Prognostic Factors for Event-Free Survival in Pediatric Patients with Hepatoblastoma Based on the 2017 PRETEXT and CHIC-HS Systems.
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ABSTRACT:
SUBMITTER: Yoon HM
PROVIDER: S-EPMC6769992 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Objective: To explore a CT-based radiomics model for preoperative prediction of event-free survival (EFS) in patients with hepatoblastoma and to compare its performance with that of a clinicopathologic model. Patients and Methods: Eighty-eight patients with histologically confirmed hepatoblastoma (mean age: 2.28 ± 2.72 years) were recruited from two institutions between 2002 and 2019 for this retrospective study. They were divided into a training cohort (65 patients from institution A) and a validation cohort (23 patients from institution B). Radiomics features were extracted manually from pretreatment CT images in the portal venous (PV) phase. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to construct a "radiomics signature" and radiomics score (Rad-score) for EFS prediction. Then, a nomogram incorporating the Rad-score, updated staging system, and significant variables of clinicopathologic risk (age, alpha-fetoprotein (AFP) level, histology subtype, tumor diameter) as the radiomic model, clinicopathologic model, and combined clinicopathologic-radiomic model were built for EFS estimation in the training cohort, the performance of which was assessed in an external-validation cohort with respect to clinical usefulness, discrimination, and calibration. Results: Nine survival-relevant features were selected for a radiomics signature and Rad-score building. Multivariable analysis revealed that histology subtype (P = 0.01), PV (P = 0.001) invasion, and metastasis (P = 0.047) were independent risk factors of EFS. Patients were divided into low- and high-risk groups based on the Rad-score with a cutoff of 0.08 according to survival outcome. The radiomics signature-incorporated nomogram showed good performance (P < 0.001) for EFS estimation (C-Index: 0.810; 95% CI: 0.738-0.882), which was comparable with that of the clinicopathological model for EFS estimation (C-Index: 0.81 vs. 0.85). The radiomics-based nomogram failed to show incremental prognostic value compared with that using the clinicopathologic model. The combined model (radiomics signature plus clinicopathologic parameters) showed significant improvement in the discriminatory accuracy, along with good calibration and greater net clinical benefit, of EFS (C-Index: 0.88; 95% CI: 0.829-0.933). Conclusion: The radiomics signature can be used as a prognostic indicator for EFS in patients with hepatoblastoma. A combination of the radiomics signature and clinicopathologic risk factors showed better performance in terms of EFS prediction in patients with hepatoblastoma, which enabled precise clinical decision-making.
| S-EPMC8086552 | biostudies-literature
Project description:ObjectiveThe aim of this study was to compare the accuracy of the Children's Oncology Group (COG) risk stratification system to the Children's Hepatic tumor International Collaboration (CHIC) risk stratification system for predicting the prognosis of Chinese children with hepatoblastoma (HB).MethodsClinicopathological data of 86 patients diagnosed with HB between January 2014 and December 2017 were retrieved. The study endpoints were the 1- and 3-year overall survival (OS) and disease-free survival (DFS) were analyzed to evaluate the predictive value.ResultsThe 1-, 3-year OS and DFS of the 86 patients were 86.0%, 76.3%, and 74.4%, 54.0%, respectively. Univariate analyses revealed that age at diagnosis had a significant role in prognosis for both OS and DFS, along with PRETEXT staging and metastasis at diagnosis. Multivariate analysis showed that metastasis at diagnosis (HR 3.628, 95% CI 1.404-9.378, P=0.008), PRETEXT staging system (HR 2.176, 95% CI 1.230-3.849, P=0.008) and age at diagnosis (HR 2.268, 95% CI 1.033-4.982, P=0.041) were independent factors for OS. For DFS, the independent factors were the PRETEXT staging system (HR 2.241, 95% CI 1.533-3.277, P<0.001) and age at diagnosis (HR 1.792, 95% CI 1.018-3.154, P=0.043). Both COG and CHIC risk stratification systems could effectively predict the prognosis of children with HB for OS. For DFS, the CHIC risk stratification system was more effective. In addition, the CHIC risk stratification system had a higher c-index (OS 0.743, DFS 0.730), compared to the COG risk stratification system (OS 0.726, DFS 0.594).ConclusionAge at diagnosis played a significant role in prognosis. Compared to the COG risk stratification system, the CHIC risk stratification system was superior in predicting the survival of Chinese children with HB.
