Prognostic Model For Patients Research Articles

Identification of Context-Specific Fitness Genes Associated With Metabolic Rearrangements for Prognosis and Potential Treatment Targets for Liver Cancer.

Liver cancer is the most frequent fatal malignancy. Furthermore, there is a lack of effective therapeutics for this cancer type. To construct a prognostic model for potential beneficiary screens and identify novel treatment targets, we used an adaptive daisy model (ADaM) to identify context-specific fitness genes from the CRISPR-Cas9 screens database, DepMap. Functional analysis and prognostic significance were assessed using data from TCGA and ICGC cohorts, while drug sensitivity analysis was performed using data from the Liver Cancer Model Repository (LIMORE). Finally, a 25-gene prognostic model was established. Patients were then divided into high- and low-risk groups; the high-risk group had a higher stemness index and shorter overall survival time than the low-risk group. The C-index, time-dependent ROC curves, and multivariate Cox regression analysis confirmed the excellent prognostic ability of this model. Functional enrichment analysis revealed the importance of metabolic rearrangements and serine/threonine kinase activity, which could be targeted by trametinib and is the key pathway in regulating liver cancer cell viability. In conclusion, the present study provides a prognostic model for patients with liver cancer and might help in the exploration of novel therapeutic targets to ultimately improve patient outcomes.

Prognostic model in patients with metastatic urothelial carcinoma receiving immune checkpoint inhibitors after platinum failure

Immune checkpoint inhibitors (ICIs) have become a standard treatment for metastatic urothelial carcinoma (mUC) after platinum-based chemotherapy. However, the prognostic factors for patients with mUC receiving ICIs are not well established. We retrospectively collected clinical and laboratory data and reviewed the survival outcomes of patients with mUC who were treated with ICIs after platinum-based chemotherapy. We used univariate and multivariable Cox proportional hazard models to identify independent prognostic factors, and the concordance index (C-index) to evaluate the performance of the new prognostic model. In addition, bootstrap analysis was employed for internal validation of the prognostic model. A total of 224 patients were included in the study. With a median follow-up of 10.5 months (interquartile range, 5.1-17.4 months), median overall survival (OS) was 13.6 months (95% confidence interval [CI], 9.7-17.3 months). In multivariable analysis, independent prognostic factors predicting adverse OS were the presence of liver metastasis (LM), hypoalbuminemia, and neutrophil-lymphocyte ratio (NLR) >5. When patients were categorized into 3 risk groups, median OS was not reached (NR) (95% CI, 17.3-NR), 9.5 months (6.8-NR), and 2.9 months (2.3-4.4) for patients with a score of 0, 1, and 2+, respectively. The C-index for the new model was 0.763 (95% CI, 0.739-0.787). A novel prognostic model, including LM, hypoalbuminemia, and NLR, was developed and validated to estimate OS in patients with platinum-refractory disease on second- or subsequent-line ICI therapy. Further investigations, including prospective validation, are needed.

A Prognostic Model for Breast Cancer Based on Cancer Incidence-Related DNA Methylation Pattern.

Breast cancer (BC) is the most diagnosed cancer and the leading cause of cancer-related deaths in women. The purpose of this study was to develop a prognostic model based on BC-related DNA methylation pattern. A total of 361 BC incidence-related probes (BCIPs) were differentially methylated in blood samples from women at high risk of BC and BC tissues. Twenty-nine of the 361 BCIPs that significantly correlated with BC outcomes were selected to establish the BCIP score. BCIP scores based on BC-related DNA methylation pattern were developed to evaluate the mortality risk of BC. The correlation between overall survival and BCIP scores was assessed using Kaplan–Meier, univariate, and multivariate analyses. In BC, the BCIP score was significantly correlated with malignant BC characteristics and poor outcomes. Furthermore, we assessed the BCIP score-related gene expression profile and observed that genes with expressions associated with the BCIP score were involved in the process of cancer immunity according to GO and KEGG analyses. Using the ESTIMATE and CIBERSORT algorithms, we discovered that BCIP scores were negatively correlated with both T cell infiltration and immune checkpoint inhibitor response markers in BC tissues. Finally, a nomogram comprising the BCIP score and BC prognostic factors was used to establish a prognostic model for patients with BC, while C-index and calibration curves were used to evaluate the effectiveness of the nomogram. A nomogram comprising the BCIP score, tumor size, lymph node status, and molecular subtype was developed to quantify the survival probability of patients with BC. Collectively, our study developed the BCIP score, which correlated with poor outcomes in BC, to portray the variation in DNA methylation pattern related to BC incidence.

