Gene Expression Patterns Associated with Survival in Glioblastoma.

The aim of this study was to investigate gene expression alterations associated with overall survival (OS) in glioblastoma (GBM). Using the Nanostring nCounter platform, we identified four genes (COL1A2, IGFBP3, NGFR, and WIF1) that achieved statistical significance when comparing GBM with non-neoplastic brain tissue. The four genes were included in a multivariate Cox Proportional Hazard model, along with age, extent of resection, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation, to create a unique glioblastoma prognostic index (GPI). The GPI score inversely correlated with survival: patient with a high GPI had a median OS of 7.5 months (18-month OS = 9.7%) whereas patients with a low GPI had a median OS of 20.1 months (18-month OS = 54.5%; log rank p-value = 0.004). The GPI score was then validated in 188 GBM patients from The Cancer Genome Atlas (TCGA) from a national data base; similarly, patients with a high GPI had a median OS of 10.5 months (18-month OS = 12.4%) versus 16.9 months (18-month OS = 41.5%) for low GPI (log rank p-value = 0.0003). We conclude that this novel mRNA-based prognostic index could be useful in classifying GBM patients into risk groups and refine prognosis estimates to better inform treatment decisions or stratification into clinical trials.

Unlocking the potential: A novel prognostic index signature for acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by challenges in treatment, including drug resistance and frequent relapse. Recent research highlights the crucial roles of tumor microenvironment (TME) in assisting tumor cell immune escape and promoting tumor aggressiveness. This study delves into the interplay between AML and TME. Through the exploration of potential driver genes, we constructed an AML prognostic index (AMLPI). Cross-platform data and multi-dimensional internal and external validations confirmed that the AMLPI outperforms existing models in terms of areas under the receiver operating characteristic curves, concordance index values, and net benefits. High AMLPIs in AML patients were indicative of unfavorable prognostic outcomes. Immune analyses revealed that the high-AMLPI samples exhibit higher expression of HLA-family genes and immune checkpoint genes (including PD1 and CTLA4), along with lower T cell infiltration and higher macrophage infiltration. Genetic variation analyses revealed that the high-AMLPI samples associate with adverse variation events, including TP53 mutations, secondary NPM1 co-mutations, and copy number deletions. Biological interpretation indicated that ALDH2 and SPATS2L contribute significantly to AML patient survival, and their abnormal expression correlates with DNA methylation at cg12142865 and cg11912272. Drug response analyses revealed that different AMLPI samples tend to have different clinical selections, with low-AMLPI samples being more likely to benefit from immunotherapy. Finally, to facilitate broader access to our findings, a user-friendly and publicly accessible webserver was established and available at http://bioinfor.imu.edu.cn/amlpi. This server provides tools including TME-related AML driver genes mining, AMLPI construction, multi-dimensional validations, AML patients risk assessment, and figures drawing.

A Correlation Evaluation Between the Peripheral Blood Index and the Prognosis of Advanced Esophageal Squamous Cell Carcinoma Patients Treated with Camrelizumab.

Purpose: This study aimed to investigate the relationship between peripheral blood indices and the efficacy and prognosis of advanced esophageal squamous cell carcinoma (ESCC) patients treated with camrelizumab. Patients and Methods: We retrospectively analyzed 64 patients who received camrelizumab for advanced ESCC at the Second People's Hospital of Lianyungang City between July 2020 and June 2022. The study included examination of the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the systemic inflammation index (SII), the lymph-to-monocytes ratio (LMR), the absolute lymphocyte count (ALC), and lactate dehydrogenase (LDH). We used multivariate logistic regression analysis to explore the link existing between peripheral blood and the efficacy of treatment. Determination of potential prognostic factors for Progression-free survival (PFS) and Overall survival (OS) using Cox regression analysis. The nomogram model was developed based on the results of the Cox multivariate analysis. Patients were divided into three groups according to the reduction in LDH and LDL levels before treatment, and the Kaplan-Meier survival curves for the three groups were compared and ROC curves for LDH combined with PLR were plotted. Results: Lower LDH (OR=6.237, 95% CI: 1.625-23.944) were independently associated with disease control rates(DCR). LDH was independently correlated with PFS (HR: 0.227 95% CI: 0.099-0.517). LDH and PLR were independently linked to OS. The C index of the nomogram model is 0.819, indicating good predictive performance. Kaplan-Meier Survival Curve suggested better OS in patients with reduced pretreatment LDH and PLR. The area under the ROC curve showed that the LDH index combined with the PLR index predicts patient survival better than the index alone. Conclusion: LDH combined with PLR predicted prognosis in patients with ESCC treated with camrelizumab.

