Abstract
SIGNIFICANCE: For most patients with pituitary adenomas, surgical resection represents a viable therapeutic option, particularly in cases with endocrine symptoms or local mass effects. Diagnostic imaging, including MRI and computed tomography, is employed clinically to plan pituitary adenoma surgery. However, these methods cannot provide surgical guidance information in real time to improve resection rates and reduce risks of damage to normal tissue during tumor debulking. AIM: Here, we present the development of a handheld Raman spectroscopy system that can be seamlessly integrated with transsphenoidal surgery workflows to allow live discrimination of all normal intracranial anatomical structures, including the pituitary gland, and potentially tissue abnormalities such as adenomas. APPROACH: A fiber-optic probe was developed with a form factor compatible with endoscopic systems for endonasal surgeries. The instrument was evaluated in an ex vivo experimental protocol designed to assess its ability to distinguish normal intracranial structures. A total of 274 in situ spectroscopic measurements were acquired from six lamb heads, targeting key anatomical structures encountered in surgery. Support vector machine models were developed to classify tissue types based on their spectral signatures. RESULTS: Binary classification models successfully distinguished the pituitary gland from other tissue structures with a sensitivity and a specificity of 100%. In addition, a four-class predictive model enabled > 95% accuracy in situ discrimination of four structures of most importance during pituitary adenoma tumor resection, i.e., the pituitary gland, the sella turcica (ST) bone, the optic chiasm, and the ST dura mater. CONCLUSIONS: This work sets the stage for the clinical deployment of Raman spectroscopy as an intraoperative real-time decision support system during transsphenoidal surgery, with future work focused on clinical integration and the generalization of the approach to include the detection of tissue abnormalities, such as pituitary adenomas.