Development and approval of a Lasso score based on nutritional and inflammatory parameters to predict prognosis in patients with glioma
ObjectivesPreoperative 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), exhibit promise as prognostic markers for glioma. This study evaluated the prog...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1280395/full |
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| Summary: | ObjectivesPreoperative 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), exhibit promise as prognostic markers for glioma. This study evaluated the prognostic value of a combined scoring system incorporating NLR, PLR, MLR, and PNI, and developed a nomogram to predict glioma prognosis.MethodsData on preoperative NLR, PLR, MLR, and PNI were collected from 380 patients with pathologically diagnosed glioma (266 in the training cohort, 114 in the validation cohort). The Least Absolute Shrinkage and Selection Operator (Lasso) was employed to select relevant hematological indicators and generate a Lasso score. A nomogram was constructed utilizing Cox regression and Lasso variable selection. This nomogram incorporated the Lasso score, age, pathological type, chemotherapy status, and Ki67 expression to predict overall survival (OS). Model performance was evaluated utilizing Harrell’s c-index, calibration curves, DCA, and clinical utility (stratification into low-risk and high-risk groups), and verified utilizing the independent validation cohort.ResultsA total of 380 glioma patients were enrolled and separated into training (n = 266) and validation (n = 114) cohorts. The two cohorts demonstrated no significant differences in baseline characteristics. NLR, PLR, MLR, and PNI from the training dataset were utilized for Lasso calculation. Multivariable analysis indicated that age, pathological grade, chemotherapy status, Ki-67 expression, and the Lasso score were independent predictors of OS and were then included in the nomogram. The nomogram model based on the training cohort had a C index of 0.742 (95% CI: 0.700-0.783) and AUC values of 0.802, 0.775, and 0.815 for ROC curves at 1, 3, and 5 years after surgery. The validation cohort derived a similar C-index of 0.734 (95% CI: 0.671–0.798) and AUC values of 0.785, 0.778, and 0.767 at 1, 3, and 5 years, respectively. The nomogram demonstrated good calibration in both cohorts, indicating strong agreement between predicted and observed outcomes. The threshold probabilities for DCA at 1-, 3-, and 5-years post-surgery in the training and validation cohorts were 0.08~k0.74, 0.25~0.80, and 0.08~0.89, and 0.13~0.60, 0.28~0.81, and 0.25~0.88, respectively.ConclusionsA nomogram incorporating a Lasso score effectively predicted prognosis in glioma patients. However, its performance did not significantly exceed that of standard clinical nomograms. |
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| ISSN: | 2234-943X |