Performance of Brain Metastasis Velocity and Initial Brain Metastasis Velocity in a Latin American LINAC-Based Stereotactic Radiosurgery Cohort

Performance of Brain Metastasis Velocity and Initial Brain Metastasis Velocity in a Latin American LINAC-Based Stereotactic Radiosurgery Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Performance of Brain Metastasis Velocity and Initial Brain Metastasis Velocity in a Latin American LINAC-Based Stereotactic Radiosurgery Cohort J. F. Franco-García, I. Vázquez-Aldana, A. Salazar-Pigeon, D. B. Díaz-Simental, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8642010/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Brain metastasis velocity (BMV) and initial brain metastasis velocity (iBMV) have been proposed as dynamic metrics to describe intracranial metastatic behavior in patients treated with stereotactic radiosurgery (SRS). However, their prognostic relevance in linear accelerator (LINAC)-based practice and Latin American populations remains incompletely characterized. Methods: We performed a retrospective cohort study of adult patients with brain metastases (BMs) treated with LINAC-based SRS between 2020 and 2025. iBMV was defined as the number of brain metastases at first SRS divided by the time from primary cancer diagnosis to intracranial metastasis detection. BMV was calculated in patients who developed distant brain failure (DBF) as the rate of new metastases from initial SRS to first DBF. Overall survival was analyzed using Kaplan–Meier estimates and Cox proportional hazards modeling. Results: The iBMV cohort included 127 patients, of whom 62 developed DBF and were evaluable for BMV analysis. iBMV did not significantly stratify overall survival. In contrast, BMV demonstrated a strong association with survival following DBF. Patients with low BMV experienced significantly prolonged survival compared with intermediate and high BMV groups. BMV remained independently associated with survival after adjustment for relevant clinical covariates. Conclusions: Within this contemporary LINAC-based Latin American cohort, BMV provided clinically meaningful prognostic information after intracranial progression, whereas iBMV did not confer prognostic value at initial presentation. Incorporation of BMV may improve post-progression risk stratification and inform salvage treatment strategies. Brain metastases Stereotactic radiosurgery Brain metastasis velocity Initial brain metastasis velocity Prognosis Overall survival Figures Figure 1 Figure 2 Figure 3 Introduction Brain metastases (BMs) are the most common intracranial malignancy in adults and a major cause of neurological morbidity in patients with advanced cancer [1–3]. Improvements in systemic therapy have prolonged survival, increasing the population at risk for intracranial progression and shifting treatment priorities toward durable intracranial control with minimized neurotoxicity. Stereotactic radiosurgery (SRS) has largely replaced whole-brain radiotherapy (WBRT) for selected patients with limited intracranial disease, achieving high local control while preserving neurocognitive function [4–6]. However, improved survival has been accompanied by a rising incidence of distant brain failure (DBF) after upfront SRS, complicating salvage decision-making [7,8]. Prognostic models such as recursive partitioning analysis and the Graded Prognostic Assessment were developed to estimate survival at diagnosis using baseline clinical characteristics [9–12]. Although these tools remain useful, they provide a static assessment and do not reflect the evolving intracranial disease trajectory observed after SRS [13,14]. To address this limitation, velocity-based metrics have been proposed. Brain metastasis velocity (BMV) quantifies the rate at which new BMs emerge following initial SRS and has been shown to correlate with survival and the need for subsequent intracranial interventions [10,12]. Initial brain metastasis velocity (iBMV) was later introduced to capture the tempo of intracranial dissemination before the first SRS by incorporating the interval from primary tumor diagnosis to BMs detection [11,15]. Despite increasing interest, most studies evaluating BMV and iBMV have been conducted in Gamma Knife–based cohorts from North America and Europe [16–18]. Data evaluating these metrics in linear accelerator (LINAC)-based SRS and in Latin American populations remains sparse. The present study aimed to assess the prognostic performance of BMV and iBMV in a contemporary Latin American cohort treated exclusively with LINAC-based SRS. Methods Study Design and Population This retrospective single-center cohort study included consecutive adult patients (≥ 18 years) with histologically confirmed solid tumors and BMs treated with LINAC-based SRS at the Instituto Nacional de Neurología y Neurocirugía Manuel Velasco Suárez (Mexico City, Mexico) between January 2020 and January 2025. SRS was delivered as the initial local intracranial treatment. Patients with prior WBRT, insufficient follow-up imaging, or incomplete data were excluded. A total of 127 patients satisfied the requirements for iBMV analysis. The BMV cohort consisted of 62 patients who developed DBF and received salvage SRS. Stereotactic radiosurgery procedure Patients underwent LINAC-based SRS (6-MV photons). Individualized biological effective doses (BED) based on lesion characteristics and proximity to the organ-at-risk were used in treatment planning, utilizing fused contrast-enhanced MRI and planning CTs in accordance with international guidelines. Image guidance ensured adequate localization. SRS followed neurosurgical intervention (biopsy or resection) when indicated by the multidisciplinary tumor board. Clinical Data and Imaging Evaluation Baseline variables included age, sex, Karnofsky Performance Status (KPS), primary tumor histology, extracranial disease status, number of BMs, prior neurosurgical procedures, prescription dose, and total irradiated intracranial volume. Follow-up consisted of contrast-enhanced brain MRI every 3–6 months, with response and failure patterns assessed using Response-Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. Definition of Velocity Metrics BMV was defined in accordance with the original description by Farris et al. [10]. For each patient, only the first episode of DBF was considered. BMV was analyzed as a continuous variable and categorized into low ( 13 metastases/year) groups using previously reported thresholds [10,12]. iBMV was computed as proposed by Soike et al. [11]. All patients in this cohort had metachronous BM (minimum interval: 42 days; median: 16 months), with complete documentation of both primary diagnosis and BM detection dates; no cases of synchronous presentation were included. iBMV was analyzed as a continuous variable and dichotomized into low (< 2.0 metastases/year) and high (≥ 2.0 metastases/year) groups in accordance with prior literature and time-dependent ROC analyses [11,15]. Outcomes Overall survival (OS) was the primary endpoint. For iBMV, OS was measured from BM diagnosis; for BMV, OS was measured from the first DBF. Survival analyses employed Kaplan–Meier estimates, log-rank testing, and Cox proportional hazards regression. Model discrimination was evaluated using Harrell’s C-index and time-dependent ROC curves at 12 and 24 months. Statistical analysis Continuous variables are reported as medians (range/IQR as available) and categorical variables as counts (%). Group comparisons used Fisher’s exact test for categorical variables and Wilcoxon rank-sum or Kruskal–Wallis tests for continuous variables, as appropriate. OS was estimated using Kaplan–Meier methods and compared using log-rank tests. Cox proportional hazards models were used to evaluate associations with survival, reporting hazard ratios (HRs) with 95% confidence intervals (CIs). Multivariable models included covariates significant in univariable analyses and/or clinically relevant factors (e.g., age, KPS, irradiated volume, prescription dose). Model discrimination was assessed using Harrell’s C-index and time-dependent ROC curves at 12 and 24 months following established methodology [19]. The proportional hazards assumption was verified using Schoenfeld residuals. Analyses were performed in R (version 4.5.2), with two-sided p < 0.05. Results Cohort characteristics The iBMV cohort included 127 patients. Median age was 55 years, and median KPS was 90. The median number of BM at initial SRS was 2, and the median total irradiated intracranial volume was 8.9 cm³. Using the predefined threshold, 70 patients (55.1%) were classified as low iBMV and 57 (44.9%) as high iBMV. Primary tumor distribution differed across iBMV strata (notably breast vs lung representation), while other baseline variables were broadly comparable. (Table 1 ). 