美国哈佛医学院Faisal Mahmood研究团队在研究中取得进展。他们通过多模式深度学习完成了泛癌组织学-基因组综合分析。该项研究成果发表在2022年8月8日出版的《癌细胞》上。

研究人员使用多模态深度学习对14种癌症类型的病理学全幻灯片图像和分子谱数据进行了整合分析。研究人员研发的弱监督、多模式深度学习算法能够融合这些异构模式来预测结果并发现与不良和有利结果相关的预后特征。研究人员在交互式开放访问数据库中，对14种癌症类型在疾病和患者水平的预后形态学和分子相关性进行了分析，以实现进一步探索、生物标志物的发现和特征评估。

据介绍，快速兴起的计算病理学在基于组织学图像进行客观预后建模方面具有可观的前景。然而，大多数预后模型仅基于组织学或基因组学，并未解决如何整合这些数据源以研发图像组学联合预后模型。此外，从这些预后模型中识别可解释的形态学和分子特征是有意义的。

附：英文原文

Title: Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Author: Richard J. Chen, Ming Y. Lu, Drew F.K. Williamson, Tiffany Y. Chen, Jana Lipkova, Zahra Noor, Muhammad Shaban, Maha Shady, Mane Williams, Bumjin Joo, Faisal Mahmood

Issue&Volume: 2022/08/08

Abstract: The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these data sources can be integrated to develop joint image-omic prognostic models. Additionally, identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We use multimodal deep learning to jointly examine pathology whole-slide images and molecular profile data from 14 cancer types. Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover prognostic features that correlate with poor and favorable outcomes. We present all analyses for morphological and molecular correlates of patient prognosis across the 14 cancer types at both a disease and a patient level in an interactive open-access database to allow for further exploration, biomarker discovery, and feature assessment.

DOI: 10.1016/j.ccell.2022.07.004

Source: https://www.cell.com/cancer-cell/fulltext/S1535-6108(22)00317-8