Preoperative prediction of lymph node metastasis using deep learning-based features

Renee Cattell; Jia Ying; Lan Lei; Jie Ding; Shenglan Chen; Mario Serrano Sosa; Chuan Huang

doi:10.1186/s42492-022-00104-5

Preoperative prediction of lymph node metastasis using deep learning-based features

Vis Comput Ind Biomed Art. 2022 Mar 7;5(1):8. doi: 10.1186/s42492-022-00104-5.

Authors

Renee Cattell^{1

2}, Jia Ying¹, Lan Lei^{3

4}, Jie Ding^{1

5}, Shenglan Chen^{1

6}, Mario Serrano Sosa¹, Chuan Huang^{7

8}

Affiliations

¹ Department of Biomedical Engineering, Stony Brook University, NY, 11794, Stony Brook, USA.
² Department of Radiation Oncology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA.
³ Program in Public Health, Stony Brook Medicine, Stony Brook, NY, 11794, USA.
⁴ Department of Medicine, Northside Hospital Gwinnett, GA, 30046, Lawrenceville, USA.
⁵ Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
⁶ Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China.
⁷ Department of Biomedical Engineering, Stony Brook University, NY, 11794, Stony Brook, USA. chuan.huang@stonybrookmedicine.edu.
⁸ Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA. chuan.huang@stonybrookmedicine.edu.

Abstract

Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm², which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm²) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.

Keywords: Breast cancer; Deep learning; Lymph node metastasis; Prediction model; Radiomics.

Abstract

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