# Prediction of early distant brain failure after stereotactic radiotherapy for brain metastases

Moncef Morjani, Brieg Dissaux, Olivier Pradier, Solène Querellou, Romuald Seizeur, Victor Nguyen, Ulrike Schick, François Lucia, Gurvan Dissaux, Vincent Bourbonne

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## To cite this version:

Moncef Morjani, Brieg Dissaux, Olivier Pradier, Solène Querellou, Romuald Seizeur, et al. Prediction of early distant brain failure after stereotactic radiotherapy for brain metastases. Neuro-Oncology Advances, 2025, 7 (1), pp.vdaf151. 10.1093/noajnl/vdaf151. hal-05237598

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Neuro-Oncology Advances

7(1), vdaf151, 2025 | https://doi.org/10.1093/noajnl/vdaf151 | Advance Access date 10 July 2025

Prediction of early distant brain failure after
stereotactic radiotherapy for brain metastases

Moncef Morjani, Brieg Dissaux, Olivier Pradier, Solène Querellou, Romuald Seizeur, Victor Nguyen, Ulrike Schick, François Lucia, Gurvan Dissaux, Vincent Bourbonne®

All author affiliations are listed at the end of the article

Corresponding Author: Vincent Bourbonne, MD, PhD, Radiation Oncology Unit, University Hospital, Boulevard Tanguy Prigent, 29200 Brest, France (vincent.bourbonne@chu-brest.fr or bourbonne.vincent@gmail.com).

Abstract

**Background.** Stereotactic radiotherapy (SRT) is the main treatment for patients with 1 to 3–5 brain metastases (BMs) but with the compromise of a higher risk of distant brain failure (DBF) in comparison with whole brain radiotherapy. Early DBF has a significant negative impact on overall survival (OS). We propose to build and validate a prediction model of early DBF.

**Methods.** Early DBF was defined as the appearance of unknown BMs on the first post-SRT magnetic resonance imaging (MRI). A nomogram was built for the prediction of early DBF using patients from a cohort of 537 SRT courses. The nomogram was evaluated for early DBF classification using the chi-squared test. Prediction of DBF-free survival and OS was tested using Kaplan–Meier curves and the log-rank test. The model was validated on an external cohort of 160 subsequently delivered SRT courses.

**Results.** In the cohort of 537 SRT courses, early DBF occurred in 17% cases. The number of BMs and the DS-GPA score were significant predictors of early DBF. The nomogram demonstrated robust performances for early DBF classification ($\chi^2$ 23.7, *P* < .0001), DBF-free survival ($\chi^2$ 35.5 *P* < .0001, Figure 1), and OS ($\chi^2$ 38.9, *P* < .0001). On the validation cohort, the same nomogram achieved a $\chi^2$ of 15.3, *P* = .0005 for DBF-free survival and a $\chi^2$ of 8.6, *P* = .01 for OS.

**Conclusion.** Our study provides a robust predictive model for early DBF, validated in an independent cohort. This nomogram could improve clinical outcomes and treatment personalization.

Key Points

* Early distant brain failure (eDBF) after stereotactic radiotherapy correlates with worse survival.

* Nomogram combining DS-GPA and the number of brain metastasis BM predicts eDBF.

* The nomogram was externally validated and could improve patient selection.

The management of brain metastases (BM) has rapidly evolved in recent years.¹ Stereotactic radiotherapy (SRT) has revolutionized the treatment of brain metastases, offering a precise and effective therapeutic option. It is estimated that 20–40% of cancer patients will develop BM during the course of their disease, a prevalence that is likely to grow due to the increased efficacy of systemic treatments and thus longer overall survival. Large-scale international clinical trials conducted over

the past decade have established that SRT is the treatment of choice for managing patients with up to 3–5 BMs.²,³

However, despite its localized effectiveness, SRS presents the risk of distant brain failure (DBF). In comparison, whole-brain radiotherapy (WBRT) can treat occult brain metastases but at the cost of significant toxicity, impacting patients' quality of life, particularly in terms of cognitive decline and overall well-being.⁴,⁵ The selection of patients remains a
## Importance of the Study

The study addresses the pressing need to enhance treatment strategies for patients with brain metastases. With the advent of stereotactic radiotherapy (SRT) as a promising therapeutic option, understanding the predictors of early distant brain failure (DBF) becomes paramount. By elucidating factors associated with early DBF

and developing a prognostic model, this study provides insights into patient prognosis and treatment response. These findings may contribute to refining clinical decision-making but also pave the way for personalized therapeutic interventions aimed at improving overall survival and quality of life for patients undergoing SRT.

challenge, and there is ongoing debate between the two main therapeutic approaches for managing BMs. The major challenge lies in predicting which patients are at the highest risk of DBF to personalize treatment strategies and optimize clinical outcomes.