Construction and validation of the prognostic model for patients with neuroendocrine cervical carcinoma: a competing risk nomogram analysis

PurposeNeuroendocrine cervical carcinoma (NECC) is an uncommon malignancy of the female reproductive system. This study aimed to evaluate cancer-specific mortality and to construct prognostic nomograms for predicting the survival of patients with NECC.Methodswe assembled the patients with NECC diagnosed between 2004 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Meanwhile, we identified other patients with NECC from the Wenling Maternal and Child Health Care Hospital between 2002 to 2017. Fine and Gray’s test and Kaplan–Meier methods were used to evaluate cancer-specific mortality and overall survival (OS) rates, respectively. Nomograms were constructed for predicting cancer-specific survival (CSS) and OS for patients with NECC. The developed nomograms were validated both internally and externally.Resultsa total of 894 patients with NECC were extracted from the SEER database, then classified into the training cohort (n = 628) and the internal validation cohort (n = 266). Besides, 106 patients from the Wenling Maternal and Child Health Care Hospital served as an external validation cohort. Nomograms for predicting CSS and OS were constructed on clinical predictors. The validation of nomograms was calculated by calibration curves and concordance indexes (C-indexes). Furthermore, the developed nomograms presented higher areas under the receiver operating characteristic (ROC) curves when compared to the FIGO staging system.Conclusionswe established the first competing risk nomograms to predict the survival of patients with NECC. Such a model with high predictive accuracy could be a practical tool for clinicians.

Clinical laboratory tests associated with survival in patients with metastatic renal cell carcinoma: A Laboratory Wide Association Study (LWAS).

Prognostic models for patients with metastatic renal cell carcinoma (mRCC) include select laboratory values. These models have important limitations, including reliance on a limited array of laboratory tests, and use of dichotomous ("high-low") cutoffs. We applied a Laboratory-Wide Association Study (LWAS) framework to systematically evaluate common clinical laboratory results associated with survival for patients diagnosed with mRCC. We used laboratory data for 3,385 patients diagnosed with mRCC from 2002 to 2017. We developed a LWAS framework, to examine the association with 53 common clinical laboratory tests results (641,712 measurements) and overall survival. We employed false-discovery rate to test the association of multiple laboratory tests with survival, and validated these results using 3 separate cohorts to generate a standardized hazard ratio (sHR), reported for a 1 standard deviation unit change in each laboratory test. The LWAS approach confirmed the association of laboratory values currently used in prognostic models with survival, including calcium (HR 1.35, 95%CI 1.24-1.48), leukocyte count (HR 1.40, 95%CI 1.30-1.51), platelet count (HR 1.36, 95%CI 1.27-1.51), and hemoglobin (HR 0.79, 95%CI 0.72-0.86). Use of these tests as continuous variables improved model performance. LWAS also identified acute phase reactants associated with survival not typically included in prognostic models, including serum albumin (HR 0.66, 95%CI 0.61-0.72), ferritin (HR 1.25, 95%CI 1.08-1.45), alkaline phosphatase (HR 1.31, 95%CI 1.23-1.40), and C-reactive protein (HR 1.70, 95%CI 1.14-2.53). Routinely measured laboratory tests can refine current prognostic models, facilitate comparisons across clinical trial cohorts, and match patients with specific systemic therapies.

