Prediction Model of Axillary Lymph Node Status Using an Automated Breast Volume Ultrasound (ABVS) Radiomics Nomogram in Early Breast Cancer with Negative Axillary Ultrasound

Qianqing Ma ¹ Junli Wang ² Zhengzheng Tu ³ Jingwen She ⁴ Zhu Jianhui ³ Chaoxue Zhang ^4*

• ¹ First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui Province, China
• ² Second People's Hospital of Wuhu, Wuhu, Anhui Province, China
• ³ Anhui University, Hefei, Anhui Province, China
• ⁴ Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China

Background: Construction and validation of an automated breast volume ultrasound (ABVS)-based nomogram for assessing axillary lymph node (ALNs) metastasis in axillary ultrasound (AUS)-negative early breast cancer. Methods: A retrospective study of 174 patients with AUS-negative early-stage breast cancer was divided into a training and test with a ratio of 7:3. Radiomics features were extracted by combining images of intra-tumor and peri-tumor ABVS. Select the best classifier from 3 machine learning techniques to build Model 1and radiomics-score (RS). Differences in ER, PR, Her-2, Ki-67 expression were analyzed for intra-tumoral and peri-tumoral habitat radiomics features. Model 2 (based on sonogram features) and Model 3 (based on RS and sonogram features) were constructed by multivariate logistic regression. Efficiency of the models was evaluated by the area under the curve (AUC). Plotting the nomogram and evaluating its treatment in ALN≥3 according to Model 2 and Model 3. Result: Intratumoral and peritumoral 5 mm radiomics features were screened using least absolute shrinkage and selection operator (LASSO), and logistic regression was used as a classifier to build the best-performing Model 1. Using unsupervised cluster analysis, intratumoral and peritumoral 5mm were classified into 3 habitats, and 2 / 29 they differed in PR and Her-2 expression. Model 2 (combining diameter and microcalcification) and Model 3 (combining RS and microcalcification) were created by multivariate logistic regression. Model 3 achieves the highest AUC in both the training (0.827) and validation (0.768) sets. The Nomo-score was calculated based on nomogram-model2 and nomogram-model3, revealing a positive correlation between ALN burden and Nomo-score. Combined with the optimal thresholds, nomogram-model2 screened 54.6%-100% of patients with ALN ≥3 and nomogram-model3 screened 81.8%-100% of patients with ALN ≥3. Conclusion: The ABVS-based nomogram is an effective tool for assessing ALN metastasis, and it can provide a preoperative basis for individualized treatment of breast cancer.