Contrast-enhanced computed tomography for diagnosing the intraductal component and small invasive foci of Breast Cancer

Breast Cancer ◽  
2001 ◽  
Vol 8 (1) ◽  
pp. 10-15 ◽  
Author(s):  
Sadako Akashi-Tanaka ◽  
Takashi Fukutomi ◽  
Kunihisa Miyakawa ◽  
Takeshi Nanasawa ◽  
Kaneyuki Matsuo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Contrast Enhanced ◽  
Intraductal Component ◽  
Enhanced Computed Tomography ◽  
Contrast Enhanced Computed Tomography

scholarly journals Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

2021 ◽  
Vol 11 ◽  
Author(s):  
Chunmei Yang ◽  
Jing Dong ◽  
Ziyi Liu ◽  
Qingxi Guo ◽  
Yue Nie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Classification Accuracy ◽  
Cancer Metastasis ◽  
Support Vector ◽  
Axillary Lymph ◽  
Model Based ◽  
Contrast Enhanced ◽  
Enhanced Computed Tomography ◽  
Contrast Enhanced Computed Tomography

BackgroundThe use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.PurposeThe aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.MethodsWe retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).ResultsThe radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer.ConclusionThe radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.

Sign in / Sign up

Export Citation Format

Share Document