AUTHOR=Li Chunxiao , Huang Haibo , Chen Ying , Shao Sihui , Chen Jing , Wu Rong , Zhang Qi 

TITLE=Preoperative Non-Invasive Prediction of Breast Cancer Molecular Subtypes With a Deep Convolutional Neural Network on Ultrasound Images

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.848790

DOI=10.3389/fonc.2022.848790

ISSN=2234-943X

ABSTRACT=Purpose: This study aimed to develop a deep convolutional neural network (DCNN) model to classify molecular subtypes of breast cancer from ultrasound (US) images together with clinical information.
Methods: A total of 1012 breast cancer patients with 2284 US images (center 1) were collected as the main cohort for training and internal testing. Another cohort of 117 breast cancer cases with 153 US images (center 2) was used as the external testing cohort. Patients were grouped according to thresholds of nodule sizes of 20mm and age of 50 years old. The DCNN models were constructed based on the US images and the clinical information to predict the molecular subtypes of breast cancer. A BI-RADS lexicon model was built on the same data based on morphological and clinical description parameters for diagnostic performance comparison. The diagnostic performance was assessed through the accuracy, sensitivity, specificity, Youden’s index (YI) and area under receiver operating characteristic curve (AUC).
Results: Our DCNN model achieved better diagnostic performance than the BI-RADS lexicon model in differentiating molecular subtypes of breast cancer both in the main cohort and external testing cohort (all p < 0.001). In the main cohort, when classifying luminal A from non-luminal A subtypes, our model obtained an AUC of 0.776 (95% CI, 0.649-0.885) for patients older than 50 years and 0.818 (95% CI, 0.726-0.902) for those with tumor sizes ≤ 20 mm. For young patients ≤ 50 years old, the AUC value of our model for detecting triple-negative breast cancer was 0.712 (95% CI, 0.538-0.874). In the external testing cohort, when classifying luminal A from non-luminal A subtypes for patients older than 50 years, our DCNN model achieved an AUC of 0.686 (95% CI, 0.567-0.806).
Conclusions: We employed a DCNN model to predict the molecular subtypes of breast cancer based on the US images. Our model can be valuable depending on the patient ages and nodule sizes.