Prognostic Model For Patients Research Articles

Identification and validation of a novel prognostic model based on anoikis‑related genes in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a hematological cancer prevalent worldwide. Anoikis-related genes (ARGs) are crucial in the progression of cancer and metastasis of tumors. However, their role in AML needs to be clarified. In the present study, differential analysis was performed on data from The Cancer Genome Atlas database to identify differentially expressed ARGs (DE-ARGs). Subsequently, a prognostic model for patients with AML was constructed using univariate Cox, Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses. This model was based on four key DE-ARGs [lectin galactoside-binding soluble 1 (LGALS1), integrin subunit α 4 (ITGA4), hepatocyte growth factor (HGF) and Ras homolog gene family member C (RHOC)]. Independent prognostic factors for AML included prior treatment, age, risk scores and diagnosis. A nomogram was constructed based on these factors to aid clinical decision-making. Furthermore, bone marrow samples were collected from individuals diagnosed with AML and healthy donors to validate the expression of the identified ARGs using reverse transcription-quantitative PCR. The mRNA levels of LGALS1 and RHOC were significantly higher, while those of ITGA4 and HGF were significantly lower in patients with AML than in healthy donors (all P<0.05). The results of the present study expands the understanding of the function of ARGs in AML, providing a new theoretical basis for the treatment of AML.

Validation of the CNS-IPI-C Prognostic Model in Patients with Systemic DLBCL in the Real World

Validation of the CNS-IPI-C Prognostic Model in Patients with Systemic DLBCL in the Real World

Criteria and prognostic models for patients with hepatocellular carcinoma undergoing liver transplantation.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Development and validation of a new prognostic model for patients with acute-on-chronic liver failure in intensive care unit.

Cirrhotic patients with acute-on-chronic liver failure (ACLF) in the intensive care unit (ICU) have a poor but variable prognoses. Accurate prognosis evaluation can guide the rational management of patients with ACLF. However, existing prognostic scores for ACLF in the ICU environment lack sufficient accuracy. To develop a new prognostic model for patients with ACLF in ICU. Data from 938 ACLF patients in the Medical Information Mart for Intensive Care (MIMIC) database were used to develop a new prognostic model (MIMIC ACLF) for ACLF. Discrimination, calibration and clinical utility of MIMIC ACLF were assessed by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA), respectively. MIMIC ACLF was then externally validated in a multiple-center cohort, the Electronic Intensive Care Collaborative Research Database and a single-center cohort from the Second Hospital of Hebei Medical University in China. The MIMIC ACLF score was determined using nine variables: ln (age) × 2.2 + ln (white blood cell count) × 0.22 - ln (mean arterial pressure) × 2.7 + respiratory failure × 0.6 + renal failure × 0.51 + cerebral failure × 0.31 + ln (total bilirubin) × 0.44 + ln (internationalized normal ratio) × 0.59 + ln (serum potassium) × 0.59. In MIMIC cohort, the AUROC (0.81/0.79) for MIMIC ACLF for 28/90-day ACLF mortality were significantly greater than those of Chronic Liver Failure Consortium ACLF (0.76/0.74), Model for End-stage Liver Disease (MELD; 0.73/0.71) and MELD-Na (0.72/0.70) (all P < 0.001). The consistency between actual and predicted 28/90-day survival rates of patients according to MIMIC ACLF score was excellent and superior to that of existing scores. The net benefit of MIMIC ACLF was greater than that achieved using existing scores within the 50% threshold probability. The superior predictive accuracy and clinical utility of MIMIC ACLF were validated in the external cohorts. We developed and validated a new prognostic model with satisfactory accuracy for cirrhotic patients with ACLF hospitalized in the ICU. The model-based risk stratification and online calculator might facilitate the rational management of patients with ACLF.

A Prognostic Methylation-Driven Two-Gene Signature in Medulloblastoma.

Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that one-third of patients will not achieve long-term survival. Conventional prognostic parameters have limited and unreliable correlations with MB outcome, presenting a major challenge for patients' clinical improvement. Acknowledging this issue, our aim was to build a gene signature and evaluate its potential as a new prognostic model for patients with the disease. In this study, we used six datasets totaling 1679 samples including RNA gene expression and DNA methylation data from primary MB as well as control samples from healthy cerebellum. We identified methylation-driven genes (MDGs) in MB, genes whose expression is correlated with their methylation. We employed LASSO regression, incorporating the MDGs as a parameter to develop the prognostic model. Through this approach, we derived a two-gene signature (GS-2) of candidate prognostic biomarkers for MB (CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall survival (OS) with Kaplan-Meier analysis. We evaluated its robustness and accuracy with receiver operating characteristic curves predicting OS at 1, 3, and 5 years in multiple independent datasets. The GS-2 showed highly significant results as an independent prognostic biomarker compared to traditional MB markers. The methylation-regulated GS-2 risk score model can effectively classify patients with MB into high and low-risk, reinforcing the importance of this epigenetic modification in the disease. Such genes stand out as promising prognostic biomarkers with potential application for MB treatment.

Abstract 7351: Delineating the prostate cancer tumor microenvironment through scRNA-seq profiling at different timepoints

Abstract Distant metastasis remains the leading cause of death in patients with prostate cancer (PCa), however, the underlying mechanisms of this process remain unclear. Using samples from genetically-engineered mouse models (GEMMs) of PCa, we have identified eight different stromal cell states linked to epithelial mutations that may impact PCa progression to metastatic stages. To further investigate the unique intercellular interactions in the tumor microenvironment (TME) involved in mediating disease progression, we use single cell RNA sequencing (scRNA-seq) to profile primary PCa samples from different GEMMs including TRAMP, Hi-MYC, and FVBN, at different timepoints. Analysis of the scRNA-seq data revealed a significant enrichment of three clusters, designated as stromal c6 (s6), epithelial c7 (e7), and epithelial c10 (e10), exclusively in TRAMP, each expressing proliferative markers such as Mki67 and Top2a. To investigate the intercellular interactions among these clusters, we conducted ligand-receptor (LR) interaction analysis using CellChat. Notably, s6 is expressing the genes encoding ligands Bmp4, Fn1, and Igf1, which interact with the receptors Bmpr1b/Bmpr2, Itga3/Itgb1, and Igf1r, respectively expressed in e7. Subsequently, the transcriptomic profiles of these cells were used to build and validate a robust gene signature that can predict metastasis and survival in patients with PCa using a large cohort of 1239 samples, 930 of which were used for training while the remaining 309 samples were used for testing the model’s performance. In conclusion, these results underscore the role of the TME mediating PCa progression and highlight the potential of leveraging the TME to extract predictive and prognostic features which can be later employed to build predictive and prognostic models for patients with PCa. Citation Format: Itzel Valencia, Hubert Pakula, Mohamed Omar, Massimo Loda, Luigi Marchionni. Delineating the prostate cancer tumor microenvironment through scRNA-seq profiling at different timepoints [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7351.

Prognostic Models for Chronic Low Back Pain Outcomes in Primary Care Are at High Risk of Bias and Lack Validation-High-Quality Studies Are Needed: A Systematic Review.

OBJECTIVE: To provide an updated overview of available prognostic models for people with chronic low back pain (LBP) in primary care. DESIGN: Prognosis systematic review LITERATURE SEARCH: We searched for relevant studies on MEDLINE, Embase, Web of Science, and CINAHL databases (up to July 13, 2022), and performed citation tracking in Web of Science. STUDY SELECTION CRITERIA: We included observational (cohort or nested case-control) studies and randomized controlled trials that developed or validated prognostic models for adults with chronic LBP in primary care. The outcomes of interest were physical functioning, pain intensity, and health-related quality of life at any follow-up time-point. DATA SYNTHESIS: Data were extracted using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and the Prediction model Risk of Bias Assessment Tool (PROBAST) tool was used to evaluate the risk of bias of the models. Due to the number of studies retrieved and the heterogeneity, we reported the results descriptively. RESULTS: Ten studies (out of 5593 hits screened) with 34 models met our inclusion criteria, of which six are development studies and four are external validation studies. Five studies reported the area under the curve of the models (ranging from 0.48 to 0.84), whereas no study reported calibration indices. The most promising model is the Örebro Musculoskeletal Pain Screening Questionnaire Short-Form. CONCLUSIONS: Given the high risk of bias and lack of external validation, we cannot recommend that clinicians use prognostic models for patients with chronic LBP in primary care settings. J Orthop Sports Phys Ther 2024;54(5):1-13. Epub 15 February 2024. doi:10.2519/jospt.2024.12081.

