Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status.
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ABSTRACT:
SUBMITTER: Soltani M
PROVIDER: S-EPMC8290179 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Prediction of overall survival based on multimodal MRI of brain tumor patients is a difficult problem. Although survival also depends on factors that cannot be assessed via preoperative MRI such as surgical outcome, encouraging results for MRI-based survival analysis have been published for different datasets. We assess if and how established radiomic approaches as well as novel methods can predict overall survival of brain tumor patients on the BraTS challenge dataset. This dataset consists of multimodal preoperative images of 211 glioblastoma patients from several institutions with reported resection status and known survival. In the official challenge setting, only patients with a reported gross total resection (GTR) are taken into account. We therefore evaluated previously published methods as well as different machine learning approaches on the BraTS dataset. For different types of resection status, these approaches are compared to a baseline, a linear regression on patient age only. This naive approach won the 3rd place out of 26 participants in the BraTS survival prediction challenge 2018. Previously published radiomic signatures show significant correlations and predictiveness to patient survival for patients with a reported subtotal resection. However, for patients with reported GTR, none of the evaluated approaches was able to outperform the age-only baseline in a cross-validation setting, explaining the poor performance of approaches based on radiomics in the BraTS challenge 2018.
| S-EPMC6857096 | biostudies-literature
Project description:Background:Glioblastoma (GBM) is the most common malignant central nervous system tumor, and MGMT promoter hypermethylation in this tumor has been shown to be associated with better prognosis. We evaluated the capacity of radiomics features to add complementary information to MGMT status, to improve the ability to predict prognosis. Methods:159 patients with untreated GBM were included in this study and divided into training and independent test sets. 286 radiomics features were extracted from the magnetic resonance images acquired prior to any treatments. A least absolute shrinkage selection operator (LASSO) selection followed by Kaplan-Meier analysis was used to determine the prognostic value of radiomics features to predict overall survival (OS). The combination of MGMT status with radiomics was also investigated and all results were validated on the independent test set. Results:LASSO analysis identified 8 out of the 286 radiomic features to be relevant which were then used for determining association to OS. One feature (edge descriptor) remained significant on the external validation cohort after multiple testing (p=0.04) and the combination with MGMT identified a group of patients with the best prognosis with a survival probability of 0.61 after 43 months (p=0.0005). Conclusion:Our results suggest that combining radiomics with MGMT is more accurate in stratifying patients into groups of different survival risks when compared to with using these predictors in isolation. We identified two subgroups within patients who have methylated MGMT: one with a similar survival to unmethylated MGMT patients and the other with a significantly longer OS.
| S-EPMC6363013 | biostudies-literature
Project description:Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) patients is highly desired by clinicians and oncologists. Radiomic research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, first-order, intensity-based volume and shape-based and textural radiomic features are extracted from fluid-attenuated inversion recovery (FLAIR) and T1ce MRI data. The region of interest is further decomposed with stationary wavelet transform with low-pass and high-pass filtering. Further, radiomic features are extracted on these decomposed images, which helped in acquiring the directional information. The efficiency of the proposed algorithm is evaluated on Brain Tumor Segmentation (BraTS) challenge training, validation, and test datasets. The proposed approach achieved 0.695, 0.571, and 0.558 on BraTS training, validation, and test datasets. The proposed approach secured the third position in BraTS 2018 challenge for the OS prediction task.
| S-EPMC7417437 | biostudies-literature
Project description:PurposeApproximately 12,000 glioblastomas are diagnosed annually in the United States. The median survival rate for this disease is 12 months, but individual survival rates can vary with patient-specific factors, including extent of surgical resection (EOR). The goal of our investigation is to develop a reliable strategy for personalized survival prediction and for quantifying the relationship between survival, EOR, and adjuvant chemoradiotherapy.Patients and methodsWe used accelerated failure time (AFT) modeling using data from 721 newly diagnosed patients with glioblastoma (from 1993 to 2010) to model the factors affecting individualized survival after surgical resection, and we used the model to construct probabilistic, patient-specific tools for survival prediction. We validated this model with independent data from 109 patients from a second institution.ResultsAFT modeling using age, Karnofsky performance score, EOR, and adjuvant chemoradiotherapy produced a continuous, nonlinear, multivariable survival model for glioblastoma. The median personalized predictive error was 4.37 months, representing a more than 20% improvement over current methods. Subsequent model-based calculations yield patient-specific predictions of the incremental effects of EOR and adjuvant therapy on survival.ConclusionNonlinear, multivariable AFT modeling outperforms current methods for estimating individual survival after glioblastoma resection. The model produces personalized survival curves and quantifies the relationship between variables modulating patient-specific survival. This approach provides comprehensive, personalized, probabilistic, and clinically relevant information regarding the anticipated course of disease, the overall prognosis, and the patient-specific influence of EOR and adjuvant chemoradiotherapy. The continuous, nonlinear relationship identified between expected median survival and EOR argues against a surgical management strategy based on rigid EOR thresholds and instead provides the first explicit evidence supporting a maximum safe resection approach to glioblastoma surgery.
