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. 2023 Mar 14;23(1):239.
doi: 10.1186/s12885-023-10704-w.

Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence

Chao Zhang  1   2 , Lisha Qi  1   2 , Jun Cai  2   3 , Haixiao Wu  1   2 , Yao Xu  1   2 , Yile Lin  2 , Zhijun Li  1   2 , Vladimir P Chekhonin  2   4 , Karl Peltzer  2   5 , Manqing Cao  1 , Zhuming Yin  1 , Xin Wang  2   6 , Wenjuan Ma  7   8
Affiliations

Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence

Chao Zhang et al. BMC Cancer. .

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.

Keywords: Artificial Intelligence; Breast Cancer; Image; Metastasis; Prediction.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1
The flowchart of the proposed distant metastasis prediction system
Fig. 2
Fig. 2
AUC of each radiomics model for 1-, 3-, and 5-year risk on training set (A-C) and validation set (D-F)
Fig. 3
Fig. 3
Construction of the clinicomics-based prediction model for non-distant metastasis (DM). A A nomogram was developed in the training data set with clinicopathological characteristics and RadScore. Calibration curves and ROC of the nomogram for the training set (B and D) and validation set (C and E). F Decision curve analysis derived from the validation cohort
Fig. 4
Fig. 4
Construction of the clinicomics-based prediction model for bone metastasis (BM). A A nomogram was developed in the training data set with clinicopathological characteristics and RadScore. Calibration curves and ROC of the nomogram for the training set (B and D) and validation set (C and E)
Fig. 5
Fig. 5
Construction of the clinicomics-based prediction model for visceral metastasis (VM). A A nomogram was developed in the training data set with clinicopathological characteristics and RadScore. Calibration curves and ROC of the nomogram for the training set (B and D) and validation set (C and E)
See this image and copyright information in PMC

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