Several prognostic tools have been developed to assess the prognosis of patients with BM and can be used to guide their management. The Recursive Partitioning Analysis (RPA),⁶ Disease Specific-Graded Prognostic Assessment (DS-GPA),⁷ and Eligibility Quotient (EQ)⁸ were developed for this purpose but have several limitations. Choosing overall survival (OS) as the primary endpoint does not take into account the impact of DBF on quality of life.

Recent advances in medical oncology, such as immune checkpoint inhibitors and targeted therapies,⁹⁻¹³ have revolutionized the management of patients with metastatic solid cancer, with some showing improved intracerebral efficacy due to increased crossing of the blood-brain barrier (BBB).¹⁴ Developing models based solely on OS would hinder the assessment of the benefits of these new therapies. DBF, particularly early DBF, could be used as a surrogate endpoint. Indeed, several studies have shown that patients with symptomatic cerebral recurrences have poorer survival rates and generate higher healthcare costs than asymptomatic patients whose recurrences have been detected by routine surveillance imaging.

We hypothesize that early DBF can be considered a surrogate marker for selecting patients for SRT. Therefore, this report provides a review of early DBF rates and early DBF risk factors in a large monocentric cohort and aims at developing and validating a prediction model for early DBF.

(MRI) performed after SRT) was unavailable (not performed or not retrievable) and patients whose brain metastases were not treated in totality were also excluded. This study was conducted in accordance with the declaration of Helsinki and has been approved by the internal ethics committee of the Brest University Hospital (29BTC23.0143). No patients expressed opposition to the use of their data.

## Aim of the Study and Primary Outcome

Our study aims to demonstrate that early DBF after SRT significantly impacts overall survival in patients. The primary endpoint was the impact of early DBF on overall survival. The secondary endpoints were to identify the risk factors for early DBF and develop a nomogram using these risk factors to predict the patients at the highest risk.

## Imaging and Delineation

The planning CT scan (Philips, Somatom) was obtained with a slice thickness of 1.5 mm. Patients were immobilized using a thermoformed mask (BrainLAB, Feldkirchen, Germany). The CT scan was merged with contrast enhanced T1-weighted MRI, generally for 3D acquisition with a slice thickness of 1 mm. This MRI was performed in the last two weeks prior to SRT, and is used to define the gross tumour volume (GTV) and organs at risk (OAR). The GTV was defined as the macroscopic contrast-enhancing lesion on contrast enhanced T1-weighted MRI and was equal to the clinical target volume (CTV). The planning target volume (PTV) was obtained by adding an isotropic margin of 2 mm in all directions.

## Materials and Methods

### Patient Selection

This is a retrospective, monocentric study conducted by selecting patients treated with intracerebral stereotactic radiotherapy (SRT) at the Brest University Hospital for brain metastasis (BMs) of a primary solid cancer between January 1, 2014 and June 3, 2024. The exclusion criteria were as follows: primary tumor types other than lung cancer, breast cancer, melanoma, renal cell cancer (RCC), or digestive cancer; patients with primary brain tumors; patients treated with normofractionated external cerebral radiotherapy; and patients who were minors or under legal protection (guardianship, curatorship). Patients for whom MRI1 (defined as the first magnetic resonance imaging

### Treatment Planning

A total dose of 21, 23.1, and 27 in 3 fractions or 30 Gy in 5 fractions was prescribed, depending on various clinical and radiological parameters, including the size of the BM, the presence of subacute or acute neurological symptoms, the proximity of the OARs and the anatomical position. Treatment plans were generated for a TrueBeamTM STX Linac (Varian Medical Systems, Palo Alto, CA) equipped with a standard Millennium MultiLeaf (MLC) collimator with 120 leaves (2.5 mm thickness at isocentre and up to 8 cm, followed by 5 mm thickness from 8 to 22 cm). Each patient was planned using the volumetric modulated arc therapy (VMAT) technique with 6x beams using the Pinnacle treatment planning system (Philips, v9.10 or 16.2).
## Outcome Evaluation

Outcomes were evaluated by neurological examination and brain MRI. Clinical and radiological follow-up further continued every 3 months. Radiological response on MRI was assessed by an expert neuroradiologist according to the Response Assessment for Brain Metastases (RANO-BM) criteria (13): local control was defined as the absence of new radiographic enhancing abnormality in the irradiated areas on MR imaging, and early DBF was defined as the appearance of unknown BMs on MRI. According to international guidelines, the first post-SRT MRI assessment was usually performed between 2 and 3 months¹,¹⁵⁻¹⁷ after SRT.