A Prognostic Model for Patients with Hepatocellular Carcinoma Based on Hypoxia‐Related Long Noncoding RNAs

Objective. The prognosis of patients with hepatocellular carcinoma (HCC) varies greatly due to the hypoxic environment and multiple factors driving metastasis progress. In this study, we aimed to correlate the expression of hypoxia‐related long noncoding RNAs (lncRNAs) with the survival of patients with HCC to develop a prognostic model for HCC. Methods. The Pearson correlation analysis was used to screen hypoxia‐related lncRNAs between differentially expressed lncRNAs by analyzing lncRNA expression profiles in The Cancer Genome Atlas (TCGA) database and 200 hypoxia genes downloaded from the Molecular Signatures Database (M5891). The univariate and multivariate Cox regression analyses were used to identify significantly predictive hypoxia‐related lncRNAs, and a prognostic model based on these factors was constructed to predict the survival of HCC. The Kaplan‐Meier (K‐M) survival analysis and receiver operating characteristic curve (ROC) were performed to evaluate the performance of the model. Results. A total of 490 hypoxia‐related lncRNAs were screened out. A prognostic model comprising 10 significantly predictive hypoxia‐related lncRNAs was constructed by the multivariate Cox regression analysis. The hypoxia‐related risk scores were calculated and were divided into high‐risk and low‐risk groups. The K‐M survival analysis showed a lower overall survival rate of patients in the high‐risk group (P < 0.05). ROC analysis showed that the AUC value of hypoxia‐related risk score was 0.799, demonstrating that the hypoxia‐related risk score was an independent prognosis factor of HCC. Conclusion. Our study indicates that identified 10 key hypoxia‐related lncRNAs have potential prognostic values for HCC patients and may provide new targets for the treatment of HCC.

New prognostic model for patients with advanced gastric cancer: Fluoropyrimidine/platinum doublet for first-line chemotherapy.

BACKGROUNDNew prognostic factors have been reported in patients with metastatic or recurrent gastric cancer (MRGC), necessitating modifications to the previous prognostic model.AIMTo develop a new model, MRGC patients who received fluoropyrimidines/ platinum doublet chemotherapy between 2008 and 2015 were analyzed.METHODSA total of 1883 patients was divided into a training set (n = 937) and an independent validation set (n = 946).RESULTSMultivariate analysis showed that the following six factors were associated with poor overall survival (OS) in the training set: Eastern Cooperative Oncology Group performance score ≥ 2 and bone metastasis (2 points each), peritoneal metastasis, high alkaline phosphatase level, low albumin level, and high neutrophil-lymphocyte ratio (1 point each). A prognostic model was developed by stratifying patients into good (0-1 point), moderate (2-3 points), and poor (≥ 4 points) risk groups. In the validation set, the median OS of the three risk groups was 15.8, 10.1, and 5.7 mo, respectively, and those differences were significant (P < 0.001).CONCLUSIONWe identified six factors readily measured in clinical practice that are predictive of poor prognosis in patients with MRGC. The new model is simpler than the old and more easily predicts OS.

A Prognostic Model for Patients With Oligometastatic Disease Treated With Stereotactic Body Radiation Therapy

