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MEAI:一个人工智能平台,可直接从原发性乳腺癌预测远处转移和淋巴结转移。

MEAI: an artificial intelligence platform for predicting distant and lymph node metastases directly from primary breast cancer.

机构信息

School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China.

Department of Vascular Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.

出版信息

J Cancer Res Clin Oncol. 2023 Sep;149(11):9229-9241. doi: 10.1007/s00432-023-04787-y. Epub 2023 May 18.

Abstract

PURPOSE

Breast cancer patients typically have decent prognoses, with a 5-year survival rate of more than 90%, but when the disease metastases to lymph node or distant, the prognosis drastically declines. Therefore, it is essential for future treatment and patient survival to quickly and accurately identify tumor metastasis in patients. An artificial intelligence system was developed to recognize lymph node and distant tumor metastases on whole-slide images (WSIs) of primary breast cancer.

METHODS

In this study, a total of 832 WSIs from 520 patients without tumor metastases and 312 patients with breast cancer metastases (including lymph node, bone, lung, liver, and other) were gathered. Based on the WSIs were randomly divided into the training and testing cohorts, a brand-new artificial intelligence system called MEAI was built to identify lymph node and distant metastases in primary breast cancer.

RESULTS

The final AI system attained an area under the receiver operating characteristic curve of 0.934 in a test set of 187 patients. In addition, the potential for AI system to increase the precision, consistency, and effectiveness of tumor metastasis detection in patients with breast cancer was highlighted by the AI's achievement of an AUROC higher than the average of six board-certified pathologists (AUROC 0.811) in a retrospective pathologist evaluation.

CONCLUSION

The proposed MEAI system can provide a non-invasive approach to assess the metastatic probability of patients with primary breast cancer.

摘要

目的

乳腺癌患者的预后通常较好,5 年生存率超过 90%,但当疾病转移到淋巴结或远处时,预后则急剧下降。因此,快速准确地识别患者的肿瘤转移对于未来的治疗和患者生存至关重要。本研究开发了一种人工智能系统,用于识别原发性乳腺癌全切片图像(WSI)中的淋巴结和远处肿瘤转移。

方法

本研究共收集了 520 例无肿瘤转移和 312 例乳腺癌转移(包括淋巴结、骨、肺、肝等)患者的 832 张 WSI。基于 WSI 被随机分为训练和测试队列,构建了一个名为 MEAI 的全新人工智能系统,用于识别原发性乳腺癌中的淋巴结和远处转移。

结果

在 187 例患者的测试集中,最终的 AI 系统获得了 0.934 的受试者工作特征曲线下面积。此外,通过回顾性病理评估,AI 系统的 AUROC 高于六位认证病理学家的平均水平(AUROC 0.811),这突显了 AI 系统提高乳腺癌患者肿瘤转移检测的精度、一致性和有效性的潜力。

结论

所提出的 MEAI 系统可以为评估原发性乳腺癌患者的转移概率提供一种非侵入性的方法。

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