## Statistical Analyses

For descriptive analysis, continuous variables are presented with the mean, standard deviation, median, confidence intervals, and quartiles. Categorical variables are reported with frequencies and percentages. Cerebral progression-free survival (PFS) was defined from the end of SRT to the time of any new central nervous system progression. Overall survival (OS) was calculated from the end of SRT to death or the last date of follow-up. The Kaplan-Meier method was used to estimate PFS and OS.

Regarding the primary endpoint, patients were stratified based on early DBF at MRI₁ (eDBF), and their respective Kaplan-Meier curves were compared using the log-rank test.

To study the prognostic role of individual variables for eDBF, univariable logistic regression was initially used. Features with a P-value < .05 were further processed for multivariable analysis using logistic regression to estimate the independent association of these variables.

The prognostic factors analyzed included the DS-GPA (stratified in 5 classes: 0 = 0.0–0.5, I = 1–1.5, II = 2–2.5, III = 3–3.5, and IV if ≥ 4), clinical status at the time of treatment (recurrence, disease discovery, oligo-progression), Karnofsky Performance Status (KPS), number of brain metastases as a continuous variable, previous RT (WBRT or any type of RT) and types of associated systemic treatments (immunotherapy, chemotherapy, or targeted therapies) during SRT treatment, within a 1-month window before and after SRT. The potential crossing of the blood-brain barrier (BBB) by associated systemic treatments was also studied.

After multivariable analysis, selected features were dichotomized based on their correlation with eDBF by maximizing the Youden Index. The nomogram was then built assigning points based on the results of an additional logistic regression combining the dichotomized features of interest. Points for the nomogram relied on the hazard ratio (HR) that resulted from the multivariable logistic regression. The feature with the maximum HR was given 100 points in case of the feature being positive (and 0 points if negative). The remaining features were given a proportional amount of points using a rule of third approach, comparing every HR to the highest HR.

All statistical analyses were performed on the cohort of SRT courses delivered between January 1, 2014, and

July 15, 2022, further defined as the development cohort. The nomogram was then validated on the cohort of SRT courses delivered between July 16, 2022, and June 3, 2024 (validation cohort).

Evaluation of a previously developed model by Natarajan et al.¹⁸ is also provided. This model is based on the number of treated BMs (1–2 = 35 points or > 2 = 100 points if melanoma and 1 = 0 point vs ≥ 2 = 45 points if non-melanoma), the history of WBRT (yes = 0 point vs no = 15 points) and the tome from cancer diagnosis to initial metastases (≥ 0 point or ≤ 5 years = 45 points). Patients with a score higher than 85 were considered at high risk whereas those with a score ≤ 85 were at low risk of DBF.

All statistical analyses were performed using SPSS Statistics v24.0 and MedCalc Statistical Software version 15.8 (MedCalc Software bvba, Ostend, Belgium, https://www.medcalc.org, 2015).

## Results

### Patients Characteristics

Between January 1, 2014 and July 15, 2022, 435 adult patients with solid cancers were treated with SRT at the Brest University Hospital, accounting for a total of 537 treatment courses. The population was well-balanced between men (53%) and women (47%), with a median age of 64 years. The most common primary tumor was lung cancer (70.6%), particularly pulmonary adenocarcinoma (73.4%). At the time of treatment, the clinical status was most often progression (58%). Regarding the DS-GPA score, the majority of patients (74%) had a score between 1.5 and 3 (Table 1). With a median follow-up of 12.8 months (CI<sub>95%</sub> 11.1–15.6), the median DBF-free survival was 15.8 months (CI<sub>95%</sub> 11.2–20.2) and the median OS was 12.9 months (CI<sub>95%</sub> 11.1–15.6).

The validation cohort consisted of 121 patients, 129 SRT courses delivered between July 16, 2022 and June 1, 2024. The median DS-GPA score was 2.0 (CI<sub>95%</sub> 2.0–2.5). The median follow-up was 10.1 months (CI<sub>95%</sub> 9.1–11.3) while the median DBF-free survival was 12.8 months (CI<sub>95%</sub> 9.3–15.3) and the median OS was 11.1 months (CI<sub>95%</sub> 6.8–15.1).

The main clinical data for both cohorts are summarized in Table 1.

