Qingwu Wu1, Jianning Chen2, Yong Ren3, Huijun Qiu4, Lianxiong Yuan5, Huiyi Deng4, Yana Zhang4, Rui Zheng4, Haiyu Hong6, Yueqi Sun7, Xinyue Wang4, Xuekun Huang4, Chunkui Shao2, Haotian Lin8, Lanqing Han9, Qintai Yang10. 1. Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China; Department of Allergy, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China. 2. Department of Pathology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China. 3. Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou 510735, China; Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518107, China. 4. Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China. 5. Department of Science and Research, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China. 6. Department of Otolaryngology-Head and Neck Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai 519020, China. 7. Department of Otorhinolaryngology-Head and Neck Surgery, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518107, China. 8. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, China. 9. Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou 510735, China. Electronic address: hanlance@tsinghua-gd.org. 10. Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China; Department of Allergy, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China. Electronic address: yangqint@mail.sysu.edu.cn.
Abstract
BACKGROUND: artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of inflammatory cells for cellular phenotyping diagnosis of nasal polyps and to explore the clinical significance of different phenotypes of nasal polyps on the WSI. METHODS: a total of 453 patients were enrolled in our study. For the development of AICEP 2.0, 179 patients (WSIs) were obtained from the Third Affiliated Hospital of Sun Yat-Sen University (3HSYSU) from January 2008 to December 2018. A total of 24,625 patches were automatically extracted from the regions of interest under a 400× HPF by Openslide and the number of inflammatory cells in these patches was counted by two pathologists. For the application of AICEP 2.0 in a prospective cohort, 158 patients aged 14-70 years old with chronic rhinosinusitis with nasal polyps (CRSwNP) who had undergone endoscopic sinus surgery at 3HSYSU from June 2020 to December 2020 were included for preoperative demographic characteristics. For the application of AICEP 2.0 in a retrospective cohort, 116 patients with CRSwNP who had undergone endoscopic sinus surgery from May 2016 to June 2017 were enrolled for the recurrence rate. The proportion of inflammatory cells of these patients on WSI was calculated by our AICEP 2.0. FINDINGS: for AICEP 2.0, the mean absolute errors of the ratios of eosinophils, lymphocytes, neutrophils, and plasma cells were 1.64%, 2.13%, 1.06%, and 1.22%, respectively. The four phenotypes of nasal polyps were significantly different in clinical characteristics (including asthma, itching, sneezing, total IgE, peripheral eosinophils%, tissue eosinophils%, tissue neutrophils%, tissue lymphocytes%, tissue plasma cells%, and recurrence rate; P <0.05), but there were no significant differences in age distribution, onset time, total VAS score, Lund-Kennedy score, or Lund-Mackay score. The percentage of peripheral eosinophils was positively correlated with the percentage of tissue eosinophils (r = 0.560, P <0.001) and negatively correlated with tissue lymphocytes% (r = -0.489, P <0.001), tissue neutrophils% (r = -0.225, P = 0.005), and tissue plasma cells% (r = -0.266, P = 0.001) in WSIs.
BACKGROUND: artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of inflammatory cells for cellular phenotyping diagnosis of nasal polyps and to explore the clinical significance of different phenotypes of nasal polyps on the WSI. METHODS: a total of 453 patients were enrolled in our study. For the development of AICEP 2.0, 179 patients (WSIs) were obtained from the Third Affiliated Hospital of Sun Yat-Sen University (3HSYSU) from January 2008 to December 2018. A total of 24,625 patches were automatically extracted from the regions of interest under a 400× HPF by Openslide and the number of inflammatory cells in these patches was counted by two pathologists. For the application of AICEP 2.0 in a prospective cohort, 158 patients aged 14-70 years old with chronic rhinosinusitis with nasal polyps (CRSwNP) who had undergone endoscopic sinus surgery at 3HSYSU from June 2020 to December 2020 were included for preoperative demographic characteristics. For the application of AICEP 2.0 in a retrospective cohort, 116 patients with CRSwNP who had undergone endoscopic sinus surgery from May 2016 to June 2017 were enrolled for the recurrence rate. The proportion of inflammatory cells of these patients on WSI was calculated by our AICEP 2.0. FINDINGS: for AICEP 2.0, the mean absolute errors of the ratios of eosinophils, lymphocytes, neutrophils, and plasma cells were 1.64%, 2.13%, 1.06%, and 1.22%, respectively. The four phenotypes of nasal polyps were significantly different in clinical characteristics (including asthma, itching, sneezing, total IgE, peripheral eosinophils%, tissue eosinophils%, tissue neutrophils%, tissue lymphocytes%, tissue plasma cells%, and recurrence rate; P <0.05), but there were no significant differences in age distribution, onset time, total VAS score, Lund-Kennedy score, or Lund-Mackay score. The percentage of peripheral eosinophils was positively correlated with the percentage of tissue eosinophils (r = 0.560, P <0.001) and negatively correlated with tissue lymphocytes% (r = -0.489, P <0.001), tissue neutrophils% (r = -0.225, P = 0.005), and tissue plasma cells% (r = -0.266, P = 0.001) in WSIs.
Authors: Antonio Mario Bulfamante; Francesco Ferella; Austin Michael Miller; Cecilia Rosso; Carlotta Pipolo; Emanuela Fuccillo; Giovanni Felisati; Alberto Maria Saibene Journal: Eur Arch Otorhinolaryngol Date: 2022-10-19 Impact factor: 3.236
Authors: Constanze A Jakwerth; Jose Ordovas-Montanes; Simon Blank; Carsten B Schmidt-Weber; Ulrich M Zissler Journal: Cells Date: 2022-04-20 Impact factor: 7.666