Molecular Imaging of Endometriosis Tissues using Desorption Electrospray Ionization Mass Spectrometry.

Endometriosis is a pathologic condition affecting approximately 10% of women in their reproductive years. Characterized by abnormal growth of uterine endometrial tissue in other body areas, endometriosis can cause severe abdominal pain and/or infertility. Despite devastating consequences to patients' quality of life, the causes of endometriosis are not fully understood and validated diagnostic markers for endometriosis have not been identified. Molecular analyses of ectopic and eutopic endometrial tissues could lead to enhanced understanding of the disease. Here, we apply desorption electrospray ionization (DESI) mass spectrometry (MS) imaging to chemically and spatially characterize the molecular profiles of 231 eutopic and ectopic endometrial tissues from 89 endometriosis patients. DESI-MS imaging allowed clear visualization of endometrial glandular and stromal regions within tissue samples. Statistical models built from DESI-MS imaging data allowed classification of endometriosis lesions with overall accuracies of 89.4%, 98.4%, and 98.8% on training, validation, and test sample sets, respectively. Further, molecular markers that are significantly altered in ectopic endometrial tissues when compared to eutopic tissues were identified, including fatty acids and glycerophosphoserines. Our study showcases the value of MS imaging to investigate the molecular composition of endometriosis lesions and pinpoints metabolic markers that may provide new knowledge on disease pathogenesis.

Performance of the MasSpec Pen for Rapid Diagnosis of Ovarian Cancer.

BACKGROUND: Accurate tissue diagnosis during ovarian cancer surgery is critical to maximize cancer excision and define treatment options. Yet, current methods for intraoperative tissue evaluation can be time intensive and subjective. We have developed a handheld and biocompatible device coupled to a mass spectrometer, the MasSpec Pen, which uses a discrete water droplet for molecular extraction and rapid tissue diagnosis. Here we evaluated the performance of this technology for ovarian cancer diagnosis across different sample sets, tissue types, and mass spectrometry systems. METHODS: MasSpec Pen analyses were performed on 192 ovarian, fallopian tube, and peritoneum tissue samples. Samples were evaluated by expert pathologists to confirm diagnosis. Performance using an Orbitrap and a linear ion trap mass spectrometer was tested. Statistical models were generated using machine learning and evaluated using validation and test sets. RESULTS: High performance for high-grade serous carcinoma (n = 131; clinical sensitivity, 96.7%; specificity, 95.7%) and overall cancer (n = 138; clinical sensitivity, 94.0%; specificity, 94.4%) diagnoses was achieved using Orbitrap data. Variations in the mass spectra from normal tissue, low-grade, and high-grade serous ovarian cancers were observed. Discrimination between cancer and fallopian tube or peritoneum tissues was also achieved with accuracies of 92.6% and 87.9%, respectively, and 100% clinical specificity for both. Using ion trap data, excellent results for high-grade serous cancer vs normal ovarian differentiation (n = 40; clinical sensitivity, 100%; specificity, 100%) were obtained. CONCLUSIONS: The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.

Laparoscopic Major Vascular Injury Simulation Using a Synthetic Compared With Porcine Model.

BACKGROUND: Major vascular injury training may improve clinical skills and reduce patient morbidity during gynecologic laparoscopy; thus, reliable models for simulation should be identified. METHOD: Two laparoscopic major vascular injury simulations using synthetic or live porcine models were constructed. The primary surgeon was given the opportunity to complete both simulations. After obtaining peritoneal access, the surgeon quickly encountered a major vascular injury. Degrading vital signs and estimated blood loss coupled with the replay of a human heartbeat that increased in volume and intensity were provided to heighten tension during the synthetic simulation. EXPERIENCE: Twenty-two gynecologic surgery educators evaluated the simulations. Educators considered the porcine model superior to the synthetic model with regard to tissue handling. The synthetic model simulation was found to be equivalent to the porcine model on how likely the simulation would be able to improve performance in a clinical setting. Educators were more likely to implement the synthetic simulation over the porcine simulation. CONCLUSION: The synthetic model was found to be more feasible and as effective as the porcine model to simulate and teach the initial management steps of major vascular injury at laparoscopy by gynecologic educators.