Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia.
Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia.
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DOI：
10.1002/path.5795
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发表时间：
2022-01
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旨在协助诊断淋巴肿瘤的基于人工智能的工具仍然有限。这些工具的开发可以增加价值，作为淋巴瘤所涉及的组织样本的评价的诊断辅助。一个常见的诊断问题是确定慢性淋巴细胞白血病（CLL）进展为加速CLL（aCLL）或转化为弥漫性大B细胞淋巴瘤（Richter转化; RT）的患者发展为疾病进展。CLL、aCLL和RT的形态学评估在诊断上具有挑战性。使用已建立的CLL进展/转化的诊断标准，我们设计了四种基于细胞学（核大小和核强度）和结构（细胞密度和细胞与最近邻距离）特征的人工智能构建的生物标志物。我们分析了单独实施这些生物标志物的预测价值，然后以迭代顺序的方式区分组织样本与CLL，aCLL和RT。我们的模型，基于这四个形态学生物标志物属性，实现了强大的分析准确性。这项研究表明，使用基于人工智能的工具识别的生物标志物可用于协助对发展侵袭性疾病特征的CLL患者的组织样本进行诊断评估。
Artificial intelligence-based tools designed to assist in the diagnosis of lymphoid neoplasms remain limited. The development of such tools can add value as a diagnostic aid in the evaluation of tissue samples involved by lymphoma. A common diagnostic question is the determination of chronic lymphocytic leukemia (CLL) progression to accelerated CLL (aCLL) or transformation to diffuse large B-cell lymphoma (Richter transformation; RT) in patients who develop progressive disease. The morphologic assessment of CLL, aCLL, and RT can be diagnostically challenging. Using established diagnostic criteria of CLL progression/transformation, we designed four artificial intelligence-constructed biomarkers based on cytologic (nuclear size and nuclear intensity) and architectural (cellular density and cell to nearest-neighbor distance) features. We analyzed the predictive value of implementing these biomarkers individually and then in an iterative sequential manner to distinguish tissue samples with CLL, aCLL, and RT. Our model, based on these four morphologic biomarker attributes, achieved a robust analytic accuracy. This study suggests that biomarkers identified using artificial intelligence-based tools can be used to assist in the diagnostic evaluation of tissue samples from patients with CLL who develop aggressive disease features.
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DOI：
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期刊：
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期刊：
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