{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T01:57:34Z","timestamp":1793239054372,"version":"4.5.0"},"reference-count":37,"publisher":"IOP Publishing","issue":"7","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62371328"],"award-info":[{"award-number":["62371328"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2018YFA0701700"],"award-info":[{"award-number":["2018YFA0701700"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2024,4,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) present many similar clinical features. However, there are significant differences in the progression of nAMD and PCV. and it is crucial to make accurate diagnosis for treatment. In this paper, we propose a structure-radiomic fusion network (DRFNet) to differentiate PCV and nAMD in optical coherence tomography (OCT) images.\n                    <jats:italic>Approach.<\/jats:italic>\n                    The subnetwork (RIMNet) is designed to automatically segment the lesion of nAMD and PCV. Another subnetwork (StrEncoder) is designed to extract deep structural features of the segmented lesion. The subnetwork (RadEncoder) is designed to extract radiomic features from the segmented lesions based on radiomics. 305 eyes (155 with nAMD and 150 with PCV) are included and manually annotated CNV region in this study. The proposed method was trained and evaluated by 4-fold cross validation using the collected data and was compared with the advanced differentiation methods.\n                    <jats:italic>Main results.<\/jats:italic>\n                    The proposed method achieved high classification performace of nAMD\/PCV differentiation in OCT images, which was an improvement of 4.68 compared with other best method.\n                    <jats:italic>Significance<\/jats:italic>\n                    . The presented structure-radiomic fusion network (DRFNet) has great performance of diagnosing nAMD and PCV and high clinical value by using OCT instead of indocyanine green angiography.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad2ca0","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T17:22:25Z","timestamp":1708708945000},"page":"075012","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["DRFNet: a deep radiomic fusion network for nAMD\/PCV differentiation in OCT images"],"prefix":"10.1088","volume":"69","author":[{"given":"Erwei","family":"Shen","sequence":"first","affiliation":[{"name":"Soochow University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenmao","family":"Wang","sequence":"additional","affiliation":[{"name":"Shantou University and the Chinese University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Lin","sequence":"additional","affiliation":[{"name":"Shantou University and the Chinese University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingquan","family":"Meng","sequence":"additional","affiliation":[{"name":"Soochow University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weifang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Soochow University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Shi","sequence":"additional","affiliation":[{"name":"Soochow University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinjian","family":"Chen","sequence":"additional","affiliation":[{"name":"Soochow University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0676-4610","authenticated-orcid":false,"given":"Haoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Shantou University and the Chinese University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7873-9778","authenticated-orcid":true,"given":"Dehui","family":"Xiang","sequence":"additional","affiliation":[{"name":"Soochow University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"pmbad2ca0bib1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","type":"journal-article","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"pmbad2ca0bib2","doi-asserted-by":"publisher","first-page":"198","DOI":"10.3341\/kjo.2016.30.3.198","type":"journal-article","article-title":"Optical coherence tomography-based diagnosis of polypoidal choroidal vasculopathy in korean patients","volume":"30","author":"Chang","year":"2016","journal-title":"Korean J. Ophthalmol."},{"key":"pmbad2ca0bib3","doi-asserted-by":"publisher","first-page":"7130","DOI":"10.1038\/s41598-021-86526-2","type":"journal-article","article-title":"Deeplearning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration","volume":"11","author":"Chou","year":"2021","journal-title":"Sci. Rep."},{"key":"pmbad2ca0bib4","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1016\/j.ajo.2014.08.025","type":"journal-article","article-title":"Sensitivity and specificity of spectral-domain optical coherence tomography in detecting idiopathic polypoidal choroidal vasculopathy","volume":"158","author":"De Salvo","year":"2014","journal-title":"Am. J. Ophthalmol."},{"key":"pmbad2ca0bib5","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1109\/TMI.2019.2898414","type":"journal-article","article-title":"Attention to lesion: lesion-aware convolutional neural network for retinal optical coherence tomography image classification","volume":"38","author":"Fang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbad2ca0bib6","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","type":"journal-article","article-title":"An introduction to roc analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"pmbad2ca0bib7","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1038\/s41433-021-01688-7","type":"journal-article","article-title":"Evolving treatment paradigms for PCV","volume":"36","author":"Fenner","year":"2022","journal-title":"Eye"},{"key":"pmbad2ca0bib8","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1038\/s41598-022-05903-7","type":"journal-article","article-title":"Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images","volume":"12","author":"Han","year":"2022","journal-title":"Sci. Rep."},{"key":"pmbad2ca0bib10","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","type":"conference-proceedings","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"pmbad2ca0bib11","doi-asserted-by":"publisher","first-page":"7132","DOI":"10.1109\/CVPR.2018.00745","type":"conference-proceedings","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"pmbad2ca0bib12","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1200\/JCO.2015.65.9128","type":"journal-article","article-title":"Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer","volume":"34","author":"Huang","year":"2016","journal-title":"J. Clin. Oncol."},{"key":"pmbad2ca0bib13","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1186\/s12967-023-03937-7","type":"journal-article","article-title":"Emerging therapeutic strategies for unmet need in neovascular age-related macular degeneration","volume":"21","author":"Khachigian","year":"2023","journal-title":"J. Transl. Med."},{"key":"pmbad2ca0bib14","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","type":"journal-article","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"LAerts","year":"2014","journal-title":"Nat. Commun."