Clinical trial NCT05202626: Detection and Biopsy Guidance of Nasopharyngeal Carcinoma Based on Artificial Intelligence and Endoscopic Images
Official title: Detection and Biopsy Guidance of Nasopharyngeal Carcinoma Based on Artificial Intelligence and Endoscopic Images：a Multi-center Prospective Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Nasopharyngeal Carcinoma
Image-Guided Biopsy
Keywords: Artificial Intelligence
Classification
Nasopharyngeal Carcinoma
Nasopharyngoscope
Image-Guided Biopsy
Lead sponsor: Chinese Academy of Sciences
Sponsor class: OTHER_GOV
Collaborators: name: Sun Yat-sen University
name: Guangdong Provincial People's Hospital
name: Nanfang Hospital, Southern Medical University
name: Fujian Cancer Hospital
name: Hainan People's Hospital
First posted: 2022-01-21
Enrollment: 100
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The patient was found to have a nasopharyngeal lesion through the nasopharyngeal endoscopy and the clinican considered it necessary to perform an endoscopic image-guided biopsy.

Brief summary: Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for nasopharyngeal carcinoma identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal carcinoma upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies and guide biopsy under endoscopic examination based on deep learning.

Detailed description: Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for nasopharyngeal carcinoma identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal carcinoma upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies and guide biopsy under endoscopic examination based on deep learning.

Eligibility criteria: Inclusion Criteria:

* The patient was found to have a nasopharyngeal lesion through the nasopharyngeal endoscopy and the clinicans considered it necessary to perform an biopsy.
* Hemilateral lesion with limited size.

Exclusion Criteria:

* Patients with nasopharyngeal cancer, oropharyngeal cancer, hypopharyngeal cancer, etc. who have already been treated.

Primary outcomes:
measure: Aera under the receiver operating characteristic curve (AUC)
timeFrame: three months
Secondary outcomes:
measure: Accuray
timeFrame: three months
Other outcomes:
measure: Statistical significance
timeFrame: Three years
Locations:
city: Fuzhou
country: China
facility: Fujian Provincial Cancer Hospital
state: Fujian
city: Guangzhou
country: China
facility: Guangdong Provincial People's Hospital
state: Guangdong
city: Guangzhou
country: China
facility: Nanfang Hospital, Southern Medical University
state: Guangdong
city: Guangzhou
country: China
facility: Sun Yat-Sen University Cancer Center
state: Guangdong
city: Haikou
country: China
facility: Hainan Provincial People's Hospital
state: Hainan
Study officials:
References:
citation: Zwakenberg MA, Halmos GB, Wedman J, van der Laan BFAM, Plaat BEC. Evaluating Laryngopharyngeal Tumor Extension Using Narrow Band Imaging Versus Conventional White Light Imaging. Laryngoscope. 2021 Jul;131(7):E2222-E2231. doi: 10.1002/lary.29361. Epub 2021 Jan 4.
citation: Li C, Jing B, Ke L, Li B, Xia W, He C, Qian C, Zhao C, Mai H, Chen M, Cao K, Mo H, Guo L, Chen Q, Tang L, Qiu W, Yu Y, Liang H, Huang X, Liu G, Li W, Wang L, Sun R, Zou X, Guo S, Huang P, Luo D, Qiu F, Wu Y, Hua Y, Liu K, Lv S, Miao J, Xiang Y, Sun Y, Guo X, Lv X. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies. Cancer Commun (Lond). 2018 Sep 25;38(1):59. doi: 10.1186/s40880-018-0325-9.