In vitro cell line and in vivo murine models have historically dominated pre-clinical cancer research. These models can be expensive and time consuming and lead to only a small percentage of anti-cancer drugs gaining a license for human use. Large animal models that reflect human disease have high translational value; these can be used to overcome current pre-clinical research limitations through the integration of drug development techniques with surgical procedures and anesthetic protocols, along with emerging fields such as implantable medical devices. Ovine pulmonary adenocarcinoma (OPA) is a naturally-occurring lung cancer that is caused by the jaagsiekte sheep retrovirus. The disease has similar histological classification and oncogenic pathway activation to that of human lung adenocarcinomas making it a valuable model for studying human lung cancer. Developing OPA models to include techniques used in the treatment of human lung cancer would enhance its translational potential, making it an excellent research tool in assessing cancer therapeutics. In this study we developed a novel OPA model to validate the ability of miniaturized implantable O2 and pH sensors to monitor the tumor microenvironment. Naturally-occurring pre-clinical OPA cases were obtained through an on-farm ultrasound screening programme. Sensors were implanted into OPA tumors of anesthetized sheep using a CT-guided trans-thoracic percutaneous implantation procedure. This study reports the findings from 9 sheep that received sensor implantations. Time taken from initial CT scans to the placement of a single sensor into an OPA tumor was 45 ± 5 min, with all implantations resulting in the successful delivery of sensors into tumors. Immediate post-implantation mild pneumothoraces occurred in 4 sheep, which was successfully managed in all cases. This is, to the best of our knowledge, the first description of the use of naturally-occurring OPA cases as a pre-clinical surgical model. Through the integration of techniques used in the treatment of human lung cancer patients, including ultrasound, general anesthesia, CT and surgery into the OPA model, we have demonstrated its translational potential. Although our research was tailored specifically for the implantation of sensors into lung tumors, we believe the model could also be developed for other pre-clinical applications.