Quercetin Attenuates Cancer‐Associated Fibroblast Activation via Tumor‐Stromal Interactions and Demonstrates Its Clinical Value in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is a rapidly progressing malignancy with a poor prognosis. Quercetin is a flavonoid compound with various biological benefits that can be extracted from Chinese herbs or daily foods. Quercetin has anticancer properties in various types of cancers. However, its therapeutic effects and potential mechanisms in PDAC have not been investigated extensively. Here, we confirmed the therapeutic effect of quercetin in PDAC using a mouse model. Based on high‐throughput RNA sequencing (RNA‐seq) and bioinformatic analysis, we propose that quercetin is involved in stromal infiltration of PDAC. Quercetin attenuates the activation of cancer‐associated fibroblasts (CAFs) via tumor‐stromal interaction. Meanwhile, we have identified two quercetin‐related prognostic models for patients with PDAC. Finally, we proposed a downstream target of quercetin, the ITGB4 gene, which could be a potential therapeutic target for PDAC.

Validation of the CRAFITY score for predicting prognosis in patients with hepatocellular carcinoma undergoing transarterial chemoembolization combined with systemic therapy

BackgroundThe CRAFITY score can predict survival and radiological response to systemic front-line treatment in patient with hepatocellular carcinoma (HCC). AimsIn this study, we aimed to externally validate the CRAFITY score and compare it with other simple prognostic models in patients with unresectable HCC who underwent transarterial chemoembolization (TACE) combined with systemic therapies. MethodsPatients with unresectable HCC who underwent TACE combined with tyrosine kinase inhibitors (TKIs), with or without immune checkpoint inhibitors (ICIs), were retrospectively analyzed to determine whether the CRAFITY score can predict overall survival (OS) through multivariate analysis and Kaplan‒Meier curves. Prediction of 3- and 6-month objective response rates (ORRs) was validated by the area under the curve (AUC). ResultsAmong 147 patients with unresectable HCC, 113 received TACE ​+ ​TKI and 34 received TACE ​+ ​TKI ​+ ​ICI. The CRAFITY score was identified as an independent predictor for OS (P ​< ​0.001) and showed a good ability to categorize all included patients into low-, moderate-, and high-risk groups (P ​< ​0.001). For predicting the 3- and 6-month ORRs, the CRAFITY score exhibited poor performance, with AUCs of 0.555 and 0.515, respectively. ConclusionThe CRAFITY score is effective in predicting OS but demonstrates limited accuracy in predicting 3- and 6-month ORRs for patients with unresectable HCC undergoing TACE combined with systemic therapies.

Non-small cell lung cancer with synchronous brain metastases: Identification of prognostic factors in a retrospective multicenter study (HOT 1701).

Non-small-cell lung cancer (NSCLC) is associated with a high incidence of brain metastasis (BM), and the prognosis of patients with NSCLC and BM is poor. This study aimed to identify the prognostic factors and elucidate the survival rates of Japanese patients with NSCLC and BM at initial diagnosis. HOT 1701 is a retrospective multicenter study of patients with NSCLC and BM at initial diagnosis. The medical records of all consecutive patients diagnosed with advanced or recurrent NSCLC and BM at 14 institutions of the Hokkaido Lung Cancer Clinical Study Group Trial (HOT) in Japan were reviewed. The participants were categorized based on the presence or absence of driver mutations. The Kaplan-Meier method was used to estimate median overall survival (OS). Univariate and multivariate analyses were performed to identify prognostic factors in these patients. Among 566 patients with NSCLC and BM, the median OS was 11.8 months. Patients with driver mutations survived longer than those without driver mutations. The univariate and multivariate analyses revealed 6 independent prognostic factors: age ≥65 years, poor performance status, T factor, absence of driver gene mutations, presence of extracranial metastases, and number of BM. According to the prognostic score based on these 6 factors, the patients were stratified into 3 risk groups: low-, intermediate-, and high-risk, with median OS of 27.8, 12.2, and 2.8 months, respectively. We developed a new prognostic model for patients with NSCLC and BM, which may help determine prognosis at diagnosis.

Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities.

While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival. A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership. Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each). Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs. To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM. DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01). A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC. Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity. •A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. •The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. •A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.

Development and Validation of an Individualized and Weighted Prognostic Model in Patients with Newly Diagnosed Multiple Myeloma

Introduction Multiple myeloma (MM) is a malignancy of terminally differentiated antibody-secreting plasma cells in the bone marrow with highly variable survival outcomes. Precise risk stratification not only plays a vital role in predicting patient prognoses, but critically can be used to develop risk-adapted treatment regimens. However, current risk stratification models do not adequately account for the heterogeneity of patients with new-diagnosed multiple myeloma (NDMM). Methods In this retrospective, multicohort study, we collected clinical data from 1792 NDMM patients and formulated a weighted Myeloma Prognostic Score System (MPSS) risk model to predict overall survival (OS). Construction of the MPSS model was carried out based on the National Longitudinal Cohort of Hematological Diseases in China (NCT04645199) cohort, external validation cohort was created using TT2, TT3a, and TT3b cohorts from a series of randomized prospective studies from the University of Arkansas for Medical Sciences (UAMS). Results Each risk factor was defined as its weighted value respectively according to their hazard ratio for OS (thrombocytopenia 2, elevated LDH 1, ISS III 2, one high-risk cytogenetic aberration [HRA] 1, and ≥2 HRA 2 points). In the training cohort, patients were furtherly stratified into four risk groups: MPSS I (22.5%, 0 points), II (17.6%, 1 points), III (38.6%, 2-3 points), and IV (21.3%, 4-7 points). MPSS risk stratification showed optimal discrimination, as well as calibration, of four risk groups with median OS of 91.0, 69.8, 45.0, and 28.0 months, for patients in MPSS I to IV groups (P&amp;lt; 0.001), respectively. Importantly, the MPSS model retained its prognostic value in the internal validation cohort and an independent external validation cohort, and exhibited significant risk distribution compared with conventional prognostic models (R-ISS, R2-ISS, and MASS). Conclusions In conclusion, we formulated and validated the MPSS risk model to predict the prognosis of patients with MM using readily available standard clinical and genetic test data. The established MPSS profile shows a better performance in risk discrimination than the current R-ISS, R2-ISS, and MASS. A score-based risk stratification is derived, and identifies patients with high and ultra-high risk of death after diagnosis and may therefore aid the development of more personalized treatment strategies, especially for patients for whom current therapies are likely to fail.

A Novel Prognostic Model for Patients with Acute Myeloid Leukemia in First Relapse with Improved Prognostic Accuracy

