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Nasopharyngeal Cancinoma (NPC)NPCNCT06829147

A Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma(NPC)

An observational study of Nasopharyngeal Cancinoma (NPC) and Lymph Node Metastasis, run by Sun Yat-sen University at 1 site in China.

Recruiting

Looking for participants now. Contact the study team or a site below to ask about taking part.

Sites and contacts
Official record
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Trial summary

(I) AI Model for Diagnosing Lymph Node Metastasis We developed an AI model to help diagnose whether a single lymph node in nasopharyngeal cancer has spread. The model uses MRI images of the lymph node and the area around it. It includes: 1.Automatically identifying the lymph nodes and the primary tumor. Read more about this study

Who is it for?
Any age
How many people
500 participants
Where
1 siteChina
Study dates
19 Nov 2024to 1 Sep 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);
  • MRI scan was performed at the initial diagnosis (before anti-tumor treatment), and transverse and coronal MRI images before treatment were available, including T1-weighted, T2-weighted and T1-enhanced scanning sequences.
  • PET/CT scan was performed at the initial diagnosis (before anti-tumor treatment)
  • When MRI and PET/CT were inconsistent in judging the benign or malignant nature of lymph nodes, the patient agreed to undergo cervical lymph node puncture and pathological examination.

Who cannot join

  • The patient has undergone cervical lymph node radiotherapy for any reason
  • Combined with other malignant tumors

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

China1 site, 1 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

Other trials in China

About this study

Official title: Development and Validation of a Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma Using Histologic Whole Slide Images and Time-dependent Magnetic Resonance Images

(I) AI Model for Diagnosing Lymph Node Metastasis We developed an AI model to help diagnose whether a single lymph node in nasopharyngeal cancer has spread. The model uses MRI images of the lymph node and the area around it. It includes: 1.Automatically identifying the lymph nodes and the primary tumor. 2.Analyzing MRI images of the lymph node and surrounding area. 3.Using MRI scans before and after chemotherapy to track changes in the lymph node.

(II) AI Model for Predicting Lymph Node Metastasis We created an AI model that predicts whether a lymph node in a specific area has cancer. This model uses a combination of the primary tumor's pathology and MRI images of both the tumor and lymph node. It also tracks changes in the lymph node over time. The model includes: 1.Analyzing the tumor's pathology to identify specific lymphatic structures. 2.Using MRI scans to predict the likelihood of metastasis in a single lymph node. 3.Examining MRI scans before and after chemotherapy to help determine if the lymph node has metastasized.

(III) Verifying and Analyzing the Benefits of the AI Model We are testing the AI model to see how well it works and its potential benefits, including: 1.Checking if the AI can correct past diagnoses of recurrent lymph nodes in nasopharyngeal cancer, which could help guide treatment plans for radiotherapy. 2.Testing the model using biopsy results from head and neck cancer patients to see if it can accurately detect negative lymph nodes. 3.Running clinical trials to test the AI model's safety and effectiveness in guiding radiation treatment for upper neck and single lymph node areas in nasopharyngeal cancer. 4.Analyzing the economic benefits of using the AI model in radiation treatment for nasopharyngeal cancer.

Read the detailed description

(I) AI Model for Assisting Diagnosis of Lymph Node Metastasis in Nasopharyngeal Carcinoma Based on MRI Features of the Lymph Node and Surrounding 3mm Area.

An AI model is developed to assist in diagnosing whether a single lymph node in nasopharyngeal carcinoma has metastasized, based on the MRI features of the lymph node itself and its surrounding 3mm area. Specifically, the model includes: 1.Automatic segmentation of the lymph node and primary lesion using a semi-supervised AI model. 2.Construction of a single dual-view AI model based on baseline MRI features of the lymph node itself and its surrounding 3mm area. 3.Development of a dual-time series dual-view AI model based on MRI features of the lymph nodes before and after induction chemotherapy.

(II) Multimodal AI Model for Predicting Lymph Node Metastasis. A multimodal AI model is constructed to predict metastasis of a single station lymph node and diagnose lymph node metastasis "from surface to point." This is based on pathological features of the nasopharyngeal primary lesion, MRI images, lymph node location, and other factors. Specifically, the model includes: 1.Construction of an AI model based on H\&E stained digital pathology of the primary lesion to extract features of the tertiary lymphatic structure. 2.Development of a multimodal AI model integrating pathological features of the nasopharyngeal primary lesion and baseline MRI images of both the primary lesion and lymph node to predict the probability of lymph node metastasis in a single station. 3.Construction of a single or dual-time series multimodal AI model using the probability of lymph node metastasis and baseline MRI or two MRI scans before and after induction chemotherapy to diagnose whether a single lymph node in the station has metastasized.