Efficacy and safety of first-line PD-L1/PD-1 inhibitors in limited-stage small cell lung cancer: a multicenter propensity score matched retrospective study.

Background: The prognosis of small cell lung cancer (SCLC) patients is poor, and the standard first-line treatment for limited-stage small cell lung cancer (LS-SCLC) is still chemotherapy and thoracic radiotherapy. The primary objectives of our study were to confirm the superior efficacy of first-line immune checkpoint inhibitors (ICIs) plus etoposide and platinum (EP) for LS-SCLC and find crucial biomarkers. Methods: We analyzed LS-SCLC patients from three medical centers, employing propensity score matching for group comparability. Survival outcomes were estimated by Kaplan-Meier and Cox regression analyses. Additionally, we conducted univariate and multivariate analyses to investigate potential predictive factors. Results: Among 150 patients in our study, we successfully matched 41 pairs. The median overall survival (OS) was 29.5 months in the EP + ICIs group and 20.0 months in the EP group {hazard ratio (HR) =0.64 [95% confidence interval (CI): 0.41-1.02], P=0.059}. The median progression-free survival (PFS) was significantly extended in the EP + ICIs group (14.6 months), compared to the EP group (8.6 months) [HR =0.42 (95% CI: 0.28-0.63), P<0.001]. After matching, patients receiving chemo-immunotherapy had a median OS of 36.1 months, significantly surpassing those receiving chemotherapy alone (19.0 months) [HR =0.51 (95% CI: 0.28-0.93), P=0.02]. And the patients in the EP + ICIs group also had longer PFS after matching [HR =0.42 (95% CI: 0.25-0.71), P=0.001]. No significant difference in the objective response rate (ORR) and treatment-related adverse events (trAEs) between the two groups was found (ORR: EP: 81.0%, EP + ICIs: 90.0%, P=0.14; trAEs: EP: grade 1-2, 49.3%; grade 3-4, 42.5%; EP + ICIs: grade 1-2, 40.0%; grade 3-4, 49.1%, P=0.62). The multivariate analysis presented that the history of immunotherapy [EP + PD-1 inhibitors: HR =0.33 (95% CI: 0.17-0.62), P=0.001; EP + PD-L1 inhibitors: HR =0.18 (95% CI: 0.06-0.60), P=0.005] and baseline lung immune prognostic index (LIPI) [intermediate: HR =2.22 (95% CI: 1.20-4.13), P=0.01; poor: HR =2.03 (95% CI: 0.71-5.77), P=0.18] were independent prognostic factors for PFS among all LS-SCLC cases. However, no independent prognostic factor was identified for OS. Conclusions: Our real-world data showed promising clinical efficacy and tolerable safety of first-line programmed cell death protein 1 (PD-1) inhibitors or programmed cell death ligand 1 (PD-L1) inhibitors in cases with LS-SCLC. Additionally, LIPI may serve as a valuable prognostic factor.

Usefulness of the Palliative Prognostic Index in Predicting Prognosis when Considering the Transition from Hospital to Home Care in Patients with Terminal Stage Cancer.

BACKGROUND: No accurate prognostic tool is available for patients with cancer who spend their final days at home. In this study, we examined whether performance status (PS) and the palliative prognostic index (PPI), a well-known prognostic tool in palliative care units, could be used to predict prognosis in the home care setting at the time of intervention by home physicians. SUBJECTS AND METHODS: Using medical records, we conducted a retrospective analysis of 132 patients who were referred to the Home Clinic Naginoki for home care for terminal stages of carcinoma in situ. Based on the status at the time of the first visit, the PPI-Low group was defined as those scoring six or below and the PPI-High group as those scoring greater than six. RESULTS: The PPI-high group had a significantly poorer prognosis within 21 days than the PPI-low group (21-day-OS; Low 71.4% vs. High 13.2%; p<0.001). The Eastern Cooperative Oncology Group (ECOG) PS alone predicted better prognosis in the group with PS of one or two (21-day survival 90.1%), and the PPI score further significantly stratified the prognosis for patients with PS three or four, with a trend toward poor prognosis (p ≤ 0.005). CONCLUSION: ECOG PS 1 or 2 has a favorable prognosis and that using PPI in ECOG PS 3 or 4 leads to a more accurate prognosis prediction. PPI evaluated during the hospital-based treatment of patients with terminal cancer can also be used to predict prognosis if the patient is transitioned to a home care environment.

Predicting central nervous system relapse in primary breast diffuse large B-cell lymphoma using the stage-modified IPI score: A retrospective cohort study.

