{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T07:09:21Z","timestamp":1778224161395,"version":"3.51.4"},"reference-count":48,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"vor","delay-in-days":283,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007300","name":"China Medical University","doi-asserted-by":"publisher","award":["2019120823"],"award-info":[{"award-number":["2019120823"]}],"id":[{"id":"10.13039\/501100007300","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p><jats:italic>Purpose<\/jats:italic>. We want to develop a model for predicting lymph node status based on positron emission computed tomography (PET) images of untreated ovarian cancer patients. We use the feature map formed by wavelet transform and the parameters obtained by image segmentation to build the model. The model is expected to help clinicians and provide additional information about what to do with first\u2010visit patients. <jats:italic>Materials and Methods<\/jats:italic>. Our study included 224 patients with ovarian cancer. We have chosen two main methods to extract information from images. On the one hand, we segmented the image to extract the parameters to evaluate the clustering effect. On the other hand, we used wavelet transform to extract the image\u2019s texture information to form the image\u2019s feature map. Based on the above two kinds of information, we used residual neural network and support vector machine for modeling. <jats:italic>Results<\/jats:italic>. We established a model to predict lymph node metastasis in patients with primary ovarian cancer using PET images. On the training set, our accuracy was 0.8854, AUC: 0.9472, CI: 0.9098\u20100.9752, sensitivity was 0.9865, and specificity was 0.7952. On the test set, our accuracy was 0.9104, AUC: 0.9259, CI: 0.8417\u20100.9889, sensitivity was 0.8125, and specificity was 1.0000. <jats:italic>Conclusions<\/jats:italic>. We used wavelet transform to process the preoperative medical images of ovarian cancer patients, and the residual neural network can effectively predict the lymph node metastasis of ovarian cancer patients, which is undoubted of great significance for patients\u2019 staging and treatment options.<\/jats:p>","DOI":"10.1155\/2022\/9646846","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T03:20:53Z","timestamp":1665544853000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Prediction Model of Residual Neural Network for Pathological Confirmed Lymph Node Metastasis of Ovarian Cancer"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1040-2324","authenticated-orcid":false,"given":"Huanchun","family":"Yao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4116-949X","authenticated-orcid":false,"given":"Xinglong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21551"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)62146-7"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajpath.2015.11.011"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32552-2"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-022-13861-y"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1810858"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(21)00531-3"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijgo.2013.10.001"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.31"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejogrb.2015.06.011"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11101748"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-018-5067-1"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5006"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2018.11.007"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.86517"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10082-4"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/app12147314"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0324-9"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-16777-6"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2021.12.010"},{"key":"e_1_2_9_21_2","first-page":"221","article-title":"Clinicopathological analysis of ovarian tumors--a study on five years samples","volume":"12","author":"Swamy G.","year":"2010","journal-title":"Nepal Medical College Journal"},{"key":"e_1_2_9_22_2","volume-title":"nipy\/nibabel: 3.0. 0","author":"Brett M.","year":"2019"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.3.013074"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/make1010025"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-163129"},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"GhazaliK. H. MansorM. F. MustafaM. M. andHussainA. Feature Extraction Technique Using Discrete Wavelet Transform for Image Classification 2007 5th Student Conference on Research and Development 2007 Selangor Malaysia 1\u20134 https:\/\/doi.org\/10.1109\/SCORED.2007.4451366 2-s2.0-50449089280.","DOI":"10.1109\/SCORED.2007.4451366"},{"key":"e_1_2_9_27_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition 2016 Las Vegas NV USA 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2011.01.112"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13193-016-0601-6"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.11110175"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.bioeng.2.1.511"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1191\/0962280203sm339ra"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.3233\/XST-200715"},{"key":"e_1_2_9_34_2","unstructured":"WangD. KhoslaA. GargeyaR. IrshadH. andBeckA. H. Deep learning for identifying metastatic breast cancer 2016 https:\/\/arxiv.org\/abs\/1606.05718."},{"key":"e_1_2_9_35_2","unstructured":"ChenH. LiC. LiX. WangG. HuW. LiY. LiuW. SunC. YaoY. TengY. andGrzegorzekM. Gashis-transformer: a multi-scale visual transformer approach for gastric histopathology image classification https:\/\/arxiv.org\/abs\/2104.14528."},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.24920\/003963"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-022-02958-5"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896920"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107885"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-022-00388-9"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102022"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20180986"},{"key":"e_1_2_9_43_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.01364"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-021-01393-1"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-07731-1"},{"key":"e_1_2_9_46_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.11625"},{"key":"e_1_2_9_47_2","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.3132"},{"key":"e_1_2_9_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2423261"}],"container-title":["BioMed Research International"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2022\/9646846.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2022\/9646846.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/9646846","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T20:17:53Z","timestamp":1735676273000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2022\/9646846"}},"subtitle":[],"editor":[{"given":"Chen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["10.1155\/2022\/9646846"],"URL":"https:\/\/doi.org\/10.1155\/2022\/9646846","archive":["Portico"],"relation":{},"ISSN":["2314-6133","2314-6141"],"issn-type":[{"value":"2314-6133","type":"print"},{"value":"2314-6141","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"2022-08-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-09-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-10-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9646846"}}