},{"key":"pmbad2ca0bib15","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","type":"journal-article","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"pmbad2ca0bib16","doi-asserted-by":"publisher","first-page":"2999","DOI":"10.1109\/ICCV.2017.324","type":"other","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"pmbad2ca0bib17","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1097\/IAE.0000000000000794","type":"journal-article","article-title":"Distinguishing polypoidal choroidal vasculopathy from typical neovascular age-related macular degeneration based on spectral domain optical coherence tomography","volume":"36","author":"Liu","year":"2016","journal-title":"Retina"},{"key":"pmbad2ca0bib18","doi-asserted-by":"publisher","first-page":"3431","DOI":"10.1109\/CVPR.2015.7298965","type":"other","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"pmbad2ca0bib19","doi-asserted-by":"publisher","DOI":"10.1016\/j.xops.2023.100292","type":"journal-article","article-title":"Joint multimodal deep learning-based automatic segmentation of indocyanine green angiography and oct images for assessment of polypoidal choroidal vasculopathy biomarkers","volume":"3","author":"Loo","year":"2023","journal-title":"Ophthalmol. Sci."},{"key":"pmbad2ca0bib20","first-page":"2024","type":"journal-article","article-title":"Differential diagnosis between polypoidal choroidal vasculopathy (pcv) and age-related macular degeneration (amd) using deep neural network","volume":"61","author":"Ma","year":"2020","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"pmbad2ca0bib21","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.compbiomed.2022.105319","type":"journal-article","article-title":"Clinical explainable differential diagnosis of polypoidal choroidal vasculopathy and age-related macular degeneration using deep learning","volume":"143","author":"Ma","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"pmbad2ca0bib22","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.743769","type":"journal-article","article-title":"Mf-net: Multi-scale information fusion network for cnv segmentation in retinal oct images","volume":"15","author":"Meng","year":"2021","journal-title":"Front. Neurosci."},{"key":"pmbad2ca0bib23","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1109\/3DV.2016.79","type":"other","article-title":"V-net: fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"pmbad2ca0bib24","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","type":"conference-proceedings","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"pmbad2ca0bib25","isbn-type":"print","author":"Steinwart","year":"2008","ISBN":"https:\/\/id.crossref.org\/isbn\/9780387772424","type":"book"},{"key":"pmbad2ca0bib26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CVPR.2015.7298594","type":"conference-proceedings","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"pmbad2ca0bib27","doi-asserted-by":"publisher","first-page":"2818","DOI":"10.1109\/CVPR.2016.308","type":"conference-proceedings","article-title":"Rethinking the inception architecture for computer Ision","author":"Szegedy","year":"2016"},{"key":"pmbad2ca0bib28","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1167\/tvst.11.2.6","type":"journal-article","article-title":"Diagnosis of polypoidal choroidal vasculopathy from fluorescein angiography using deep learning","volume":"11","author":"Tsai","year":"2022","journal-title":"Transl. Vision Sci. Technol."},{"key":"pmbad2ca0bib29","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1167\/tvst.12.5.12","type":"journal-article","article-title":"Deep learning for diagnosing and segmenting choroidal neovascularization in OCT angiography in a large real-world data set","author":"Wang","year":"2023","journal-title":"Trans. Vis. Sci. Tech."},{"key":"pmbad2ca0bib30","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TMI.2021.3112716","type":"journal-article","article-title":"Mstganet: automatic drusen segmentation from retinal oct images","volume":"41","author":"Wang","year":"2021","journal-title":"IEEE Trans. on Med. Imaging"},{"key":"pmbad2ca0bib31","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.preteyeres.2016.04.002","type":"journal-article","article-title":"Age-related macular degeneration and polypoidal choroidal vasculopathy in asians","volume":"53","author":"Wong","year":"2016","journal-title":"Prog. Retinal Eye Res."},{"key":"pmbad2ca0bib32","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1167\/tvst.11.10.16","type":"journal-article","article-title":"Application of deep learning for automated detection of polypoidal choroidal vasculopathy in spectral domain optical coherence tomography","volume":"11","author":"Wongchaisuwat","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"pmbad2ca0bib33","doi-asserted-by":"publisher","first-page":"6122","DOI":"10.1364\/BOE.400816","type":"journal-article","article-title":"Ia-net: informative attention convolutional neural network for choroidal neovascularization segmentation in oct images","volume":"11","author":"Xi","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"pmbad2ca0bib34","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s00530-017-0582-5","type":"journal-article","article-title":"Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior","volume":"25","author":"Xi","year":"2019","journal-title":"Multimed. Syst."},{"key":"pmbad2ca0bib35","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1136\/bjophthalmol-2020-315817","type":"journal-article","article-title":"Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks","volume":"105","author":"Xu","year":"2021","journal-title":"Br. J. Ophthalmol."},{"key":"pmbad2ca0bib36","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s00417-019-04493-x","type":"journal-article","article-title":"Utility of a public-available artificial intelligence in diagnosis of polypoidal choroidal vasculopathy","volume":"258","author":"Yang","year":"2020","journal-title":"Graefe\u2019s Archive Clin. Exp. Ophthalmol."},{"key":"pmbad2ca0bib37","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","type":"journal-article","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"pmbad2ca0bib9","doi-asserted-by":"publisher","first-page":"8792","DOI":"10.5555\/3327546.3327555","type":"book","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume":"31","author":"Zhang","year":"2018"}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2ca0","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2ca0\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2ca0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2ca0\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2ca0\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2ca0\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2ca0\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2ca0\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:17:19Z","timestamp":1778779039000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad2ca0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,21]]},"references-count":37,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,3,21]]},"published-print":{"date-parts":[[2024,4,7]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/ad2ca0","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,21]]},"assertion":[{"value":"DRFNet: a deep radiomic fusion network for nAMD\/PCV differentiation in OCT images","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2023-11-08","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-02-23","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-03-21","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}