Objective: The existing Central Nervous System-International Prognostic Index (CNS-IPI) provides insufficient guidance for predicting central nervous system (CNS) relapse in individuals with primary breast diffuse large B-cell lymphoma (DLBCL). This retrospective cohort study sought to examine the potential of the stage-modified IPI in predicting CNS relapse within this specific patient population. Patients and methods: We examined the baseline characteristics of 76 consecutive patients diagnosed with primary breast DLBCL, calculating the stage-modified IPI score for each individual. Utilizing a competing risk regression (CRR) model, we conducted both univariate and multivariate analyses to explore the relationship between potential prognostic factors and the occurrence of CNS relapse. Results: In our cohort, the rates of CNS disease at 2 and 5 years since the diagnosis of primary breast DLBCL are 3.9% and 7.8%, respectively. Among patients experiencing CNS relapse, 80% presented with a parenchymal brain mass. Individuals with a high stage-modified IPI score (1-3 points) had a significantly higher incidence of CNS relapse (p = 0.031), a shorter time from the initial diagnosis of primary breast DLBCL to the first CNS relapse (p = 0.010), as well as relapse at any site (p = 0.012), compared to those with a low score (0 points). Univariate analysis identified stage (Hazard Ratio (HR): 4.098, p = 0.024), stage-modified IPI score (HR: 11.582, p = 0.012), and radiation therapy (HR: 5.784, p = 0.026) as significant risk factors. In multivariate analysis, in addition to radiation therapy (HR: 7.258, p = 0.012), the stage-modified IPI score (1-3 points versus 0 points) emerged as an independent and reliable predictor for CNS relapse (HR: 12.945, p = 0.016). Conclusion: Our study underscores the significance of stage-modified IPI scores in predicting CNS relapse for patients with primary breast DLBCL. Validation of these findings through further research is essential, along with exploring potential prevention and intervention approaches.

Resilience improvement through a multicomponent physical and cognitive intervention for older people: the DanzArTe emotional well-being technology project.

BACKGROUND: Resilience is a crucial component of successful aging. However, which interventions might increase resilience in older adults is yet unclear. AIMS: This study aims to assess the feasibility and the physical and psychological effects of a technology-based multicomponent dance movement intervention that includes physical, cognitive, and sensory activation in older people living in community-dwelling and nursing home. METHODS: DanzArTe program consists of four sessions on a weekly basis, using a technological platform that integrates visual and auditory contents in real time. 122 participants (mean age = 76.3 ± 8.8 years, 91 females = 74.6%) from seven nursing homes and community-dwelling subjects were assessed, before and after the intervention, with the Resilience Scale-14 items (RES-14), the Multidimensional Prognostic Index (MPI), the Psychological General Well-Being Index (PGWBI-S), and the Client Satisfaction Questionnaire-8 (CSQ-8). Mann-Whitney and Wilcoxon signed-ranks tests were used for statistical analyses. RESULTS: At baseline significant differences in MPI and RES-14 between community-dwelling and nursing home residents were observed (p < 0.001 for both analyses). After the intervention, resilience significantly increased in total sample (RES-14 mean T1 = 74.6 Vs. T2 = 75.7) and in the nursing home residents (RES-14 mean T1 = 68.1 Vs. T2 = 71.8). All participants showed high overall satisfaction for DanzArTe program (CSQ-8 mean = 23.9 ± 4.4). No differences in MPI and PGWBI-S were observed. DISCUSSION: DanzArTe was a feasible intervention and high appreciated by all older adults. Nursing home residents revealed improvements in resilience after DanzArTe program. CONCLUSION: The DanzArTe technology-based multi-component intervention may improve resilience in older people living in nursing homes.

Multivariate prognostic index and triplet regimen efficacy predictive index in locally advanced and metastatic gastric cancer: pooled analysis from three clinical trials using individual patient data.

