A Novel Deep Learning Radiomics Nomogram Integrating B-Mode Ultrasound and Contrast-Enhanced Ultrasound for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer.
Authors
Affiliations (6)
Affiliations (6)
- School of Medicine, Nankai University, 94 Weijin Road, Tianjin 300071, China.
- College of Medicine and Biological Information Engineering, Northeastern University, 195 Chuangxin Road, Shenyang 110169, China.
- Department of Ultrasound, the First Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China.
- Department of Ultrasound, Peking University First Hospital, Beijing 100034, China.
- Department of Ultrasound, Cancer Hospital Chinese Academy of Medical Sciences, Beijing 100021, China.
- School of Medicine, Nankai University, 94 Weijin Road, Tianjin 300071, China; Department of Ultrasound, the First Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China. Electronic address: [email protected].
Abstract
This study aimed to develop a deep learning radiomics nomogram (DLRN) that integrated B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) images for preoperative lymphovascular invasion (LVI) prediction in invasive breast cancer (IBC). Total 981 patients with IBC from three hospitals were retrospectively enrolled. Of 834 patients recruited from Hospital I, 688 were designated as the training cohort and 146 as the internal test cohort, whereas 147 patients from Hospitals II and III were assigned to constitute the external test cohort. Deep learning and handcrafted radiomics features of BMUS and CEUS images were extracted from breast cancer to construct a deep learning radiomics (DLR) signature. The DLRN was developed by integrating the DLR signature and independent clinicopathological parameters. The performance of the DLRN is evaluated with respect to discrimination, calibration, and clinical benefit. The DLRN exhibited good performance in predicting LVI, with areas under the receiver operating characteristic curves (AUCs) of 0.885 (95% confidence interval [CI,0.858-0.912), 0.914 (95% CI, 0.868-0.960) and 0.914 (95% CI, 0.867-0.960) in the training, internal test, and external test cohorts, respectively. The DLRN exhibited good stability and clinical practicability, as demonstrated by the calibration curve and decision curve analysis. In addition, the DLRN outperformed the traditional clinical model and the DLR signature for LVI prediction in the internal and external test cohorts (all p < 0.05). The DLRN exhibited good performance in predicting LVI, representing a non-invasive approach to preoperatively determining LVI status in IBC.