Introduction Despite high rates of complete remission (CR) after intensive induction chemotherapy in newly diagnosed AML, relapse is frequent and associated with poor outcome with a median overall survival (OS) of 3-5 months. While cytogenetic and molecular markers are associated with outcome in newly diagnosed patients, the prognostic value in the relapsed setting is unclear, even though it may facilitate therapeutic decision making. Genetic data was only limitedly available when earlier models for relapsed patients were developed, such as reported by HOVON-SAKK (Breems, JCO 2005) and GOELAMS (Chevallier, Leukemia, 2011). Therefore, a comprehensive analysis of clinical, cytogenetic and molecular variables in a prognostic model for outcome after relapse is needed. We set out to evaluate the impact of these variables on outcome after first relapse in the current therapeutic era, and develop a new prognostic model and compare that model with earlier models. Methods A total of 946 (44%) relapsing patients out of 2127 newly diagnosed AML patients who participated in prospective HOVON-SAKK trials of intensive induction chemotherapy between 2000-2018 were included in this analysis. A random survival forest machine learning algorithm was used to identify top markers impacting 1 year survival after relapse among 45 candidate predictors, including 7 clinical parameters, and 9 cytogenetic and 29 molecular abnormalities present at diagnosis. Cox regression with backward selection was performed to obtain hazard ratios (HRs) from relevant predictors identified by the random survival forest algorithm. A clinical prognostic tool was developed with points assigned to each marker based on the rounded logarithm of HR. Four groups were defined based on quartiles of the individual predictions. Prognostic accuracy of risk models was assessed using Harrel's C-index. Results The median age of relapsed patients was 58 years (range: 18-81), with 56% being male. Adverse risk AML according to ELN2022 risk was found in 48% of patients, whereas favorable and intermediate risk were classified in 22% and 31%, respectively. Prior allogeneic stem cell transplantation (SCT) in first CR was performed in 33% of patients, while 14% received autologous SCT. The median time between CR and relapse was 14 months (range: 0-124). The median follow-up time after relapse for patients alive was 49 months (range: 0-124). The random survival forest identified 16 parameters that were associated with 1 year OS based on variable importance, including: age at relapse ≥60 years, relapse-free interval ≤1 year, white blood cell count ≥10 x 10e9/L at diagnosis, prior allogeneic SCT, absence of t(8;21)/inv16, and presence of complex or monosomal karyotype (CK/MK), -5 or del(5q), -7, t(v;11q23), chromosome 17 alterations (-17, del(17p) or abn(17p)), FLT3-ITD, and mutations found in either TP53 with VAF ≥10%, DNMT3A, EZH2, NPM1, or NRAS. From these parameters, nine remained significant with Cox regression: age (HR 1.48, p&amp;lt;0.01), relapse free interval ≤1 year (HR 1.74, p&amp;lt;0.01), white blood cell count ≥10 x 10e9/L at diagnosis (HR 1.23, p=0.02), prior allogeneic SCT (HR 1.58, p&amp;lt;0.01), CK/MK (HR 1.53, p&amp;lt;0.01), no t(8;21)/inv(16) (HR 2.34, p&amp;lt;0.01), t(v;11q23) (HR 1.51, p=0.02), mutated TP53 with VAF ≥10% (HR 1.70, p&amp;lt;0.01), FLT3-ITD (HR 1.32, p&amp;lt;0.01). Next, weights were assigned to these variables based on the multivariate HRs (Figure 1A). Scores were calculated for individual patients and four groups were defined. OS at 1 year estimated 54±3% in the favorable risk group (≤5 points), 35±3% in the intermediate group (6-7 points), 18±3% in the high risk group (8-9 points), and 8±2% in the very high risk group (≥10 points) (Figure 1B). The prognostic accuracy for 1 year OS as measured by the C-index was 0.70. In contrast, in this cohort C-indices for the HOVON-SAKK and GOELAMS models were 0.65 and 0.64, respectively. In addition, comparing the earlier models with the new model, 50% of patients in the adverse group of the HOVON-SAKK model were classified to the favorable (14%) and intermediate group (36%) in the new model. In the GOELAMS model, 42% of intermediate risk patients were considered high (10%) or very high risk (32%) in the new model. Conclusion Analysis of clinical, cytogenetic and molecular variables identified nine variables for a new prognostic model which was associated with distinct OS at 1 year after first AML relapse and improved prognostic accuracy.

