Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI

NCT06831357 | Recruiting

• 1 other identifier: B2025-062-01
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 2

Brief Summary

An AI model was developed to predict the likelihood of distant metastasis in patients with nasopharyngeal cancer based on pathology slides and MRI scans of the primary tumor. The model was validated using data from multiple centers. It was then applied to patients with advanced stages who were recommended to undergo PET/CT scans based on the NCCN or CSCO guidelines. This AI model can accurately screen patients with high risk of distant metastasis at the time of initial diagnosis to receive PET/CT, avoid excessive examination of patients with low risk of distant metastasis, save medical resources and reduce the economic burden on patients.

Conditions

Nasopharyngeal Cancinoma (NPC); Distant Metastasis

Keywords

Nasopharyngeal Cancinoma (NPC); Distant Metastasis; PET/CT; MRI; whole slide imaging; deep learning model

Outcome Measures

Primary Outcomes (1)

• Negative predictive value

  NPV measures the proportion of predicted negative cases that are actually negative. It tells us how reliable the model is when it predicts a negative outcome.

  through study completion, an average of 2 year

Secondary Outcomes (1)

• Sensitivity, specificity, and positive predictive value

  through study completion, an average of 2 year

Study Arms (1)

Prospective Validation Cohort

Prospective patient enrollment to validate the diagnostic efficacy of the AI model

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with pathologically confirmed nasopharyngeal carcinoma

You may qualify if:

• A. The primary lesion was pathologically confirmed as nasopharyngeal carcinoma (WHO classification is I, II and III); B. The stage was T3-4 or N2-3, and the nasopharynx + neck MRI plain scan and enhanced scan were performed to confirm the nasopharyngeal and cervical lymph node lesions, and PET/CT or conventional examination (chest CT plain scan + enhanced scan, upper abdominal CT or MRI plain scan + enhanced scan or abdominal color Doppler ultrasound or ultrasound angiography, and whole body bone imaging) was performed to screen for distant metastases.

You may not qualify if:

• Previous history of other malignant tumors (such as other head and neck squamous cell carcinomas, thyroid cancer, breast cancer, esophageal cancer, etc.).

Sponsors & Collaborators

• Sun Yat-sen University: lead
• First Affiliated Hospital, Sun Yat-Sen University: collaborator
• Fifth Affiliated Hospital, Sun Yat-Sen University: collaborator
• Affiliated Cancer Hospital & Institute of Guangzhou Medical University: collaborator
• The Affiliated Panyu Center Hospital of Guangzhou Medical University: collaborator
• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: collaborator
• Qingyuan People's Hospital: collaborator

Study Sites (2)

Department of Radiation Oncology, Sun Yat-sen University Cancer Center

Guangzhou, Guangdong, 510060, China

NOT YET RECRUITING

Sun Yat-sen University Cancer Center

Guangzhou, Guangdong, 510060, China

RECRUITING

Related Publications (8)

• Zhong L, Dong D, Fang X, Zhang F, Zhang N, Zhang L, Fang M, Jiang W, Liang S, Li C, Liu Y, Zhao X, Cao R, Shan H, Hu Z, Ma J, Tang L, Tian J. A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study. EBioMedicine. 2021 Aug;70:103522. doi: 10.1016/j.ebiom.2021.103522. Epub 2021 Aug 11.

  PMID: 34391094 BACKGROUND

• Qiang M, Li C, Sun Y, Sun Y, Ke L, Xie C, Zhang T, Zou Y, Qiu W, Gao M, Li Y, Li X, Zhan Z, Liu K, Chen X, Liang C, Chen Q, Mai H, Xie G, Guo X, Lv X. A Prognostic Predictive System Based on Deep Learning for Locoregionally Advanced Nasopharyngeal Carcinoma. J Natl Cancer Inst. 2021 May 4;113(5):606-615. doi: 10.1093/jnci/djaa149.