<h3>Purpose/Objective(s)</h3> Stereotactic body radiation therapy (SBRT) is an increasingly important modality in the management of patients with oligometastatic disease (OMD). Though prospective clinical trials have demonstrated the benefits of SBRT in a variety of oligometastatic settings, there is currently limited data to guide patient selection and provide long-term prognostic information for OMD patients. The purpose of this study was to create a clinical prognostic model for overall survival (OS) for OMD patients treated with SBRT. <h3>Materials/Methods</h3> A large, retrospective multi-institutional database of OMD patients treated with SBRT provided the data for model construction. Recursive partitioning analysis (RPA) was used to generate a prognostic model for OS that could account for complex interactions between baseline patient characteristics. The model was generated using a training set (75% of all samples) and internally validated using the reserved testing set. Model performance in the training and test sets were evaluated using log-rank tests, Harrell's C-statistic and time-dependent area under the receiver operating characteristics curve (AUC). All analyses were carried out in R. <h3>Results</h3> A total of 1,033 patients were included in the analysis. RPA for OS revealed three risk groups. The low-risk group consisted of younger (< 55) patients with favorable primary sites (hormone receptor/Her2-positive breast cancer, colorectal cancer or renal cell carcinoma) as well as any patient with a prostate cancer primary; the high-risk group consisted of patients with any other primary site who presented with non-pulmonary OMD within 24 months of the diagnosis of the primary disease; and the intermediate-risk group consisted of all other patients. The 5-year OS was 77.5 % (95% confidence interval: 63.1-91.9%), 32.3% (25.1-39.5%) and 12.1% (2.9-21.4%), respectively, for the low, intermediate and high-risk groups. Log-rank tests for difference in survival between the risk groups in both the training and test sets were highly significant (<i>P</i> < 0.0001). The model possessed good discriminative power with a C-statistic of 0.68 and time-dependent AUC of 0.72 in the training set, and there was an expected small reduction in these statistics in the test set (C-statistic: 0.65, AUC: 0.67). <h3>Conclusion</h3> An internally validated prognostic model for OS with good ability to distinguish between low, intermediate and high-risk OMD patients was generated. Subsequent external validation will be undertaken to demonstrate the robustness of this model to new data.

A prognostic model for patients with lymphoma and COVID-19: a multicentre cohort study

AbstractLymphoma represents a heterogeneous hematological malignancy (HM), which is characterized by severe immunosuppression. Patients diagnosed of coronavirus disease 2019 (COVID-19) during the course of HM have been described to have poor outcome, with only few reports specifically addressing lymphoma patients. Here, we investigated the clinical behavior and clinical parameters of a large multicenter cohort of adult patients with different lymphoma subtypes, with the aim of identifying predictors of death. The study included 856 patients, of whom 619 were enrolled prospectively in a 1-year frame and were followed-up for a median of 66 days (range 1-395). Patients were managed as outpatient (not-admitted cohort, n = 388) or required hospitalization (n = 468), and median age was 63 years (range 19-94). Overall, the 30- and 100-days mortality was 13% (95% confidence interval (CI), 11% to 15%) and 23% (95% CI, 20% to 27%), respectively. Antilymphoma treatment, including anti-CD20 containing regimens, did not impact survival. Patients with Hodgkin's lymphoma had the more favorable survival, but this was partly related to significantly younger age. The time interval between lymphoma diagnosis and COVID-19 was inversely related to mortality. Multivariable analysis recognized 4 easy-to-use factors (age, gender, lymphocyte, and platelet count) that were associated with risk of death, both in the admitted and in the not-admitted cohort (HR 3.79 and 8.85 for the intermediate- and high-risk group, respectively). Overall, our study shows that patients should not be deprived of the best available treatment of their underlying disease and indicates which patients are at higher risk of death. This study was registered with ClinicalTrials.gov, NCT04352556.

51: A Prognostic Model for Patients with Oligometastatic Disease Treated with Stereotactic Body Radiation Therapy

51: A Prognostic Model for Patients with Oligometastatic Disease Treated with Stereotactic Body Radiation Therapy