62 patients (48.8%) developed DBF and underwent salvage SRS, constituting the BMV cohort. At first DBF, median age was 54.5 years and median KPS remained 90. BMV stratification yielded 31 low (50.0%), 17 intermediate (27.4%), and 14 high (22.6%) cases. Higher BMV was associated with a greater number of new metastases at progression and shorter time to progression. (Table 2 ). Table 1 Characteristics and outcomes by iBMV classification (n = 127) Characteristic Low iBMV (n = 70) High iBMV (n = 57) p-value N 70 57 Age, y 55 (30–79) 55 (20–88) 0.367 Sex - Female, n (%) 57 (81.4) 35 (61.4) 0.016 Sex - Male, n (%) 13 (18.6) 22 (38.6) KPS 90 (40–100) 90 (30–100) 0.056 Primary tumor - Breast, n (%) 32 (45.7) 20 (35.1) 0.012 Primary tumor - Lung, n (%) 10 (14.3) 23 (40.4) Primary tumor - Melanoma, n (%) 4 (5.7) 1 (1.8) Primary tumor - Renal, n (%) 7 (10) 5 (8.8) Primary tumor - Other, n (%) 17 (24.3) 8 (14) Primary disease control - Yes, n (%) 18 (25.7) 12 (21.1) 0.444 Surgery - Biopsy, n (%) 2 (2.9) 0 (0) 0.407 Surgery - GTR, n (%) 14 (20) 8 (14) Surgery - STR, n (%) 4 (5.7) 5 (8.8) Surgery - None, n (%) 48 (68.6) 44 (77.2) SRS - Circular arcs, n (%) 12 (17.1) 10 (17.5) 0.763 SRS - Dynamic arcs, n (%) 1 (1.4) 0 (0) SRS - Static fields, n (%) 3 (4.3) 4 (7) SRS - IMRT, n (%) 16 (22.9) 9 (15.8) SRS - VMAT, n (%) 38 (54.3) 33 (57.9) Response - Complete, n (%) 23 (32.9) 13 (22.8) 0.489 Response - Partial, n (%) 15 (21.4) 14 (24.6) Response - Progression, n (%) 32 (45.7) 30 (52.6) Volume, cc 8.9 (2.3–15.7) 8.6 (2.2–20.2) 0.529 BED¹⁰, 1 fraction (Gy) 70.4 (60.0-70.4) 65.1 (60.0-70.4) 0.065 BED¹⁰, 3 fractions (Gy) 51.3 (51.3–51.3) 51.3 (51.3–55.6) 0.113 BED¹⁰, 5 fractions (Gy) 48.0 (48.0–48.0) 48.0 (48.0–48.0) 0.185 Time primary to mets, m 31 (14.9–63.4) 8.6 (5.9–15.5) < 0.001 Survival, m 19 (10.3–29) 18.6 (8.8–35.7) 0.942 Data are presented as median (range) for continuous variables and n (%) for categorical variables. P values were calculated using Fisher’s exact test for categorical variables and the Wilcoxon rank-sum test for continuous variables. KPS Karnofsky Performance Status, SRS stereotactic radiosurgery, GTR gross total resection, STR subtotal resection, IMRT intensity-modulated radiation therapy, VMAT volumetric modulated arc therapy, BED biologically effective dose. Table 2 Characteristics and outcomes at first DBF by BMV classification (n = 62) Characteristic Low BMV (n = 31) Intermediate BMV (n = 17) High BMV (n = 14) p-value N 31 17 14 Age, y 53 (29–79) 55 (40–69) 55 (42–88) 0.569 Sex - Female, n (%) 20 (64.5) 15 (88.2) 9 (64.3) 0.192 Sex - Male, n (%) 11 (35.5) 2 (11.8) 5 (35.7) KPS 90 (60–100) 90 (60–90) 80 (30–90) 0.409 Primary tumor - Breast, n (%) 16 (51.6) 8 (47.1) 7 (50) 0.897 Primary tumor - Lung, n (%) 3 (9.7) 4 (23.5) 3 (21.4) Primary tumor - Melanoma, n (%) 2 (6.5) 1 (5.9) 1 (7.1) Primary tumor - Renal, n (%) 3 (9.7) 0 (0) 1 (7.1) Primary tumor - Other, n (%) 7 (22.6) 4 (23.5) 2 (14.3) Primary disease control - Yes, n (%) 10 (32.3) 2 (11.8) 1 (7.1) 0.180 Surgery - Biopsy, n (%) 0 (0) 1 (5.9) 0 (0) 0.743 Surgery - GTR, n (%) 6 (19.4) 1 (5.9) 2 (14.3) Surgery - STR, n (%) 1 (3.2) 0 (0) 0 (0) Surgery - None, n (%) 23 (74.2) 14 (82.4) 12 (85.7) SRS - Circular arcs, n (%) 2 (6.5) 2 (11.8) 5 (35.7) 0.009 SRS - Static fields, n (%) 3 (9.7) 0 (0) 4 (28.6) SRS - IMRT, n (%) 5 (16.1) 2 (11.8) 2 (14.3) SRS - VMAT, n (%) 21 (67.7) 13 (76.5) 3 (21.4) Volume, cc 9 (3.4–12.2) 12.5 (3-26.6) 4.9 (2.2–44.8) 0.519 BED¹⁰, 1 fraction (Gy) 70.4 (60.0-70.4) 70.4 (60.0-70.4) 70.4 (52.8–70.4) 0.715 BED¹⁰, 3 fractions (Gy) 51.3 (51.3–55.6) 51.3 (51.3–51.3) 51.3 (51.3–51.3) 0.891 BED¹⁰, 5 fractions (Gy) 48.0 (48.0–48.0) 48.0 (42.8–48.0) 48.0 (48.0–48.0) 0.344 Initial metastases, n 3 (1–5) 3 (1–6) 4.5 (3-9.8) 0.066 New metastases post-SRS, n 1 (1–2) 3 (2–6) 9.5 (6–14) < 0.001 Time to progression, m 15 (10.2–25.4) 4.7 (2.7–11.5) 5.3 (3.5-6) < 0.001 Survival, m 35.2 (24–50.6) 10.4 (7.9–21.8) 12.3 (9.2–24.2) < 0.001 Data are presented as median (range) for continuous variables and n (%) for categorical variables. P values were calculated using Fisher’s exact test for categorical variables and the Kruskal–Wallis test for continuous variables. KPS Karnofsky Performance Status, SRS stereotactic radiosurgery, DBF distant brain failure, GTR gross total resection, STR subtotal resection, IMRT intensity-modulated radiation therapy, VMAT volumetric modulated arc therapy, BED biologically effective dose. iBMV overall survival With a median follow-up of 14.5 months, 73 deaths (57.5%) were recorded in the iBMV cohort. Median OS from BM diagnosis was 27.4 months (95% CI 21.6–33.5). Kaplan–Meier curves showed no meaningful separation by iBMV category (Fig. 1). Median OS was 25.7 months in the low iBMV group and 29.4 months in the high iBMV group (log-rank p = 0.80). In Cox regression, iBMV was not associated with OS when modeled categorically (HR 0.94, 95% CI 0.59–1.50; p = 0.802) or continuously (HR 1.00, 95% CI 0.98–1.02; p = 0.941). After adjustment for age, KPS, total irradiated volume, and prescription dose, iBMV remained non-significant (HR 1.08, 95% CI 0.67–1.76; p = 0.742). Lower KPS, larger irradiated volume, and lower prescription dose were associated with shorter survival. Time-dependent ROC analyses for iBMV showed limited discrimination (AUC 0.57 at 12 months; 0.53 at 24 months), consistent with weak prognostic utility at presentation. Figure 1. Overall survival by iBMV classification (n = 127). Kaplan–Meier curves for overall survival (OS) from the date of BM diagnosis, stratified by iBMV category (low < 2.0 vs high ≥ 2.0 metastases/year). Shaded areas indicate 95% confidence intervals. Differences between curves were assessed using the log-rank test. BMV survival after distant brain failure In the BMV cohort, time from initial SRS to first DBF differed significantly across BMV strata, with the longest time to progression observed in low BMV patients. Kaplan–Meier analysis demonstrated pronounced OS differences from the time of first DBF (Fig. 2 ). Median OS was 45.5 months (95% CI 35.2–not estimable) in the low BMV group, versus 10.4 months (95% CI 8.0–not estimable) in the intermediate group and 12.3 months (95% CI 10.2–not estimable) in the high group (log-rank p < 0.001). In univariable Cox regression, low BMV was associated with improved survival compared with intermediate–high BMV (HR 0.23, 95% CI 0.11–0.45; p < 0.001). Higher KPS and lower total irradiated intracranial volume were also associated with longer survival. In multivariable analysis including BMV classification, KPS, and irradiated volume, BMV retained independent prognostic value; low BMV was associated with a 72% reduction in the hazard of death (HR 0.28, 95% CI 0.14–0.59; p < 0.001). The proportional hazards assumption was satisfied for both velocity-based metrics, with no evidence of violation for BMV (Schoenfeld residuals p = 0.145) or iBMV (Schoenfeld residuals p = 0.128). BMV demonstrated superior discrimination (C-index = 0.719, 95% CI 0.665–0.773) compared with iBMV (C-index = 0.487, 95% CI 0.424–0.551). Time-dependent ROC performance and calibration statement Time-dependent ROC curves demonstrated good to excellent discrimination for BMV when predicting OS after DBF, with AUC values of 0.78 at 12 months and 0.81 at 24 months. iBMV showed poor discrimination within its cohort, as noted above (Fig. 3 ). Because these metrics are derived from different cohorts and time origins, side-by-side AUC values should be interpreted as within-cohort discrimination rather than a direct head-to-head comparison. Kaplan–Meier curves for overall survival (OS) from the date of first DBF, stratified by BMV category (low 13 metastases/year). Shaded areas indicate 95% confidence intervals. Differences between curves were assessed using the log-rank test. Time-dependent ROC curves evaluating discrimination for overall survival prediction. (A) 12-month ROC curves for iBMV (n = 127) and BMV (n = 62). (B) 24-month ROC curves for iBMV (n = 127) and BMV (n = 62). The diagonal line indicates no discrimination (AUC = 0.5). AUC, area under the curve. Discussion In this single-center Latin American cohort treated with contemporary LINAC-based SRS, we observed differential prognostic performance between velocity-based metrics. While iBMV did not provide meaningful prognostic discrimination at initial presentation, BMV robustly predicted survival following DBF and remained independently associated with outcomes after multivariable adjustment. The proportional hazards assumption was satisfied for both metrics, supporting the validity of the Cox regression analyses. The absence of prognostic value for iBMV contrasts with the initial report by Soike et al. [11], but aligns with subsequent work emphasizing limitations and cohort sensitivity [15]. In our cohort, patients were selected for upfront SRS—typically reflecting preserved performance status and limited intracranial disease burden—which may reduce the variance that iBMV is intended to capture. Additionally, iBMV depends on timing from primary diagnosis, a component influenced by surveillance patterns, systemic therapy era, and tumor-specific diagnostic pathways, all of which can differ across settings. By contrast, BMV measures observed intracranial kinetics after SRS, arguably closer to the biological behavior relevant to salvage decision-making. Our findings are consistent with prior validations showing BMV’s association with survival and the need for salvage therapy [10,12,16–18]. We extend those observations to a LINAC-based platform, supporting the notion that BMV is not platform-dependent and may generalize beyond