Background: To construct an effective prognostic index to predict overall survival (OS) and triplet regimen efficacy for advanced gastric cancer (AGC) patients treated with platinum-based and fluorouracil-based chemotherapy. Objectives: Between 2011 and 2021, 679 patients from two randomized phase III trials and one phase II trial were enrolled. Designs: We collected 11 baseline clinicopathological and 14 hematological parameters to establish a prognostic index. Methods: Univariate and multivariate Cox analyses were used to screen prognostic factors, and a prognostic index nomogram was conducted. Results: Seven prognostic factors were identified: primary tumor site in the non-proximal gastric area, signet-ring cell carcinoma (SRCC)/mucinous carcinoma, peritoneal metastasis, neutrophil count higher than the upper limit of normal value (ULN), lymphocyte count lower than the lower limit of normal value, lactate dehydrogenase level higher than the ULN, and alkaline phosphatase level higher than the ULN as significant for prognosis. A prognostic nomogram named the Fudan advanced gastric cancer prognostic risk score (FARS) index was constructed, and patients in the high-risk group had significantly shorter OS than those in the low-risk group (median OS, 15.5 versus 8.0 months, p < 0.001). The areas under the curve of the FARS index for 1-, 2-, and 3-year OS were 0.70, 0.72, and 0.77, respectively. A validation and external cohort verified the prognostic value of the FARS index. Moreover, three triplet regimen efficacy parameters were identified: SRCC/mucinous adenocarcinoma, primary tumor location in the non-proximal gastric area, and peripheral neutrophil count higher than the ULN; a TRIS index was subsequently conducted. In patients with any two of the three parameters, the triplet regimen showed significantly longer OS than the doublet regimen (p = 0.018). Conclusion: The constructed FARS index to predict the OS of AGC patients and the TRIS index to screen out the dominant population for triplet regimens can be used to aid clinical decision-making and individual risk stratification.

A prognostic index in locally advanced and metastatic gastric cancer To date, no recognized systematic prognostic score has been established for advanced gastric cancer (AGC). Our research aims to construct an effective prognostic index to predict overall survival (OS) for AGC patients to aid clinical decision-making and individual risk stratification. In our research, seven prognostic factors were identified: primary tumor site in the non-proximal gastric area, signet-ring cell carcinoma (SRCC)/mucinous carcinoma, peritoneal metastasis, neutrophil count higher than the upper limit of normal value (ULN), lymphocyte count lower than the lower limit of normal value, lactate dehydrogenase level higher than the ULN, and alkaline phosphatase level higher than the ULN as significant for prognosis. A prognostic index named the Fudan advanced gastric cancer prognostic risk score (FARS) index was constructed, and patients in the high-risk group had significantly shorter OS than those in low-risk group (median OS, 15.5 months vs. 8.0 months, P < 0.001). Moreover, three triplet regimen efficacy parameters were identified: SRCC/mucinous adenocarcinoma, primary tumor location in the non-proximal gastric area, and peripheral neutrophil count higher than the ULN; a TRIS index was subsequently conducted. In patients with any two of the three parameters, the triplet regimen showed significantly longer OS than the doublet regimen (P = 0.018).

An artificial intelligence-driven predictive model for pediatric allogeneic hematopoietic stem cell transplantation using clinical variables.

BACKGROUND: Hematopoietic stem cell transplantation (HSCT) is a procedure with high morbidity and mortality. Identifying patients for maximum benefit and risk assessment is crucial in the decision-making process. This has led to the development of predictive risk models for HSCT in adults, which have limitations when applied to pediatric population. Our goal was to develop an automatic learning algorithm to predict survival in children with malignant disorders undergoing HSCT. METHODS: We studied allogenic HSCTs performed on children with malignant disorders at a third-level hospital between 1991 and 2021. Survival was analyzed using the Kaplan-Meier method, log-rank test for the univariate analysis, and Cox regression for the multivariate analysis. A prognostic index was constructed based on these findings. Lastly, we constructed a predictive model using a random forest algorithm to forecast 1-year survival after HSCT. RESULTS: We analyzed 229 HSCTs in 201 patients with a median follow-up of 1.64 years. Variables that impacted on the multivariate analysis were older age (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.12-1.76, p = .003), oldest period of HSCT (HR 0.46, 95% CI 0.29-0.73, p < .001), and mismatched donor (HR 2.65, 95% CI 1.51-4.65, p = .001). Our prognostic index was associated with 3-year overall survival (OS; p < .001). A random forest was developed using as variables: diagnosis, age, year of HSCT, time from diagnosis to HSCT, disease stage, donor type, and conditioning. This achieved 72% accuracy in predicting 1-year OS. CONCLUSIONS: Our index and random forest was effective in predicting 1-year survival. However, further validation in diverse populations is necessary to establish their generalizability.

Prediction of the Nottingham prognostic index and molecular subtypes of breast cancer through multimodal magnetic resonance imaging.

