Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer - PubMed (original) (raw)

doi: 10.1038/s41467-020-15027-z.

Zhao Yao # 2, Yini Huang # 1, Yanyan Yu # 3, Yun Wang 1, Yubo Liu 1, Rushuang Mao 1, Fei Li 1, Yang Xiao 3, Yuanyuan Wang 2 4, Yixin Hu 1, Jinhua Yu 5 6, Jianhua Zhou 7

Affiliations

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Xueyi Zheng et al. Nat Commun. 2020.

Erratum in

Abstract

Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1

Fig. 1. Patient recruitment workflow.

In total, 584 out of 1342 patients were included according to the selection criteria. The included patients were examined by conventional US and SWE, and had complete clinical information needed for the study.

Fig. 2

Fig. 2. Comparison of receiver operating characteristic (ROC) curves between different models for predicting disease-free axilla (N0) and any axillary metastasis (N+(≥1)).

DLR deep learning radiomics. Numbers in parentheses are areas under the receiver operating characteristic curves. Source data are provided as a Source Data file.

Fig. 3

Fig. 3. Receiver operating characteristic (ROC) curves comparison between different models for predicting low metastatic burden of axillary disease (N+(1–2)) and heavy metastatic burden of axillary disease (N+(≥3)).

DLR deep learning radiomics. Numbers in parentheses are areas under the receiver operating characteristic curves. Source data are provided as a Source Data file.

Fig. 4

Fig. 4. The confusion matrix of predicting metastasis among disease-free axilla (N0), low metastatic burden of axillary disease (N+(1–2)) and heavy metastatic burden of axillary disease (N+(≥3)).

Source data are provided as a Source Data file.

Fig. 5

Fig. 5. Visualization of two patient examples.

Each example shows the gray-scale US image and corresponding heart map, and the red region represents a larger weight, which can be decoded by the color bar on the right. Image a shows that the low echo area inside the tumor is valuable for predicting ALN status, while it is the tumor boundary for image b.

Fig. 6

Fig. 6. The overall pipeline of the model.

The parallel pre-trained ResNet model encodes the input images to features which be combined with clinical parameters. Then the combined features be classified by an SVM model.

Similar articles

Cited by

References

    1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J. Clin. 2018;68:7–30. - PubMed
    1. Ahmed M, Purushotham AD, Douek M. Novel techniques for sentinel lymph node biopsy in breast cancer: a systematic review. Lancet Oncol. 2014;15:e351–e362. - PubMed
    1. Lyman GH, et al. Sentinel lymph node biopsy for patients with early-stage breast cancer: american society of clinical oncology clinical practice guideline update. J. Clin. Oncol. 2017;35:561–564. - PubMed
    1. Giuliano AE, et al. Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: The ACOSOG Z0011 (Alliance) Randomized Clinical Trial. JAMA. 2017;318:918–926. - PMC - PubMed
    1. Giuliano AE, et al. Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis: a randomized clinical trial. JAMA. 2011;305:569–575. - PMC - PubMed

Publication types

MeSH terms

LinkOut - more resources