Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Nature. 2021 Jun;594(7861):106-110. doi: 10.1038/s41586-021-03512-4. Epub 2021 May 5.
Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour. Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour. 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.
原发灶不明癌症（CUP）是一组具有挑战性的诊断，其中肿瘤原发部位无法确定。这是一个很大的挑战，因为现代治疗方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确定肿瘤的起源。然而，基因组检测并非总是进行，并且在资源有限的环境中缺乏临床渗透。在这里，为了克服这些挑战，我们提出了一种基于深度学习的算法——通过深度学习评估肿瘤起源（TOAD）——该算法可以使用常规获取的组织学幻灯片提供原发肿瘤起源的鉴别诊断。我们使用具有已知原发起源的肿瘤全切片图像来训练一个模型，该模型可以同时识别肿瘤是原发还是转移，并预测其起源部位。在我们的已知原发起源肿瘤验证集上，该模型的 top-1 准确率为 0.83，top-3 准确率为 0.96，而在我们的外部验证集上，它的 top-1 和 top-3 准确率分别为 0.80 和 0.93。我们进一步整理了一个包含 317 例 CUP 病例的数据集，对这些病例进行了鉴别诊断。我们的模型预测结果与 61%的病例一致，top-3 一致率为 82%。TOAD 可以用作辅助工具，对复杂的转移性肿瘤和 CUP 病例进行鉴别诊断，也可以与辅助检查和广泛的诊断性检查结合使用或替代这些检查，以减少 CUP 的发生。