### Survival Impact of Early DBF

Early DBF on MRI₁ occurred in 17% of all treatment courses and was significantly associated with a worse OS. Mean survival was 31.4 months (CI<sub>95%</sub> 27.8–35.1) vs 17.2 months (CI<sub>95%</sub> 11.2–23.1) in case of early DBF. Similarly, median survival was 12.9 months (CI<sub>95%</sub> 11.1–15.6) vs 6.4 months (CI<sub>95%</sub> 4.6–8.1) when early DBF occurred (HR 2.3 CI<sub>95%</sub> 1.7–3.0, P < .0001; Figure 1).

### Factors Associated with Early DBF

Among all the evaluated prognostic factors, only the number of brain metastases and the DS-GPA score were significantly associated with early DBF on univariable
<table><caption>**Table 1.** Characteristics of Treatment Courses in the Training and Validation Cohorts</caption><thead><tr><th></th><th></th><th>Training cohort</th><th>Validation cohort</th></tr></thead><tbody><tr><td>Number of TC</td><td></td><td>n = 537</td><td>N = 129</td></tr><tr><td rowspan="2">Gender (nb, %)</td><td>Male</td><td>287 (53.4)</td><td>68 (52.7)</td></tr><tr><td>Female</td><td>250 (46.6)</td><td>61 (47.3)</td></tr><tr><td>Age (median, IC95%)</td><td></td><td>63.8 years (62.8–64.8)</td><td>64.6 years (62.1–66.6)</td></tr><tr><td rowspan="5">Histology of the primary tumour (nb, %)</td><td>Lung</td><td>379 (70.6)</td><td>105 (81.4)</td></tr><tr><td>Breast</td><td>63 (11.7)</td><td>10 (7.8)</td></tr><tr><td>Melanoma</td><td>46 (8.6)</td><td>7 (5.4)</td></tr><tr><td>RCC</td><td>21 (3.9)</td><td>1 (0.8)</td></tr><tr><td>GIT</td><td>28 (5.2)</td><td>6 (4.7)</td></tr><tr><td rowspan="3">Clinical setting (nb, %)</td><td>Recurrence</td><td>88 (16.4)</td><td>13 (10.1)</td></tr><tr><td>Disease discovery</td><td>142 (26.4)</td><td>50 (38.8)</td></tr><tr><td>Oligo Progression</td><td>307 (57.2)</td><td>66 (51.2)</td></tr><tr><td>DS-GPA-score (median, IC95%)</td><td></td><td>2.5 (2.5–2.5)</td><td>2.0 (2.0–2.5)</td></tr><tr><td rowspan="5">DS-GPA-score (nb, %)</td><td>0–0.5</td><td>28 (5.2)</td><td>2 (1.6)</td></tr><tr><td>1–1.5</td><td>0 (0.0)</td><td>32 (24.8)</td></tr><tr><td>2–2.5</td><td>196 (36.5)</td><td>63 (48.8)</td></tr><tr><td>3–3.5</td><td>208 (38.7)</td><td>27 (20.9)</td></tr><tr><td>4</td><td>105 (19.6)</td><td>5 (3.9)</td></tr><tr><td rowspan="2">Nb of treated BMs</td><td>Median, IC<sub>95%</sub></td><td>1 (1–1)</td><td>1 (1–2)</td></tr><tr><td>Mean, IC<sub>95%</sub></td><td>1.8 (1.7–1.9)</td><td>2.1 (1.8–2.4)</td></tr><tr><td>Previous WBRT (nb, %)</td><td></td><td>31 (5.8)</td><td>1 (0.8)</td></tr><tr><td rowspan="4">Nb of previous RT course (nb, %)</td><td>0</td><td>390 (72.6)</td><td>111 (86.0)</td></tr><tr><td>1</td><td>114 (21.2)</td><td>15 (11.6)</td></tr><tr><td>2</td><td>30 (5.6)</td><td>3 (2.3)</td></tr><tr><td>3</td><td>3 (0.6)</td><td>-</td></tr><tr><td rowspan="6">Type of concomitant treatment (nb, %)</td><td>No systemic treatment</td><td>123 (22.9)</td><td>23 (17.8)</td></tr><tr><td>Targeted therapy</td><td>94 (17.5)</td><td>14 (10.9)</td></tr><tr><td>Immune checkpoint inhibitor</td><td>109 (20.3)</td><td>32 (24.8)</td></tr><tr><td>Chemotherapy</td><td>134 (25.0)</td><td>21 (16.3)</td></tr><tr><td>Chemotherapy + Immune checkpoint inhibitor</td><td>38 (7.1)</td><td>37 (28.7)</td></tr><tr><td>Chemotherapy + Targeted therapy</td><td>37 (6.9)</td><td>2 (1.6)</td></tr><tr><td rowspan="2">Known BBB crossing (nb, %)</td><td>Targeted therapy + Immune checkpoint inhibitor</td><td>2 (0.4)</td><td>-</td></tr><tr><td></td><td>267 (49.7)</td><td>98 (76.0)</td></tr></tbody></table>