| S-EPMC4876349 | biostudies-literature
Project description:BackgroundRadiological characteristics may reflect the biological features of brain tumors and may be associated with genetic alterations that occur in tumorigenesis. This study aimed to investigate the relationship between radiological features and IDH1 status as well as their predictive value for survival of glioblastoma patients.MethodsThe clinical information and MR images of 280 patients with histologically confirmed glioblastoma were retrospectively reviewed. The radiological characteristics of tumors were examined on MR images, and the IDH1 status was determined using DNA sequencing for all cases. The Kaplan-Meier method and Cox regression model were used to identify prognostic factors for progression-free and overall survival.ResultsThe IDH1 mutation was associated with longer progression-free survival (P = .022; hazard ratio, 0.602) and overall survival (P = .018; hazard ratio, 0.554). In patients with the IDH1 mutation, tumor contrast enhancement and peritumoral edema indicated worse progression-free survival (P = .015 and P = .024, respectively) and worse overall survival (P = .024 and P = .032, respectively). For tumors with contrast enhancement, multifocal contrast enhancement of the tumor lesion was associated with poor progression-free survival (P = .002) and poor overall survival (P = .010) in patients with wild-type IDH1 tumors.ConclusionsCombining the radiological features and IDH1 status of a tumor allows more accurate prediction of survival outcomes in glioblastoma patients. The complementary roles of genetic changes and radiological features of tumors should be considered in future studies.
| S-EPMC4799681 | biostudies-literature
Project description:In adults, pilocytic astrocytomas (PA) account for less than 2% of gliomas, resulting in uncertainty regarding the clinical course and optimal treatment, particularly in cases where gross total resection (GTR) could not be achieved. Moreover, information on molecular markers and their prognostic impact is sparse. In order to improve risk stratification, we analyzed our institutional series of 58 patients aged 17 years and older with histology-proven intracranial PA World Health Organization grade I for clinical and molecular prognosticators. Anaplastic and NF1-associated tumors were excluded. O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status was determined by pyrosequencing or 450k/850k DNA methylation array. A univariate log-rank test and multivariate StepAIC were applied to identify prognostic factors. The median age was 30 years (range 17-66). Tumors were located in the cerebral/cerebellar hemispheres, midline structures and cerebello-pontine angle in 53%, 38% and 9%. MGMT promoter methylation was present in eight patients (14%). GTR (39/58 patients) significantly reduced the likelihood of tumor recurrence (p = 0.0001). Tumor relapse occurred in 16 patients (28%) after a median progression-free survival (PFS) of 135 months (range 6-153 months); there was one tumor-related death. PFS at 5 and 10 years was 67% and 53%. In multivariate analysis, PFS was significantly prolonged in patients with GTR (HR 0.1; CI 0.03-0.37; p < 0.001), unmethylated MGMT promoter (HR 0.18; CI 0.05-0.64; p = 0.009) and midline tumors (HR 0.21; CI 0.06-0.78; p = 0.02). In conclusion, MGMT promoter methylation status and tumor location were identified as novel prognostic factors in adult PAs, pointing at distinct molecular subtypes and detecting patients in need of close observance and intensified treatment.
| S-EPMC6721291 | biostudies-literature
Project description:PurposeConstruction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.Materials and methodsA total of 134 eligible GBM patients were selected from The Cancer Genome Atlas. These patients were separated into the long-term and short-term survival groups according to the median of individual survival indicators: overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Then, the patients were divided into a training set and a validation set in a ratio of 2:1. Radiomics features (n = 5,152) were extracted from multiple regions of the GBM using multiparametric MRI. Then, radiomics signatures that are related to the three survival indicators were respectively constructed using the analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) regression for each patient in the training set. Based on a Cox proportional hazards model, the radiomics model was further constructed by combining the signature and clinical risk factors.ResultsThe constructed radiomics model showed a promising discrimination ability to differentiate in the training set and validation set of GBM patients with survival indicators of OS, PFS, and DSS. Both the four MRI modalities and five tumor subregions have different effects on the three survival indicators of GBM. The favorable calibration and decision curve analysis indicated the clinical decision value of the radiomics model. The performance of models of the three survival indicators was different but excellent; the best model achieved C indexes of 0.725, 0.677, and 0.724, respectively, in the validation set.ConclusionOur results show that the proposed radiomics models have favorable predictive accuracy on three survival indicators and can provide individualized probabilities of survival stratification for GBM patients by using multiparametric and multiregional MRI features.
