Author:
Zhang Chao,Qi Lisha,Cai Jun,Wu Haixiao,Xu Yao,Lin Yile,Li Zhijun,Chekhonin Vladimir P.,Peltzer Karl,Cao Manqing,Yin Zhuming,Wang Xin,Ma Wenjuan
Abstract
Abstract
Background
Breast 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.
Methods
We 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.
Results
Three 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.
Conclusion
Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.
Funder
Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Cancer Research,Genetics,Oncology
Cited by
1 articles.
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