**Abbreviations:** TC = Treatment course, nb = number, IC95% = 95% Confidence Interval, RCC = Renal Cell Carcinoma, GIT = Gastro-Intestinal Tumour, DS-GPA = Disease Specific Graded Prognostic Assessment, BMs = Brain Metastases, BBB = Blood Brain Barrier.

logistic regression. Nor the histology of the primary cancer, the history of RT including WBRT or the use of concomitant systemic treatment were found to be significantly associated with the risk of early DBF. On multivariable analysis, both the number of brain metastases and the DS-GPA score remained significantly associated with early DBF. The DS-GPA score appeared as a protective factor (HR 0.7, IC95% 0.5–0.9, P=.005) while the number of treated brain metastasis was a negative factor (HR 1.2, IC95% 1.0–1.4, P=.02) (Table 2). Performing the univariate and multivariate analyses on the overall cohort did not impact the results (Supplementary Table 1).

## Prognostic Nomogram for Early DBF

The DBF nomogram was thus based on the DS-GPA score and the number of BMs. Based on the Youden Index, the DS-GPA score was dichotomized using a cut-off of 2 while the cut-off for BMs was 1. Patients with more than 1 BM were given 100 points while those who were treated for an unique BM were given 0 point. Regarding the DS-GPA score, patients in which the DS-GPA score was strictly above 2 were deducted 90 points while patients with a DS-GPA score under or equal to 2 were given 0 point. The final score was then normalized in order to have a final
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Overall survival according to the early DBF status. Abbreviation: eDBF: early Distant Brain Failure.</figcaption></figure>

range between 0 and 200 (+ 100 points for all SRT courses) and stratified in 3 risk levels: low [0–100[, moderate [100–200], and high (200) risks.

Respectively, 37.4%, 41.3%, and 21.2% of the SRT courses were classified at low, moderate, and high risks; correlating with eDBF rates of 7.5%, 21.2%, and 27.2% (χ2 23.7, *P* < .0001). As a comparison, the model by Natarajan et al.¹⁸ and the DS-GPA resulted in a χ2 of 13.6 (*P* = .0002) and 14.2 (*P* = .007), respectively.

The eDBF nomogram was significantly associated with PFS (χ2 35.5, *P* < .0001, Figure 2a). In comparison to low-risk courses, moderate, and high-risk SRTs were associated with shorter PFS, HR 1.9 (CI95% 1.4–2.5), and HR 2.5 (CI95% 1.8–3.5), respectively.

As a comparison, the nomogram by Natarajan et al. and the DS-GPA resulted in a χ2 of 20.9 (HR 1.80, CI95% 1.40–2.31, *P* < .0001, Figure 2b) and 24.7 (*P* = .0001, Figure 2c), respectively.

As for PFS, the eDBF nomogram was also significantly associated with OS (χ2 38.9, *P* < .0001), as seen in Figure 3. When comparing the moderate- and high-risk groups to the low-risk SRT courses, HR was 1.8 (CI95% 1.4–2.2) and 2.0 (CI95% 1.5–2.6), respectively. The nomogram by Natarajan et al. was predictive of OS (χ2 9.2, *P* = .002) while the DS-GPA was also validated (χ2 36.7, *P* < .0001).

## External Validation of the Nomogram

Early DBF occurred in 15.5% of the SRT courses. The same nomogram combining the number of BMs and the DS-GPA was validated on both early DBF prediction (χ2 of 8.28,

*P* = .02), DBF-free survival (χ2 of 15.27, *P* = .0005), and OS (χ2 of 8.58, *P* = .01). In comparison, the model by Natarajan et al. was not validated for the prediction of early DBF (χ2 of 2.90, *P* = .09), DBF-free survival (χ2 of 3.64, *P* = .06) nor OS (χ2 of 0.38, *P* = .54). Similarly, the DS-GPA alone was not prognostic of early DBF (χ² of 5.42, *P* = .25) or DBF-free survival (χ² of 6.27, *P* = .18). The DS-GPA was significantly associated with OS (χ² of 9.56, *P* = .05).

