Radiomic analysis of magnetic resonance imaging predicts brain metastases velocity and clinical outcome after upfront radiosurgery

Abstract

Background: Brain metastasis velocity (BMV) predicts outcomes after initial distant brain failure (DBF) following upfront stereotactic radiosurgery (SRS). We developed an integrated model of clinical predictors and pre-SRS MRI-derived radiomic scores (R-scores) to identify high-BMV (BMV-H) patients upon initial identification of brain metastases (BMs).

Methods: In total, 256 patients with BMs treated with upfront SRS alone were retrospectively included. R-scores were built from 1246 radiomic features in 2 target volumes by using the Extreme Gradient Boosting algorithm to predict BMV-H groups, as defined by BMV at least 4 or leptomeningeal disease at first DBF. Two R-scores and 3 clinical predictors were integrated into a predictive clinico-radiomic (CR) model.

Results: The related R-scores showed significant differences between BMV-H and low BMV (BMV-L), as defined by BMV less than 4 or no DBF (P < .001). Regression analysis identified BMs number, perilesional edema, and extracranial progression as significant predictors. The CR model using these 5 predictors achieved a bootstrapping corrected C-index of 0.842 and 0.832 in the discovery and test sets, respectively. Overall survival (OS) after first DBF was significantly different between the CR-predicted BMV-L and BMV-H groups (median OS: 26.7 vs 13.0 months, P = .016). Among patients with a diagnosis-specific graded prognostic assessment of 1.5-2 or 2.5-4, the median OS after initial SRS was 33.8 and 67.8 months for CR-predicted BMV-L, compared to 13.5 and 31.0 months for CR-predicted BMV-H (P < .001 and <.001), respectively.

Conclusion: Our CR model provides a novel approach showing good performance to predict BMV and clinical outcomes.

Keywords: brain metastases velocity; distant brain failure; machine learning; neuro-oncology; radiomics.

Figure 1.

(A) Recruitment pathway of patients with BMs. (B) Development process of radiomic signatures and clinico-radiomic model for BMV risk category.

Figure 2.

(A) Clinico-radiomic (CR) model for BMV prediction presented with a nomogram scaled by the proportional regression coefficient of predictors. (B) Receiver operating characteristic analysis of the CR model. (C) CR model calibration curve in the discovery cohort and (D) test cohort.

Figure 3.

(A and B) Overall survival (OS) curves since the first DBF scaled by (A) BMV status and by (B) CR-predicted BMV status with Kaplan–Meier analysis. (C, D, E, and F) OS curves since initial SRS scaled by CR-predicted BMV status with Kaplan–Meier analysis for (C) all patients and (D) patients with a diagnosis-specific graded prognostic assessment (DS-GPA) of 1.5–2, (E) 2.5–4, or (F) 0–1.

Figure 4.

(A and B) Top 5 important radiomic features of (A) Radi_Tumor and (B) Radi_Edge according to SHAP value. (C and D) Box plot illustrations of the correlation between metastases number and top 5 important radiomic features of (C) Radi_Tumor or (D) Radi_Edge. (E and F) Box plot illustrations of the correlation between perilesional edema and top 5 important radiomic features of (E) Radi_Tumor and (F) Radi_Edge.