| S-EPMC8655336 | biostudies-literature
Project description:PurposePrevious studies on the effect of tumor location on overall survival in glioblastoma have found conflicting results. Based on statistical maps, we sought to explore the effect of tumor location on overall survival in a population-based cohort of patients with glioblastoma and IDH wild-type astrocytoma WHO grade II-III with radiological necrosis.MethodsPatients were divided into three groups based on overall survival: < 6 months, 6-24 months, and > 24 months. Statistical maps exploring differences in tumor location between these three groups were calculated from pre-treatment magnetic resonance imaging scans. Based on the results, multivariable Cox regression analyses were performed to explore the possible independent effect of centrally located tumors compared to known prognostic factors by use of distance from center of the third ventricle to contrast-enhancing tumor border in centimeters as a continuous variable.ResultsA total of 215 patients were included in the statistical maps. Central tumor location (corpus callosum, basal ganglia) was associated with overall survival < 6 months. There was also a reduced overall survival in patients with tumors in the left temporal lobe pole. Tumors in the dorsomedial right temporal lobe and the white matter region involving the left anterior paracentral gyrus/dorsal supplementary motor area/medial precentral gyrus were associated with overall survival > 24 months. Increased distance from center of the third ventricle to contrast-enhancing tumor border was a positive prognostic factor for survival in elderly patients, but less so in younger patients.ConclusionsCentral tumor location was associated with worse prognosis. Distance from center of the third ventricle to contrast-enhancing tumor border may be a pragmatic prognostic factor in elderly patients.
| S-EPMC8195961 | biostudies-literature
Project description:BackgroundConditional survival is the survival probability after already surviving a predefined time period. This may be informative during follow-up, especially when adjusted for tumor characteristics. Such prediction models for patients with resected pancreatic cancer are lacking and therefore conditional survival was assessed and a nomogram predicting 5-year survival at a predefined period after resection of pancreatic cancer was developed.MethodsThis population-based study included patients with resected pancreatic ductal adenocarcinoma from the Netherlands Cancer Registry (2005-2016). Conditional survival was calculated as the median, and the probability of surviving up to 8 years in patients who already survived 0-5 years after resection was calculated using the Kaplan-Meier method. A prediction model was constructed.ResultsOverall, 3082 patients were included, with a median age of 67 years. Median overall survival was 18 months (95% confidence interval 17-18 months), with a 5-year survival of 15%. The 1-year conditional survival (i.e. probability of surviving the next year) increased from 55 to 74 to 86% at 1, 3, and 5 years after surgery, respectively, while the median overall survival increased from 15 to 40 to 64 months at 1, 3, and 5 years after surgery, respectively. The prediction model demonstrated that the probability of achieving 5-year survival at 1 year after surgery varied from 1 to 58% depending on patient and tumor characteristics.ConclusionsThis population-based study showed that 1-year conditional survival was 55% 1 year after resection and 74% 3 years after resection in patients with pancreatic cancer. The prediction model is available via www.pancreascalculator.com to inform patients and caregivers.
| S-EPMC7311496 | biostudies-literature
Project description:We aimed to determine the histopathological characteristics and prognosis of curatively resected pancreatic ductal adenocarcinoma (PDAC) showing intratumoral necrosis on preoperative CT or MRI. This study consecutively included 102 patients who underwent upfront surgery with margin-negative resection from 2012 to 2020. All patients underwent both pancreatic CT and MRI within 1 month before surgery. Two radiologists independently assessed CT/MRI findings, including the presence of CT- and MRI-detected necrosis. Histopathological characteristics of PDACs according to CT or MRI detection of necrosis were evaluated. Disease-free survival (DFS) and overall survival (OS) were assessed by the Kaplan-Meier method and the Cox proportional hazards model. Among the 102 PDAC patients, 14 patients (13.7%) had CT-detected necrosis, and 16 patients (15.7%) had MRI-detected necrosis, of which 9 showed both CT- and MRI-detected necrosis. PDACs with CT- or MRI-detected necrosis demonstrated a significantly higher degree of histopathological necrosis than those without (p < 0.001). Multivariable analysis revealed that tumor size (hazard ratio [HR], 1.19; p = 0.040), tumor location (HR, 0.46; p = 0.009), and MRI-detected necrosis (HR, 2.64; p = 0.002) had independent associations with DFS. Only MRI-detected necrosis was significantly associated with OS (HR, 2.59; p = 0.004). Therefore, MRI-detected necrosis might be a potential imaging predictor of poor survival after curative resection of PDAC.
| S-EPMC9688323 | biostudies-literature