A Population-Based Study on Performance of Clinical Prognostic Models in Patients with Diffuse Large B-Cell Lymphoma Younger Than 70 Years

Background: In diffuse large B-cell lymphoma (DLBCL), the International Prognostic Index (IPI) and age-adjusted IPI (aaIPI) remain the primary prognostic tool for outcome predictions and risk stratification in clinical trials. Several prognostic indices with the same five risk factors included in the IPI have been developed for DLBCL patients, including Revised IPI (R-IPI) and National Comprehensive Cancer Network IPI (NCCN-IPI), showing a decreased prognostic value of the IPI. Additionally, an age-adjusted DLBCL-Prognostic Index (aaDLBCL-PI) developed in patients younger than years combining similar variables (excluding age and stage), but with the addition of albumin, showed better accuracy than the IPI. Younger patients without comorbidities and a history of cancer are typically candidates for clinical trials, but validation studies of prognostic indices in this population are mostly lacking. Aim: This study aimed to compare five clinical models (IPI, aaIPI, R-IPI, NCCN-IPI, and aaDLBCL-PI) in patients with DLBCL younger than 70 years identified from the Danish Lymphoma Registry (LYFO). Methods: Patients diagnosed between 2000 and 2021 and identified through the LYFO were included if they fulfilled the following criteria: 1) newly diagnosed DLBCL; 2) age &amp;lt;70 years; 3) treated with at least one cycle of immunochemotherapy (R-CHOP/R-CHOP-like); 4) availability of all parameters for calculation of all five prognostic models; and 5) no previous/concomitant malignancy (excluding non-melanoma skin cancer). Patients with isolated central nervous system lymphoma were excluded. Discriminatory ability was calculated using the concordance index (c-index), with 1 indicating perfect discrimination and 0.5 indicating random guess. Model fitness was compared with the Akaike Information Criterion (AIC), with the lower values indicating a better fit of a model and the difference ≥10 between models indicating a significant improvement in model fitness. Results: Of 3,184 DLBCL patients younger than 70 years, 2,456 had all parameters available for calculating five prognostic models and were included in the final analysis. The median age at diagnosis was 59 years (range 18-69) with a slight male predominance (n=1,440, 58.6%). Most patients (n=1,619, 65.9%) had advanced (III/IV) stage of disease and were in good performance status 0-1 (n=2,165, 88.2%). More than one extranodal localization was diagnosed in 738 patients (30%), and more than half of our population (n=1,462, 59%) had elevated LDH levels. All models stratified patients into four risk groups except R-IPI, which provided three risk groups. The proportion of patients allocated to the low-risk group according to the IPI, aaIPI, R-IPI, aaDLBCL-PI, and NCCN-IPI were 32.9%, 19.7%, 12.3%, 17.4%, and 15.3%, respectively. The lowest proportion of patients in the high-risk group was identified by NCCN-IPI (6.3%), followed by IPI (14.8%), aaIPI (20.2%), aaDLBCL-PI (22.0%), and R-IPI (40.7%), respectively. The median follow-up was 78.1 months (range 0.2-244 months). As presented in Table 1, NCCN-IPI produced the highest absolute 3- and 5-year difference between high and low-risk groups and was the only model with less than 50% of surviving patients in the high-risk groups in respective survival time points. Moreover, the highest c-index was calculated for NCCN-IPI (0.709), followed by IPI (0.703), aaDLBCL-PI (0.692), R-IPI (0.679), and aaIPI (0.667). NCCN-IPI showed a statistically significant difference in discriminatory ability compared to all models except IPI. The lowest AIC was found in NCCN-IPI (9,231), along with IPI (9,295), aaDLBCL-PI (9,369), R-IPI (9,310), and aaIPI (9,408). When patients aged ≤60 were selected (n=1,328), NCCN-IPI again produced the highest absolute 3- and 5-year difference between high and low-risk groups. Moreover, NCCN-IPI had the highest c-index and lowest AIC (0.758; 3,068) compared to IPI (0.742; 3,118), aaIPI (0.730; 3,124), R-IPI (0.729; 3,109), and aaDLBCL-PI (0.751; 3,110). Conclusion: In this retrospective population-based study investigating 2,456 patients with DLBCL 18-69 years of age, we found NCCN-IPI superior to IPI and, in particular, aaIPI and R-IPI. Therefore, we suggest reporting NCCN-IPI and IPI for risk stratification in future studies and clinical trials.

