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

Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 88-98页

作者： Renee Cattell Jia Ying Lan Lei Jie Ding Shenglan Chen mario serrano sosa Chuan Huang Department of Biomedical Engineering Stony Brook UniversityNY 11794 Stony BrookUSA Department of Radiation Oncology Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA Program in Public Health Stony Brook MedicineStony BrookNY 11794USA Department of Medicine Northside Hospital GwinnettGA 30046 LawrencevilleUSA Department of Radiation Oncology Medical College of WisconsinMilwaukeeWI 53226USA Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Department of Radiology Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794US

Lymph node involvement increases the risk of breast cancer *** accurate non-invasive assessment of nodal involvement is valuable in cancer staging,surgical risk,and cost *** has been proposed to pre-operatively predict sentinel lymph node(SLN)status;however,radiomic models are known to be sensitive to acquisition *** 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 ***,this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar *** contrast-enhancement images from 198 patients(67 positive SLNs)were used in this *** these subjects,163 had an in-plane resolution of 0.7×0.7 mm^(2),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 mm^(2))were treated as independent testing set for *** methods were employed:(1)conventional radiomics(CR),and(2)DLB features which replaced hand-curated features with pre-trained VGG-16 *** threshold determined using the training set was applied to the independent validation and testing *** feature reduction,feature selection,model creation procedures were used for both *** 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 *** interestingly,these improvements were seen particularly in the independent testing set of dissimilar *** could indicate that DLB features can ultimately result in a mor

关键词： Deep learning Radiomics Prediction model Lymph node metastasis Breast cancer

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