Cancer mortality is mostly related to metastasis. Metastasis is currently prognosed via histopathology, disease-statistics, or genetics; those are potentially inaccurate, not rapidly available and require known markers. We had developed a rapid (~ 2 h) mechanobiology-based approach to provide early prognosis of the clinical likelihood for metastasis. Specifically, invasive cell-subsets seeded on impenetrable, physiological-stiffness polyacrylamide gels forcefully indent the gels, while non-invasive/benign cells do not. The number of indenting cells and their attained depths, the mechanical invasiveness, accurately define the metastatic risk of tumors and cell-lines. Utilizing our experimental database, we compare the capacity of several machine learning models to predict the metastatic risk. Models underwent supervised training on individual experiments using classification from literature and commercial-sources for established cell-lines and clinical histopathology reports for tumor samples. We evaluated 2-class models, separating invasive/non-invasive (e.g. benign) samples, and obtained sensitivity and specificity of 0.92 and 1, respectively; this surpasses other works. We also introduce a novel approach, using 5-class models (i.e. normal, benign, cancer-metastatic-non/low/high) that provided average sensitivity and specificity of 0.69 and 0.91. Combining our rapid, mechanical invasiveness assay with machine learning classification can provide accurate and early prognosis of metastatic risk, to support choice of treatments and disease management.

癌症死亡率主要与转移有关。目前通过组织病理学、疾病统计或遗传学预测转移；这些可能不准确，无法快速获得并且需要已知的标记。我们开发了一种快速（约 2 小时）基于机械生物学的方法，以提供转移临床可能性的早期预后。具体来说，在不可穿透的、生理硬度的聚丙烯酰胺凝胶上播种的侵入性细胞亚群会强烈地压入凝胶，而非侵入性/良性细胞则不会。缩进细胞的数量及其达到的深度、机械侵袭性，准确地定义了肿瘤和细胞系的转移风险。利用我们的实验数据库，我们比较了几种机器学习模型预测转移风险的能力。模型使用来自文献和商业来源的分类对已建立的细胞系和肿瘤样本的临床组织病理学报告进行了个别实验的监督训练。我们评估了 2 类模型，分离侵入性/非侵入性（例如良性）样本，分别获得 0.92 和 1 的敏感性和特异性；这超越了其他作品。我们还介绍了一种新方法，使用 5 类模型（即正常、良性、癌症转移非/低/高），提供 0.69 和 0.91 的平均敏感性和特异性。将我们的快速机械侵袭性测定与机器学习分类相结合，可以提供准确和早期的转移风险预后，以支持治疗选择和疾病管理。