Results in the development and validation cohort for DBF rates according to the prediction model are summarized in Table 3 while KM curves for PFS/OS in the validation cohort are respectively available as Supplementary Figures 1a/2a for the eDBF nomogram, 1b/2b for the model by Natarajan et al. and 1c/2c for the DS-GPA. When focusing on sub-analyses per-histology and thus pooling the development and validation cohorts, the eDBF prediction model performed robustly as shown in the Supplementary Table 2.

## Discussion

Our study demonstrated that early DBF is negatively associated with overall survival (OS), indicating that patients who experience early DBF have a poorer prognosis. Additionally, we identified prognostic factors for early distant brain failure (DBF) after stereotactic radiotherapy (SRT) in patients with brain metastases. The results showed that early DBF was significantly associated with both the number of metastases and the DS-GPA score. These simple factors were combined in a nomogram that
<table><caption>**Table 2.** Predictive Factors of Early DBF on MRI<sub>i</sub>.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">Univariable analysis</th><th colspan="2">Multivariable analysis</th></tr><tr><th>P value</th><th>HR (IC95%)</th><th>P value</th><th>HR</th></tr></thead><tbody><tr><td>Clinical status at the time of treatment<br>(ref = metastatic initially)</td><td>.23</td><td></td><td></td><td></td></tr><tr><td>Oligoprogression</td><td>.56</td><td>1.17 (0.69-1.97)</td><td></td><td></td></tr><tr><td>Recurrence</td><td>.25</td><td>0.63 (0.29-1.39)</td><td></td><td></td></tr><tr><td>Gender</td><td>.54</td><td>0.87 (0.56-1.36)</td><td></td><td></td></tr><tr><td>Age</td><td>.47</td><td>1.01 (0.99-1.03)</td><td></td><td></td></tr><tr><td>Concomitant Systemics therapies</td><td>.37</td><td></td><td></td><td></td></tr><tr><td>Crossing BBB</td><td>.80</td><td>1.07 (0.65-1.76)</td><td></td><td></td></tr><tr><td>Nb of BM (continuous)</td><td>.002</td><td>1.29 (1.10-1.52)</td><td>0.02<sup>†</sup></td><td>1.21 (1.03-1.44)</td></tr><tr><td>RPA<br>(ref = RPA = 1)</td><td>.28</td><td></td><td></td><td></td></tr><tr><td>RPA = 2</td><td>.31</td><td>1.34 (0.77-2.36)</td><td></td><td></td></tr><tr><td>RPA = 3</td><td>.12</td><td>2.31 (0.80-6.69)</td><td></td><td></td></tr><tr><td>DS-GPA score (continuous)</td><td>.001</td><td>0.61 (0.45-0.81)</td><td>0.005<sup>†</sup></td><td>0.66 (0.49-0.88)</td></tr><tr><td>Histology<br>(ref = lung)</td><td>.49</td><td></td><td></td><td></td></tr><tr><td>Melanoma</td><td>.13</td><td>1.74 (0.85-3.54)</td><td></td><td></td></tr><tr><td>Breast</td><td>.84</td><td>0.93 (0.45-1.92)</td><td></td><td></td></tr><tr><td>RCC</td><td>.38</td><td>0.52 (0.12-2.28)</td><td></td><td></td></tr><tr><td>GIT</td><td>.90</td><td>1.07 (0.39-2.92)</td><td></td><td></td></tr><tr><td>Previous WBRT</td><td>.79</td><td>0.88 (0.33-2.34)</td><td></td><td></td></tr><tr><td>Nb of previous RT course (ref = 0)</td><td>.24</td><td></td><td></td><td></td></tr><tr><td></td><td>.61</td><td>0.86 (0.48-1.54)</td><td></td><td></td></tr><tr><td></td><td>.08</td><td>2.10 (0.92-4.80)</td><td></td><td></td></tr><tr><td></td><td>.47</td><td>2.46 (0.22-27.47)</td><td></td><td></td></tr></tbody></table>

**Abbreviations:** HR: Hazard Ratio, BBB: Blood-Brain Barrier, BM = brain metastases, RPA: Recursive Partitioning Analysis, DS-GPA: Disease Specific Graded Prognostic Assessment, WBRT: Whole Brain Radiotherapy. †: p < 0,05 in multivariate analysis, RCC = Renal cell cancer, GIT = Gastro-Intestinal Tumour.

robustly predicted early DBF as well as appeared prognostic for DBF-free survival and OS.

Previous studies have demonstrated that the number of brain metastases is a critical factor in determining the likelihood of progression after SRT. Our findings are consistent with these studies, reinforcing the notion that patients with multiple brain metastases are at higher risk of early DBF. Additionally, the DS-GPA score, which takes into account various prognostic factors including age, Karnofsky performance status, and the presence of extracranial metastases, was also found to be a significant predictor. This underscored the utility of comprehensive scoring systems in assessing patient prognosis.

