A New Predictive Model for Radiation Necrosis Risk based on PTV-enriched Blood Inflammatory Biomarkers in Patients with Brain Metastases Treated with Stereotactic Radiosurgery
International Journal of Radiation Oncology*Biology*Physics, 2026
Jablonska P., Serrano D., Galán N., Barranco J., Leon S., Zelaya V., Robledano R., Echeveste J., Rico M., Flamarique S., Cuenca T., Moreno-Jiménez M., Bosch-Barrera J., Calvo A., Aristu J.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Oncology | Patient Stratification | Plasma |  Olink Target 96 |
Abstract
Background
Radiation necrosis (RN) is an adverse event following stereotactic radiosurgery (SRS) for brain metastases (BMs). The planning target volume (PTV) size is a potential yet suboptimal predictor of RN, that unlike V12, remains independent of the number of fractions delivered. Prior authors demonstrated that radiation-induced brain injury can be traced in peripheral blood. This study aimed to identify predictive biomarkers of symptomatic RN at the time of SRS to develop a risk prediction model based on inflammatory proteomic plasma markers and the PTV as dosimetric surrogate.
Methods
A retrospective study design was conducted using plasma samples from BMs patients treated with SRS (stereotactic radiosurgery, single fraction) or FSRT (fractionated stereotactic radiotherapy, 3-6 fractions). The Olink 96 Target Immuno-Oncology Panel was performed to analyze 92 related human proteins using oligonucleotide-bound antibodies and real-time polymerase-chain reaction. Statistical analyses included ROC curves and multivariable Cox proportional hazards (PH) regression to evaluate clinical parameters, PTV, and blood biomarkers to predict RN risk. Multiplex immunophenotyping was performed on available RN tissue samples to assess immune infiltration.
Results
A total of 47 BMs patients were analyzed (21 cases with RN and 26 without). Cox regression analysis identified PTV and the inflammation-related blood biomarkers MUC-16 and CXCL11 as independent predictors of RN. A high Necrosis Prediction Index (NPI) combining PTV with MUC-16 and CXCL11 was significantly associated with higher risk (HR=2.543 [1.615 – 4.005], p<0.0001) of RN development. ROC analysis demonstrated that the NPI effectively distinguished patients with RN, achieving an AUC of 0.808. An exploratory independent analysis found that baseline levels of other proinflammatory blood biomarkers (i.e. CD8a and IL-8) could also be associated with RN. Tissue analysis confirmed a pro-inflammatory microenvironment in RN compared to tumor recurrence.ConclusionsThis hypothesis-generating study suggests the combination of PTV, CXCL11 and MUC-16 may predict development of RN, warranting further validation.