Gamma Knife–dominant datasets. From a clinical standpoint, velocity-based metrics may support individualized salvage strategies. Patients with low BMV experienced substantially longer survival after DBF, suggesting that repeated salvage SRS may be appropriate and could allow deferral of WBRT, thereby reducing neurocognitive risk [4–6,20]. Conversely, intermediate/high BMV identifies patients with rapid intracranial dissemination in whom earlier WBRT or alternative strategies may be reasonable, particularly when repeated SRS becomes impractical or when disease biology suggests diffuse intracranial risk [7,21]. As systemic therapies continue to evolve and improve intracranial control for some histologies [25–30], dynamic prognostic tools may complement baseline indices and better reflect real-time disease behavior. A key interpretive point is that iBMV and BMV were evaluated in different cohorts and from different time origins; thus, comparisons of discrimination metrics must be interpreted carefully. This is consistent with established methodological guidance regarding time-dependent ROC estimation and comparisons across differing risk sets [19]. These findings should be interpreted as hypothesis-generating. Limitations include the retrospective design, single-center setting, and potential residual confounding. Additionally, systemic therapy heterogeneity may influence intracranial kinetics. Nevertheless, the use of standardized definitions, consistent imaging follow-up patterns, and established velocity thresholds supports internal validity. Prospective multi-institutional evaluation in Latin American settings would further clarify generalizability and potential integration into clinical pathways. Conclusions In a contemporary Latin American cohort treated with LINAC-based stereotactic radiosurgery, BMV was a strong and independent predictor of overall survival after distant brain failure, with excellent discriminatory performance. iBMV velocity did not provide meaningful prognostic stratification at initial presentation in this SRS-selected population. These findings support incorporating BMV at the time of intracranial progression to refine risk stratification and guide salvage treatment decisions, including the selection of patients for repeated SRS versus earlier consideration of whole-brain radiotherapy. Declarations Conflict of Interest The authors declare that they have no conflicts of interest. Ethics Approval This study was reviewed and approved by the Ethics and Research Committee of the Instituto Nacional de Neurología y Neurocirugía Manuel Velasco Suárez (Mexico City, Mexico). Approval number 118/25. Consent to Participate Given the retrospective nature of the study and the use of anonymized clinical data, the requirement for informed consent was waived by the Ethics and Research Committee. Consent for Publication Not applicable. Funding The authors received no specific funding for this study. Author Contribution J.F.F.G., I.A.A and S.M.J. conceived and designed the study. J.F.F.G., D.B.D.S., A.G.B., G.G.C., J.V.Á.A. and A.B.M.P. collected the data. J.F.F.G. and A.S.P. performed the statistical analysis. J.F.F.G. and H.M.J. drafted the manuscript. S.M.J. and I.A.A supervised the study and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript. Data Availability The datasets generated and/or analyzed during the current study are stored in the institutional repository and are available from the corresponding author upon reasonable request. References Gavrilovic IT, Posner JB (2005) Brain metastases: epidemiology and pathophysiology. J Neurooncol 75:5–14. https://doi.org/10.1007/s11060-004-8093-6 Nayak L, Lee EQ, Wen PY (2012) Epidemiology of brain metastases. Curr Oncol Rep 14:48–54. https://doi.org/10.1007/s11912-011-0203-y Achrol AS, Rennert RC, Anders C et al (2019) Brain metastases. Nat Rev Dis Primers 5:5. https://doi.org/10.1038/s41572-018-0055-y Chang EL, Wefel JS, Hess KR et al (2009) Neurocognition in patients with brain metastases treated with radiosurgery or radiosurgery plus whole-brain irradiation. Lancet Oncol 10:1037–1044. https://doi.org/10.1016/S1470-2045(09)70263-3 Brown PD, Jaeckle K, Ballman KV et al (2016) Effect of radiosurgery alone vs radiosurgery with whole-brain radiotherapy on cognitive function. JAMA 316:401–409. https://doi.org/10.1001/jama.2016.9839 Yamamoto M, Serizawa T, Shuto T et al (2014) Stereotactic radiosurgery for patients with multiple brain metastases. Lancet Oncol 15:387–395. https://doi.org/10.1016/S1470-2045(14)70061-0 Tsao MN, Rades D, Wirth A et al (2012) Radiotherapeutic and surgical management for newly diagnosed brain metastases. Radiother Oncol 105:262–273. https://doi.org/10.1016/j.radonc.2012.10.004 Minniti G, Scaringi C, Paolini S et al (2016) Repeated stereotactic radiosurgery for patients with recurrent brain metastases. J Neurooncol 126:91–97. https://doi.org/10.1007/s11060-015-1959-4 Gaspar L, Scott C, Rotman M et al (1997) Recursive partitioning analysis of prognostic factors in brain metastases. Int J Radiat Oncol Biol Phys 37:745–751 DOI not available (pre-DOI era) Farris M, McTyre ER, Cramer CK et al (2017) Brain metastasis velocity: a novel prognostic metric predictive of survival. Int J Radiat Oncol Biol Phys 98:131–141. https://doi.org/10.1016/j.ijrobp.2017.01.224 Soike MH, McTyre ER, Hughes RT et al (2020) Initial brain metastasis velocity predicts outcomes following stereotactic radiosurgery. J Neurooncol 146:357–365. https://doi.org/10.1007/s11060-019-03403-2 Hughes RT, McTyre ER, LeCompte MC et al (2019) Validation of brain metastasis velocity in a multi-institutional cohort. J Neurooncol 142:131–138. https://doi.org/10.1007/s11060-018-03045-6 Sperduto PW, Berkey B, Gaspar LE et al (2008) A new prognostic index and comparison to RPA. Int J Radiat Oncol Biol Phys 70:510–514. https://doi.org/10.1016/j.ijrobp.2007.06.074 Sperduto PW, Kased N, Roberge D et al (2012) Effect of tumor subtype on survival and the graded prognostic assessment. J Clin Oncol 30:419–425. https://doi.org/10.1200/JCO.2011.37.4889 Hughes RT, Soike MH, Farris M et al (2021) Limitations of initial brain metastasis velocity as a prognostic tool. Pract Radiat Oncol 11:e1–e9. https://doi.org/10.1016/j.prro.2020.07.009 McTyre ER, Johnson AJ, Ruiz J et al (2017) Predictors of distant brain failure after stereotactic radiosurgery. Neuro Oncol 19:123–131. https://doi.org/10.1093/neuonc/now170 Lehrer EJ, Peterson JL, Zaorsky NG et al (2019) Single versus multiple fraction stereotactic radiosurgery. Radiother Oncol 134:32–38. https://doi.org/10.1016/j.radonc.2018.12.002 Robin TP, Jones BL, Amini A et al (2018) Radiosurgery alone is associated with favorable outcomes. Cancer 124:1947–1955. https://doi.org/10.1002/cncr.31269 Blanche P, Dartigues JF, Jacqmin-Gadda H (2013) Estimating and comparing time-dependent ROC curves. Stat Med 32:5381–5397. https://doi.org/10.1002/sim.5958 Brown PD, Ahluwalia MS, Khan OH et al (2018) Whole-brain radiotherapy for brain metastases: evolution or revolution? J Clin Oncol 36:483–491. https://doi.org/10.1200/JCO.2017.75.9589 Li J, Bentzen SM, Renschler M et al (2007) Regression after whole-brain radiation therapy. J Clin Oncol 25:1260–1266. https://doi.org/10.1200/JCO.2006.09.2534 Linskey ME, Andrews DW, Asher AL et al (2010) The role of stereotactic radiosurgery in brain metastases. J Neurooncol 96:45–68. https://doi.org/10.1007/s11060-009-0073-1 Minniti G, Clarke E, Lanzetta G et al (2011) Risk of brain radionecrosis after radiosurgery. Radiat Oncol 6:48. https://doi.org/10.1186/1748-717X-6-48 Flickinger JC, Kondziolka D, Maitz AH et al (1994) Gamma Knife radiosurgery for multiple brain metastases. Int J Radiat Oncol Biol Phys 30:121–129 DOI not available Tawbi HA, Forsyth PA, Algazi A et al (2018) Combined nivolumab and ipilimumab in melanoma brain metastases. N Engl J Med 379:722–730. https://doi.org/10.1056/NEJMoa1805453 Goldberg SB, Gettinger SN, Mahajan A et al (2016) Pembrolizumab for patients with untreated brain metastases. Lancet Oncol 17:976–983. https://doi.org/10.1016/S1470-2045(16)30053-5 Sperduto PW, Mesko S, Li J et al (2020) Survival in patients with brain metastases by histology. J Clin Oncol 38:3773–3784. https://doi.org/10.1200/JCO.20.01255 Lehrer EJ, McGee HM, Peterson JL et al (2021) Emerging systemic therapies and radiosurgery outcomes. Neuro Oncol 23:202–214. https://doi.org/10.1093/neuonc/noaa186 Aoyama H, Shirato H, Tago M et al (2006) Stereotactic radiosurgery plus whole-brain radiotherapy vs radiosurgery alone. JAMA 295:2483–2491. https://doi.org/10.1001/jama.295.21.2483 Sahgal A, Aoyama H, Kocher M et al (2015) Phase 3 trials of stereotactic radiosurgery with or without WBRT. Lancet Oncol 16:1049–1060. https://doi.org/10.1016/S1470-2045(15)00076-0 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8642010","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":578267556,"identity":"df3be0a8-49eb-4a3b-a17a-413d056c864f","order_by":0,"name":"J. F. Franco-García","email":"","orcid":"","institution":"Instituto Nacional de Neurología y Neurocirugía","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"F.","lastName":"Franco-García","suffix":""},{"id":578267557,"identity":"e82c8b59-d321-47f5-a191-21afd376fb21","order_by":1,"name":"I. Vázquez-Aldana","email":"","orcid":"","institution":"Instituto