Assessment of COVID-19 progression on day 5 from symptoms onset

BackgroundA major limitation of current predictive prognostic models in patients with COVID-19 is the heterogeneity of population in terms of disease stage and duration. This study aims at identifying a panel of clinical and laboratory parameters that at day-5 of symptoms onset could predict disease progression in hospitalized patients with COVID-19.MethodsProspective cohort study on hospitalized adult patients with COVID-19. Patient-level epidemiological, clinical, and laboratory data were collected at fixed time-points: day 5, 10, and 15 from symptoms onset. COVID-19 progression was defined as in-hospital death and/or transfer to ICU and/or respiratory failure (PaO2/FiO2 ratio < 200) within day-11 of symptoms onset. Multivariate regression was performed to identify predictors of COVID-19 progression. A model assessed at day-5 of symptoms onset including male sex, age > 65 years, dyspnoea, cardiovascular disease, and at least three abnormal laboratory parameters among CRP (> 80 U/L), ALT (> 40 U/L), NLR (> 4.5), LDH (> 250 U/L), and CK (> 80 U/L) was proposed. Discrimination power was assessed by computing area under the receiver operating characteristic (AUC) values.ResultsA total of 235 patients with COVID-19 were prospectively included in a 3-month period. The majority of patients were male (148, 63%) and the mean age was 71 (SD 15.9). One hundred and ninety patients (81%) suffered from at least one underlying illness, most frequently cardiovascular disease (47%), neurological/psychiatric disorders (35%), and diabetes (21%). Among them 88 (37%) experienced COVID-19 progression. The proposed model showed an AUC of 0.73 (95% CI 0.66–0.81) for predicting disease progression by day-11.ConclusionAn easy-to-use panel of laboratory/clinical parameters computed at day-5 of symptoms onset predicts, with fair discrimination ability, COVID-19 progression. Assessment of these features at day-5 of symptoms onset could facilitate clinicians’ decision making. The model can also play a role as a tool to increase homogeneity of population in clinical trials on COVID-19 treatment in hospitalized patients.

Allele frequency deviation (AFD) as a new prognostic model to predict overall survival in lung adenocarcinoma (LUAD)

BackgroundLung adenocarcinoma (LUAD) remains one of the world’s most known aggressive malignancies with a high mortality rate. Molecular biological analysis and bioinformatics are of great importance as they have recently occupied a large area in the studies related to the identification of various biomarkers to predict survival for LUAD patients. In our study, we attempted to identify a new prognostic model by developing a new algorithm to calculate the allele frequency deviation (AFD), which in turn may assist in the early diagnosis and prediction of clinical outcomes in LUAD.MethodFirst, a new algorithm was developed to calculate AFD using the whole-exome sequencing (WES) dataset. Then, AFD was measured for 102 patients, and the predictive power of AFD was assessed using Kaplan–Meier analysis, receiver operating characteristic (ROC) curves, and area under the curve (AUC). Finally, multivariable cox regression analyses were conducted to evaluate the independence of AFD as an independent prognostic tool.ResultThe Kaplan–Meier analysis showed that AFD effectively segregated patients with LUAD into high-AFD-value and low-AFD-value risk groups (hazard ratio HR = 1.125, 95% confidence interval CI 1.001–1.26, p = 0.04) in the training group. Moreover, the overall survival (OS) of patients who belong to the high-AFD-value group was significantly shorter than that of patients who belong to the low-AFD-value group with 42.8% higher risk and 10% lower risk of death for both groups respectively (HR for death = 1.10; 95% CI 1.01–1.2, p = 0.03) in the training group. Similar results were obtained in the validation group (HR = 4.62, 95% CI 1.22–17.4, p = 0.02) with 41.6%, and 5.5% risk of death for patients who belong to the high and low-AFD-value groups respectively. Univariate and multivariable cox regression analyses demonstrated that AFD is an independent prognostic model for patients with LUAD. The AUC for 5-year survival were 0.712 and 0.86 in the training and validation groups, respectively.ConclusionAFD was identified as a new independent prognostic model that could provide a prognostic tool for physicians and contribute to treatment decisions.

Construction and evaluation of prognostic models for esophageal cancer patients with distant and non-distant metastases: providing a reference process for clinical diagnosis and treatment.

