Title: Transformer-Based Hematological Malignancy Prediction from Peripheral Blood Smears in a Real-World Cohort Show Chinese title

Title: 基于Transformer的现实世界队列中从外周血涂片预测血液系统恶性肿瘤

Abstract: Peripheral blood smears remain a cornerstone in the diagnosis of hematological neoplasms, offering rapid and valuable insights that inform subsequent diagnostic steps. However, since neoplastic transformations typically arise in the bone marrow, they may not manifest as detectable aberrations in peripheral blood, presenting a diagnostic challenge. In this paper, we introduce cAItomorph, an explainable transformer-based AI model, trained to classify hematological malignancies based on peripheral blood cytomorphology. Our data comprises peripheral blood single-cell images from 6115 patients with diagnoses confirmed by cytomorphology, cytogenetics, molecular genetics, and immunophenotyping from bone marrow samples, and 495 healthy controls, eight coarse classes. cAItomorph leverages the DinoBloom hematology foundation model and aggregates image encodings via a transformer-based architecture into a single vector. It achieves an overall accuracy of 0.72 in eight disease classification, with F1 scores of 0.76 for acute leukemia, 0.80 for myeloproliferative neoplasms and 0.94 for healthy cases. The overall accuracy increases to 0.87 in top-2 predictions. cAItomorph achieves high sensitivity for acute leukemia cases in external test sets. By analyzing attention heads, we demonstrate clinically relevant cell-level attentions in both internal and external test sets. Moreover, our model's calibrated prediction probabilities reduce the false discovery rate from 13.5% to 8.7% without missing any acute leukemia cases, thereby decreasing the number of unnecessary bone marrow aspirations based on peripheral blood smears. This study highlights the potential of AI-assisted diagnostics in hematological malignancies, illustrating how models trained on real-world data could enhance diagnostic accuracy and reduce invasive procedures. Show Chinese abstract

Abstract: 外周血涂片仍然是诊断血液系统肿瘤的基石，提供快速且有价值的信息，为后续诊断步骤提供依据。 然而，由于肿瘤性转化通常发生在骨髓中，它们可能不会在外周血中表现出可检测的异常，这带来了诊断上的挑战。 在本文中，我们介绍了cAItomorph，一种基于Transformer的可解释AI模型，该模型通过外周血细胞形态学对血液系统恶性肿瘤进行分类。 我们的数据包括来自6115名经细胞形态学、细胞遗传学、分子遗传学和免疫表型分析确认诊断的骨髓样本患者的外周血单细胞图像，以及495名健康对照者，分为八个粗类。 cAItomorph利用DinoBloom血液学基础模型，并通过基于Transformer的架构将图像编码聚合为一个向量。 它在八种疾病分类中的总体准确率为0.72，其中急性白血病的F1得分为0.76，骨髓增殖性肿瘤的F1得分为0.80，健康病例的F1得分为0.94。 在top-2预测中，总体准确率提高到0.87。 cAItomorph在外部测试集中的急性白血病病例上表现出高灵敏度。 通过分析注意力头，我们在内部和外部测试集中展示了具有临床相关性的细胞级注意力。 此外，我们的模型校准后的预测概率将假阳性率从13.5%降低到8.7%，而没有遗漏任何急性白血病病例，从而减少了基于外周血涂片的不必要的骨髓穿刺数量。 本研究突显了人工智能辅助诊断在血液系统恶性肿瘤中的潜力，说明了在真实世界数据上训练的模型如何提高诊断准确性并减少侵入性操作。