Day 15. Deep learning radiomics can predict axillary lymphnode status in early-stage breast cancer

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

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.

Accurately identify axillary lymph nodes in patients with early breast cancer （ALN） To determine the appropriate axillary treatment 、 It is of great significance to avoid unnecessary axillary surgery and complications . ad locum , We report the deep learning radiomics of conventional ultrasound and shear wave elastography in breast cancer （DLR） For patients with early breast cancer before surgery ALN State prediction . Combination of clinical parameters DLR In predicting disease-free axillary and axillary metastasis ALN It has the best diagnostic performance in terms of status , In the trial queue , Subject operating characteristic curve （AUC） The area under is 0.902（95% confidence interval [CI]：0.843,0.961）. This clinical parameter is combined DLR It can also distinguish between low metastatic load and heavy metastatic load of axillary disease ,AUC by 0.905（95%CI:0.814,0.996）. Our study provides a noninvasive imaging biomarker to predict patients with early breast cancer ALN Transfer scope of .