Search

Yunxia Huang,1,* Yi Guo,2,* Qin Xiao,3,* Shuyu Liang,2 Qiang Yu,4 Lang Qian,1 Jin Zhou,1 Jian Le,1 Yuchen Pei,5 Lei Wang,4 Cai Chang,1 Sheng Chen,4 Shichong Zhou1 1Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China; 2Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China; 3Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, People’s Republic of China; 4Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China; 5Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shichong Zhou, Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People’s Republic of China, Tel +86 21-64175590, Email sczhou@hotmail.com Sheng Chen, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People’s Republic of China, Tel +86 21-64175590, Email 0456177@fudan.edu.cnPurpose: The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures.Patients and Methods: Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features.Results: Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic–gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic–pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC.Conclusion: The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.Keywords: triple-negative breast cancer, radiomics, somatic mutation, signaling pathway

PurposeTo develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound.Materials and MethodsA total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated.ResultsA significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the “stiff rim” sign, minimum elastic modulusof the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0–4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617–0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%–62.1%), and a specificity of 68.99% (95% CI, 64.5%–73.3%) in predicting axillary LN metastasis.ConclusionA 0–4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.