ArticleBMC cancer2023
Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Clinical applications of MRI-based artificial intelligence in spinal metastases: A systematic review.Journal of bone oncology · 2026Review
- Artificial Intelligence and Digital Pathology for Preoperative Lymphovascular Invasion and Metastatic Risk Prediction in Breast Cancer: Bridging the Biopsy-Resection Gap.Journal of clinical medicine · 2026Review
- Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.Scientific reports · 2026Article
- Survival outcomes in patients with metachronous breast cancer bone metastasis: an 11-year single-institution retrospective study.American journal of cancer research · 2026Article
- Rutin-encapsulated manganese carbonate nanoparticles for 'liver-dark/tumor-bright' magnetic resonance imaging and inhibition of breast cancer with liver metastases.Regenerative biomaterials · 2026Article
- Development trends and knowledge framework of artificial intelligence (AI) applications in oncology by years: a bibliometric analysis from 1992 to 2022.Discover oncology · 2024Article
- Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.BMC women's health · 2024Article
- Insights into the performance of PREDICT tool in a large Mainland Chinese breast cancer cohort: a comparative analysis of versions 3.0 and 2.2.The oncologist · 2024Article
- AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter.Scientific reports · 2024Article
- Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study.Frontiers in oncology · 2024Article
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Authors and funding
13 authors.
Funding
Abstract
backgroundBreast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis.
methodsWe retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data.
resultsThree clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients.
conclusionOur study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.
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