Clinical risk factors and prognostic model for patients with bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation.

Bronchiolitis obliterans syndrome (BOS) is a common and potentially devastating noninfectious pulmonary complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Currently, predictive tools for BOS are not available. We aimed to identify the clinical risk factors and establish a prognostic model for BOS in patients who undergo allo-HSCT. We retrospectively identified a cohort comprising 195 BOS patients from 6100 consecutive patients who were allografted between 2008 and 2022. The entire cohort was divided into a derivation cohort and a validation cohort based on the time of transplantation. Via multivariable Cox regression methods, declining forced expiratory volume at 1 s (FEV1) to <40%, pneumonia, cGVHD except lung, and respiratory failure were found to be independent risk factors for the 3-year mortality of BOS. A risk score called FACT was constructed based on the regression coefficients. The FACT model had an AUC of 0.863 (95% CI: 0.797-0.928) in internal validation and 0.749 (95% CI: 0.621-0.876) in external validation. The calibration curves showed good agreement between the FACT-predicted probabilities and actual observations. The FACT risk score will help to identify patients at high risk and facilitate future research on developing novel, effective interventions to personalize treatment.

New prognostic model for hospitalized patients with alcoholic cirrhosis and Maddrey's discriminant function

Few studies have investigated the prognosis of patients with non-severe alcoholic hepatitis (Non-SAH). The study aimed to develop a new prognostic model for patients with especially Non-SAH. We extracted 316 hospitalized patients with alcoholic cirrhosis without severe alcoholic hepatitis, defined as Maddrey's discriminant function score lower than 32, from the retrospective Korean Acute-on-Chronic Liver Failure (KACLiF) cohort to develop a new prognostic model (training set), and validated it in 419 patients from the prospective KACLiF cohort (validation set). Prognostic factors for death and liver transplantation were analyzed to construct a prognostic model. Twenty-one and 24 patients died within 6 months in both sets, respectively. In the training set, the highest area under the curve (AUC) of conventional prognostic models was 0.765, 0.732, and 0.684 for 1-, 3-, and 6-month mortality, respectively. Refractory ascites, vasopressor use, and hyponatremia were independently associated with mortality of cirrhotic patients with Non-SAH. The new model consisted of four variables: past deterioration, neutrophil proportion > 70%, Na < 128 mmol/L, and vasopressor use. It showed the highest accuracy for short-term mortality in the training and validation sets (0.803 and 0.786; 0.797 and 0.776; and 0.789 and 0.721 for 1-, 3-, and 6-month mortality, respectively). There is a group of patients with high risk among those classified as Non-SAH. The new model will help stratifying cirrhotic patients with Non-SAH more accurately in terms of prognosis. The patients with high Non-SAH score need to monitor closely and might be considered for preemptive liver transplantation. ClinicalTrials.gov identifier: NCT02650011.

Risk factors and a prognostic model for patients with borderline resectable locally advanced T3-4N0-1 non-small cell lung cancer: a population-based study.

Non-small cell lung cancer (NSCLC) is a malignant disease with a significant morbidity rate. For patients diagnosed with borderline resectable locally advanced (T3-4 invasion and N0-1) NSCLC, the optimal treatment and prognosis are still under debate. This study aimed to develop a predictive nomogram that could assess the prognosis of these patients and optimize clinical decision-making. Between 2010 to 2015, the survival, demographic and clinical characteristics of patients with borderline resectable locally advanced T3-4N0-1 NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Cox proportional hazard regression analyses were conducted to identify potential factors, which were further utilized to develop a dynamic nomogram for personalized prediction. Internal and external validation were conducted to verify the predictive accuracy of the nomogram. Totally, 5,054 eligible records were enrolled into the study cohort. The included patients were divided into a training cohort (n=3,538) and a validation cohort (n=1,516) in a 7:3 ratio. Nine independent prognostic factors (including age, gender, primary site, lymph node removal, differentiation grade, T stage, N stage, histology and adjuvant chemotherapy) were finally included into the nomogram. The developed nomogram exhibited favorable discriminative ability with the C-index =0.71. Moreover, the calibration curves demonstrated excellent agreement between predicted and observed outcomes in both the training and validation cohorts. Notably, subgroup analyses revealed that neoadjuvant chemotherapy was significantly associated with a better overall survival (OS) (P<0.05) in patients staged as T3-4N1. In this study, we developed and validated a prognostic model to assist in clinical decision-making for patients with borderline resectable locally advanced T3-4N0-1 NSCLC. Our findings suggested that patients with T3-4N1 stage disease may derive significant benefits from neoadjuvant chemotherapy.

