Department of Chemistry, The University of Texas at Austin, Austin, TX.
Department of Women's Health, Dell Medical School, The University of Texas at Austin, Austin, TX.
Clin Chem. 2019 May;65(5):674-683. doi: 10.1373/clinchem.2018.299289. Epub 2019 Feb 15.
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.
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.
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.
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.
在卵巢癌手术中进行准确的组织诊断对于最大限度地切除癌症并确定治疗方案至关重要。然而，目前用于术中组织评估的方法可能既耗时又主观。我们开发了一种手持式和生物兼容的设备，与质谱仪结合使用，称为 MasSpec Pen，它使用离散的水滴进行分子提取和快速组织诊断。在这里，我们评估了该技术在不同样本集、组织类型和质谱系统中用于卵巢癌诊断的性能。
对 192 个卵巢、输卵管和腹膜组织样本进行了 MasSpec Pen 分析。通过专家病理学家评估样本以确认诊断。测试了使用轨道阱和线性离子阱质谱仪的性能。使用机器学习生成统计模型，并使用验证集和测试集进行评估。
使用轨道阱数据实现了高级别浆液性癌（n = 131；临床灵敏度为 96.7%；特异性为 95.7%）和总体癌症（n = 138；临床灵敏度为 94.0%；特异性为 94.4%）的高诊断性能。观察到正常组织、低级别和高级别浆液性卵巢癌的质谱谱差异。还实现了癌症与输卵管或腹膜组织之间的区分，准确率分别为 92.6%和 87.9%，且临床特异性均为 100%。使用离子阱数据，高级别浆液性癌与正常卵巢分化的区分（n = 40；临床灵敏度为 100%；特异性为 100%）获得了极好的结果。
MasSpec Pen 与机器学习一起为卵巢浆液性癌预测提供了强大的分子模型，因此具有用于快速准确卵巢癌诊断的临床应用潜力。
