Nasopharyngeal Cancinoma (NPC)NCT06831357
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
An observational study of Nasopharyngeal Cancinoma (NPC) and Distant Metastasis, run by Sun Yat-sen University at 2 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Nasopharyngeal Cancinoma (NPC), Distant Metastasis
- How many people
- 500 participants
- Where
- 2 sitesChina
- Study dates
- 15 Feb 2025to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with pathologically confirmed nasopharyngeal carcinoma
Who may be able to join
- 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.
Who cannot join
- Previous history of other malignant tumors (such as other head and neck squamous cell carcinomas, thyroid cancer, breast cancer, esophageal cancer, etc.).
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Not yet recruiting
Department of Radiation Oncology, Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, 510060
Pu-Yun OuYang, 86+020-87342925, ouyangpy@sysucc.org.cn
Pu-Yun OuYang
- Recruiting
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, 510060
Pu-Yun OuYang, +86 18565382769, ouyangpy@sysucc.org.cn
Pu-Yun OuYang, ouyangpy@sysucc.org.cn
About this study
Official title: Development and Multicenter Validation of a Deep Learning Model Based on Whole Slide Imaging and Magnetic Resonance Imaging of the Nasopharynx and Lymph Nodes to Predict Distant Metastases at Diagnosis in Nasopharyngeal Carcinoma
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.
Read the detailed description
An AI model was constructed based on HE-stained pathological sections of the primary lesion and MRI of the nasopharynx and neck to predict the probability of distant metastasis at the first visit, and the AI model was fully verified by multicenter data; the AI model was applied to T3-4 or N2-3 patients who were recommended to undergo PET/CT examination according to the NCCN and CSCO guidelines, and the threshold of the AI model when the negative predictive value for predicting M0 was not less than 95% was determined, providing theoretical support for patients predicted by AI to be exempted from PET/CT examination.
- Conditions
- Nasopharyngeal Cancinoma (NPC), Distant Metastasis
- Keywords
- Nasopharyngeal Cancinoma (NPC), Distant Metastasis, PET/CT, MRI, whole slide imaging, deep learning model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Prospective Validation Cohort | Prospective patient enrollment to validate the diagnostic efficacy of the AI model |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: through study completion, an average of 2 year
Secondary outcomes (1)
Sensitivity, specificity, and positive predictive value
Sensitivity, specificity, and positive predictive value of AI in predicting distant metastasis at the threshold corresponding to a negative predictive value of 95%.
Measured: through study completion, an average of 2 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Feb 2025
- Study start15 Feb 2025 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Nasopharyngeal Cancinoma (NPC)Studies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- First Affiliated Hospital, Sun Yat-Sen University, Fifth Affiliated Hospital, Sun Yat-Sen University, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, The Affiliated Panyu Center Hospital of Guangzhou Medical University, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Qingyuan People's Hospital
- Responsible party
- Principal investigator, Pu-Yun OuYang, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06831357
- Sponsor's protocol ID
- B2025-062-01
- Status last verified
- Feb 2025
- First submitted
- 14 Feb 2025
- Last updated
- 25 Feb 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data (IPD) might not be shared due to concerns about patient privacy, ethical considerations, or institutional policies. Restrictions may also arise from data protection regulations, confidentiality agreements, or the potential risk of re-identification. Additionally, if the data includes sensitive medical information, sharing may require special approvals or de-identification processes that are not feasible.
Publications
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.
PubMed 34391094Qiang 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.
PubMed 32970812Lin 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.
PubMed 30912722OuYang 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.
PubMed 37090437OuYang 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.
PubMed 37680944Sun 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.
PubMed 31319091Tang 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.
PubMed 23857969Xiao 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.
PubMed 33420610
Study at a glance
From the registry record, last updated by the sponsor on 25 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Multicenter Validation of a Deep Learning Model Based on Whole Slide Imaging and Magnetic Resonance Imaging of the Nasopharynx and Lymph Nodes to Predict Distant Metastases at Diagnosis in Nasopharyngeal Carcinoma
- ClinicalTrials.gov ID
- NCT06831357
- Acronym / sponsor ID
- B2025-062-01
- Conditions
- Nasopharyngeal Cancinoma (NPC), Distant Metastasis
- Keywords
- Nasopharyngeal Cancinoma (NPC), Distant Metastasis, PET/CT, MRI, whole slide imaging, deep learning model
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- First Affiliated Hospital, Sun Yat-Sen University, Fifth Affiliated Hospital, Sun Yat-Sen University, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, The Affiliated Panyu Center Hospital of Guangzhou Medical University, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Qingyuan People's Hospital
- Study start
- 15 Feb 2025 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 18 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
An AI model was constructed based on HE-stained pathological sections of the primary lesion and MRI of the nasopharynx and neck to predict the probability of distant metastasis at the first visit, and the AI model was fully verified by multicenter data; the AI model was applied to T3-4 or N2-3 patients who were recommended to undergo PET/CT examination according to the NCCN and CSCO guidelines, and the threshold of the AI model when the negative predictive value for predicting M0 was not less than 95% was determined, providing theoretical support for patients predicted by AI to be exempted from PET/CT examination.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prospective Validation Cohort | – | –Prospective patient enrollment to validate the diagnostic efficacy of the AI model |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with pathologically confirmed nasopharyngeal carcinoma
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (1 listed)
- Previous history of other malignant tumors (such as other head and neck squamous cell carcinomas, thyroid cancer, breast cancer, esophageal cancer, etc.).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Negative predictive value
Time frame: through study completion, an average of 2 year
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.
1 secondary outcome
Sensitivity, specificity, and positive predictive value
Time frame: through study completion, an average of 2 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Feb 2025.
- Background: PubMed 34391094Zhong 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.
- Background: PubMed 32970812Qiang 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.
- Background: PubMed 30912722Lin 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.
- Background: PubMed 37090437OuYang 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.
- Background: PubMed 37680944OuYang 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.
- Background: PubMed 31319091Sun 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.
- Background: PubMed 23857969Tang 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.
- Background: PubMed 33420610Xiao 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.
- Individual participant dataSharing planned: No. Individual participant data (IPD) might not be shared due to concerns about patient privacy, ethical considerations, or institutional policies. Restrictions may also arise from data protection regulations, confidentiality agreements, or the potential risk of re-identification. Additionally, if the data includes sensitive medical information, sharing may require special approvals or de-identification processes that are not feasible.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Nasopharyngeal Cancinoma (NPC) and Distant Metastasis, run by Sun Yat-sen University at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 25 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Pu-Yun OuYang (+8618565382769, ouyangpy@sysucc.org.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Negative predictive value (time frame: through study completion, an average of 2 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06831357, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Feb 2025.