Affiliation:
1. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute , Beijing , People’s Republic of China
2. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Breast Cancer Center, Peking University Cancer Hospital & Institute , People’s Republic of China
3. Chinese University of Hong Kong (Shenzhen) School of Medicine, Shenzhen Research Institute of Big Data , People’s Republic of China
Abstract
AbstractBackgroundThe diagnostic effectiveness of traditional imaging techniques is insufficient to assess the response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC), especially for pathological complete response (pCR). A radiomics model based on computed tomography (CT) could be helpful.Patients and MethodsProspective consecutive breast cancer patients with positive axillary LNs initially were enrolled, who received NAC prior to surgery. Chest contrast-enhanced thin-slice CT scan was performed both before and after the NAC (recorded as the first and the second CT respectively), and on both of them, the target metastatic axillary LN was identified and demarcated layer by layer. Using pyradiomics-based software that was independently created, radiomics features were retrieved. A pairwise machine learning workflow based on Sklearn (https://scikit-learn.org/) and FeAture Explorer was created to increase diagnostic effectiveness. An effective pairwise auto encoder model was developed by the improvement of data normalization, dimensionality reduction, and features screening scheme as well as the comparison of the prediction effectiveness of the various classifiers,ResultsA total of 138 patients were enrolled, and 77 (58.7%) in the overall group achieved pCR of LN after NAC. Nine radiomics features were finally chosen for modeling. The AUCs of the training group, validation group, and test group were 0.944 (0.919-0.965), 0.962 (0.937-0.985), and 1.000 (1.000-1.000), respectively, and the corresponding accuracies were 0.891, 0.912, and 1.000.ConclusionThe pCR of axillary LNs in breast cancer following NAC can be precisely predicted using thin-sliced enhanced chest CT-based radiomics.
Funder
University Cancer Hospital
Beijing Municipal Science and Technology Commission
Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support
Publisher
Oxford University Press (OUP)
Cited by
1 articles.
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