ROI-guided virtual narrow band imaging for laryngeal cancer screening.
Authors
Affiliations (9)
Affiliations (9)
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Department of Otorhinolaryngology, The People's Hospital of Wenshan Prefecture, Wenshan, Yunnan, China.
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
- Department of Otorhinolaryngology, Kunming Tongren Hospital, Kunming, Yunnan, China.
- Department of Endoscopy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030001, China.
- Department of Otolaryngology-Head and Neck Surgery, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
- Department of Otolaryngology-Head and Neck Surgery, Dalian Municipal Friendship Hospital, Dalian, Liaoning, China.
- Department of Otorhinolaryngology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China.
- Department of Otolaryngology-Head and Neck Surgery, Yongchuan People's Hospital of Chongqing, Chongqing, China.
Abstract
White light laryngoscopy is widely available but can miss subtle vascular changes associated with early laryngeal neoplasia. We developed a region-of-interest (ROI)-guided cycle-consistent generative adversarial network (CycleGAN) that translates white light imaging (WLI) images into virtual narrow band imaging (NBI) images, while emphasizing expert-annotated lesion regions. The framework was trained and evaluated on 775 weakly paired WLI-NBI image pairs and assessed with technical image quality metrics, subjective reader scoring, a visual Turing test, and a multi-center reader study involving 12 otolaryngologists from nine institutions. The model preserved lesion-region structure better than global background structure and improved overall reader accuracy compared with WLI alone (81.5% versus 73.4%). Assistance mainly increased sensitivity for junior readers and specificity for senior readers. These findings support virtual NBI as a potential software-based adjunct where optical NBI is unavailable, while requiring further external and small-lesion validation.