PURPOSE: To explore the ability of intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and background parenchyma enhancement (BPE) to predict the Nottingham prognostic index (NPI) and molecular subtypes of breast cancer (BC). MATERIALS AND METHODS: In this study, 93 patients with BC were included, and they all underwent DKI, IVIM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) examinations. The corresponding mean kurtosis value (MK), pure diffusion (MD), perfusion fraction (f), pseudo diffusion coefficient (D*), true diffusion coefficient (D), and BPE were measured. We used logistic regression analysis to investigate the relevance between the NPI, molecular subtypes and variables. The diagnostic efficacy was analyzed using receiver operating characteristic curves (ROC). RESULTS: The MD and D values of the high-level NPI group were significantly lower than those of the low-level NPI group (p < 0.01), and the f value of the high-level NPI group was obviously higher than that of low-level NPI group (p < 0.001). The area under curve (AUC) of the combined model (f + D) was 0.824. Comparing with non-Luminal subtypes, the Luminal subtypes showed obviously lower MK, f and D*, and the AUC of the combined model (MK + f + D*) was 0.785. In comparison to other subtypes, the MK and D* values of triple-negative subtype were higher than other subtypes, and the combined model (MK + D*) represented an AUC of 0.865. CONCLUSION: The quantitative parameters of DKI and IVIM have vital value in predicting the NPI and molecular subtypes of BC, while BPE could not provide additional information. Besides, these combined models can obviously improve the prediction performance.

Prognostic iron-metabolism signature robustly stratifies single-cell characteristics of hepatocellular carcinoma.

Cancer immunotherapy has shown to be a promising method in treating hepatocellular carcinoma (HCC), but suboptimal responses in patients are attributed to cellular and molecular heterogeneity. Iron metabolism-related genes (IRGs) are important in maintaining immune system homeostasis and have the potential to help develop new strategies for HCC treatment. Herein, we constructed and validated the iron-metabolism gene prognostic index (IPX) using univariate Cox proportional hazards regression and LASSO Cox regression analysis, successfully categorizing HCC patients into two groups with distinct survival risks. Then, we performed single-sample gene set enrichment analysis, weighted correlation network analysis, gene ontology enrichment analysis, cellular lineage analysis, and SCENIC analysis to reveal the key determinants underlying the ability of this model based on bulk and single-cell transcriptomic data. We identified several driver transcription factors specifically activated in specific malignant cell sub-populations to contribute to the adverse survival outcomes in the IPX-high subgroup. Within the tumor microenvironment (TME), T cells displayed significant diversity in their cellular characteristics and experienced changes in their developmental paths within distinct clusters identified by IPX. Interestingly, the proportion of Treg cells was increased in the high-risk group compared with the low-risk group. These results suggest that iron-metabolism could be involved in reshaping the TME, thereby disrupting the cell cycle of immune cells. This study utilized IRGs to construct a novel and reliable model, which can be used to assess the prognosis of patients with HCC and further clarify the molecular mechanisms of IRGs in HCC at single-cell resolution.

Development and Internal Validation of a Model for Predicting Overall Survival in Subjects with MAFLD: A Cohort Study.

Background & Aims: Fatty liver disease with metabolic dysfunction (MAFLD) is a new concept proposed to replace the previous concept of Non-Alcoholic Hepatic Steatosis (NAFLD). We developed and internally validated a prognostic model to predict the likelihood of death in a cohort of subjects with MAFLD. Methods: Our work involved two steps: the first was the construction of a bootstrapped multivariable Cox model for mortality risk prognosis and the second was its validation. Results: The study cohort included 1506 subjects, of which 907 were used for internal validation. Discriminant measures for the final model were R2D 0.6845 and Harrell's C 0.8422 in the development and R2D 0.6930 and Harrell's C 0.8465 in the validation. We used the nine independent prognostic factors selected by the LASSO Cox procedure and fitted by the bootstrap Cox survival model, and observed ß were: Gender 0.356 1.42 (p < 0.008), Age 0.146 (p < 0.001), Glycemia 0.004 (p < 0.002), Total Cholesterol -0.0040 (p < 0.009), Gamma Glutamyl Transpeptidase 0.009 (p < 0.001), SBP 0.009 (p < 0.036), DBP -0.016 (p < 0.041), ALP 0.008 (p < 0.071) and Widowhood 0.550 (p < 0.001). Conclusions: We produced and validated a model to estimate the probability of death in subjects with MAFLD. The instruments we used showed satisfactory predictive capabilities.

Beneficial Use of the Combination of Gemcitabine and Dacarbazine in Advanced Soft Tissue Sarcomas: Real-World Data.

BACKGROUND: The combination of gemcitabine and dacarbazine has exhibited efficacy in terms of progression-free survival (PFS) and overall survival (OS) for aSTSs, albeit without robust confirmation from larger clinical trials. METHODS: We conducted a retrospective study in a single institution involving aSTS patients treated with gemcitabine and dacarbazine. RESULTS: 95 patients were assessed, pointing to a benefit in PFS of 3.5 months and an OS of 14.2 months. Patients with translocated histotypes had better PFS, while those with platelet-lymphocyte ratios (PLRs) surpassing a specific threshold or lower albumin levels had poorer overall survival. CONCLUSIONS: This study validates previous findings from three phase I-II trials, affirming the utility of this treatment approach in routine clinical practice.

