addition, the prognostic genes were validated in the human TSCC cell line UM1 and the human oral keratinocyte (HOK) cell line using quantitative real-time polymerase chain reaction (qRT-PCR) analysis. Results: A total of 70 candidate genes related to invasion were identified in the TCGA-TSCC cohort. DFS data were subsequently constructed, and 6 prognostic genes, HMGN2, MYL12B, ACTB, PPP1CA, PSMB9, and IFITM3, were identified. The TSCC samples were divided into high- and low-risk groups in the TCGA training, TCGA test, and GSE41116 cohorts, respectively. In particular, patients with TSCC in the low-risk group had longer DFS than those in the high-risk group. Furthermore, qRT-PCR analysis confirmed that the expression levels of the 6 prognostic genes were significantly greater in the TSCC cell line UM1 than in the HOK cell line. Conclusion: This study identified new invasion-related target genes related to poor prognosis in TSCC patients, providing new insights into the underlying mechanisms of TSCC invasion.

Prognostic model development for risk of curve progression in adolescent idiopathic scoliosis: a prospective cohort study of 127 patients.

The study's purpose was to develop and internally validate a prognostic survival model exploring baseline variables for adolescent idiopathic scoliosis curve progression. A longitudinal prognostic cohort analysis was performed on trial data (n = 135) including girls and boys, Cobb angle 25-40°, aged 9-17 years, remaining growth > 1 year, and previously untreated. Prognostic outcome was defined as curve progression of Cobb angle of > 6° prior to skeletal maturity. 34 candidate prognostic variables were tested. Time-to-event was measured with 6-month intervals. Cox proportional hazards regression survival model (CoxPH) was used for model development and validation in comparison with machine learning models (66.6/33.3 train/test data set). The models were adjusted for treatment exposure. The final primary prognostic model included 127 patients, predicting progress with acceptable discriminative ability (concordance = 0.79, 95% confidence interval [CI] 0.72-0.86). Significant prognostic risk factors were Risser stage of 0 (HR 4.6, CI 2.1-10.1, P < 0.001), larger major curve Cobb angle (HRstandardized1.5, CI 1.1-2.0, P = 0.005), and higher score on patient-reported pictorial Spinal Appearance Questionnaire (pSAQ) (HRstandardized 1.4, CI 1.0-1.9, P = 0.04). Treatment exposure, entered as a covariate adjustment, contributed significantly to the final model (HR 3.1, CI 1.5-6.0, P = 0.001). Sensitivity analysis displayed that CoxPH maintained acceptable discriminative ability (AUC 0.79, CI 0.65-0.93) in comparison with machine learning algorithms. The prognostic model (Risser stage, Cobb angle, pSAQ, and menarche) predicted curve progression of > 6° Cobb angle with acceptable discriminative ability. Adding patient report of the pSAQ may be of clinical importance for the prognosis of curve progression.

Survival analysis and prognostic nomogram for patients with cholangiocarcinoma after radical resection in Asia

BackgroundCCA has a poor prognosis. Different anatomical subtypes are characterized by distinct clinical features, surgical options, and prognoses, which can potentially impact survival outcomes following radical resection. In addition to the malignancy of CCA itself, clinical staging and treatment methods are the main factors that can affect survival. This study aims to update a more reliable prediction model for the prognosis of CCA based on different anatomical locations. MethodsA total of 1172 CCA patients (305 iCCA, 467 pCCA, and 400 dCCA) who underwent surgical resection between 2015 and 2022 were included in the analysis. The covariates included in the analysis were age, sex, tumor diameter, differentiation grade, T stage, N stage, M stage, neural invasion, cancer thrombus, history of hepatitis B or biliary calculi, and receipt of adjuvant chemotherapy. The data were randomly divided into training (80 %) and validation cohort (20 %). ResultsWe developed a nomogram of the sensitive model and calculated concordance indices of different constructed prognostic survival models. Meanwhile, we validated the effectiveness of the nomogram model and compared it with the TNM system through decision curve analysis (DCA) and internal cohort validation. The nomogram model had a better net benefit than the TNM system at any given threshold for iCCA, pCCA, and dCCA, regardless of their location. ConclusionsWe have updated the prognostic model for OS in CCA patients who underwent radical resection according to the different tumor locations. This model can effectively predict OS and has the potential to facilitate individual clinical decision-making.

