We present NaroNet, a Machine Learning framework that integrates the multiscale spatial, in situ analysis of the tumor microenvironment (TME) with patient-level predictions into a seamless end-to-end learning pipeline. Trained only with patient-level labels, NaroNet quantifies the phenotypes, neighborhoods, and neighborhood interactions that have the highest influence on the predictive task. We validate NaroNet using synthetic data simulating multiplex-immunostained images with adjustable probabilistic incidence of different TMEs. Then we apply our model to two real sets of patient tumors, one consisting of 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancers, and the other consisting of 372 35-plex mass cytometry images from 283 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions while associating those predictions to the presence of specific TMEs. This inherent interpretability could be of great value both in a clinical setting and as a tool to discover novel biomarker signatures.

翻译：我们介绍NaroNet, 这是一种机器学习框架, 将肿瘤微环境的多尺度空间和现场分析与病人水平预测结合到无缝端到端学习管道中。 仅用病人等级标签进行训练, NaroNet量化了对预测任务影响最大的苯型、 邻里和邻里互动。 我们使用合成数据模拟多克斯- 超隐蔽图像和不同TME的可调整概率发生率来验证NaroNet。 然后我们将模型应用到两组真正的病人肿瘤中, 其中一组由12个高等级内分癌症的336个七色多色多色模拟图象组成,另一组由283个乳腺癌患者的372个双倍质量细胞测量图象组成。 在合成和真实的数据集中, NaroNet提供出色的预测,同时将这些预测与具体的TME的存在联系起来。 这种内在解释在临床环境中和作为发现新生物标志工具都具有巨大价值。