Recurrence prediction of invasive ductal carcinoma from preoperative contrast-enhanced computed tomography using deep convolutional neural network.
Authors
Affiliations (4)
Affiliations (4)
- Division of Central Radiology, Niigata Cancer Center Hospital, 2-15-3 Kawagishi-cho, Chuo-ku, Niigata-shi, Niigata 951-8566, Japan.
- Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata-shi, Niigata, 951-8518, Japan.
- Department of Breast Surgery, Niigata Cancer Center Hospital, 2-15-3 Kawagishi-cho, Chuo-ku, Niigata-shi, Niigata 951-8566, Japan.
- Department of Radiology, Niigata Cancer Center Hospital, 2-15-3 Kawagishi-cho, Chuo-ku, Niigata-shi, Niigata 951-8566, Japan.
Abstract
Predicting the risk of breast cancer recurrence is crucial for guiding therapeutic strategies, including enhanced surveillance and the consideration of additional treatment after surgery. In this study, we developed a deep convolutional neural network (DCNN) model to predict recurrence within six years after surgery using preoperative contrast-enhanced computed tomography (CECT) images, which are widely available and effective for detecting distant metastases. This retrospective study included preoperative CECT images from 133 patients with invasive ductal carcinoma. The images were classified into recurrence and no-recurrence groups using ResNet-101 and DenseNet-201. Classification performance was evaluated using the area under the receiver operating curve (AUC) with leave-one-patient-out cross-validation. At the optimal threshold, the classification accuracies for ResNet-101 and DenseNet-201 were 0.73 and 0.72, respectively. The median (interquartile range) AUC of DenseNet-201 (0.70 [0.69-0.72]) was statistically higher than that of ResNet-101 (0.68 [0.66-0.68]) (p < 0.05). These results suggest the potential of preoperative CECT-based DCNN models to predict breast cancer recurrence without the need for additional invasive procedures.