Alberto Paderno, Francesca Pia Villani, Milena Fior, Giulia Berretti, Francesca Gennarini, Gabriele Zigliani, Emanuela Ulaj, Claudia Montenegro, Alessandra Sordi, Claudio Sampieri, Giorgio Peretti, Sara Moccia, Cesare Piazza
{"title":"上消化道癌症的实例分割:部位特异性结果。","authors":"Alberto Paderno, Francesca Pia Villani, Milena Fior, Giulia Berretti, Francesca Gennarini, Gabriele Zigliani, Emanuela Ulaj, Claudia Montenegro, Alessandra Sordi, Claudio Sampieri, Giorgio Peretti, Sara Moccia, Cesare Piazza","doi":"10.14639/0392-100X-N2336","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To achieve instance segmentation of upper aerodigestive tract (UADT) neoplasms using a deep learning (DL) algorithm, and to identify differences in its diagnostic performance in three different sites: larynx/hypopharynx, oral cavity and oropharynx.</p><p><strong>Methods: </strong>A total of 1034 endoscopic images from 323 patients were examined under narrow band imaging (NBI). The Mask R-CNN algorithm was used for the analysis. The dataset split was: 935 training, 48 validation and 51 testing images. Dice Similarity Coefficient (Dsc) was the main outcome measure.</p><p><strong>Results: </strong>Instance segmentation was effective in 76.5% of images. The mean Dsc was 0.90 ± 0.05. The algorithm correctly predicted 77.8%, 86.7% and 55.5% of lesions in the larynx/hypopharynx, oral cavity, and oropharynx, respectively. The mean Dsc was 0.90 ± 0.05 for the larynx/hypopharynx, 0.60 ± 0.26 for the oral cavity, and 0.81 ± 0.30 for the oropharynx. The analysis showed inferior diagnostic results in the oral cavity compared with the larynx/hypopharynx (p < 0.001).</p><p><strong>Conclusions: </strong>The study confirms the feasibility of instance segmentation of UADT using DL algorithms and shows inferior diagnostic results in the oral cavity compared with other anatomic areas.</p>","PeriodicalId":6890,"journal":{"name":"Acta Otorhinolaryngologica Italica","volume":"43 4","pages":"283-290"},"PeriodicalIF":2.1000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/be/49/aoi-2023-04-283.PMC10366566.pdf","citationCount":"0","resultStr":"{\"title\":\"Instance segmentation of upper aerodigestive tract cancer: site-specific outcomes.\",\"authors\":\"Alberto Paderno, Francesca Pia Villani, Milena Fior, Giulia Berretti, Francesca Gennarini, Gabriele Zigliani, Emanuela Ulaj, Claudia Montenegro, Alessandra Sordi, Claudio Sampieri, Giorgio Peretti, Sara Moccia, Cesare Piazza\",\"doi\":\"10.14639/0392-100X-N2336\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To achieve instance segmentation of upper aerodigestive tract (UADT) neoplasms using a deep learning (DL) algorithm, and to identify differences in its diagnostic performance in three different sites: larynx/hypopharynx, oral cavity and oropharynx.</p><p><strong>Methods: </strong>A total of 1034 endoscopic images from 323 patients were examined under narrow band imaging (NBI). The Mask R-CNN algorithm was used for the analysis. The dataset split was: 935 training, 48 validation and 51 testing images. Dice Similarity Coefficient (Dsc) was the main outcome measure.</p><p><strong>Results: </strong>Instance segmentation was effective in 76.5% of images. The mean Dsc was 0.90 ± 0.05. The algorithm correctly predicted 77.8%, 86.7% and 55.5% of lesions in the larynx/hypopharynx, oral cavity, and oropharynx, respectively. The mean Dsc was 0.90 ± 0.05 for the larynx/hypopharynx, 0.60 ± 0.26 for the oral cavity, and 0.81 ± 0.30 for the oropharynx. The analysis showed inferior diagnostic results in the oral cavity compared with the larynx/hypopharynx (p < 0.001).</p><p><strong>Conclusions: </strong>The study confirms the feasibility of instance segmentation of UADT using DL algorithms and shows inferior diagnostic results in the oral cavity compared with other anatomic areas.</p>\",\"PeriodicalId\":6890,\"journal\":{\"name\":\"Acta Otorhinolaryngologica Italica\",\"volume\":\"43 4\",\"pages\":\"283-290\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/be/49/aoi-2023-04-283.PMC10366566.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Otorhinolaryngologica Italica\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.14639/0392-100X-N2336\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OTORHINOLARYNGOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Otorhinolaryngologica Italica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.14639/0392-100X-N2336","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OTORHINOLARYNGOLOGY","Score":null,"Total":0}
Instance segmentation of upper aerodigestive tract cancer: site-specific outcomes.
Objective: To achieve instance segmentation of upper aerodigestive tract (UADT) neoplasms using a deep learning (DL) algorithm, and to identify differences in its diagnostic performance in three different sites: larynx/hypopharynx, oral cavity and oropharynx.
Methods: A total of 1034 endoscopic images from 323 patients were examined under narrow band imaging (NBI). The Mask R-CNN algorithm was used for the analysis. The dataset split was: 935 training, 48 validation and 51 testing images. Dice Similarity Coefficient (Dsc) was the main outcome measure.
Results: Instance segmentation was effective in 76.5% of images. The mean Dsc was 0.90 ± 0.05. The algorithm correctly predicted 77.8%, 86.7% and 55.5% of lesions in the larynx/hypopharynx, oral cavity, and oropharynx, respectively. The mean Dsc was 0.90 ± 0.05 for the larynx/hypopharynx, 0.60 ± 0.26 for the oral cavity, and 0.81 ± 0.30 for the oropharynx. The analysis showed inferior diagnostic results in the oral cavity compared with the larynx/hypopharynx (p < 0.001).
Conclusions: The study confirms the feasibility of instance segmentation of UADT using DL algorithms and shows inferior diagnostic results in the oral cavity compared with other anatomic areas.
期刊介绍：
Acta Otorhinolaryngologica Italica first appeared as “Annali di Laringologia Otologia e Faringologia” and was founded in 1901 by Giulio Masini.
It is the official publication of the Italian Hospital Otology Association (A.O.O.I.) and, since 1976, also of the Società Italiana di Otorinolaringoiatria e Chirurgia Cervico-Facciale (S.I.O.Ch.C.-F.).
The journal publishes original articles (clinical trials, cohort studies, case-control studies, cross-sectional surveys, and diagnostic test assessments) of interest in the field of otorhinolaryngology as well as clinical techniques and technology (a short report of unique or original methods for surgical techniques, medical management or new devices or technology), editorials (including editorial guests – special contribution) and letters to the Editor-in-Chief.
Articles concerning science investigations and well prepared systematic reviews (including meta-analyses) on themes related to basic science, clinical otorhinolaryngology and head and neck surgery have high priority.
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