ArticleJournal of cancer research and clinical oncology2023
MEAI: an artificial intelligence platform for predicting distant and lymph node metastases directly from primary breast cancer.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Article
- Invasion and metastasis in cancer: molecular insights and therapeutic targets.Signal transduction and targeted therapy · 2025Review
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
- Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review.Journal of cancer research and clinical oncology · 2023Review
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeBreast 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.
methodsIn 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.
resultsThe 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.
conclusionThe proposed MEAI system can provide a non-invasive approach to assess the metastatic probability of patients with primary breast cancer.
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