Nacional de Neurología y Neurocirugía","correspondingAuthor":false,"prefix":"","firstName":"I.","middleName":"","lastName":"Vázquez-Aldana","suffix":""},{"id":578267558,"identity":"d7e666e7-211d-45d8-9037-264c3087cbec","order_by":2,"name":"A. Salazar-Pigeon","email":"","orcid":"","institution":"Instituto Nacional de Neurología y Neurocirugía","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"","lastName":"Salazar-Pigeon","suffix":""},{"id":578267559,"identity":"814e3bed-fbe0-4402-9376-f13204392fdc","order_by":3,"name":"D. B. Díaz-Simental","email":"","orcid":"","institution":"Instituto Nacional de Neurología y Neurocirugía","correspondingAuthor":false,"prefix":"","firstName":"D.","middleName":"B.","lastName":"Díaz-Simental","suffix":""},{"id":578267560,"identity":"7f318a41-5de3-46f4-90e0-0e142036abeb","order_by":4,"name":"A. García-Bitar","email":"","orcid":"","institution":"Instituto Nacional de Neurología y Neurocirugía","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"","lastName":"García-Bitar","suffix":""},{"id":578267561,"identity":"5dd25d47-64e6-4016-b4ad-be36ee530e1f","order_by":5,"name":"G. Gómez-Castro","email":"","orcid":"","institution":"Instituto Nacional de Neurología y Neurocirugía","correspondingAuthor":false,"prefix":"","firstName":"G.","middleName":"","lastName":"Gómez-Castro","suffix":""},{"id":578267562,"identity":"dd4c11fc-9609-4c44-a677-6395fa424834","order_by":6,"name":"H. Medina-Julio","email":"","orcid":"","institution":"Instituto Nacional de Neurología y Neurocirugía","correspondingAuthor":false,"prefix":"","firstName":"H.","middleName":"","lastName":"Medina-Julio","suffix":""},{"id":578267563,"identity":"c42e834e-df3c-45f4-b950-a22ffa6775c0","order_by":7,"name":"A. B. Moy-Pérez","email":"","orcid":"","institution":"Autonomous University of Queretaro","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"B.","lastName":"Moy-Pérez","suffix":""},{"id":578267564,"identity":"e07c563e-67b8-4221-8efb-2963b6a7639f","order_by":8,"name":"J. V. Álvarez-Aguillón","email":"","orcid":"","institution":"Autonomous University of Queretaro","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"V.","lastName":"Álvarez-Aguillón","suffix":""},{"id":578267565,"identity":"051ef540-336b-4a0a-86e0-b6a44cc4e7d8","order_by":9,"name":"S. Moreno-Jiménez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYHACgwMwFojBw8DeABK0IEULD4gykMCrBY0vkQAmcao3bz+88cDHHTaJ29ubHx6ubKuV4Z/5/OqGHwUSDPzt3QnYtMicSSs4OPNMWuKcM8cMDp5tO84jcTun7GYP0GESZ85uwKZFgiHH4DBv2+HEGRI5DAcb247xMNzOSbvBA9RiIJGLXQv/G5CW/wgt8jfPpN38g0+LBNiWAzAtNTwGN9iP3cZri8QzoF/ako1n8AD90nDuAI/hmRy22zIGEjw4/cKfvPnDxzY72RnszY8/NpTV2csdP/7s5ps/NnL87b1YtaCDw0DMA44pHmKUg0AdELM/IFb1KBgFo2AUjAwAAKejahKJseyQAAAAAElFTkSuQmCC","orcid":"","institution":"Instituto Nacional de Neurología y Neurocirugía","correspondingAuthor":true,"prefix":"","firstName":"S.","middleName":"","lastName":"Moreno-Jiménez","suffix":""}],"badges":[],"createdAt":"2026-01-19 17:51:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8642010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8642010/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101073732,"identity":"261d622c-1907-448c-b3b5-46c59085eace","added_by":"auto","created_at":"2026-01-25 10:17:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2494385,"visible":true,"origin":"","legend":"","description":"","filename":"PerformanceofBrainMetastasisVelocityandInitialBrainMetastasisVelocityinaLatinAmericanLINACBasedStereotacticRadiosurgeryCohortREV150126.docx","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/c47838f7ac897ad092d04f02.docx"},{"id":101204796,"identity":"61ec1f62-97cb-4a95-a2c0-43275291b6ca","added_by":"auto","created_at":"2026-01-27 09:43:57","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10767,"visible":true,"origin":"","legend":"","description":"","filename":"f8095b192fd646ff97ae26cc862109e4.json","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/5564c5868e627bc5f2526382.json"},{"id":101073730,"identity":"607c245f-64f0-4499-834e-b33edfbe8287","added_by":"auto","created_at":"2026-01-25 10:17:56","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100349,"visible":true,"origin":"","legend":"","description":"","filename":"f8095b192fd646ff97ae26cc862109e41enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/b58d732b6e580bbbb9fa2299.xml"},{"id":101073737,"identity":"1f40e803-9c03-4de6-8d7f-8efc2c4318bc","added_by":"auto","created_at":"2026-01-25 10:17:56","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26765,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/29fde0333ebd97710bc16c50.png"},{"id":101205037,"identity":"4325cd94-af6c-4dfd-80c4-a63734eaf250","added_by":"auto","created_at":"2026-01-27 09:45:36","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19997,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/339bcedd204bbe9ec5badbcd.png"},{"id":102298504,"identity":"05b7fbdc-9ccf-46c0-9b82-40c0d3a74729","added_by":"auto","created_at":"2026-02-10 10:40:50","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29458,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/e213351a0a1b4318ec42d557.png"},{"id":101205882,"identity":"3ffb9a21-0e19-4a7b-90df-119d2db29d95","added_by":"auto","created_at":"2026-01-27 09:50:28","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93840,"visible":true,"origin":"","legend":"","description":"","filename":"f8095b192fd646ff97ae26cc862109e41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/70404b21fdc445140f58ebee.xml"},{"id":101073735,"identity":"42f2d220-bf04-4586-a46a-2209886fa038","added_by":"auto","created_at":"2026-01-25 10:17:56","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109761,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/e93486651e7eee8d9aadaca5.html"},{"id":101073727,"identity":"5721dd07-4977-444a-9b7a-068c0ce9328c","added_by":"auto","created_at":"2026-01-25 10:17:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival by iBMV classification (n = 127).\u003cbr\u003e\n \u003c/strong\u003eKaplan–Meier \u0026nbsp;curves for overall survival (OS) from the date of BM diagnosis, stratified by \u0026nbsp;iBMV category (low \u0026lt;2.0 vs high ≥2.0 metastases/year). Shaded areas \u0026nbsp;indicate 95% confidence intervals. Differences between curves were assessed \u0026nbsp;using the log-rank test.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/f669517da7981c0e7bd7940a.png"},{"id":101073728,"identity":"5ecb68f3-877b-4ec2-a094-da034f7d1e56","added_by":"auto","created_at":"2026-01-25 10:17:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival after first DBF by BMV classification (n = 62).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan–Meier curves for overall survival (OS) from the date of first DBF, stratified by BMV category (low \u0026lt;4, intermediate 4–13, and high \u0026gt;13 metastases/year). Shaded areas indicate 95% confidence intervals. Differences between curves were assessed using the log-rank test.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/85bad745926f321bde4f05e4.png"},{"id":101073729,"identity":"79c98298-cb87-4595-b8e7-3b1ed99a8be6","added_by":"auto","created_at":"2026-01-25 10:17:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-dependent ROC curves for OS: iBMV vs BMV (12 and 24 months).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTime-dependent ROC curves evaluating discrimination for overall survival prediction. (A) 12-month ROC curves for iBMV (n=127) and BMV (n=62). (B) 24-month ROC curves for iBMV (n=127) and BMV (n=62). The diagonal line indicates no discrimination (AUC=0.5). AUC, area under the curve.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/15ce8e161d723c9148193863.png"},{"id":102397205,"identity":"0f9e5254-cc55-44de-ab60-c141c2008f43","added_by":"auto","created_at":"2026-02-11 10:10:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1123169,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8642010/v1/d0901807-b935-4c03-9d7c-4ab1e0a4e5c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance of Brain Metastasis Velocity and Initial Brain Metastasis Velocity in a Latin American LINAC-Based Stereotactic Radiosurgery Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBrain metastases (BMs) are the most common intracranial malignancy in adults and a major cause of neurological morbidity in patients with advanced cancer [1\u0026ndash;3]. Improvements in systemic therapy have prolonged survival, increasing the population at risk for intracranial progression and shifting treatment priorities toward durable intracranial control with minimized neurotoxicity. Stereotactic radiosurgery (SRS) has largely replaced whole-brain radiotherapy (WBRT) for selected patients with limited intracranial disease, achieving high local control while preserving neurocognitive function [4\u0026ndash;6]. However, improved survival has been accompanied by a rising incidence of distant brain failure (DBF) after upfront SRS, complicating salvage decision-making [7,8].