AI-guided histopathology predicts brain metastasis in lung cancer patients.
AI-guided histopathology predicts brain metastasis in lung cancer patients.
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人工智能引导的组织病理学可预测肺癌患者的脑转移。
DOI：
10.1002/path.6263
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Cote,RichardJ
中科院分区：
文献类型：
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作者：
Zhou,Haowen;Watson,Mark;Bernadt,CoryT;Lin,StevenSiyu;Lin,Chieh-Yu;Ritter,JonH;Wein,Alexander;Mahler,Simon;Rawal,Sid;Govindan,Ramaswamy;Yang,Changhuei;Cote,RichardJ
近一半的早期和局部晚期（I-III期）非小细胞肺癌（NSCLC）患者可能发生脑转移。没有可靠的组织病理学或分子手段来识别那些可能发生脑转移的人。我们试图确定深度学习（DL）是否可以应用于I-III期NSCLC患者的常规H&E染色原发性肿瘤组织切片，以预测脑转移的发展。对158例I-III期NSCLC患者的诊断载玻片进行全载玻片成像，这些患者随访至少5年，以确定是否发生脑转移（Met+，65例患者）或无进展（Met-，93例患者）。通过首先选择118个病例（45个Met+，73个Met-）来训练和验证DL算法，同时使用40个单独的病例（20个Met+，20个Met-）作为测试集，进行了三次单独的迭代。将DL算法结果与四位专家病理学家的盲态审查进行比较。基于DL的算法能够区分脑转移的最终发展，准确率为87%（p< 0.0001），而四位病理学家的平均值为57.3%，并且似乎在预测I期患者的脑转移方面特别有用。DL算法似乎专注于一组复杂的组织学特征。使用常规H&E染色载玻片的基于DL的算法可以识别可能发生脑转移的患者，以及在长期（>5年）随访期间保持无病的患者，因此可以避免全身治疗。© 2024 The Authors.The Journal of Pathology由John Wiley & Sons Ltd代表英国和爱尔兰病理学会出版。
Brain metastases can occur in nearly half of patients with early and locally advanced (stage I–III) non‐small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E‐stained primary tumor tissue sections from stage I–III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I–III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met−, 93 patients) were subjected to whole‐slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met−) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met−) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL‐based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p< 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL‐based algorithms using routine H&E‐stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow‐up and may thus be spared systemic therapy. © 2024 The Authors.The Journal of Pathologypublished by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.