Although the current treatment for esophageal cancer has great technological progress, the 5-year survival rate of patients is not optimistic. About 70% of patients with esophageal cancer are at an advanced stage at first diagnosis. These patients are prone to distant metastasis, and the prognosis is poor. Therefore, understanding the risk factors for distant metastasis in patients with esophageal cancer, combined with the prognosis of the patient, can aid in choosing the optimal diagnosis and treatment plan. Ultimately, it will improve the patient's survival time and quality of life. This research aims to construct a model for the risk assessment of distant metastasis in patients with esophageal cancer and prognostic models for patients with distant and non-distant metastases. The Surveillance Epidemiology and End Results (SEER) database was used to select patients with esophageal cancer from 2010 to 2015. The optimal cutoff point was selected for the age and tumor size variables using X-tile. The nomogram was constructed using R software (The R Foundation for Statistical Computing). Gender, grade, T stage, N stage, and tumor size were independent risk factors associated with distant metastasis in patients with esophageal cancer. The concordance index (C-index) of the nomogram prediction model for whether the patient will have distant metastasis was 0.609. Age, grade, T stage, N stage, and tumor size were independent risk factors affecting the prognosis without distant metastasis. The C-index of the nomogram prediction model for patients with distant metastases was 0.590. Age and T stage were independent risk factors affecting the prognosis of patients with distant metastases. The C-index of the nomogram prediction model was 0.543. The combination of radiotherapy, chemotherapy, and primary surgery yielded the best overall survival for both patients with distant metastases and patients with non-distant metastases. A comprehensive assessment of the risk of distant metastasis in patients with esophageal cancer, combined with prognosis prediction, is necessary to provide patients with a reasonable treatment plan.

Establishment of Prognostic Nomograms for Predicting the Survival of HR-Positive, HER2-Negative Metastatic Breast Cancer Patients Treated with Everolimus.

BackgroundThere are no clinically available prognostic models for patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer treated with everolimus. We aimed to develop a tool to predict the progression-free survival (PFS) and overall survival (OS) of these patients and to identify optimal candidates who would benefit from everolimus-based treatment in this heterogeneous patient population.MethodsThe clinical data of patients with HR+, HER2- metastatic breast cancer receiving everolimus between May 2012 and January 2018 at Sun Yat-sen University Cancer Center were retrospectively retrieved. Based on potential prognostic factors derived from multivariate Cox analysis, we established predictive nomogram models for PFS and OS and evaluated their predictive values by means of the concordance index (C-index). Calibration curves were used to estimate the consistency between the actual observations and the nomogram-predicted probabilities.ResultsA total of 116 patients with HR+, HER2- metastatic breast cancer were enrolled in this study. Three independent prognostic factors, including the line of everolimus in the metastatic setting, everolimus clinical benefit rate and number of liver metastatic lesions, were identified from the multivariate Cox analysis. Prognostic models for individual survival prediction were established and graphically presented as nomograms. The C-index was 0.738 (95% confidence interval [CI]: 0.710–0.767) for the PFS nomogram and 0.752 (95% CI: 0.717–0.788) for the OS nomogram, which showed favourable discrimination. The calibration curves for the probabilities of 6-, 9-, and 12-month PFS and 1-, 2-, and 3-year OS suggested satisfactory consistency between the actual observations and the predicted probabilities.ConclusionWe constructed convenient nomogram models for patients with HR+, HER2- metastatic breast cancer to individually predict their potential benefits from everolimus in the metastatic setting. The models showed good performance in terms of accuracy, discrimination capacity and clinical application value.

Identification and Validation of a Novel Inflammatory Response-Related Gene Signature for the Prognosis of Colon Cancer.

PurposeThe inflammatory response plays a crucial role in the occurrence and development of colon cancer. In this study, we aimed to explore a novel prognostic model for patients with colon cancer (COAD) based on inflammatory response-related genes.MethodsInflammatory response-related genes were obtained from Molecular Signatures database. Univariate and multivariate Cox regression analyses were used for model construction based on TCGA dataset. GSE39582 dataset and qRT-PCR dataset were used for validation. Gene set variation analysis and gene set enrichment analysis were performed to explore the potential regulatory pathways. The immune cell infiltration level was analyzed via CIBERSORT. Immunohistochemistry analysis and experiments were used to explore the function of genes in model.ResultsIn this study, a novel prognostic signature was identified using stepwise Cox proportional hazards regression analysis based on TCGA dataset. The results were subsequently validated in 562 patients from GSE39582 and a qRT-PCR data set from 70 tumor samples. Functional analysis indicated that the tumor microenvironment and immune cell infiltrate were different between high- and low-risk groups. Additionally, IHC results showed that the protein levels of prognostic genes were significantly different between COAD tissues and adjacent non-tumorous tissues, and prognostic genes could regulate the malignant phenotype of COAD cells.ConclusionOverall, the inflammation-related gene signature can be used for prognostic prediction in patients with COAD.

