Rapid diagnosis and tumor margin assessment during pancreatic cancer surgery with the MasSpec Pen technology

Abstract

Significance Surgical removal of pancreatic cancer remains the only option for a cure. To verify the extent of tumor removal, surgeons rely on pathologic evaluation of frozen sections of surgical margins. However, this process can be challenging, time consuming, and subjective. Here, we used the MasSpec Pen to rapidly distinguish pancreatic cancer from healthy pancreatic and bile duct tissues by generating classification models based on the molecular signatures acquired from tissue. We evaluated this technology in an operating room during pancreatic surgeries and used these classification models to predict on data obtained in vivo and ex vivo with high performance. Our results suggest that the MasSpec Pen platform has the potential to improve and expedite margin evaluation during pancreatic cancer surgery. Intraoperative delineation of tumor margins is critical for effective pancreatic cancer surgery. Yet, intraoperative frozen section analysis of tumor margins is a time-consuming and often challenging procedure that can yield confounding results due to histologic heterogeneity and tissue-processing artifacts. We have previously described the development of the MasSpec Pen technology as a handheld mass spectrometry–based device for nondestructive tissue analysis. Here, we evaluated the usefulness of the MasSpec Pen for intraoperative diagnosis of pancreatic ductal adenocarcinoma based on alterations in the metabolite and lipid profiles in in vivo and ex vivo tissues. We used the MasSpec Pen to analyze 157 banked human tissues, including pancreatic ductal adenocarcinoma, pancreatic, and bile duct tissues. Classification models generated from the molecular data yielded an overall agreement with pathology of 91.5%, sensitivity of 95.5%, and specificity of 89.7% for discriminating normal pancreas from cancer. We built a second classifier to distinguish bile duct from pancreatic cancer, achieving an overall accuracy of 95%, sensitivity of 92%, and specificity of 100%. We then translated the MasSpec Pen to the operative room and predicted on in vivo and ex vivo data acquired during 18 pancreatic surgeries, achieving 93.8% overall agreement with final postoperative pathology reports. Notably, when integrating banked tissue data with intraoperative data, an improved agreement of 100% was achieved. The result obtained demonstrate that the MasSpec Pen provides high predictive performance for tissue diagnosis and compatibility for intraoperative use, suggesting that the technology may be useful to guide surgical decision-making during pancreatic cancer surgeries.