1615P A new prognostic model of overall survival (OS) in patients (pts) with metastatic hormone sensitive prostate cancer (mHSPC)

1615P A new prognostic model of overall survival (OS) in patients (pts) with metastatic hormone sensitive prostate cancer (mHSPC)

Development a nomogram prognostic model for survival in heart failure patients based on the HF-ACTION data

BackgroundThe risk assessment for survival in heart failure (HF) remains one of the key focuses of research. This study aims to develop a simple and feasible nomogram model for survival in HF based on the Heart Failure-A Controlled Trial Investigating Outcomes of Exercise TraiNing (HF-ACTION) to support clinical decision-making.MethodsThe HF patients were extracted from the HF-ACTION database and randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Multivariate Cox regression was used to identify and integrate significant prognostic factors to form a nomogram, which was displayed in the form of a static nomogram. Bootstrap resampling (resampling = 1000) and cross-validation was used to internally validate the model. The prognostic performance of the model was measured by the concordance index (C-index), calibration curve, and the decision curve analysis.ResultsThere were 1394 patients with HF in the overall analysis. Seven prognostic factors, which included age, body mass index (BMI), sex, diastolic blood pressure (DBP), exercise duration, peak exercise oxygen consumption (peak VO2), and loop diuretic, were identified and applied to the nomogram construction based on the training cohort. The C-index of this model in the training cohort was 0.715 (95% confidence interval (CI): 0.700, 0.766) and 0.662 (95% CI: 0.646, 0.752) in the validation cohort. The area under the ROC curve (AUC) value of 365- and 730-day survival is (0.731, 0.734) and (0.640, 0.693) respectively in the training cohort and validation cohort. The calibration curve showed good consistency between nomogram-predicted survival and actual observed survival. The decision curve analysis (DCA) revealed net benefit is higher than the reference line in a narrow range of cutoff probabilities and the result of cross-validation indicates that the model performance is relatively robust.ConclusionsThis study created a nomogram prognostic model for survival in HF based on a large American population, which can provide additional decision information for the risk prediction of HF.

A personalized prognostic model for long-term survival in patients with intrahepatic cholangiocarcinoma: a retrospective cohort study.

This study aimed to determine the optimal cutoff points for age and tumor size of patients with intrahepatic cholangiocarcinoma (ICC) and to establish and verify a predictive nomogram of overall survival at 1, 3, and 5 years. From the SEER (Surveillance, Epidemiology, and End Results) database, 1,325 ICC patients were selected and randomly divided into training and testing cohorts at a 7:3 ratio. Using the X-tile software, age and tumor size were classified into 3 subgroups: ≤61, 62-74, and ≥75 years and ≤35, 36-55, and ≥56 mm. Subsequently, univariate and multivariate Cox regression analyses were performed using the R software in the training cohort to determine independent risk factors, compile the prediction nomogram, and verify it with the testing cohort findings. The C-indexes of the new prediction nomograms in the training and testing cohorts were 0.738 (95% confidence interval [CI], 0.718-0.758) and 0.750 (95% CI, 0.72-0.78), respectively. Furthermore, the areas under the 1-, 3-, and 5-year receiver operating characteristic (ROC) curves based on the nomogram were 0.792, 0.853, and 0.838, respectively, higher than the ROC based on the 7th and 8th editions of the American Joint Cancer Commission (AJCC) staging system. This study established and verified a prognostic nomogram that improved the accuracy of the 1-, 3-, and 5-year survival predictions for ICC patients, compared with that based on the 7th and 8th editions of the AJCC staging system, and can help clinicians make personalized survival predictions.

External Validation of a Prognostic Model for Survival of Patients With Abdominal Aortic Aneurysms Treated by Endovascular Aneurysm Repair

External Validation of a Prognostic Model for Survival of Patients With Abdominal Aortic Aneurysms Treated by Endovascular Aneurysm Repair

Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy.

No robust predictive biomarkers exist to identify non-small cell lung cancer (NSCLC) patients likely to benefit from immune checkpoint inhibitor (ICI) therapies. The aim of this study was to explore the role of delta-radiomics features in predicting the clinical outcomes of patients with advanced NSCLC who received ICI therapy. Data of 179 patients with advanced NSCLC (stages IIIB-IV) from two institutions (Database 1 =133; Database 2 =46) were retrospectively analyzed. Patients in the Database 1 were randomly assigned into training and validation dataset, with a ratio of 8:2. Patients in Database 2 were allocated into testing dataset. Features were selected from computed tomography (CT) images before and 6-8 weeks after ICI therapy. For each lesion, a total of 1,037 radiomic features were extracted. Lowly reliable [intraclass correlation coefficient (ICC) <0.8] and redundant (r>0.8) features were excluded. The delta-radiomics features were defined as the relative net change of radiomics features between two time points. Prognostic models for progression-free survival (PFS) and overall survival (OS) were established using the multivariate Cox regression based on selected delta-radiomics features. A clinical model and a pre-treatment radiomics model were established as well. The median PFS (after therapy) was 7.0 [interquartile range (IQR): 3.4, 9.1] (range, 1.4-13.2) months. To predict PFS, the model established based on the five most contributing delta-radiomics features yielded Harrell's concordance index (C-index) values of 0.708, 0.688, and 0.603 in the training, validation, and testing databases, respectively. The median survival time was 12 (IQR: 8.7, 15.8) (range, 2.9-23.3) months. To predict OS, a promising prognostic performance was confirmed with the corresponding C-index values of 0.810, 0.762, and 0.697 in the three datasets based on the seven most contributing delta-radiomics features, respectively. Furthermore, compared with clinical and pre-treatment radiomics models, the delta-radiomics model had the highest area under the curve (AUC) value and the best patients' stratification ability. The delta-radiomics model showed a good performance in predicting therapeutic outcomes in advanced NSCLC patients undergoing ICI therapy. It provides a higher predictive value than clinical and the pre-treatment radiomics models.