Early Changes in LIPI Score Predict Immune-Related Adverse Events: A Propensity Score Matched Analysis in Advanced Non-Small Cell Lung Cancer Patients on Immune Checkpoint Blockade.

In advanced cancer patients undergoing immune checkpoint blockade, the burden of immune-related adverse events (irAEs) is high. The need for reliable biomarkers for irAEs remains unfulfilled in this expanding therapeutic field. The lung immune prognostic index (LIPI) is a noninvasive measure of systemic inflammation that has consistently shown a correlation with survival in various cancer types when assessed at baseline. This study sought to determine whether early changes in the LIPI score could discriminate the risk of irAEs and different survival outcomes in advanced non-small cell lung cancer (NSCLC) patients receiving PD-(L)1 blockade-based therapies. We included consecutive patients diagnosed with metastatic NSCLC who received pembrolizumab, nivolumab, or atezolizumab as second-line therapy following platinum-based chemotherapy, or first-line pembrolizumab either alone or in combination with platinum-based chemotherapy. The LIPI score relied on the combined values of derived neutrophil/lymphocyte ratio (dNLR) and lactate dehydrogenase. Their assessment at baseline and after two cycles of treatment allowed us to categorize the population into three subgroups with good (LIPI-0), intermediate (LIPI-1), and poor (LIPI-2) prognosis. Between April 2016 and May 2023, we enrolled a total of 345 eligible patients, 165 (47.8%) and 180 (52.2%) of whom were treated as first- and second-line at our facility, respectively. After applying propensity score matching, we considered 83 relevant patients in each cohort with a homogeneous distribution of all characteristics across the baseline LIPI subgroups. There was a noticeable change in the distribution of LIPI categories due to a significant decrease in dNLR values during treatment. Although no patients shifted to a worse prognosis category, 20 (24.1%) transitioned from LIPI-1 to LIPI-0, and 7 (8.4%) moved from LIPI-2 to LIPI-1 (p < 0.001). Throughout a median observation period of 7.3 (IQR 3.9-15.1) months, a total of 158 irAEs (63.5%) were documented, with 121 (48.6%) and 39 (15.7%) patients experiencing mild to moderate and severe adverse events, respectively. Multivariate logistic regression analysis showed that the classification and changes in the LIPI score while on treatment were independent predictors of irAEs. The LIPI-0 group was found to have significantly increased odds of experiencing irAEs. Following a median follow-up period of 21.1 (95% CI 17.9-25.8) months, the multivariable Cox model confirmed LIPI categorization at any given time point as a significant covariate with influence on overall survival, irrespective of the treatment line. These findings suggest that reassessing the LIPI score after two cycles of treatment could help pinpoint patients particularly prone to immune-related toxicities. Those who maintain a good LIPI score or move from the intermediate to good category would be more likely to develop irAEs. The continuous assessment of LIPI provides prognostic insights and could be useful for predicting the benefit of PD-(L)1 checkpoint inhibitors.

Secondary central nervous system involvement in patients with diffuse large B-cell lymphoma treated with rituximab combined CHOP therapy - a supplementary analysis of JCOG0601.

Secondary central nervous system involvement (sCNSi) in diffuse large B-cell lymphoma (DLBCL) is fatal. However, its features in patients with sCNSi who are categorized as lower risk by international prognostic index (IPI) or CNS-IPI are not yet fully understood. In the present analysis, we evaluated DLBCL patients who developed sCNSi at their first progression and who participated in JCOG0601, most of whom were lower risk by IPI. Of 409 patients, 21 (5.1%) developed sCNSi during a median follow-up of 4.9 years. Five-year cumulative incidence of sCNSi were 5.1%; and 4.0%, 5.3%, and 11.5% at low, intermediate, and high risk of CNS-IPI, respectively. The most common locations of extranodal lesions at the time of registration in patients with sCNSi were the stomach (n = 4), paranasal cavity (n = 3), and bone marrow (n = 2). In univariable analysis, paranasal cavity lesion was a high-risk factor for sCNSi (subdistribution hazard ratio, 4.34 [95% confidence interval 1.28-14.73]). Median overall survival after sCNSi was 1.3 years, with a 2-year overall survival rate of 39.3%. The incidence of sCNSi in DLBCL patients at lower risk of CNS-IPI was low, as previously reported, but paranasal cavity lesion might indicate high risk for organ involvement. CLINICAL TRIAL REGISTRATION: JCOG0601 was registered in the UMIN Clinical Trials Registry (UMIN000000929, date of registration; December 04, 2007) and the Japan Registry of Clinical Trials (jRCTs031180139, date of registration; February 20, 2019).