Multi-omics approaches establishing histone modification based prognostic model in glioma patients and further verification of the carcinogenesis mechanism.

Glioblastoma (GBM) emerges as the most common malignant brain tumor. Histone modifications, as an epigenetic regulatory mechanism of gene expression, are closely associated with malignant tumors. Gene set related to histone modification was extracted from the MSigDB database, and scored by the function of AddModuleScore. Pearson correlation analysis was utilized using the "rcorr" function of "Hmisc" R package. Genes were screened out using the LASSO Cox analysis. TCGA-GBM and CGGA_array_301 cohorts were employed for constructing model and validation. We calculated immune infiltration scores using microenvironment cell populations counter (MCPcounter), single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms. U87-MG and CHG-5 cell lines were utilized to evaluate expression level of TMEM176A by western blot (WB). Transwell, EDU, colony formation analysis (CFA), and CKK-8 assays were conducted to investigate cell proliferation and migration rate. The malignant cells in GBM patients exhibited notable activation in the TGF-β and hypoxia pathway. Histone modifications were associated with adhesion and neuron development in GBM. We identified a model with five significant genes, namely NBEAL1, AEBP1, TMEM176A, FASTK, and CD81, with prognostic efficacy. Additionally, we observed increased infiltration of T cells and CD8+ T cells in the high-risk (HR) group. 5-Fluorouracil_1073 and Taselisib_1561 were predicted as potential treatment options for GBM patients, while ABT737_1910 and Wnt_C59-1622 exhibited superior response in GBM patients of the HR group. A spike in the TP53 mutation rate was observed in the HR group. TMEM176A played a role in regulating cell proliferation and migration in vitro. We presented a novel prognostic model for patients with GBM, based on histone modification-related genes. In addition, we identified the crucial role of the TMEM176A in the regulation of GBM carcinogenic phenotypes for the first time.

Individual risk and prognostic value prediction by machine learning for distant metastasis in pulmonary sarcomatoid carcinoma: a large cohort study based on the SEER database and the Chinese population.

This study aimed to develop diagnostic and prognostic models for patients with pulmonary sarcomatoid carcinoma (PSC) and distant metastasis (DM). Patients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into a training set and internal test set at a ratio of 7 to 3, while those from the Chinese hospital were assigned to the external test set, to develop the diagnostic model for DM. Univariate logistic regression was employed in the training set to screen for DM-related risk factors, which were included into six machine learning (ML) models. Furthermore, patients from the SEER database were randomly divided into a training set and validation set at a ratio of 7 to 3 to develop the prognostic model which predicts survival of patients PSC with DM. Univariate and multivariate Cox regression analyses have also been performed in the training set to identify independent factors, and a prognostic nomogram for cancer-specific survival (CSS) for PSC patients with DM. For the diagnostic model for DM, 589 patients with PSC in the training set, 255 patients in the internal and 94 patients in the external test set were eventually enrolled. The extreme gradient boosting (XGB) algorithm performed best on the external test set with an area under the curve (AUC) of 0.821. For the prognostic model, 270 PSC patients with DM in the training and 117 patients in the test set were enrolled. The nomogram displayed precise accuracy with AUC of 0.803 for 3-month CSS and 0.869 for 6-month CSS in the test set. The ML model accurately identified individuals at high risk for DM who needed more careful follow-up, including appropriate preventative therapeutic strategies. The prognostic nomogram accurately predicted CSS in PSC patients with DM.