| S-EPMC7708347 | biostudies-literature
Project description:Purpose Children with acute myeloid leukemia (AML) whose disease is refractory to standard induction chemotherapy therapy or who experience relapse after initial response have dismal outcomes. We sought to comprehensively profile pediatric AML microRNA (miRNA) samples to identify dysregulated genes and assess the utility of miRNAs for improved outcome prediction. Patients and Methods To identify miRNA biomarkers that are associated with treatment failure, we performed a comprehensive sequence-based characterization of the pediatric AML miRNA landscape. miRNA sequencing was performed on 1,362 samples-1,303 primary, 22 refractory, and 37 relapse samples. One hundred sixty-four matched samples-127 primary and 37 relapse samples-were analyzed by using RNA sequencing. Results By using penalized lasso Cox proportional hazards regression, we identified 36 miRNAs the expression levels at diagnosis of which were highly associated with event-free survival. Combined expression of the 36 miRNAs was used to create a novel miRNA-based risk classification scheme (AMLmiR36). This new miRNA-based risk classifier identifies those patients who are at high risk (hazard ratio, 2.830; P ? .001) or low risk (hazard ratio, 0.323; P ? .001) of experiencing treatment failure, independent of conventional karyotype or mutation status. The performance of AMLmiR36 was independently assessed by using 878 patients from two different clinical trials (AAML0531 and AAML1031). Our analysis also revealed that miR-106a-363 was abundantly expressed in relapse and refractory samples, and several candidate targets of miR-106a-5p were involved in oxidative phosphorylation, a process that is suppressed in treatment-resistant leukemic cells. Conclusion To assess the utility of miRNAs for outcome prediction in patients with pediatric AML, we designed and validated a miRNA-based risk classification scheme. We also hypothesized that the abundant expression of miR-106a could increase treatment resistance via modulation of genes that are involved in oxidative phosphorylation.
| S-EPMC5721230 | biostudies-literature
Project description:BackgroundThe objective of this study was to prospectively evaluate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as an early imaging indicator of tumor histologic response to preoperative chemotherapy and as a possible prognostic factor for event-free survival (EFS) and overall survival in pediatric patients with newly diagnosed, nonmetastatic osteosarcoma who were treated on a single, multi-institutional phase 2 trial.MethodsThree serial DCE-MRI examinations at week 0 (before treatment), week 9, and week 12 (tumor resection) were performed in 69 patients with nonmetastatic osteosarcoma to monitor the response to preoperative chemotherapy. Four DCE-MRI kinetic parameters (the influx volume transfer constant [K(trans) ], the efflux rate constant [k(ep) ], the relative extravascular extracellular space [v(e) ], and the relative vascular plasma space [v(p) ]) and the corresponding differences (ΔK(trans) , Δk(ep) , Δv(e) , and Δv(p) ) of averaged kinetic parameters between the outer and inner halves of tumors were calculated to assess their associations with tumor histologic response, EFS, and overall survival.ResultsThe parameters K(trans) , v(e) , v(p) , and k(ep) decreased significantly from week 0 to week 9 and week 12. The parameters K(trans) , v(p) , and Δk(ep) at week 9 were significantly different between responders and nonresponders (P = .046, P = .021, and P = .008, respectively). These 3 parameters were indicative of histologic response. The parameter Δv(e) at week 0 was a significant prognostic factor for both EFS (P = .02) and overall survival (P = .03).ConclusionsDCE-MRI was identified as a prognostic factor for EFS and overall survival before treatment on this trial and was indicative of a histologic response to neoadjuvant therapy. Further studies are needed to verify these findings with other treatment regimens and establish the potential role of DCE-MRI in the development of risk-adapted therapy for osteosarcoma.
| S-EPMC3310962 | biostudies-literature
Project description:High-risk pediatric B-ALL patients experience 5-year negative event rates up to 25%. Although some biomarkers of relapse are utilized in the clinic, their ability to predict outcomes in high-risk patients is limited. Here, we propose a random survival forest (RSF) machine learning model utilizing interpretable genomic inputs to predict relapse/death in high-risk pediatric B-ALL patients. We utilized whole exome sequencing profiles from 156 patients in the TARGET-ALL study (with samples collected at presentation) further stratified into training and test cohorts (109 and 47 patients, respectively). To avoid overfitting and facilitate the interpretation of machine learning results, input genomic variables were engineered using a stepwise approach involving univariable Cox models to select variables directly associated with outcomes, genomic coordinate-based analysis to select mutational hotspots, and correlation analysis to eliminate feature co-linearity. Model training identified 7 genomic regions most predictive of relapse/death-free survival. The test cohort error rate was 12.47%, and a polygenic score based on the sum of the top 7 variables effectively stratified patients into two groups, with significant differences in time to relapse/death (log-rank P = 0.001, hazard ratio = 5.41). Our model outperformed other EFS modeling approaches including an RSF using gold-standard prognostic variables (error rate = 24.35%). Validation in 174 standard-risk patients and 3 patients who failed to respond to induction therapy confirmed that our RSF model and polygenic score were specific to high-risk disease. We propose that our feature selection/engineering approach can increase the clinical interpretability of RSF, and our polygenic score could be utilized for enhance clinical decision-making in high-risk B-ALL.