Given the prognostic impact of early DBF, choosing the right treatment (SRT vs WBRT vs systemic therapy) is of paramount importance. While SRT lowers the risk of cognitive decline compared to WBRT, it is associated with an increased risk of DBF. The balance between these therapies often relies on clinical expertise and a variety of clinical factors. Previous approaches have been developed to help in the selection of patients for SRT. By calculating the cumulative number of new BMs that developed after a first radiosurgery course, Brain Metastasis Velocity (BMV) appeared as a dominant

predictor of OS<sup>19</sup> and was further externally validated.<sup>20</sup> Farris et al. have defined BMV as the cumulative number of new brain metastases that developed over time since first stereotactic radiotherapy in years.<sup>19</sup> While BMV was significantly associated with whole-brain radiotherapy and overall survival,<sup>21</sup> it can only be used after the first eDBF. As we focused on early DBF prediction tools, BMV was not calculable in our cohort. Assessing the correlation between the two approaches is of clear interest. Our predictive model for early DBF proved effective in accurately predicting early DBF, outperforming the score published by Natarajan et al.<sup>18</sup> This comparison underscored the robustness of our model in forecasting early DBF in patients with brain metastases. Additionally, we validated our nomogram on a new cohort of patients treated at Brest University Hospital, and the score was confirmed to be reliable on this independent cohort. This validation further supports the utility and accuracy of our predictive model in clinical practice. While having a shorter median follow-up, the validation cohort consists of patients who received modern systemic therapies such as immune checkpoint inhibitors, targeted therapies. Doing so enables clinicians to directly implement our tool in recently treated patients.
<figure><img src="image_4.png" /></figure>

<table><thead><tr><th>Number at risk</th><th>Group: Low</th><th>201</th><th>90</th><th>60</th><th>41</th><th>21</th><th>8</th><th>4</th><th>2</th><th>1</th><th>1</th><th>0</th></tr></thead><tbody><tr><th>Group: Moderate</th><td>222</td><td>60</td><td>27</td><td>11</td><td>9</td><td>5</td><td>4</td><td>3</td><td>2</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Group: High</th><td>114</td><td>21</td><td>8</td><td>4</td><td>3</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>

<figure><img src="image_5.png" /></figure>

<table><thead><tr><th>Number at risk</th><th>Group: Low</th><th>278</th><th>107</th><th>64</th><th>43</th><th>26</th><th>12</th><th>8</th><th>5</th><th>3</th><th>1</th><th>0</th></tr></thead><tbody><tr><th>Group: High</th><td>259</td><td>64</td><td>31</td><td>13</td><td>7</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>

<figure><img src="image_6.png" /></figure>

<table><caption>**Figure 2.** DBF-risk survival according to each model (2a: eDBF-prediction model, 2b: Natarajan et al and 2c: DS-GPA). Abbreviations: DBF: dis- tant brain failure, eDBF: early DBF, DS-GPA: Disease Specific-Graded Prognostic Assessment</caption><thead><tr><th>Number at risk</th><th>Group: 0-0.5</th><th>5</th><th>1</th><th>1</th><th>0</th><th>0</th><th>0</th><th>0</th><th>0</th><th>0</th><th>0</th><th>0</th></tr></thead><tbody><tr><th>Group: 1-1.5</th><td>81</td><td>14</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>2</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Group: 2-2.5</th><td>236</td><td>63</td><td>28</td><td>15</td><td>10</td><td>4</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Group: 3-3.5</th><td>184</td><td>79</td><td>54</td><td>31</td><td>16</td><td>6</td><td>4</td><td>2</td><td>1</td><td>1</td><td>0</td><td>0</td></tr><tr><th>Group: 4</th><td>31</td><td>14</td><td>9</td><td>7</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Overall survival according to each model (3a: eDBF-prediction model, 3b: Natarajan et al and 3c: DS-GPA). Abbreviations: DBF: distant brain failure, eDBF: early DBF, DS-GPA: Disease Specific-Graded Prognostic Assessment.</figcaption></figure>
<table><caption>**Table 3.** Comparison of Early DBF Rates According to Each Model (training and validation cohorts)</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">&lt;Training cohort</th><th colspan="4">Validation cohort</th></tr><tr><th>Risk</th><th>Chi<sub>2</sub></th><th>P value</th><th>Low</th><th>Moderate</th><th>High</th><th>Chi<sub>2</sub></th><th>P value</th><th>Low</th><th>Moderate</th><th>High</th></tr></thead><tbody><tr><td>eDBF-risk prediction</td><td>23.7</td><td>&lt; .0001</td><td>7.5%</td><td>21.2%</td><td>27.2%</td><td>8.3</td><td>.01</td><td>8.1%</td><td>11.7%</td><td>31.2%</td><td>21.8%</td></tr><tr><td>Natarajan et al</td><td>13.6</td><td>.0002</td><td>11.5%</td><td>-</td><td>23.6%</td><td>2.9</td><td>.09</td><td>10.8%</td><td>-</td><td>21.8%</td><td>0.0%</td></tr><tr><td>DS-GPA</td><td>14.2</td><td>.007</td><td>40%</td><td>Low</td><td>28.4%</td><td>High</td><td>16.1%</td><td>5.4</td><td>.25</td><td>0.0%</td><td>Intermediate</td><td>High</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Intermediate</td><td>18.2%</td><td>10.9%</td><td>Low</td><td>18.2%</td><td>20.6%</td><td>3.7%</td></tr></tbody></table>

