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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Neuro-Oncology and Neurosurgical Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1280395

Development and approval of a Lasso score based on nutritional and inflammatory parameters to predict prognosis in patients with glioma

Provisionally accepted
Huixian Li Huixian Li 1Hui Hong Hui Hong 2Jinling Zhang Jinling Zhang 3*
  • 1 The Second Medical College, Binzhou Medical University, Binzhou, Shandong Province, China
  • 2 Jinzhou Medical University, Jinzhou, Liaoning Province, China
  • 3 Linyi People's Hospital, Linyi, China

The final, formatted version of the article will be published soon.

    Objectives: Preoperative peripheral hematological indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and prognostic nutritional index (PNI) have predictive potential), exhibit promise as prognostic markers for glioma. This study aimed to exploreevaluated the predictiveprognosticprognostic value of a combined scoring system comprisingincorporating NLR, PLR, MLR, and PNI, and to developdeveloped a columnar graphical model to forecast the prognosisof gliomasnomogram to predict glioma prognosis. Methods: Data on preoperative NLR, PLR, MLR, and PNI were collected from 380 patients with pathologically diagnosed glioma patients (266 in the training cohort, 114 in the validation cohort) provided preoperative NLR, PLR, MLR, and PNI data.). The Least Absolute Shrinkage and Selection Operator (Lasso) was usedemployed to screen for select relevant hematological indicators and establish the generate a Lasso score. A nomogram was constructed utilizing Cox regression and Lasso variable selection calculated each result's nomogram. A nomogram plot model was then created using . This nomogram incorporated the Lasso score, age, pathological type, chemotherapy status, and Ki67 expression to predict overall survival (OS). The model'sModel performance was assessed usingevaluated utilizing Harrell's c-index, calibration curvecurves, DCA, and clinical utility (stratification into low-risk and high-risk categoriesgroups), and verified utilizing the independent validation cohort confirmed it. Results :: A total of 380 glioma patients were recruitedenrolled and dividedseparated into training (n = 266) and validation (n = 114) cohorts. The two cohorts haddemonstrated no significant differences in factors. baseline characteristics. NLR, PLR, MLR, and PNI were chosen from the training queue to calculate dataset were utilized for Lasso. Age, pathologic calculation. Multivariable analysis indicated that age, pathological grade, chemotherapy status, Ki-67 expression, and the Lasso score were independent OS predictors in multifactorial analysis, hence theyof OS and were then included in the nomogram model. . The nomogram model based on the training cohort had a C index of 0.742

    Keywords: Glioma, Nutrition indices, Inflammation indices, Lasso score, prognosis

    Received: 20 Aug 2023; Accepted: 03 Jan 2025.

    Copyright: © 2025 Li, Hong and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jinling Zhang, Linyi People's Hospital, Linyi, China

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