Exploring the utility of a latent variable as comprehensive inflammatory prognostic index in critically ill patients with cerebral infarction.

Objective: We introduce the comprehensive inflammatory prognostic index (CIPI), a novel prognostic tool for critically ill cerebral infarction patients, designed to meet the urgent need for timely and convenient clinical decision-making in this high-risk patient group. Methods: Using exploratory factor analysis on selected indices-neutrophil to lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and systemic immune inflammation index (SIII)-we derived CIPI, a latent variable capturing their combined predictive power. Data from 1,022 patients in the Medical Information Mart for Intensive Care (MIMIC)-IV database were used to develop CIPI-based survival models, with the robustness and area under the receiver operating characteristic curve (AUC) performance of CIPI validated against an independent dataset of 326 patients from the MIMIC-III CareVue subset. The CIPI's predictive power for in-hospital and intensive care unit (ICU) mortality was assessed through Kaplan-Meier analysis, univariate and multivariate Cox regression models, and time-dependent AUC analysis. Linearity, subgroup sensitivity analyses and interaction effects with CIPI were also evaluated. Results: CIPI was an independent prognostic factor, demonstrating a statistically significant association with in-hospital and ICU mortality, when assessed as a continuous and a categorical variable. It showed a linear relationship with mortality rates and demonstrated stability across most subgroups, with no significant interactions observed. Its predictive capabilities for in-hospital and ICU mortality among critically ill cerebral infarction patients matched those of established prognostic indices in the MIMIC database. Conclusion: Our study indicates that CIPI is a reliable and effective prognostic tool for critically ill cerebral infarction patients in predicting in-hospital and ICU mortality. Its straightforward calculation, rooted in routine blood tests, enhances its practicality, promising significant utility in clinical settings.

A prospective study on tumour response assessment methods after neoadjuvant endocrine therapy in early oestrogen receptor-positive breast cancer.

BACKGROUND: Neoadjuvant endocrine therapy (NET) in oestrogen receptor-positive (ER+) /HER2-negative (HER2-) breast cancer allows real-time evaluation of drug efficacy as well as investigation of the biological and molecular changes that occur after estrogenic deprivation. Clinical and pathological evaluation after NET may be used to obtain prognostic and predictive information of tumour response to decide adjuvant treatment. In this setting, clinical scales developed to evaluate response after neoadjuvant chemotherapy are not useful and there are not validated biomarkers to assess response to NET beyond Ki67 levels and preoperative endocrine prognostic index score (mPEPI). METHODS: In this prospective study, we extensively analysed radiological (by ultrasound scan (USS) and magnetic resonance imaging (MRI)) and pathological tumour response of 104 postmenopausal patients with ER+ /HER2- resectable breast cancer, treated with NET for a mean of 7 months prior to surgery. We defined a new score, tumour cellularity size (TCS), calculated as the product of the residual tumour cellularity in the surgical specimen and the tumour pathological size. RESULTS: Our results show that radiological evaluation of response to NET by both USS and MRI underestimates pathological tumour size (path-TS). Tumour size [mean (range); mm] was: path-TS 20 (0-80); radiological-TS by USS 9 (0-31); by MRI: 12 (0-60). Nevertheless, they support the use of MRI over USS to clinically assess radiological tumour response (rad-TR) due to the statistically significant association of rad-TR by MRI, but not USS, with Ki67 decrease (p = 0.002 and p = 0.3, respectively) and mPEPI score (p = 0.002 and p = 0.6, respectively). In addition, we propose that TCS could become a new tool to standardize response assessment to NET given its simplicity, reproducibility and its good correlation with existing biomarkers (such as ΔKi67, p = 0.001) and potential added value. CONCLUSION: Our findings shed light on the dynamics of tumour response to NET, challenge the paradigm of the ability of NET to decrease surgical volume and point to the utility of the TCS to quantify the scattered tumour response usually produced by endocrine therapy. In the future, these results should be validated in independent cohorts with associated survival data.

Prodromal Cognitive Changes as a Prognostic Indicator of Forthcoming Huntington's Disease Severity: A Retrospective Longitudinal Study.

