Background

Although there is consensus that the optimal safe margin is ≥ 5mm, obtaining clear margins (≥5 mm) intraoperatively seems to be the major challenge. We applied a molecular diagnostic method at the lipidomic level to determine the safe surgical resection margin of OSCC by desorption electrospray ionisation mass spectrometry imaging (DESI-MSI).

Methods

By overlaying mass spectrometry images with hematoxylin-eosin staining (H&E) from 18 recruited OSCC participants, the mass spectra of all pixels across the diagnosed tumour and continuous mucosal margin regions were extracted to serve as the training and validation datasets. A Lasso regression model was used to evaluate the test performance.

Findings

By leave-one-out validation, the Lasso model achieved 88.6% accuracy in distinguishing between tumour and normal regions. To determine the safe surgical resection distance and margin status of OSCC, a set of 14 lipid ions that gradually decreased from tumour to normal tissue was assigned higher weight coefficients in the Lasso model. The safe surgical resection distance of OSCC was measured using the developed 14 lipid ion molecular diagnostic model for clinical reference. The overall accuracy of predicting tumours, positive margins, and negative margins was 92.6%.

Interpretation

The spatial segmentation results based on our diagnostic model not only clearly delineated the tumour and normal tissue, but also distinguished the different status of surgical margins. Meanwhile, the safe surgical resection margin of OSCC on frozen sections can also be accurately measured using the developed diagnostic model.

Funding

This study was supported by Nanjing Municipal Key Medical Laboratory Constructional Project Funding (since 2016) and the Centre of Nanjing Clinical Medicine Tumour (since 2014).

中文翻译：

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口腔鳞状细胞癌黏膜边缘原位DESI-MSI脂质组学特征

背景

尽管人们一致认为最佳安全切缘≥5mm，但术中获得清晰的切缘（≥5mm）似乎是主要挑战。我们在脂质组水平上应用分子诊断方法，通过解吸电喷雾电离质谱成像 (DESI-MSI) 确定 OSCC 的安全手术切除边缘。

方法

通过将质谱图像与来自 18 名招募的 OSCC 参与者的苏木精-伊红染色 (H&E) 叠加，提取了诊断肿瘤和连续黏膜边缘区域的所有像素的质谱，作为训练和验证数据集。使用 Lasso 回归模型来评估测试性能。

发现

通过留一法验证，Lasso 模型在区分肿瘤和正常区域方面达到了 88.6% 的准确率。为了确定 OSCC 的安全手术切除距离和边缘状态，在 Lasso 模型中，一组 14 个从肿瘤逐渐减少到正常组织的脂质离子被分配了更高的权重系数。使用开发的14脂质离子分子诊断模型测量OSCC的安全手术切除距离，以供临床参考。预测肿瘤、阳性切缘和阴性切缘的总体准确率为 92.6%。

解释

基于我们的诊断模型的空间分割结果不仅清晰地描绘了肿瘤和正常组织，而且区分了手术切缘的不同状态。同时，使用开发的诊断模型也可以准确测量冰冻切片上 OSCC 的安全手术切除边缘。

资金

本研究得到南京市重点医学实验室建设项目资助（2016年起）和南京临床医学肿瘤中心（2014年起）的资助。