Basal cell carcinoma (BCC) accounts for 80% of skin cancers and in most cases is very treatable. However, roughly 1-10% of BCCs are considered advanced and have infiltrated into surrounding tissue such that it is challenging to clear without causing morbidity. These advanced cancers are treated with a systemic therapy inhibiting the Hedgehog signaling pathway, which is mutated in 85% of BCCs. Whilst Hedgehog inhibitors such as vismodegib have a median progression-free survival (PFS) of approximately 1 year, they come with significant toxicity that often disrupts standard treatment scheduling, and even for those that tolerate it, there is the possibility of treatment resistance. Here we present interim results from an ongoing clinical trial (NCT05651828) where we guide treatments for BCC patients using an adaptive strategy driven by imaging and mathematical modelling. Photographic images of skin lesions over time were analyzed to develop a metric for tumor burden over time and guide treatment. This metric uses a random forest algorithm to define the lesion’s current state based the color, texture, and size of a lesion to relate to its aggressiveness. We also define a system of ordinary differential equations comprising sensitive and resistant cells along with compartments to capture the drug’s unique pharmacodynamics to make predictions for future treatments. We fit the model to each patient’s lesion dynamics and predict when to start and stop therapy, iteratively driving treatment decisions to adapt treatment scheduling (either on or off) to balance growth control with toxicity and risk of drug resistance. We find considerable heterogeneity amongst patients due to the number, size, and morphology of lesions and amongst lesions within a single patient. Drug response and clearance rate of the drug may differ between patients and lesions, so pharmacokinetics and pharmacodynamics parameters need to be carefully calibrated to make good response predictions. Importantly, we define a new strategy for multiple lesions that are responding differently to treatment within the same patient. Ultimately, by combining imaging with mathematical modelling, we can predict BCC patient growth and response dynamics to make more informed treatment decisions.