PURPOSE Basal cell carcinoma (BCC) is the most commonly diagnosed cancer and the number of diagnosis is growing worldwide due to increased exposure to solar radiation and the aging population. Reduction of positive margin rates when removing BCC leads to fewer revision surgeries and consequently lower health care costs, improved cosmetic outcomes and better patient care. In this study, we propose the first use of a perioperative mass spectrometry technology (iKnife) along with a deep learning framework for detection of BCC signatures from tissue burns. METHODS Resected surgical specimen were collected and inspected by a pathologist. With their guidance, data were collected by burning regions of the specimen labeled as BCC or normal, with the iKnife. Data included 190 scans of which 127 were normal and 63 were BCC. A data augmentation approach was proposed by modifying the location and intensity of the peaks of the original spectra, through noise addition in the time and frequency domains. A symmetric autoencoder was built by simultaneously optimizing the spectral reconstruction error and the classification accuracy. Using t-SNE, the latent space was visualized. RESULTS The autoencoder achieved an accuracy (standard deviation) of 96.62 (1.35%) when classifying BCC and normal scans, a statistically significant improvement over the baseline state-of-the-art approach used in the literature. The t-SNE plot of the latent space distinctly showed the separability between BCC and normal data, not visible with the original data. Augmented data resulted in significant improvements to the classification accuracy of the baseline model. CONCLUSION We demonstrate the utility of a deep learning framework applied to mass spectrometry data for surgical margin detection. We apply the proposed framework to an application with light surgical overhead and high incidence, the removal of BCC. The learnt models can accurately separate BCC from normal tissue.

中文翻译：

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使用深度学习框架对基底细胞癌进行围手术期边缘检测：一项可行性研究。

目的基底细胞癌（BCC）是最常被诊断的癌症，由于暴露于太阳辐射和人口老龄化的增加，全世界的诊断数量也在增加。移除BCC时降低正切缘率可减少修整手术的次数，从而降低医疗保健成本，改善美容效果并改善患者护理。在这项研究中，我们建议首次使用围手术期质谱技术（iKnife）以及深度学习框架来检测组织烧伤中的BCC签名。方法切除手术标本并由病理学家检查。在他们的指导下，用iKnife烧毁标为BCC或正常的标本区域来收集数据。数据包括190次扫描，其中127次为正常，63次为BCC。通过在时域和频域中添加噪声，通过修改原始频谱峰值的位置和强度，提出了一种数据增强方法。通过同时优化频谱重构误差和分类精度，构建了对称自动编码器。使用t-SNE，可以看到潜在空间。结果当对BCC和正常扫描进行分类时，自动编码器的准确度（标准偏差）为96.62（1.35％），相对于文献中使用的基准最新技术，在统计学上有显着改善。潜在空间的t-SNE图清楚地显示了BCC和正常数据之间的可分离性，原始数据不可见。增强的数据极大地改善了基线模型的分类准确性。结论我们证明了将深度学习框架应用于质谱数据进行手术切缘检测的实用性。我们将提出的框架应用于轻手术开销，高发病率，去除BCC的应用。学习的模型可以准确地将BCC与正常组织分开。