小柯机器人

美国哈佛医学院Faisal Mahmood小组发现，基于AI的病理学可预测原发灶未知癌症的起源。这一研究成果于2021年5月5日在线发表在国际学术期刊《自然》上。

据研究人员介绍，未知原发性（CUP）起源的癌症是一个神秘的诊断类别，即无法确定肿瘤起源的主要解剖部位。这是一个巨大的挑战，因为现代疗法主要针对原发肿瘤。最近的研究集中在使用基因组学和转录组学来鉴定肿瘤的起源。但是，基因组测试并不总是实施，并且在资源匮乏的环境中缺乏临床使用性。

为了克服这些挑战，研究人员提出了一种基于深度学习的算法：通过深度学习进行肿瘤起源评估（TOAD），该算法可以使用常规获取的组织学切片对原发肿瘤的起源进行鉴别诊断。研究人员使用了具有已知原发灶的肿瘤全切片图像来训练一个模型，该模型可以同时将肿瘤鉴别为原发灶或转移灶，并预测其起源位置。在暂存的已知原发性肿瘤测试集上，该模型的top-1精度为0.83，top-3精度为0.96，而在外部测试集上，该模型的top-1和top-3精度为分别为0.80和0.93。

研究人员进一步构建了317例CUP病例的数据集，并为其分配了鉴别诊断。这个模型预测结果使61％的病例保持一致，而top-3的一致率为82％。TOAD可以用作辅助工具来对复杂的转移性肿瘤和CUP病例进行鉴别诊断，并且可以与辅助检查或广泛的诊断检查结合使用或代替其进行检查，进而减少CUP的发生。

附：英文原文

Title: AI-based pathology predicts origins for cancers of unknown primary

Author: Ming Y. Lu, Tiffany Y. Chen, Drew F. K. Williamson, Melissa Zhao, Maha Shady, Jana Lipkova, Faisal Mahmood

Issue&Volume: 2021-05-05

Abstract: Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined1,2. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour3. Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour4,5,6,7,8,9. However, genomic testing is not always performed and lacks clinical penetration in low-resource settings. Here, to overcome these challenges, we present a deep-learning-based algorithm—Tumour Origin Assessment via Deep Learning (TOAD)—that can provide a differential diagnosis for the origin of the primary tumour using routinely acquired histology slides. We used whole-slide images of tumours with known primary origins to train a model that simultaneously identifies the tumour as primary or metastatic and predicts its site of origin. On our held-out test set of tumours with known primary origins, the model achieved a top-1 accuracy of 0.83 and a top-3 accuracy of 0.96, whereas on our external test set it achieved top-1 and top-3 accuracies of 0.80 and 0.93, respectively. We further curated a dataset of 317 cases of CUP for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 61% of cases and a top-3 agreement of 82%. TOAD can be used as an assistive tool to assign a differential diagnosis to complicated cases of metastatic tumours and CUPs and could be used in conjunction with or in lieu of ancillary tests and extensive diagnostic work-ups to reduce the occurrence of CUP.

DOI: 10.1038/s41586-021-03512-4

Source: https://www.nature.com/articles/s41586-021-03512-4

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
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