**Abbreviations:** eDBF = early distant brain failure, DS-GPA = Disease Specific Graded Prognostic Assessment, For DS-GPA (Low = 0-0.5, Moderate-Low: 1-1.5, Moderate-Intermediate: 2-2.5, Moderate-High: 3-3.5, High: 4).

The negative association between early DBF and OS highlighted the need for early and effective intervention strategies. Identifying patients at high risk of early DBF allows clinicians to tailor treatment plans more effectively, potentially improving overall survival and quality of life. Several frameworks in case of high risk can be proposed: favoring combination of SRT and systemic treatment with proven intra-cranial efficacy or early post-SRT assessment. Moreover, regular post-SRT monitoring using MRI can facilitate early detection of cerebral recurrences, allowing for timely intervention and reducing the burden on healthcare systems. Delivering whole-brain radiotherapy could be an option but is limited by the higher toxicity of such treatments. This could be discussed in case of multiple BMs (> 5).

However, our study has several limitations. The retrospective nature of the study may introduce selection bias, and the single-center setting may limit the generalizability of our findings. Future research should focus on validating our predictive model in a larger, multicenter cohort and exploring the potential role of novel therapeutic agents in managing brain metastases.

## Conclusion

Our study demonstrated that early DBF is associated with the number of brain metastases and the DS-GPA score in patients treated with SRT. Moreover, early DBF is negatively associated with overall survival, indicating a poorer prognosis for these patients. These variables should be included in predictive models to identify patients at high risk of progression. Our predictive model effectively forecasts early, outperforming usual tools such as the DS-GPA. Furthermore, the validation of our nomogram on a new cohort of patients treated at Brest University Hospital confirmed its reliability. The use of early DBF as a surrogate marker for patient selection and post-SRT monitoring can enhance treatment personalization and improve clinical outcomes. Further validation of our findings in larger, multicenter studies is warranted to confirm their applicability in routine clinical practice.

## Supplementary Material

Supplementary material is available online at Neuro-Oncology Advances (https://academic.oup.com/noa).

## Keywords:

brain metastasis | early distant brain failure | overall survival | prognostic model | stereotactic radiotherapy.

*Conflict of interest statement.* The authors declare no conflicts of interest.
## Author contributions

Conceptualization: Vincent Bourbonne, Moncef Morjani; Data curation: Vincent Bourbonne; Methodology: Vincent Bourbonne, Moncef Morjani; Formal analysis: Vincent Bourbonne; Visualisation: Vincent Bourbonne; Writing (original): Vincent Bourbonne; Writing and editing: Vincent Bourbonne, Brieg Dissaux, Solène Querellou, Moncef Morjani; Supervision: Vincent Bourbonne; Validation: all authors.

## Data Availability

Data can be accessed upon request and after acceptance by the internal ethics committee of the Brest University Hospital.

## Affiliations

Radiation Oncology Department, University Hospital, Brest, France (M.M., O.P., V.N., U.S., F.L., G.D., V.B.); Radiology Department, University Hospital, Brest, France (B.D.); INSERM, GETBO UMR 1304, University of Western Brittany, Brest, France (B.D., S.Q.); INSERM, LaTIM UMR 1101, University of Western Brittany, Brest, France (O.P., R.S., U.S., F.L., V.B.); Nuclear Medicine Department, University Hospital, Brest, France (S.Q.); Neurosurgery Department, University Hospital, Brest, France (R.S.)

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