  PMID: 32970812 BACKGROUND

• Lin L, Dou Q, Jin YM, Zhou GQ, Tang YQ, Chen WL, Su BA, Liu F, Tao CJ, Jiang N, Li JY, Tang LL, Xie CM, Huang SM, Ma J, Heng PA, Wee JTS, Chua MLK, Chen H, Sun Y. Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma. Radiology. 2019 Jun;291(3):677-686. doi: 10.1148/radiol.2019182012. Epub 2019 Mar 26.

  PMID: 30912722 BACKGROUND

• OuYang PY, Zhang BY, Guo JG, Liu JN, Li J, Peng QH, Yang SS, He Y, Liu ZQ, Zhao YN, Li A, Wu YS, Hu XF, Chen C, Han F, You KY, Xie FY. Deep learning-based precise prediction and early detection of radiation-induced temporal lobe injury for nasopharyngeal carcinoma. EClinicalMedicine. 2023 Apr 4;58:101930. doi: 10.1016/j.eclinm.2023.101930. eCollection 2023 Apr.

  PMID: 37090437 BACKGROUND

• OuYang PY, He Y, Guo JG, Liu JN, Wang ZL, Li A, Li J, Yang SS, Zhang X, Fan W, Wu YS, Liu ZQ, Zhang BY, Zhao YN, Gao MY, Zhang WJ, Xie CM, Xie FY. Artificial intelligence aided precise detection of local recurrence on MRI for nasopharyngeal carcinoma: a multicenter cohort study. EClinicalMedicine. 2023 Aug 30;63:102202. doi: 10.1016/j.eclinm.2023.102202. eCollection 2023 Sep.

  PMID: 37680944 BACKGROUND

• Sun XS, Liu SL, Luo MJ, Li XY, Chen QY, Guo SS, Wen YF, Liu LT, Xie HJ, Tang QN, Liang YJ, Yan JJ, Lin DF, Bi MM, Liu Y, Liang YF, Ma J, Tang LQ, Mai HQ. The Association Between the Development of Radiation Therapy, Image Technology, and Chemotherapy, and the Survival of Patients With Nasopharyngeal Carcinoma: A Cohort Study From 1990 to 2012. Int J Radiat Oncol Biol Phys. 2019 Nov 1;105(3):581-590. doi: 10.1016/j.ijrobp.2019.06.2549. Epub 2019 Jul 15.

  PMID: 31319091 BACKGROUND

• Tang LQ, Chen QY, Fan W, Liu H, Zhang L, Guo L, Luo DH, Huang PY, Zhang X, Lin XP, Mo YX, Liu LZ, Mo HY, Li J, Zou RH, Cao Y, Xiang YQ, Qiu F, Sun R, Chen MY, Hua YJ, Lv X, Wang L, Zhao C, Guo X, Cao KJ, Qian CN, Zeng MS, Mai HQ. Prospective study of tailoring whole-body dual-modality [18F]fluorodeoxyglucose positron emission tomography/computed tomography with plasma Epstein-Barr virus DNA for detecting distant metastasis in endemic nasopharyngeal carcinoma at initial staging. J Clin Oncol. 2013 Aug 10;31(23):2861-9. doi: 10.1200/JCO.2012.46.0816. Epub 2013 Jul 15.

  PMID: 23857969 BACKGROUND

• Xiao BB, Lin DF, Sun XS, Zhang X, Guo SS, Liu LT, Luo DH, Sun R, Wen YF, Li JB, Lv XF, Han LJ, Yuan L, Liu SL, Tang QN, Liang YJ, Li XY, Guo L, Chen QY, Fan W, Mai HQ, Tang LQ. Nomogram for the prediction of primary distant metastasis of nasopharyngeal carcinoma to guide individualized application of FDG PET/CT. Eur J Nucl Med Mol Imaging. 2021 Jul;48(8):2586-2598. doi: 10.1007/s00259-020-05128-8. Epub 2021 Jan 8.

  PMID: 33420610 BACKGROUND

Central Study Contacts

Pu-Yun OuYang

CONTACT

+8618565382769; ouyangpy@sysucc.org.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Chief Physician

Study Record Dates

• First Submitted: February 14, 2025
• First Posted: February 18, 2025
• Study Start: February 15, 2025
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: February 25, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (2)