ArticleFrontiers in oncology2020
A Hematological-Related Prognostic Scoring System for Patients With Newly Diagnosed Glioblastoma.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The role of hematological biomarkers as diagnostic tool in pre-cancerous patients.Discover oncology · 2026Review
- A machine learning-driven prognostic model based on peripheral blood lymphocyte subsets in osteosarcoma.Frontiers in immunology · 2026Article
- Blood-Based Prognostic Prediction Model for Glioblastoma: Construction and Validation.Cancer management and research · 2025Article
- Sequential Evaluation of Hematology Markers as a Prognostic Factor in Glioblastoma Patients.Biomedicines · 2024Article
- Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer.BMC cancer · 2024Article
- A Clinical Prognostic Model Based on Preoperative Hematological and Clinical Parameters Predicts the Progression of Primary WHO Grade II Meningioma.Frontiers in oncology · 2021Article
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9 authors.
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Abstract
backgroundGlioblastoma is the most common primary malignant brain tumor. Recent studies have shown that hematological biomarkers have become a powerful tool for predicting the prognosis of patients with cancer. However, most studies have only investigated the prognostic value of unilateral hematological markers. Therefore, we aimed to establish a comprehensive prognostic scoring system containing hematological markers to improve the prognostic prediction in patients with glioblastoma. PATIENTS AND
methodsA total of 326 patients with glioblastoma were randomly divided into a training set and external validation set to develop and validate a hematological-related prognostic scoring system (HRPSS). The least absolute shrinkage and selection operator Cox proportional hazards regression analysis was used to determine the optimal covariates that constructed the scoring system. Furthermore, a quantitative survival-predicting nomogram was constructed based on the hematological risk score (HRS) derived from the HRPSS. The results of the nomogram were validated using bootstrap resampling and the external validation set. Finally, we further explored the relationship between the HRS and clinical prognostic factors.
resultsThe optimal cutoff value for the HRS was 0.839. The patients were successfully classified into different prognostic groups based on their HRSs (P < 0.001). The areas under the curve (AUCs) of the HRS were 0.67, 0.73, and 0.78 at 0.5, 1, and 2 years, respectively. Additionally, the 0.5-, 1-y, and 2-y AUCs of the HRS were 0.51, 0.70, and 0.79, respectively, which validated the robust prognostic performance of the HRS in the external validation set. Based on both univariate and multivariate analyses, the HRS possessed a strong ability to predict overall survival in both the training set and validation set. The nomogram based on the HRS displayed good discrimination with a C-index of 0.81 and good calibration. In the validation cohort, a high C-index value of 0.82 could still be achieved. In all the data, the HRS showed specific correlations with age, first presenting symptoms, isocitrate dehydrogenase mutation status and tumor location, and successfully stratified them into different risk subgroups.
conclusionsThe HRPSS is a powerful tool for accurate prognostic prediction in patients with newly diagnosed glioblastoma.
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