{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T19:06:58Z","timestamp":1786388818417,"version":"build-2736575974"},"reference-count":34,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Accurate diagnosis of N2 lymph node status of the resectable stage I\u2010II non\u2010small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I\u2010II NSCLC using multiview radiomics and deep learning method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum\u2010redundancy maximum\u2010relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end\u2010to\u2010end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer\u2013Lemeshow test, and decision curves, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2\u00a0+\u00a0C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant (<jats:italic>p<\/jats:italic> = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad\u00a0+\u00a0DL, Rad\u00a0+\u00a0Clinical, DL\u00a0+\u00a0Clinical, and Rad\u00a0+\u00a0DL\u00a0+\u00a0Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad\u00a0+\u00a0DL\u00a0+\u00a0Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I\u2010II NSCLC.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16177","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T17:10:56Z","timestamp":1671815456000},"page":"2049-2060","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Predicting N2 lymph node metastasis in presurgical stage I\u2010II non\u2010small cell lung cancer using multiview radiomics and deep learning method"],"prefix":"10.1002","volume":"50","author":[{"given":"Hanfei","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiology, Zhongnan Hospital of Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meiyan","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Radiology, Zhongnan Hospital of Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Guo","sequence":"additional","affiliation":[{"name":"Media Tek  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"Wuhan GE Healthcare  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Zhongnan Hospital of Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songmei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory Zhongnan Hospital of Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Radiology, Zhongnan Hospital of Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21654"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mayocp.2019.01.013"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtho.2015.09.009"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2017.00249"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2018.00005"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.182.2.1732943"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiographics.18.5.9747607"},{"issue":"11","key":"e_1_2_7_9_1","article-title":"PET\u2010CT for assessing mediastinal lymph node involvement in patients with suspected resectable non\u2010small cell lung cancer","volume":"2014","author":"Schmidt\u2010Hansen M","year":"2014","journal-title":"Cochrane Database Syst Rev"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2017.141"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2018.02.018"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5583-z"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.585767"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12901"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15027-z"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcm.2016.02.012"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt383"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab8531"},{"key":"e_1_2_7_20_1","doi-asserted-by":"crossref","unstructured":"RibeiroM T SinghS GuestrinC.\u201cWhy should i trust you?\u201d: Explaining the predictions of any classifier[C].ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM.2016:1135\u20101144.","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2012.0062"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCO.0000000000000596"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)62159-5"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/ejcts\/ezu028"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtcvs.2005.10.045"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2013.06.038"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtcvs.2012.06.050"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2013.03.050"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.603595"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.17.19074"},{"key":"e_1_2_7_31_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.585942"},{"key":"e_1_2_7_32_1","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr.2019.11.18"},{"key":"e_1_2_7_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07624-9"},{"key":"e_1_2_7_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07523-z"},{"key":"e_1_2_7_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07466-5"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.16177","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.16177","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.16177","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T10:23:48Z","timestamp":1754043828000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.16177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,6]]},"references-count":34,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["10.1002\/mp.16177"],"URL":"https:\/\/doi.org\/10.1002\/mp.16177","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,6]]},"assertion":[{"value":"2022-06-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-01-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}