\u003c/p\u003e \u003cp\u003ePrognostic models such as recursive partitioning analysis and the Graded Prognostic Assessment were developed to estimate survival at diagnosis using baseline clinical characteristics [9\u0026ndash;12]. Although these tools remain useful, they provide a static assessment and do not reflect the evolving intracranial disease trajectory observed after SRS [13,14].\u003c/p\u003e \u003cp\u003eTo address this limitation, velocity-based metrics have been proposed. Brain metastasis velocity (BMV) quantifies the rate at which new BMs emerge following initial SRS and has been shown to correlate with survival and the need for subsequent intracranial interventions [10,12]. Initial brain metastasis velocity (iBMV) was later introduced to capture the tempo of intracranial dissemination before the first SRS by incorporating the interval from primary tumor diagnosis to BMs detection [11,15].\u003c/p\u003e \u003cp\u003eDespite increasing interest, most studies evaluating BMV and iBMV have been conducted in Gamma Knife\u0026ndash;based cohorts from North America and Europe [16\u0026ndash;18]. Data evaluating these metrics in linear accelerator (LINAC)-based SRS and in Latin American populations remains sparse. The present study aimed to assess the prognostic performance of BMV and iBMV in a contemporary Latin American cohort treated exclusively with LINAC-based SRS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis retrospective single-center cohort study included consecutive adult patients (\u0026ge;\u0026thinsp;18 years) with histologically confirmed solid tumors and BMs treated with LINAC-based SRS at the Instituto Nacional de Neurolog\u0026iacute;a y Neurocirug\u0026iacute;a Manuel Velasco Su\u0026aacute;rez (Mexico City, Mexico) between January 2020 and January 2025. SRS was delivered as the initial local intracranial treatment.\u003c/p\u003e \u003cp\u003ePatients with prior WBRT, insufficient follow-up imaging, or incomplete data were excluded. A total of 127 patients satisfied the requirements for iBMV analysis. The BMV cohort consisted of 62 patients who developed DBF and received salvage SRS.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStereotactic radiosurgery procedure\u003c/h3\u003e\n\u003cp\u003ePatients underwent LINAC-based SRS (6-MV photons). Individualized biological effective doses (BED) based on lesion characteristics and proximity to the organ-at-risk were used in treatment planning, utilizing fused contrast-enhanced MRI and planning CTs in accordance with international guidelines. Image guidance ensured adequate localization. SRS followed neurosurgical intervention (biopsy or resection) when indicated by the multidisciplinary tumor board.\u003c/p\u003e\n\u003ch3\u003eClinical Data and Imaging Evaluation\u003c/h3\u003e\n\u003cp\u003eBaseline variables included age, sex, Karnofsky Performance Status (KPS), primary tumor histology, extracranial disease status, number of BMs, prior neurosurgical procedures, prescription dose, and total irradiated intracranial volume. Follow-up consisted of contrast-enhanced brain MRI every 3\u0026ndash;6 months, with response and failure patterns assessed using Response-Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria.\u003c/p\u003e\n\u003ch3\u003eDefinition of Velocity Metrics\u003c/h3\u003e\n\u003cp\u003eBMV was defined in accordance with the original description by Farris et al. [10]. For each patient, only the first episode of DBF was considered. BMV was analyzed as a continuous variable and categorized into low (\u0026lt;\u0026thinsp;4 metastases/year), intermediate (4\u0026ndash;13 metastases/year), and high (\u0026gt;\u0026thinsp;13 metastases/year) groups using previously reported thresholds [10,12].\u003c/p\u003e \u003cp\u003eiBMV was computed as proposed by Soike et al. [11]. All patients in this cohort had metachronous BM (minimum interval: 42 days; median: 16 months), with complete documentation of both primary diagnosis and BM detection dates; no cases of synchronous presentation were included. iBMV was analyzed as a continuous variable and dichotomized into low (\u0026lt;\u0026thinsp;2.0 metastases/year) and high (\u0026ge;\u0026thinsp;2.0 metastases/year) groups in accordance with prior literature and time-dependent ROC analyses [11,15].\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eOverall survival (OS) was the primary endpoint. For iBMV, OS was measured from BM diagnosis; for BMV, OS was measured from the first DBF. Survival analyses employed Kaplan\u0026ndash;Meier estimates, log-rank testing, and Cox proportional hazards regression. Model discrimination was evaluated using Harrell\u0026rsquo;s C-index and time-dependent ROC curves at 12 and 24 months.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are reported as medians (range/IQR as available) and categorical variables as counts (%). Group comparisons used Fisher\u0026rsquo;s exact test for categorical variables and Wilcoxon rank-sum or Kruskal\u0026ndash;Wallis tests for continuous variables, as appropriate. OS was estimated using Kaplan\u0026ndash;Meier methods and compared using log-rank tests. Cox proportional hazards models were used to evaluate associations with survival, reporting hazard ratios (HRs) with 95% confidence intervals (CIs). Multivariable models included covariates significant in univariable analyses and/or clinically relevant factors (e.g., age, KPS, irradiated volume, prescription dose). Model discrimination was assessed using Harrell\u0026rsquo;s C-index and time-dependent ROC curves at 12 and 24 months following established methodology [19]. The proportional hazards assumption was verified using Schoenfeld residuals. Analyses were performed in R (version 4.5.2), with two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCohort characteristics\u003c/h2\u003e \u003cp\u003eThe iBMV cohort included 127 patients. Median age was 55 years, and median KPS was 90. The median number of BM at initial SRS was 2, and the median total irradiated intracranial volume was 8.9 cm\u0026sup3;. Using the predefined threshold, 70 patients (55.1%) were classified as low iBMV and 57 (44.9%) as high iBMV. Primary tumor distribution differed across iBMV strata (notably breast vs lung representation), while other baseline variables were broadly comparable. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e62 patients (48.8%) developed DBF and underwent salvage SRS, constituting the BMV cohort. At first DBF, median age was 54.5 years and median KPS remained 90. BMV stratification yielded 31 low (50.0%), 17 intermediate (27.4%), and 14 high (22.6%) cases. Higher BMV was associated with a greater number of new metastases at progression and shorter time to progression. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics and outcomes by iBMV classification (n\u0026thinsp;=\u0026thinsp;127)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow iBMV (n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh iBMV (n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (30\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (20\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex - Female, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (81.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex - Male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (40\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (30\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Breast, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Lung, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Melanoma, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Renal, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Other, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary disease control - Yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery - Biopsy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery - GTR, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery - STR, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery - None, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS - Circular arcs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS - Dynamic arcs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS - Static fields, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS - IMRT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS - VMAT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse - Complete, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse - Partial, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse - Progression, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume, cc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9 (2.3\u0026ndash;15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6 (2.2\u0026ndash;20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBED\u0026sup1;⁰, 1 fraction (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.4 (60.0-70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.1 (60.0-70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBED\u0026sup1;⁰, 3 fractions (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.3 (51.3\u0026ndash;51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.3 (51.3\u0026ndash;55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBED\u0026sup1;⁰, 5 fractions (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0 (48.0\u0026ndash;48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.0 (48.0\u0026ndash;48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime primary to mets, m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (14.9\u0026ndash;63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6 (5.9\u0026ndash;15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival, m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (10.3\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6 (8.8\u0026ndash;35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are presented as median (range) for continuous variables and n (%) for categorical variables. P values were calculated using Fisher\u0026rsquo;s exact test for categorical variables and the Wilcoxon rank-sum test for continuous variables. KPS Karnofsky Performance Status, SRS stereotactic radiosurgery, GTR gross total resection, STR subtotal resection, IMRT intensity-modulated radiation therapy, VMAT volumetric modulated arc therapy, BED biologically effective dose.