As the field of mass spectrometry imaging continues to grow, so too do its needs for optimal methods of data analysis. One general need in image analysis is the ability to classify the underlying regions within an image, as healthy or diseased, for example. Classification, as a general problem, is often best accomplished by supervised machine learning strategies; unfortunately, conducting supervised machine learning on MS imaging files is not typically done by mass spectrometrists because a high degree of specialized knowledge is needed. To address this problem, we developed a fully open-source approach that facilitates supervised machine learning on MS imaging files, and we demonstrated its implementation on sets of cancer spheroids that either had or had not undergone chemotherapy treatment. These supervised machine learning studies demonstrated that metabolic changes induced by the monoclonal antibody, Cetuximab, are detectable but modest at 24 h, and by 72 h, the drug induces a larger and more diverse metabolic response.

中文翻译：

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如何将有监督的机器学习工具应用于 MS 成像文件：使用单克隆抗体西妥昔单抗治疗癌症球体的案例研究。

随着质谱成像领域的不断发展，其对最佳数据分析方法的需求也在不断增长。图像分析的一个普遍需求是能够将图像中的底层区域分类为健康或患病，例如。分类作为一个普遍问题，通常最好通过监督机器学习策略来完成；不幸的是，质谱仪通常不会对 MS 成像文件进行监督机器学习，因为需要高度的专业知识。为了解决这个问题，我们开发了一种完全开源的方法，可以促进对 MS 成像文件的监督机器学习，并且我们展示了它在已接受或未接受化疗治疗的癌症球体集上的实施。