BACKGROUND: Cognitive changes in Huntington's disease (HD) precede motor manifestations. ENROLL-HD platform includes four cognitive measures of information processing speed (IPS). Our group is eager to seek clinical markers in the life stage that is as close as possible to the age of onset (ie, the so called prodromal HD phase) because this is the best time for therapeutic interventions. OBJECTIVES: Our study aimed to test whether cognitive scores in prodromal ENROLL-HD mutation carriers show the potential to predict the severity of motor and behavioral changes once HD became fully manifested. METHODS: From the global ENROLL-HD cohort of 21,343 participants, we first selected a premanifest Cohort#1 (ie, subjects with Total Motor Score (TMS) <10 and Diagnostic Confidence Level (DCL) <4, N = 1.222). From this cohort, we then focused on a prodromal Cohort#2 of subjects who were ascertained to phenoconvert into manifest HD at follow-up visits (ie, subjects from 6 ≤ TMS≤9 and DCL <4 to TMS≥10 and DCL = 4, n = 206). RESULTS: The main results of our study showed that low IPS before phenoconversion in Cohort#2 predicted the severity of motor and behavioral manifestations. By combining the four IPS cognitive measures (eg, the Categorical Verbal Fluency Test; Stroop Color Naming Test; Stroop Word Reading; Symbol Digit Modalities Test), we generated a Composite Cognition Score (CCS). The lower the CCS score the higher the TMS and the apathy scores in the same longitudinally followed-up patients after phenoconversion. CONCLUSIONS: CCS might represent a clinical instrument to predict the prognosis of mutation carriers who are close to manifesting HD.

Predictive model for treatment outcomes of peripheral T-cell lymphoma, not otherwise specified, in Taiwanese patients.

PURPOSE: We aimed to explore the clinical outcomes and prognostic factors for PTCL-NOS patients in the real world. METHODS: Clinical data were retrospectively collected from adult patients with PTCL-NOS treated at a single center in Taiwan. RESULTS: 104 PTCL-NOS patients with a median age of 53.0 years were enrolled. Patients with the International Prognostic Index (IPI) or prognostic index for peripheral T-cell lymphoma (PIT) scores of zero had a longer overall survival (OS) and progression free survival (PFS), while patients with IPI or PIT scores ≥1 did poorly. For patients who are eligible for transplantation, the use of pralatrexate as salvage chemotherapy has shown better OS (2-year OS 83.3% vs. 24.4%, P = 0.011) compared to patients who did not. By multivariate analysis, age >60 years, male, B symptoms, ECOG >1, lung involvement, and thrombocytopenia were independent adverse factors for OS. Incorporating factors in multivariate analysis, we established a novel predictive index for PTCL-NOS which efficiently stratifies patients into low (0-1 factor), intermediate-1 (2 factors), intermediate-2 (3 factors), and high risk (4-6 factors) groups with 2-year OS rates of 81.5%, 32.9%, 8.8%, and 0%, respectively (P < 0.001). CONCLUSION: PTCL-NOS patients have a dismal prognosis in Taiwan. Novel agents may improve the outcomes of PTCL-NOS patients. The usefulness of the novel prognostic index for PTCL-NOS needs further validation.

Prognostic Role of NECPAL CCOMS-ICO Tool on One-Year Mortality in a Hospitalized Older Population.

Background: It is essential to establish both the appropriateness of palliative care (PC) and the prognosis in daily clinical practice to guide decision making in the management of older people with multiple advanced chronic diseases. Objectives: We assessed patients who were appropriate for PC using the NECPAL tool in a hospitalized older population and then we investigated its predictive validity on one-year mortality compared with the multidimensional prognostic index (MPI), a validated geriatric prognostic tool. Design: Prospective cohort study. Setting/Subjects: We enrolled 103 older adults hospitalized for acute medical and surgical conditions in a geriatric hospital in Italy. Measurements: The variables of interest were obtained at baseline through interviews of the ward medical staff and by consulting the computerized medical records. Long-term mortality (one-year) was assessed through the analysis of data acquired from hospital or territorial databases or through telephone contact with caregivers. Results: Mean age was 86.8 ± 7.2 years, with a female prevalence of 54.4%. Prevalence of NECPAL+ patients was 65.1%. MPI low risk: 30.1%; moderate risk: 41.7%; severe risk: 28.2%. Patients deceased during follow-up were 54.4%. NECPAL+ patients were more likely to die, even after adjusting for age, sex, and MPI score (hazard ratio [HR] 2.7, p = 0.020). All the NECPAL categories were associated with one-year mortality. MPI showed a better predictive power than NECPAL (area under the curve [AUC] 0.85 vs. 0.75, p = 0.030). After the exclusion of "Comorbidity: ≥2 concurrent diseases" item from NECPAL, its AUC increased to 0.78 with no statistically significant differences from MPI (p = 0.122). Conclusions: NECPAL is useful to identify the appropriateness of PC in hospitalized older adults, also allowing to predict long-term mortality with a performance similar to that of a validated geriatric prognostic tool.