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics and outcomes at first DBF by BMV classification (n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow BMV (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntermediate BMV (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh BMV (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (29\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (40\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (42\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex - Female, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (88.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex - Male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (60\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (60\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (30\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Breast, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Lung, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Melanoma, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Renal, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor - Other, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary disease control - Yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery - Biopsy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery - GTR, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery - STR, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery - None, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS - Circular arcs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS - Static fields, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS - IMRT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS - VMAT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume, cc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (3.4\u0026ndash;12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5 (3-26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.9 (2.2\u0026ndash;44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBED\u0026sup1;⁰, 1 fraction (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.4 (60.0-70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.4 (60.0-70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.4 (52.8\u0026ndash;70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBED\u0026sup1;⁰, 3 fractions (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.3 (51.3\u0026ndash;55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.3 (51.3\u0026ndash;51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.3 (51.3\u0026ndash;51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBED\u0026sup1;⁰, 5 fractions (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0 (48.0\u0026ndash;48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.0 (42.8\u0026ndash;48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.0 (48.0\u0026ndash;48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial metastases, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 (3-9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew metastases post-SRS, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.5 (6\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime to progression, m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (10.2\u0026ndash;25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7 (2.7\u0026ndash;11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3 (3.5-6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival, m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.2 (24\u0026ndash;50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4 (7.9\u0026ndash;21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.3 (9.2\u0026ndash;24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are presented as median (range) for continuous variables and n (%) for categorical variables. P values were calculated using Fisher\u0026rsquo;s exact test for categorical variables and the Kruskal\u0026ndash;Wallis test for continuous variables. KPS Karnofsky Performance Status, SRS stereotactic radiosurgery, DBF distant brain failure, GTR gross total resection, STR subtotal resection, IMRT intensity-modulated radiation therapy, VMAT volumetric modulated arc therapy, BED biologically effective dose.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eiBMV overall survival\u003c/h2\u003e \u003cp\u003eWith a median follow-up of 14.5 months, 73 deaths (57.5%) were recorded in the iBMV cohort. Median OS from BM diagnosis was 27.4 months (95% CI 21.6\u0026ndash;33.5). Kaplan\u0026ndash;Meier curves showed no meaningful separation by iBMV category (Fig.\u0026nbsp;1). Median OS was 25.7 months in the low iBMV group and 29.4 months in the high iBMV group (log-rank p\u0026thinsp;=\u0026thinsp;0.80). In Cox regression, iBMV was not associated with OS when modeled categorically (HR 0.94, 95% CI 0.59\u0026ndash;1.50; p\u0026thinsp;=\u0026thinsp;0.802) or continuously (HR 1.00, 95% CI 0.98\u0026ndash;1.02; p\u0026thinsp;=\u0026thinsp;0.941). After adjustment for age, KPS, total irradiated volume, and prescription dose, iBMV remained non-significant (HR 1.08, 95% CI 0.67\u0026ndash;1.76; p\u0026thinsp;=\u0026thinsp;0.742). Lower KPS, larger irradiated volume, and lower prescription dose were associated with shorter survival.\u003c/p\u003e \u003cp\u003eTime-dependent ROC analyses for iBMV showed limited discrimination (AUC 0.57 at 12 months; 0.53 at 24 months), consistent with weak prognostic utility at presentation.\u003cb\u003eFigure 1. Overall survival by iBMV classification (n\u0026thinsp;=\u0026thinsp;127).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier curves for overall survival (OS) from the date of BM diagnosis, stratified by iBMV category (low\u0026thinsp;\u0026lt;\u0026thinsp;2.0 vs high\u0026thinsp;\u0026ge;\u0026thinsp;2.0 metastases/year). Shaded areas indicate 95% confidence intervals. Differences between curves were assessed using the log-rank test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBMV survival after distant brain failure\u003c/h2\u003e \u003cp\u003eIn the BMV cohort, time from initial SRS to first DBF differed significantly across BMV strata, with the longest time to progression observed in low BMV patients. Kaplan\u0026ndash;Meier analysis demonstrated pronounced OS differences from the time of first DBF (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Median OS was 45.5 months (95% CI 35.2\u0026ndash;not estimable) in the low BMV group, versus 10.4 months (95% CI 8.0\u0026ndash;not estimable) in the intermediate group and 12.3 months (95% CI 10.2\u0026ndash;not estimable) in the high group (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In univariable Cox regression, low BMV was associated with improved survival compared with intermediate\u0026ndash;high BMV (HR 0.23, 95% CI 0.11\u0026ndash;0.45; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher KPS and lower total irradiated intracranial volume were also associated with longer survival. In multivariable analysis including BMV classification, KPS, and irradiated volume, BMV retained independent prognostic value; low BMV was associated with a 72% reduction in the hazard of death (HR 0.28, 95% CI 0.14\u0026ndash;0.59; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe proportional hazards assumption was satisfied for both velocity-based metrics, with no evidence of violation for BMV (Schoenfeld residuals p\u0026thinsp;=\u0026thinsp;0.145) or iBMV (Schoenfeld residuals p\u0026thinsp;=\u0026thinsp;0.128). BMV demonstrated superior discrimination (C-index\u0026thinsp;=\u0026thinsp;0.719, 95% CI 0.665\u0026ndash;0.773) compared with iBMV (C-index\u0026thinsp;=\u0026thinsp;0.487, 95% CI 0.424\u0026ndash;0.551).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTime-dependent ROC performance and calibration statement\u003c/h2\u003e \u003cp\u003eTime-dependent ROC curves demonstrated good to excellent discrimination for BMV when predicting OS after DBF, with AUC values of 0.78 at 12 months and 0.81 at 24 months. iBMV showed poor discrimination within its cohort, as noted above (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Because these metrics are derived from different cohorts and time origins, side-by-side AUC values should be interpreted as within-cohort discrimination rather than a direct head-to-head comparison.