Deep learning applications are emerging as promising new tools that can support the diagnosis and classification of different cancer types. While such solutions hold great potential for hematological malignancies, there have been limited studies describing the use of such applications in this field. The rapid diagnosis of double/triple-hit lymphomas (DHLs/THLs) involvingMYC,BCL2and/orBCL6rearrangements is obligatory for optimal patient care. Here, we present a novel deep learning tool for diagnosing DHLs/THLs directly from scanned images of biopsy slides. A total of 57 biopsies, including 32 in a training set (including five DH lymphoma cases) and 25 in a validation set (including 10 DH/TH cases), were included. The DHL-classifier demonstrated a sensitivity of 100%, a specificity of 87% and an AUC of 0.95, with only two false positive cases, compared to FISH. The DHL-classifier showed a 92% predictive value as a screening tool for performing conventional FISH analysis, over-performing currently used criteria. The work presented here provides the proof of concept for the potential use of an AI tool for the identification of DH/TH events. However, more extensive follow-up studies are required to assess the robustness of this tool and achieve high performances in a diverse population.
深度学习应用正成为支持不同癌症类型诊断与分类的新型工具，展现出广阔前景。尽管这类解决方案在血液系统恶性肿瘤领域具有巨大潜力，但目前描述其在该领域应用的研究仍较为有限。涉及MYC、BCL2和/或BCL6基因重排的双打击/三打击淋巴瘤（DHLs/THLs）的快速诊断是实现最佳患者诊疗的必要条件。本研究提出一种可直接通过活检切片扫描图像诊断DHLs/THLs的新型深度学习工具。共纳入57例活检样本，其中训练集32例（含5例DH淋巴瘤病例），验证集25例（含10例DH/TH病例）。与FISH检测相比，DHL分类器显示出100%的灵敏度、87%的特异度及0.95的AUC值，仅出现2例假阳性病例。作为实施传统FISH分析的筛查工具，该DHL分类器展现出92%的预测价值，其性能优于当前使用的诊断标准。本研究为人工智能工具在DH/TH事件识别中的潜在应用提供了概念验证，但尚需开展更广泛的后续研究以评估该工具的稳健性，并在多样化人群中实现高性能诊断。
肿瘤(癌症)患者之家