| S-EPMC8777142 | biostudies-literature
Project description:BACKGROUND:Atrial fibrillation (AF), one of the most common comorbidities of heart failure (HF), is associated with worse long-term prognosis in HF patients receiving cardiac resynchronization therapy (CRT). However, there is still no convenient tool to identify CRT candidates with AF who are at high risk of mortality and hospitalization due to HF. METHODS:We included 152 consecutive patients with AF for CRT in our hospital from January 2009 to July 2019. Multiple imputation was used for missing values. With imputed datasets, a multivariate Cox regression model was performed for variable selection using the backward stepwise method to predict all-cause mortality and HF readmissions. A nomogram and nomogram-based scoring system were constructed from the selected predictors. Then, internal validation and calibration were achieved by the bootstrap method, deriving the corrected concordance index and calibration curves. Sensitivity analysis was also performed to validate our selected predictors. RESULTS:Five predictors were incorporated in the nomogram, including N-terminal pro brain natriuretic protein (NT-proBNP)?>?1745?pg/mL, history of syncope, previous pulmonary hypertension, moderate or severe tricuspid regurgitation, thyroid-stimulating hormone (TSH)?>?4 mIU/L. The concordance index (0.70, 95% CI 0.62-0.77), corrected concordance index (0.67, 95% CI 0.59-0.74) and calibration curve showed optimal discrimination and calibration of the established nomogram. A significant difference in overall event-free survival was recognized by the nomogram-derived scores for patients with high risk (>?50 points), intermediate risk (21-50 points) and low risk (0-20 points) before CRT. CONCLUSION:Our internally validated nomogram may be an applicable tool for the early risk stratification of CRT candidates with AF.
| S-EPMC7222436 | biostudies-literature
Project description:Background:The goal of this study is to assess the newest survival of hepatoblastoma (HB) and the risk factors which impacted on survival by using the Surveillance, Epidemiology and End Results (SEER) database, also calculate the incidence of HB in recent years. Methods:We calculate age-adjusted incidence of HB by using SEER 21 registries. Age, sex, race, tumor size, macrovascular involvement, multifocal tumor, distant metastasis, the way of treatment, and the survival were collected for survival and analysis of prognostic factors in SEER 18 registries. Survival curves, according to different factors, were obtained by Kaplan-Meier estimates. Multivariable Cox regression models were also built. Results:The overall age-adjusted incidence of HB was 0.19 patients per 100,000 children with a statistically significant increase per year. Overall survival (OS) at 1-, 3- and 5-year for all patients were 89.3%, 84.6%, and 81.9%, respectively. Multivariate analysis showed tumor size >5 cm [hazard ratio (HR), 8.271; 95% confidence interval (CI), 1.134-60.310], multiple tumors (HR, 2.578; 95% CI, 1.424-4.668) and no-surgery treatment (HR, 7.520; 95% CI, 4.121-13.724) were independent indicators of poor prognosis. Only the age ?2-year-old (HR, 3.240; 95% CI, 1.433-7.326) and multiple tumors (HR, 2.395; 95% CI, 1.057-5.430) were the risk factors for the surgical treatment group. Conclusions:The survival of patients with HB has been greatly improved in the recent years, and at the same time, due to the application of better chemotherapy, we should re-evaluate the traditional risk indicators of prognosis in order to better apply to the clinical.
