Risk factors and risk prediction model for recurrence in medulloblastoma.

BACKGROUND

At present, there is a lack of established treatment protocols for recurrent medulloblastoma. The assessment of recurrence risk prior to treatment is of utmost importance in determining the most suitable treatment modality and intensity for medulloblastoma. Consequently, the creation of a predictive model for medulloblastoma recurrence is imperative in aiding clinical decision-making. The objective of this study is to construct an enhanced risk prediction model for relapse in medulloblastoma by integrating molecular subtyping and straightforward immune markers, such as neutrophil-to-lymphocyte ratio (NLR), into a nomogram.

METHODS

A retrospective analysis was conducted on the clinical data of 273 patients diagnosed with medulloblastoma. The NLR was calculated prior to radiotherapy, and various clinical characteristics including age, gender, molecular subtype, dissemination, and residual lesions after resection were collected. Survival analysis was performed utilizing the Kaplan-Meier method, while Cox regression models were employed to identify independent prognostic factors. Furthermore, a column chart illustrating all independent prognostic factors was generated using R. The nomogram's prognostic predictive ability was evaluated using the Concordance Index (C-index), area under the curve (AUC), and calibration curve.

RESULTS

The median progression-free survival (PFS) for the entire cohort was determined to be 63.8 months. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors that were associated with PFS in patients diagnosed with medulloblastoma. These factors included age, residual tumor volume exceeding 1.5 cm, NLR exceeding 4.5, dissemination occurring prior radiotherapy, and molecular subtype classified as Group 3. These identified factors were then utilized to construct a column chart. The nomogram C-index for the predicted PFS in the training and validation cohorts was 0.749 and 0.736, respectively. The AUC for predicting the 3-year PFS exhibited satisfactory accuracy in the validation cohort (AUC =0.71). Furthermore, the calibration curve indicated a strong concordance between the predicted and ideal values. Additionally, the Kaplan-Meier curve, based on PFS, demonstrated a statistically significant distinction between the low-risk and high-risk groups as predicted by the nomogram (P<0.001).

CONCLUSIONS

Our study revealed that the NLR prior to treatment serves as an autonomous prognostic determinant for the recurrence or metastasis of medulloblastoma subsequent to treatment. By integrating NLR with clinical variables, the utilization of a nomogram demonstrates the capability to anticipate PFS following radiotherapy in medulloblastoma patients. This nomogram exhibits potential in facilitating more accurate risk stratification, thereby guiding the implementation of personalized treatment strategies for individuals with medulloblastoma.