Physical properties of the microenvironment influence penetration of drugs into tumors. Here, we develop a mathematical model to predict the outcome of chemotherapy based on the physical laws of diffusion. The most important parameters in the model are the volume fraction occupied by tumor blood vessels and their average diameter. Drug delivery to cells, and kill thereof, are mediated by these microenvironmental properties and affected by the diffusion penetration distance after extravasation. To calculate parameter values we fit the model to histopathology measurements of the fraction of tumor killed after chemotherapy in human patients with colorectal cancer metastatic to liver (coefficient of determination R 2 = 0.94). To validate the model in a different tumor type, we input patient-specific model parameter values from glioblastoma; the model successfully predicts extent of tumor kill after chemotherapy (R 2 = 0.7-0.91). Toward prospective clinical translation, we calculate blood volume fraction parameter values from in vivo contrastenhanced computed tomography imaging from a separate cohort of patients with colorectal cancer metastatic to liver, and demonstrate accurate model predictions of individual patient responses (average relative error = 15%). Here, patient-specific data from either in vivo imaging or histopathology drives output of the model's formulas. Values obtained from standard clinical diagnostic measurements for each individual are entered into the model, producing accurate predictions of tumor kill after chemotherapy. Clinical translation will enable the rational design of individualized treatment strategies such as amount, frequency, and delivery platform of drug and the need for ancillary non-drug-based treatment.colorectal cancer liver metastasis | glioblastoma multiforme histopathology | contrast CT | patient drug response | mathematical modeling P redicting the effects of chemotherapeutic drugs on tumor behavior in patients is vital to advancing knowledge in the fight against cancer. Computational methods of "mathematical pathology" developed through quantitative analysis of human tumor tissue have the potential to provide predictions of treatment outcomes in the clinical setting (1). Here, we develop our model using colorectal cancer (CRC) metastatic to liver from one cohort of patients as an example of intratumor perfusion properties. We then assess the general applicability of our model to predict response in other tumor types, that is, glioblastoma multiforme (GBM). We prospectively apply our model in vivo to a third cohort of subjects with metastatic CRC to liver using pretreatment contrast-enhanced computed tomography (CT) scans followed by correlation histopathology after treatment and surgical excision.CRC metastatic to liver can be treated by surgical resection in the majority of cases. Metastases too large or numerous for primary excision are first treated with chemotherapy and then excised if possible, because chemotherapy alone is rarely curative (2, 3). This strategy works,...