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

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.

Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis

“…Modeling methods using radiomics algorithm and machine/deep learning had a similar diagnostic performance. This result opposes previous evidence that suggests that machine/deep learning is superior to the traditional radiomics algorithm in predicting LNM in breast cancer (3,39,42) and requires further verification.…”

“…Therefore, future prospective studies should improve the predictive performance of radiomics for the evaluation of LNM in patients with breast cancer and its clinical efficiency. Our meta-analysis also showed that studies using radiomics combined with clinical factors have higher diagnostic performance than those relying only on radiomics, which is consistent with the previous studies (3,25,26). Thus, adding clinical features to radiomic imaging improves the accuracy of diagnosing LNM in breast cancer.…”

“…The studies had 5611 patients, 2337 with LNM and 3274 without, assessed using a radiomics method. Seven studies ( 3 , 22 – 27 ) reported diagnostic performance based on radiological characteristics and clinical factors. Only one ( 10 ) used prospectively collected data.…”

“…Only one ( 10 ) used prospectively collected data. Furthermore, the studies applied radiomics based on different imaging modalities: MRI, 17 ( 3 , 11 , 22 , 23 , 28 – 39 ); ultrasound, 7 ( 14 , 24 , 25 , 27 , 40 – 42 ); CT, 2 ( 12 , 43 ); PET–CT, 2 ( 10 , 44 ); and MMG, 3 ( 13 , 26 , 45 ).…”

“…The presence and extent of axillary lymph node metastasis reflect the risk of distant recurrence and death after topical treatment. Moreover, the overall survival rate of nodepositive patients is 40% lower than node-negative (2,3). Thus, the lymph node status is crucial for treatment planning, surgical procedures, and prognosis assessment of patients (4).Currently, staging of the axilla in patients with breast cancer is evaluated with sentinel lymph node biopsy and axillary lymph node dissection.…”