Predictors of long-term survival after resection of adenocarcinoma arising from intraductal papillary mucinous neoplasm and derivation of a prognostic model: An international multicenter study (ADENO-IPMN study)

Predictors of long-term survival after resection of adenocarcinoma arising from intraductal papillary mucinous neoplasms are unknown. This study determines predictors of long-term (>5 years) disease-free survival and recurrence in adenocarcinoma arising from intraductal papillary mucinous neoplasms and derives a prognostic model for disease-free survival. Consecutive patients who underwent pancreatic resection for adenocarcinoma arising from intraductal papillary mucinous neoplasms in 18 academic pancreatic centers in Europe and Asia between 2010 to 2017 with at least 5-year follow-up were identified. Factors associated with disease-free survival were determined using Cox proportional hazards model. Internal validation was performed, and discrimination and calibration indices were assessed. In the study, 288 patients (median age, 70 years; 52% male) were identified; 140 (48%) patients developed recurrence after a median follow-up of 98 months (interquartile range, 78.4-123), 57 patients (19.8%) developed locoregional recurrence, and 109 patients (37.8%) systemic recurrence. At 5 years after resection, the overall and disease-free survival was 46.5% (134/288) and 35.0% (101/288), respectively. On Cox proportional hazards model analysis, multivisceral resection (hazard ratio, 2.20; 95% confidence interval, 1.06-4.60), pancreatic tail location (hazard ratio, 2.34; 95% confidence interval, 1.22-4.50), poor tumor differentiation (hazard ratio, 2.48; 95% confidence interval, 1.10-5.30), lymphovascular invasion (hazard ratio, 1.74; 95% confidence interval, 1.06-2.88), and perineural invasion (hazard ratio, 1.83; 95% confidence interval, 1.09-3.10) were negatively associated with long-term disease-free survival. The final predictive model incorporated 8 predictors and demonstrated good predictive ability for disease-free survival (C-index, 0.74; calibration, slope 1.00). A third of patients achieve long-term disease-free survival (>5 years) after pancreatic resection for adenocarcinoma arising from intraductal papillary mucinous neoplasms. The predictive model developed in the current study can be used to estimate the probability of long-term disease-free survival.

Immunohistochemical markers in the prognostic model for survival of patients with diffuse large B-cell lymphoma.

e19055 Background: Currently, relevant issues include the study of the role of immunohistochemical markers in patients with diffuse large B-cell lymphoma (DLBCL) with extranodal involvement to predict the outcomes of the conducted therapy. Methods: A retrospective study included 81 patients with diffuse large B-cell lymphoma (DLBCL) involving the liver and spleen with extranodal manifestations, who underwent treatment and were under outpatient monitoring in a hospital setting. The average age of the patients was 60.7±7.5 years. The expression of pan-B-cell antigens CD20, CD79A, PAX 5 (monomorphic intense nuclear expression), CD5, CD45, CD30, BCL-2, BCL-6, Ki-67, MUM1, GCET1, FOXP1 was examined in the patients. To exclude the polymorphic blastoid variant of lymphoma with CD5 expression in mantle cells, the study of cyclin D1 expression was conducted. Results: All patients began treatment with R-CHOP chemotherapy. In 37 cases, this regimen had to be replaced with DHAP in 23 (62.2%) patients due to the low effectiveness of the 1st-line chemotherapy, and in 14 (37.8%) after completing the full course (6 cycles) of treatment. Over the 6-year observation period, despite identical treatment, the 5-year survival rate was 60.5%. Among 32 (39.5%) patients, there was a fatal outcome due to the progression of the pathological process during treatment. Moderate expression was characteristic for the markers CD5, CD30, PAX-5, FOXP1, and GCET1, while high expression was observed for CD10, CD45, CD79A, Bcl-6, MUM1, and Ki-67. High expression of Bcl-2, Bcl-6, GCET1, and Ki-67 exceeding 60% is a prognostically unfavorable factor significantly impacting the survival of patients. The most unfavorable outcomes were observed with the combination of Bcl-2 and Ki-67 expression exceeding 60% along with CD-45. The combination of Ki-67 expression exceeding 60%, CD-79, and MUM1 also resulted in an unfavorable outcome in all cases. The prognostic scale, using Cox's proportional hazard, allows predicting the prognosis of diffuse large B-cell lymphoma with extranodal involvement with a probability of 87.2%. Conclusions: In our studies, it has been demonstrated that adverse immunohistochemical characteristics of diffuse large B-cell lymphoma with extranodal involvement include high expression of Bcl-2, Bcl-6, GCET1, and a Ki-67 proliferation index exceeding 60% (p&lt;0.025).