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier curves for overall survival (OS) from the date of first DBF, stratified by BMV category (low\u0026thinsp;\u0026lt;\u0026thinsp;4, intermediate 4\u0026ndash;13, and high\u0026thinsp;\u0026gt;\u0026thinsp;13 metastases/year). Shaded areas indicate 95% confidence intervals. Differences between curves were assessed using the log-rank test.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTime-dependent ROC curves evaluating discrimination for overall survival prediction. (A) 12-month ROC curves for iBMV (n\u0026thinsp;=\u0026thinsp;127) and BMV (n\u0026thinsp;=\u0026thinsp;62). (B) 24-month ROC curves for iBMV (n\u0026thinsp;=\u0026thinsp;127) and BMV (n\u0026thinsp;=\u0026thinsp;62). The diagonal line indicates no discrimination (AUC\u0026thinsp;=\u0026thinsp;0.5). AUC, area under the curve.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this single-center Latin American cohort treated with contemporary LINAC-based SRS, we observed differential prognostic performance between velocity-based metrics. While iBMV did not provide meaningful prognostic discrimination at initial presentation, BMV robustly predicted survival following DBF and remained independently associated with outcomes after multivariable adjustment. The proportional hazards assumption was satisfied for both metrics, supporting the validity of the Cox regression analyses.\u003c/p\u003e \u003cp\u003eThe absence of prognostic value for iBMV contrasts with the initial report by Soike et al. [11], but aligns with subsequent work emphasizing limitations and cohort sensitivity [15]. In our cohort, patients were selected for upfront SRS\u0026mdash;typically reflecting preserved performance status and limited intracranial disease burden\u0026mdash;which may reduce the variance that iBMV is intended to capture. Additionally, iBMV depends on timing from primary diagnosis, a component influenced by surveillance patterns, systemic therapy era, and tumor-specific diagnostic pathways, all of which can differ across settings.\u003c/p\u003e \u003cp\u003eBy contrast, BMV measures observed intracranial kinetics after SRS, arguably closer to the biological behavior relevant to salvage decision-making. Our findings are consistent with prior validations showing BMV\u0026rsquo;s association with survival and the need for salvage therapy [10,12,16\u0026ndash;18]. We extend those observations to a LINAC-based platform, supporting the notion that BMV is not platform-dependent and may generalize beyond Gamma Knife\u0026ndash;dominant datasets.\u003c/p\u003e \u003cp\u003eFrom a clinical standpoint, velocity-based metrics may support individualized salvage strategies. Patients with low BMV experienced substantially longer survival after DBF, suggesting that repeated salvage SRS may be appropriate and could allow deferral of WBRT, thereby reducing neurocognitive risk [4\u0026ndash;6,20]. Conversely, intermediate/high BMV identifies patients with rapid intracranial dissemination in whom earlier WBRT or alternative strategies may be reasonable, particularly when repeated SRS becomes impractical or when disease biology suggests diffuse intracranial risk [7,21]. As systemic therapies continue to evolve and improve intracranial control for some histologies [25\u0026ndash;30], dynamic prognostic tools may complement baseline indices and better reflect real-time disease behavior.\u003c/p\u003e \u003cp\u003eA key interpretive point is that iBMV and BMV were evaluated in different cohorts and from different time origins; thus, comparisons of discrimination metrics must be interpreted carefully. This is consistent with established methodological guidance regarding time-dependent ROC estimation and comparisons across differing risk sets [19]. These findings should be interpreted as hypothesis-generating.\u003c/p\u003e \u003cp\u003eLimitations include the retrospective design, single-center setting, and potential residual confounding. Additionally, systemic therapy heterogeneity may influence intracranial kinetics. Nevertheless, the use of standardized definitions, consistent imaging follow-up patterns, and established velocity thresholds supports internal validity. Prospective multi-institutional evaluation in Latin American settings would further clarify generalizability and potential integration into clinical pathways.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn a contemporary Latin American cohort treated with LINAC-based stereotactic radiosurgery, BMV was a strong and independent predictor of overall survival after distant brain failure, with excellent discriminatory performance. iBMV velocity did not provide meaningful prognostic stratification at initial presentation in this SRS-selected population. These findings support incorporating BMV at the time of intracranial progression to refine risk stratification and guide salvage treatment decisions, including the selection of patients for repeated SRS versus earlier consideration of whole-brain radiotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003e This study was reviewed and approved by the Ethics and Research Committee of the Instituto Nacional de Neurolog\u0026iacute;a y Neurocirug\u0026iacute;a Manuel Velasco Su\u0026aacute;rez (Mexico City, Mexico). Approval number 118/25.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003e Given the retrospective nature of the study and the use of anonymized clinical data, the requirement for informed consent was waived by the Ethics and Research Committee.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors received no specific funding for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.F.F.G., I.A.A and S.M.J. conceived and designed the study. J.F.F.G., D.B.D.S., A.G.B., G.G.C., J.V.\u0026Aacute;.A. and A.B.M.P. collected the data. J.F.F.G. and A.S.P. performed the statistical analysis. J.F.F.G. and H.M.J. drafted the manuscript. S.M.J. and I.A.A supervised the study and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are stored in the institutional repository and are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGavrilovic IT, Posner JB (2005) Brain metastases: epidemiology and pathophysiology. J Neurooncol 75:5\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11060-004-8093-6\u003c/span\u003e\u003cspan address=\"10.1007/s11060-004-8093-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNayak L, Lee EQ, Wen PY (2012) Epidemiology of brain metastases. Curr Oncol Rep 14:48\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11912-011-0203-y\u003c/span\u003e\u003cspan address=\"10.1007/s11912-011-0203-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAchrol AS, Rennert RC, Anders C et al (2019) Brain metastases. Nat Rev Dis Primers 5:5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41572-018-0055-y\u003c/span\u003e\u003cspan address=\"10.1038/s41572-018-0055-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang EL, Wefel JS, Hess KR et al (2009) Neurocognition in patients with brain metastases treated with radiosurgery or radiosurgery plus whole-brain irradiation. Lancet Oncol 10:1037\u0026ndash;1044. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1470-2045(09)70263-3\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(09)70263-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown PD, Jaeckle K, Ballman KV et al (2016) Effect of radiosurgery alone vs radiosurgery with whole-brain radiotherapy on cognitive function. JAMA 316:401\u0026ndash;409. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2016.9839\u003c/span\u003e\u003cspan address=\"10.1001/jama.2016.9839\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamoto M, Serizawa T, Shuto T et al (2014) Stereotactic radiosurgery for patients with multiple brain metastases. Lancet Oncol 15:387\u0026ndash;395. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1470-2045(14)70061-0\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(14)70061-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsao MN, Rades D, Wirth A et al (2012) Radiotherapeutic and surgical management for newly diagnosed brain metastases. Radiother Oncol 105:262\u0026ndash;273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.radonc.2012.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.radonc.2012.