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The Rio Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM).

ABSTRACT: Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the "Río Hortega University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details for glioblastoma patients who underwent total or near-total enhancing tumor resection. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans.

SUBMITTER: Cepeda S

PROVIDER: S-EPMC10551826 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM).

Cepeda Santiago S García-García Sergio S Arrese Ignacio I Herrero Francisco F Escudero Trinidad T Zamora Tomás T Sarabia Rosario R

Data in brief 20230923

Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the "Río Hortega University Hospital Glioblastoma Dataset (RHUH-GBM)," a coll ...[more]

PMID: 37808543

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Project description:BackgroundGlioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies.MethodsThis longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients.ResultsPreoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p < 0.01) in the preoperative model, while age, WHO grade 4 and chemotherapy or radiotherapy treatments (t > 4.12, p < 0.0001) were most significant in the postoperative period. Postoperative predictive models specifically targeting the glioblastoma and IDH wildtype subgroups were also performed, with an AUC of 0.76 and 0.80, respectively. The 50 combinations of distinct risk factors accommodate diverse recurrence risks among glioma patients, and the nomograms visualizes the results for clinical practice. A stratified Cox model identified many prognostic factors for long-term recurrence, thereby facilitating the enhanced formulation of perioperative care plans for patients, and glioblastoma patients displayed a median progression-free survival (PFS) of only 11 months.ConclusionThe constructed preoperative and postoperative models reliably predicted short-term postoperative glioma recurrence in a substantial patient cohort. The combinations risk factors and nomograms enhance the operability of personalized therapeutic strategies and care regimens. Particular emphasis should be placed on patients with recurrence within six months post-surgery, and the corresponding treatment strategies require comprehensive clinical investigation.

Project description:BackgroundLiver resection is the most effective treatment for intrahepatic cholangiocarcinoma. Recurrent disease is frequent; however, recurrence patterns are ill-defined and prognostic models are lacking.Study designA primary cohort of 189 patients who underwent resection for intrahepatic cholangiocarcinoma was used for recurrence patterns analysis within and after 24 months. Based on independent factors for disease-free survival identified in Cox regression analysis, preoperative and postoperative models were developed using a recursive partitioning method. Models were externally validated using a multicenter cohort of 522 resected patients (Association Française de Chirurgie intrahepatic cholangiocarcinoma study group).ResultsRecurrence within 24 months most often involved the liver (82.7%), and most recurrences after 24 months were strictly extrahepatic (61.1%). In multivariable analysis of the primary cohort, independent preoperative factors for disease-free survival were tumor size and multifocality (based on imaging); tumor size, multifocality, vascular invasion, and lymph node metastases (based on pathology) were independent postoperative factors. The preoperative model allowed patient classification into low-risk and high-risk groups for recurrence. In the validation cohort (n = 522), high-risk patients had a greater likelihood of recurrence (hazard ratio = 2.17; 95% CI, 1.74-2.72; p < 0.001). The postoperative model included tumor size, vascular invasion, and positive nodal disease on pathology and classified patients in low-, intermediate-, and high-risk groups in the primary cohort. As compared with low-risk patients in the validation cohort, intermediate- and high-risk patients were more likely to experience recurrence (hazard ratio = 1.9; 95% CI, 1.41-2.47; p < 0.001 and hazard ratio = 2.99; 95% CI, 2.08-4.31; p < 0.001, respectively).ConclusionsRecurrence patterns are time dependent. Both models as developed and validated in this study classified patients in distinct recurrence risk groups, which can guide treatment recommendations.

Project description:OBJECTIVE:To evaluate the performance of predicting early recurrence using preoperative factors only in comparison with using both pre-/postoperative factors. MATERIALS AND METHODS:We retrospectively reviewed 549 patients who had undergone curative resection for single hepatcellular carcinoma (HCC) within Milan criteria. Multivariable analysis was performed to identify pre-/postoperative high-risk factors of early recurrence after hepatic resection for HCC. Two prediction models for early HCC recurrence determined by stepwise variable selection methods based on Akaike information criterion were built, either based on preoperative factors alone or both pre-/postoperative factors. Area under the curve (AUC) for each receiver operating characteristic curve of the two models was calculated, and the two curves were compared for non-inferiority testing. The predictive models of early HCC recurrence were internally validated by bootstrap resampling method. RESULTS:Multivariable analysis on preoperative factors alone identified aspartate aminotransferase/platelet ratio index (OR, 1.632; 95% CI, 1.056-2.522; p = 0.027), tumor size (OR, 1.025; 95% CI, 0.002-1.049; p = 0.031), arterial rim enhancement of the tumor (OR, 2.350; 95% CI, 1.297-4.260; p = 0.005), and presence of nonhypervascular hepatobiliary hypointense nodules (OR, 1.983; 95% CI, 1.049-3.750; p = 0.035) on gadoxetic acid-enhanced magnetic resonance imaging as significant factors. After adding postoperative histopathologic factors, presence of microvascular invasion (OR, 1.868; 95% CI, 1.155-3.022; p = 0.011) became an additional significant factor, while tumor size became insignificant (p = 0.119). Comparison of the AUCs of the two models showed that the prediction model built on preoperative factors alone was not inferior to that including both pre-/postoperative factors {AUC for preoperative factors only, 0.673 (95% confidence interval [CI], 0.623-0.723) vs. AUC after adding postoperative factors, 0.691 (95% CI, 0.639-0.744); p = 0.0013}. Bootstrap resampling method showed that both the models were valid. CONCLUSION:Risk stratification solely based on preoperative imaging and laboratory factors was not inferior to that based on postoperative histopathologic risk factors in predicting early recurrence after curative resection in within Milan criteria single HCC patients.

Dataset Information

The Rio Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM).

Publications

The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM).

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