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The purpose of this study was to establish a DECT\u2010specified quantitative approach based on a neural network to characterize the sentinel lymph node (SLN).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>With IRB approval, we retrospectively collected a total of 229 patients (100\/229 metastasis) with biopsy proven breast cancer in this study. The chest and axillary spectral CT examinations were performed prior to the axillary lymph node (ALN) surgery. A decoupling convolution network with 11 ROIs from sequential keV (40 to 140\u00a0keV with 10\u00a0keV increment) was proposed to explicitly extract the spectral and spatial features in a DECT to predict the lymph node status. Focal loss was introduced as the loss function. The metric of the slope of the spectral Hounsfield unit curve measured at the venous phase was used as the baseline approach in comparison to our approach. In additional, a logistic model with radiomic features was also compared to our approach. The area under ROC curve (AUC) was used as the figure of merit to evaluate the classification performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>By introducing spectral convolution and focal loss, AUC on test set could be improved by 0.15 and 0.01 separately. Compared to the slope of the spectral curve with the average AUC of 0.611 and radiomic model with AUC of 0.825, the proposed approach demonstrates a considerably better performance, with test set AUC value of 0.837, by using decoupling spectral and spatial convolution together with focal loss function.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We presented a new decoupling neural network based quantification method for DECT analysis, which might have potential as a noninvasive tool to predict metastasis lymph node status for breast cancer in clinical practice.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14876","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T07:47:20Z","timestamp":1617781640000},"page":"3679-3690","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Decoupling convolution network for characterizing the metastatic lymph nodes of breast cancer patients"],"prefix":"10.1002","volume":"48","author":[{"given":"Rutong","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Mathematics Sun Yat\u2010sen University Guangzhou 510275 P.R. China"},{"name":"Guangdong Province Key Laboratory of Computational Science Sun Yat\u2010sen University Guangzhou 510275 P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology Sun Yat\u2010Sen Memorial Hospital Sun Yat\u2010Sen University Guangzhou 510120 P.R. China"},{"name":"Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Medical Research Center Sun Yat\u2010Sen Memorial Hospital Sun Yat\u2010Sen University Guangzhou 510120 P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chushan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Radiology Sun Yat\u2010Sen Memorial Hospital Sun Yat\u2010Sen University Guangzhou 510120 P.R. 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiology Sun Yat\u2010Sen Memorial Hospital Sun Yat\u2010Sen University Guangzhou 510120 P.R. China"},{"name":"Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Medical Research Center Sun Yat\u2010Sen Memorial Hospital Sun Yat\u2010Sen University Guangzhou 510120 P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Lu","sequence":"additional","affiliation":[{"name":"Guangdong Province Key Laboratory of Computational Science Sun Yat\u2010sen University Guangzhou 510275 P.R. China"},{"name":"School of Computer Science and Engineering Sun Yat\u2010sen University Guangzhou 510275 P.R. China"},{"name":"Shanghai University of Medicine &amp; Health Sciences Shanghai 201218 P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2016.71.0947"},{"key":"e_1_2_8_3_1","first-page":"31451","article-title":"Current approach of the axilla in patients with early\u2010stage breast cancer","volume":"0140","author":"Mamounas EP","year":"2017","journal-title":"Lancet"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(06)70947-0"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21253"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-010-0981-8"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2006.07.4062"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.13122637"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s003300000370"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-013-2747-z"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.breast.2009.02.004"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2017.05.003"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.13330"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019190372"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180544"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mric.2017.08.001"},{"key":"e_1_2_8_17_1","unstructured":"LinM ChenQ YanS.Network in network. arXiv preprint arXiv:13124400.2013."},{"key":"e_1_2_8_18_1","unstructured":"XceptionCF.Deep learning with depthwise separable convolutions. 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