OC-0642 A radiomics based prognostic model for patients with head and neck squamous cell carcinoma

OC-0642 A radiomics based prognostic model for patients with head and neck squamous cell carcinoma

A prognostic model in patients with advanced biliary tract cancer receiving first-line chemotherapy

Background Standard treatment of advanced biliary tract cancer (aBTC) is represented by first-line chemotherapy (CT1). However, some patients do not gain any benefit from CT1, contributing to the overall dismal prognosis of aBTC. The present study aimed to devise a prognostic model in aBTC patients receiving CT1. Methods A large panel of clinical, laboratory, and pathology variables, available before the start of CT1, were retrospectively assessed in a multi-centric cohort to determine their prognostic value on univariate and multivariate regression analysis. The variables that showed a significant correlation with overall survival (OS) were computed in a three-tier prognostic score. External validation of the prognostication performance was carried out. Results Clinical histories of 935 patients (median OS 10.3 months), with diagnosis dates ranging from 2001 to 2017, were retrieved from 14 institutions. According to multivariate analysis, Eastern Cooperative Oncology Group performance status, carbohydrate antigen 19.9, albumin levels, and neutrophil/lymphocyte ratio were strongly associated with OS (p <0.01). The prognostic score could generate a highly significant stratification (all between-group p values ≤0.001) into groups of favorable (comprising 51.5% of the sample), intermediate (39.2%), and poor prognosis (9.3%): median OS was 12.7 (CI95% 11.0–14.4), 7.1 (CI95% 5.8–8.4), and 3.2 months (CI95% 1.7–4.7), respectively. This OS gradient was replicated in the validation set (129 patients), with median OS of 12.7 (CI95% 11.0–14.3), 7.5 (CI95% 6.1–8.9), and 1.4 months (CI95% 0.1–2.7), respectively (all between-group p values ≤0.05). Conclusion A prognostic score, derived from a limited set of easily-retrievable variables, efficiently stratified a large population of unselected aBTC patients undergoing CT1. This tool could be useful to clinicians, to ascertain the potential benefit from CT1 at the start of treatment.

Prognostic Models for Patients With Gleason Score 9 Prostate Cancer: A Population-Based Study.

Purpose: Gleason score (GS) system is one of the most widely used histological grading methods for prostate cancer (PCa) all over the world. GS can be obtained by adding the primary Gleason pattern (GP) and secondary GP. Different proportions of GP 4 and GP 5 in prostate specimens can both lead to GS 9. In this study, we explored whether GP 5 + 4 or GP 4 + 5 was associated with different prognoses among patients with GS 9 PCa.Materials and methods: A retrospective population-based study was conducted on 10,124 subjects diagnosed with GS 9 PCa between 2004 and 2009 from the Surveillance, Epidemiology, and End Results program. A 1:1 propensity-score matching (PSM) was performed to balance the baseline characteristics between the GP 4 + 5 and 5 + 4 groups and to compare the prognoses between the two groups. Cox regression analysis and Fine-Gray competing risk regression models were adopted to screen the covariates significantly associated with all-cause mortality (ACM) and cancer-specific mortality (CAM).Results: GP 5 + 4 was associated with higher risks of ACM and CSM before or after PSM than GP 4 + 5. In the original cohort, there were eight independent predictors for ACM, which were age at diagnosis, race, AJCC NM stage, PSA levels, treatments, GP, and marital status, confirmed by the Cox analysis; and nine independent predictors for CSM, which were age at diagnosis, race, AJCC TNM stage, PSA levels, treatments, GP, and marital status, confirmed by the competing-risk model.Conclusion: GP 5 + 4 was associated with a poorer overall survival and cancer-specific survival compared with GP 4 + 5.

