Background: Ipilimumab/Nivolumab (Ipi/Nivo) significantly transformed outcomes for patients with melanoma brain metastases (MBM), however inter- and intra-patient responses can vary widely. The purpose of this study is to develop and calibrate an imaging-based mathematical model to predict lesion-specific immune response to Ipi/Nivo in patients with MBM to guide early local treatments such as radiotherapy and evaluate the prognostic value of identifying treatment resistant lesions. Methods: Baseline and follow-up 3D T1-W contrast MRIs of patients who solely received Ipi/Nivo after diagnosis with MBM were used to segment individual brain metastases and track serial volumetric data for each lesion to develop a mechanistic model (eq 1). After applying appropriate assumptions to our model, tumor burden (ρ), intrinsic lesion growth rate (α₀), Ipi/Nivo-induced kill rate (μ) and immune response strength (Λ) were quantified for each lesion. Growth rate shortly after the start of treatment (α₁) was calculated using the short-term model solution (eq 2). dρ/dt= (α₀- μ+μ.Λ).ρ - μ.Λ.ρ² (eq 1) ρ(t)≈e^α1.t (eq 2) Change in BM volume between baseline and last follow-up was used to classify each lesion as either ‘responder’ or ‘non-responder’; these were compared with the Wilcoxon-rank-sum test. The highest α₁value for any one BM within a patient tumor was evaluated for its potential to sort patient overall survival (OS). Because the median survival was not reached for this cohort, we determined the α₁ threshold of 0.005 to sort patients using an optimization routine that generates the maximum AUC to separate the cohort into 2 groups on an ROC curve. Kaplan-Meier (KM) curves were compared between these 2 groups. Results: In 23 patients, we evaluated 61 lesions. At the lesion level, model parameters representing μ and Λ were significantly different between responder and non-responder lesions (p-values <0.0001 and 0.0004, respectively). The highest α₁value for any one BM within a patient tumor was used to sort patient overall survival (OS) into prognostically ‘favorable’ or ‘unfavorable’ groups based on patient survival to ±1,200 days. In this limited cohort, OS KM curves by most aggressive lesion for favorable (α₁< 0.005) vs. unfavorable (α₁ > 0.005) were not significant by log-rank test (p = 0.3263). Conclusions: In this limited cohort, we demonstrate feasibility to leverage serial conventional imaging and tumor volumetric data to calibrate a mechanistic model to predict per-lesion treatment response of MBM to Ipi/Nivo. Although the p-value between groups was found to be insignificant when predicting survival using α₁, this is likely because of the small number of patients included in this study (n=23). Furthermore, when taken together with the notable AUC (=0.7917) observed in the ROC curve, this promising result supports further investigation in a larger cohort.