| S-EPMC6861810 | biostudies-literature
Project description:The tumor microenvironment can protect cancer cells from conventional anticancer therapies. Thus, targeting these protective mechanisms could eradicate therapy-resistant cancer cells and improve outcomes. Interleukin-6 (IL-6) provides extrinsic protection for several solid tumors and multiple myeloma. In pediatric acute myeloid leukemia (AML), IL-6-induced STAT3 signaling frequently becomes stronger at relapse, and increases in IL-6-induced STAT3 activity are associated with inferior survival after relapse. These findings suggested that the IL-6-induced STAT3 pathway may promote chemotherapy resistance and disease progression. Thus, we investigated the dysregulation of IL-6 levels in the bone marrow niche in pediatric patients with AML and the association between IL-6 levels and outcome. We measured levels of over 40 cytokines and growth factors in plasma from diagnostic bone marrow aspirates of 45 pediatric AML patients and 7 healthy sibling controls. Of the measured cytokines, only IL-6 levels were associated with event-free survival. Importantly, the effect of elevated IL-6 was most striking among children classified as having a low risk of relapse. In these patients, 5-year event-free survival was 82.5% ± 11% for patients with low IL-6 levels at diagnosis (n = 14) compared with 17.3% ± 11% for patients with elevated IL-6 (n = 13, log-rank P = .0003). In vitro, exogenous IL-6 reduced mitoxantrone-induced apoptosis in cell lines and primary pediatric AML samples. These results suggest that IL-6 levels at diagnosis could be used to help identify children at high risk of relapse, particularly those who are otherwise classified as low risk by current algorithms. Moreover, the IL-6 pathway could represent a target for overcoming environment-mediated chemotherapy resistance.
| S-EPMC5727855 | biostudies-literature
Project description:Glial tumors are the leading cause of cancer-related death and morbidity in children. Their diagnosis, mainly based on clinical and histopathological factors, is particularly challenging because of their high molecular heterogeneity. Thus, tumors with identical histotypes could result in variable biological behaviors and prognoses. The PATZ1 gene has been recently shown to be expressed in adult gliomas, including glioblastomas, where it correlates with the proneural subtype and with a better prognosis. Here, we analyzed the expression of PATZ1 in pediatric gliomas, first at mRNA level in a public database, and then at protein level, by immunohistochemistry, in a cohort of 52 glial brain tumors from young patients aged from 6 months to 16 years. As for adult tumors, we show that PATZ1 is enriched in glial tumors compared to the normal brain, where it correlates positively and negatively with a proneural and mesenchymal signature, respectively. Moreover, we show that PATZ1 is expressed at variable levels in our cohort of tumors. Higher expression was detected in high-grade than low-grade gliomas, suggesting a correlation with the malignancy. Among high-grade gliomas, higher levels of PATZ1 have consistently been found to correlate with worse event-free survival. Therefore, our study may imply new diagnostic opportunities for pediatric gliomas.
| S-EPMC6826955 | biostudies-literature
Project description:BackgroundHepatoblastoma (HB) is a prevalent form of liver cancer in pediatric patients, characterized by an embryonal malignant tumor. In the current study, a clinical prediction model was developed; that can effectively assess the likelihood of a patient's survival with HB.MethodsData from the Surveillance, Epidemiology, and End Results (SEER) database for cases of HB between 2010 and 2019 were used in this retrospective research. Information on clinicopathologic characteristics, therapeutic interventions, and survival outcomes were included in the data. The HB patients were randomly assigned to the training or validation cohort in a 7:3 ratio. Using univariate and multivariate Cox proportional hazards regression models, the prognostic indicators for overall survival (OS) and cancer-specific survival (CSS) were identified. The area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and concordance index (C-index) were used to evaluate the accuracy and calibration of these models. The clinical utility of the models was examined using decision curve analysis (DCA).ResultsThe multivariate Cox regression analysis revealed multiple autonomous prognostic determinants for the OS and CSS, including age, surgical interventions, and chemotherapy administration. Significantly, tumor size was found to be a strong predictor of OS. AUC values of 0.915, 0.846, and 0.847 for 1-, 3-, and 5-year OS, respectively, indicated that the nomogram-based models were highly accurate at predicting outcomes. Similarly, the AUC values for CSS were 0.871, 0.814, and 0.825. The C-index measurements, which quantify the discriminatory performance of the models, produced CSS values of 0.836 and OS values of 0.864. Furthermore, the calibration plots accurately represented the actual survival rates. Concurrently, the DCA had validated the clinical relevance of the nomogram-based models.ConclusionsThe present study successfully developed and validated user-friendly nomogram-based models, allowing for accurate assessment of OS and CSS in pediatric HB patients. These tools enable personalized survival predictions, enhance risk stratification, and strengthen clinical decision-making for managing HB.
| S-EPMC10928620 | biostudies-literature