Effect of Diameter and Number of Hepatocellular Carcinomas on Survival After Resection, Transarterial Chemoembolization, and Ablation.

Most studies predicting survival after resection, transarterial chemoembolization (TACE), and ablation analyzed diameter and number of hepatocellular carcinomas (HCCs) as dichotomous variables, resulting in an underestimation of risk variation. We aimed to develop and validate a new prognostic model for patients with HCC using largest diameter and number of HCCs as continuous variables. The prognostic model was developed using data from patients undergoing resection, TACE, and ablation in 645 Japanese institutions. The model results were shown after balanced using the inverse probability of treatment-weighted analysis and were externally validated in an international multi-institution cohort. Of 77,268 patients, 43,904 patients, including 15,313 (34.9%) undergoing liver resection, 13,375 (30.5%) undergoing TACE, and 15,216 (34.7%) undergoing ablation, met the inclusion criteria. Our model (http://www.u-tokyo-hbp-transplant-surgery.jp/about/calculation.html) showed that the 5-year overall survival (OS) in patients with HCC undergoing these procedures decreased with progressive incremental increases in diameter and number of HCCs. For patients undergoing resection, the inverse probability of treatment-weighted-adjusted 5-year OS probabilities were 10%-20% higher compared with patients undergoing TACE for 1-6 HCC lesions <10 cm and were also 10%-20% higher compared with patients undergoing ablation when the HCC diameter was 2-3 cm. For patients undergoing resection and TACE, the model performed well in the external cohort. Our novel prognostic model performed well in predicting OS after resection and TACE for HCC and demonstrated that resection may have a survival benefit over TACE and ablation based on the diameter and number of HCCs.

A novel microRNA-based prognostic model outperforms standard prognostic models in patients with acetaminophen-induced acute liver failure

Acetaminophen (APAP)-induced acute liver failure (ALF) remains the most common cause of ALF in the Western world. Conventional prognostic models, utilising markers of liver injury and organ failure, lack sensitivity for mortality prediction. We previously identified a microRNA signature that is associated with successful regeneration post-auxiliary liver transplant and with recovery from APAP-ALF. Herein, we aimed to use this microRNA signature to develop outcome prediction models for APAP-ALF. We undertook a nested, case-control study using serum samples from 194 patients with APAP-ALF enrolled in the US ALF Study Group registry (1998-2014) at early (day 1-2) and late (day 3-5) time-points. A microRNA qPCR panel of 22 microRNAs was utilised to assess microRNA expression at both time-points. Multiple logistic regression was used to develop models which were compared to conventional prognostic models using the DeLong method. Individual microRNAs confer limited prognostic value when utilised in isolation. However, incorporating them within microRNA-based outcome prediction models increases their clinical utility. Our early time-point model (AUC= 0.78, 95% CI 0.71-0.84) contained a microRNA signature associated with liver regeneration and our late time-point model (AUC= 0.83, 95% CI 0.76-0.89) contained a microRNA signature associated with cell-death. Both models were enhanced when combined with model for end-stage liver disease (MELD) score and vasopressor use and both outperformed the King's College criteria. The early time-point model combined with clinical parameters outperformed the ALF Study Group prognostic index and the MELD score. Our findings demonstrate that a regeneration-linked microRNA signature combined with readily available clinical parameters can outperform existing prognostic models for ALF in identifying patients with poor prognosis who may benefit from transplantation. While acute liver failure can be reversible, some patients will die without a liver transplant. We show that blood test markers that measure the potential for liver recovery may help improve identification of patients unlikely to survive acute liver failure who may benefit from a liver transplant.