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinniti G, Scaringi C, Paolini S et al (2016) Repeated stereotactic radiosurgery for patients with recurrent brain metastases. J Neurooncol 126:91\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11060-015-1959-4\u003c/span\u003e\u003cspan address=\"10.1007/s11060-015-1959-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaspar L, Scott C, Rotman M et al (1997) Recursive partitioning analysis of prognostic factors in brain metastases. Int J Radiat Oncol Biol Phys 37:745\u0026ndash;751 DOI not available (pre-DOI era)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarris M, McTyre ER, Cramer CK et al (2017) Brain metastasis velocity: a novel prognostic metric predictive of survival. Int J Radiat Oncol Biol Phys 98:131\u0026ndash;141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijrobp.2017.01.224\u003c/span\u003e\u003cspan address=\"10.1016/j.ijrobp.2017.01.224\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoike MH, McTyre ER, Hughes RT et al (2020) Initial brain metastasis velocity predicts outcomes following stereotactic radiosurgery. J Neurooncol 146:357\u0026ndash;365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11060-019-03403-2\u003c/span\u003e\u003cspan address=\"10.1007/s11060-019-03403-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes RT, McTyre ER, LeCompte MC et al (2019) Validation of brain metastasis velocity in a multi-institutional cohort. J Neurooncol 142:131\u0026ndash;138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11060-018-03045-6\u003c/span\u003e\u003cspan address=\"10.1007/s11060-018-03045-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSperduto PW, Berkey B, Gaspar LE et al (2008) A new prognostic index and comparison to RPA. Int J Radiat Oncol Biol Phys 70:510\u0026ndash;514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijrobp.2007.06.074\u003c/span\u003e\u003cspan address=\"10.1016/j.ijrobp.2007.06.074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSperduto PW, Kased N, Roberge D et al (2012) Effect of tumor subtype on survival and the graded prognostic assessment. J Clin Oncol 30:419\u0026ndash;425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/JCO.2011.37.4889\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2011.37.4889\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes RT, Soike MH, Farris M et al (2021) Limitations of initial brain metastasis velocity as a prognostic tool. Pract Radiat Oncol 11:e1\u0026ndash;e9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prro.2020.07.009\u003c/span\u003e\u003cspan address=\"10.1016/j.prro.2020.07.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcTyre ER, Johnson AJ, Ruiz J et al (2017) Predictors of distant brain failure after stereotactic radiosurgery. Neuro Oncol 19:123\u0026ndash;131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/neuonc/now170\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/now170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehrer EJ, Peterson JL, Zaorsky NG et al (2019) Single versus multiple fraction stereotactic radiosurgery. Radiother Oncol 134:32\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.radonc.2018.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.radonc.2018.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobin TP, Jones BL, Amini A et al (2018) Radiosurgery alone is associated with favorable outcomes. Cancer 124:1947\u0026ndash;1955. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cncr.31269\u003c/span\u003e\u003cspan address=\"10.1002/cncr.31269\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanche P, Dartigues JF, Jacqmin-Gadda H (2013) Estimating and comparing time-dependent ROC curves. Stat Med 32:5381\u0026ndash;5397. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/sim.5958\u003c/span\u003e\u003cspan address=\"10.1002/sim.5958\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown PD, Ahluwalia MS, Khan OH et al (2018) Whole-brain radiotherapy for brain metastases: evolution or revolution? J Clin Oncol 36:483\u0026ndash;491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/JCO.2017.75.9589\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2017.75.9589\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Bentzen SM, Renschler M et al (2007) Regression after whole-brain radiation therapy. J Clin Oncol 25:1260\u0026ndash;1266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/JCO.2006.09.2534\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2006.09.2534\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinskey ME, Andrews DW, Asher AL et al (2010) The role of stereotactic radiosurgery in brain metastases. J Neurooncol 96:45\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11060-009-0073-1\u003c/span\u003e\u003cspan address=\"10.1007/s11060-009-0073-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinniti G, Clarke E, Lanzetta G et al (2011) Risk of brain radionecrosis after radiosurgery. Radiat Oncol 6:48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1748-717X-6-48\u003c/span\u003e\u003cspan address=\"10.1186/1748-717X-6-48\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlickinger JC, Kondziolka D, Maitz AH et al (1994) Gamma Knife radiosurgery for multiple brain metastases. Int J Radiat Oncol Biol Phys 30:121\u0026ndash;129 DOI not available\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTawbi HA, Forsyth PA, Algazi A et al (2018) Combined nivolumab and ipilimumab in melanoma brain metastases. N Engl J Med 379:722\u0026ndash;730. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa1805453\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1805453\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldberg SB, Gettinger SN, Mahajan A et al (2016) Pembrolizumab for patients with untreated brain metastases. Lancet Oncol 17:976\u0026ndash;983. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1470-2045(16)30053-5\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(16)30053-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSperduto PW, Mesko S, Li J et al (2020) Survival in patients with brain metastases by histology. J Clin Oncol 38:3773\u0026ndash;3784. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/JCO.20.01255\u003c/span\u003e\u003cspan address=\"10.1200/JCO.20.01255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehrer EJ, McGee HM, Peterson JL et al (2021) Emerging systemic therapies and radiosurgery outcomes. Neuro Oncol 23:202\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/neuonc/noaa186\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/noaa186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAoyama H, Shirato H, Tago M et al (2006) Stereotactic radiosurgery plus whole-brain radiotherapy vs radiosurgery alone. JAMA 295:2483\u0026ndash;2491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.295.21.2483\u003c/span\u003e\u003cspan address=\"10.1001/jama.295.21.2483\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahgal A, Aoyama H, Kocher M et al (2015) Phase 3 trials of stereotactic radiosurgery with or without WBRT. Lancet Oncol 16:1049\u0026ndash;1060. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1470-2045(15)00076-0\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(15)00076-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Brain metastases, Stereotactic radiosurgery, Brain metastasis velocity, Initial brain metastasis velocity, Prognosis, Overall survival","lastPublishedDoi":"10.21203/rs.3.rs-8642010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8642010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e \u003cp\u003eBrain metastasis velocity (BMV) and initial brain metastasis velocity (iBMV) have been proposed as dynamic metrics to describe intracranial metastatic behavior in patients treated with stereotactic radiosurgery (SRS). However, their prognostic relevance in linear accelerator (LINAC)-based practice and Latin American populations remains incompletely characterized.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe performed a retrospective cohort study of adult patients with brain metastases (BMs) treated with LINAC-based SRS between 2020 and 2025. iBMV was defined as the number of brain metastases at first SRS divided by the time from primary cancer diagnosis to intracranial metastasis detection. BMV was calculated in patients who developed distant brain failure (DBF) as the rate of new metastases from initial SRS to first DBF. Overall survival was analyzed using Kaplan\u0026ndash;Meier estimates and Cox proportional hazards modeling.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe iBMV cohort included 127 patients, of whom 62 developed DBF and were evaluable for BMV analysis. iBMV did not significantly stratify overall survival. In contrast, BMV demonstrated a strong association with survival following DBF. Patients with low BMV experienced significantly prolonged survival compared with intermediate and high BMV groups. BMV remained independently associated with survival after adjustment for relevant clinical covariates.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eWithin this contemporary LINAC-based Latin American cohort, BMV provided clinically meaningful prognostic information after intracranial progression, whereas iBMV did not confer prognostic value at initial presentation. Incorporation of BMV may improve post-progression risk stratification and inform salvage treatment strategies.\u003c/p\u003e","manuscriptTitle":"Performance of Brain Metastasis Velocity and Initial Brain Metastasis Velocity in a Latin American LINAC-Based Stereotactic Radiosurgery Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-25 10:17:51","doi":"10.21203/rs.3.rs-8642010/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c5bbb53-cbc6-4430-853d-3ee03d6f7d12","owner":[],"postedDate":"January 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T11:45:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-25 10:17:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8642010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8642010","identity":"rs-8642010","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}