Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery.
Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery.
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DOI：
10.1186/s13058-017-0845-2
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发表时间：
2017-05-23
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通讯作者：
Leff DR
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作者：
St John ER;Balog J;McKenzie JS;Rossi M;Covington A;Muirhead L;Bodai Z;Rosini F;Speller AVM;Shousha S;Ramakrishnan R;Darzi A;Takats Z;Leff DR
保乳手术后切除切缘为阳性的再次手术经常发生(平均 = 为20-25%)，成本低，并导致生理和心理上的并发症。目前的利润率评估技术缓慢且劳动密集型。快速蒸发电离质谱仪(REIMS)通过电外科气雾剂的在线化学分析来确定组织的结构脂质分布，从而快速识别被解剖的组织，从而实现实时边缘评估。从体外和体内的乳房样本中产生的电外科气雾剂被吸入使用单极手机的质谱仪(MS)。通过对MS数据的多变量统计分析获得的组织鉴定结果与组织病理学进行了验证。体外分类模型是从正常和肿瘤乳腺样本的质谱库中构建的。对显著峰进行单变量和串联MS分析，以确定正常组织和癌组织之间的生化差异。体外分类模型与定制识别软件结合使用，作为智能刀(IKnife)，用于预测体外验证集的诊断。术中REMS数据是在乳房手术期间获得的，并与手术视频时间同步。使用从正常组织和肿瘤组织中获取的932个采样点和226个采样点的组织学验证的光谱数据的分类模型提供了93.4%的灵敏度和94.9%的特异度。串联质谱仪鉴定了63种磷脂和6种甘油三酯，它们导致了24种组织类型之间的光谱差异。对2 6 0例新鲜和冷冻乳腺组织标本(正常n = 16 1例，肿瘤n = 99例)的iKnife识别准确率为90.9%，特异度为98.8%。体外和术中方法产生了视觉上可比的高强度光谱。IKnife对术中电外科蒸气的解释，包括数据采集和分析，可以在平均1.80秒内完成(SD±0.40)。基于脂肪代谢的细微变化，Reims方法已经被优化，用于对不同种类的乳房组织进行实时iKnife分析，结果表明光谱分析既准确又快速。概念验证数据表明iKnife方法能够在线收集和分析术中数据。需要进一步的验证研究来确定术中REIM用于肿瘤边缘评估的准确性。本文的在线版本(doi：10.1186/s13058-0170845-2)包含补充材料，授权用户可以使用。
Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20–25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment. Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos. A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40). The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment. The online version of this article (doi:10.1186/s13058-017-0845-2) contains supplementary material, which is available to authorized users.