(III) Verification and Economic Benefit Analysis of AI Models. The AI models are subjected to thorough verification and economic benefit analysis. Specifically, the process includes: 1.Retrospective correction of historical diagnoses of in situ recurrent lymph nodes in nasopharyngeal carcinoma patients using the AI model to verify its potential benefits in guiding prescription doses for single lymph node radiotherapy. 2.Validation of the AI model using pathological results from lymph node dissection in head and neck squamous cell carcinoma patients to assess the detection rate of clinically diagnosed negative lymph nodes. 3.Prospective clinical trials to evaluate the effectiveness and safety of the AI model in guiding prescription doses for upper neck radiotherapy and single lymph node radiotherapy in nasopharyngeal carcinoma patients. 4.Economic benefit analysis to illustrate the economic value of the AI model in guiding upper neck radiotherapy for nasopharyngeal carcinoma.

Keywords
Nasopharyngeal carcinoma, Cervical lymph node metastasis, MRI, pathology, Artificial Intelligence Diagnostic Model
Medical subject headings
Lymphatic Metastasis, Nasopharyngeal Carcinoma

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

Study groups in NCT06829147
Study groupRoleWhat they receive
Prospective Validation CohortProspective 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.

  1. AUC

    AUC (Area Under the Curve) refers to the area under a performance curve, typically the ROC (Receiver Operating Characteristic) curve or PR (Precision-Recall) curve, that is used to evaluate the performance of a classification model. It is a single scalar value that provides an aggregate measure of a model's ability to distinguish between classes (e.g., positive and negative samples).

    Measured: through study completion, an average of 2 year

Secondary outcomes (1)

  1. Sensitivity and Specificity

    Sensitivity and Specificity are fundamental metrics used to evaluate the performance of a classification model, especially in medical diagnostics, machine learning, and statistics. These metrics are used to measure how well a model can correctly identify positive and negative cases.

    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.

  1. First posted17 Feb 2025
  2. Study start19 Nov 2024
  3. Primary completion1 Sep 2026 (estimated)
  4. Study completion1 Sep 2026 (estimated)

Resources

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)
IDs, dates and publications
ClinicalTrials.gov ID
NCT06829147
Sponsor's protocol ID
B2024-769-01
Acronym
NPC
Status last verified
Feb 2025
First submitted
8 Feb 2025
Last updated
17 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

  • Liu Y, Lai F, Lin B, Gu Y, Chen L, Chen G, Xiao H, Luo S, Pang Y, Xiong D, Li B, Peng S, Lv W, Alexander EK, Xiao H. Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic study. EClinicalMedicine. 2023 May 18;60:102007. doi: 10.1016/j.eclinm.2023.102007. eCollection 2023 Jun.

    PubMed 37251623

  • Wessels F, Schmitt M, Krieghoff-Henning E, Jutzi T, Worst TS, Waldbillig F, Neuberger M, Maron RC, Steeg M, Gaiser T, Hekler A, Utikal JS, von Kalle C, Frohling S, Michel MS, Nuhn P, Brinker TJ. Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. BJU Int. 2021 Sep;128(3):352-360. doi: 10.1111/bju.15386. Epub 2021 May 5.

    PubMed 33706408

  • Chen C, Zhang Y, Wu X, Shen J. The role of tertiary lymphoid structure and B cells in nasopharyngeal carcinoma: Based on bioinformatics and experimental verification. Transl Oncol. 2024 Mar;41:101885. doi: 10.1016/j.tranon.2024.101885. Epub 2024 Jan 31.

    PubMed 38295746

  • Wibawa MS, Zhou JY, Wang R, Huang YY, Zhan Z, Chen X, Lv X, Young LS, Rajpoot N. AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma. Cancers (Basel). 2023 Dec 10;15(24):5789. doi: 10.3390/cancers15245789.

    PubMed 38136336

  • Haggstrom I, Leithner D, Alven J, Campanella G, Abusamra M, Zhang H, Chhabra S, Beer L, Haug A, Salles G, Raderer M, Staber PB, Becker A, Hricak H, Fuchs TJ, Schoder H, Mayerhoefer ME. Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis. Lancet Digit Health. 2024 Feb;6(2):e114-e125. doi: 10.1016/S2589-7500(23)00203-0. Epub 2023 Dec 21.

    PubMed 38135556

  • Zhong Y, Cai C, Chen T, Gui H, Chen C, Deng J, Yang M, Yu B, Song Y, Wang T, Chen Y, Shi H, Xie D, Chen C, She Y. PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study. Eur J Nucl Med Mol Imaging. 2024 Jan;51(2):521-534. doi: 10.1007/s00259-023-06434-7. Epub 2023 Sep 19.

    PubMed 37725128

  • Lee JO, Ahn SS, Choi KS, Lee J, Jang J, Park JH, Hwang I, Park CK, Park SH, Chung JW, Choi SH. Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas. Neuro Oncol. 2024 Mar 4;26(3):571-580. doi: 10.1093/neuonc/noad202.

    PubMed 37855826

  • 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.

    PubMed 37090437

  • Jiang C, Gong B, Gao H, Zhang T, Li Z, Wang J, Zhang L. Correlation analysis of neck node levels in 960 cases of Nasopharyngeal carcinoma (NPC). Radiother Oncol. 2021 Aug;161:23-28. doi: 10.1016/j.radonc.2021.05.020. Epub 2021 May 25.

    PubMed 34044063

  • Zhu GL, Zhang XM, Yang KB, Tang LL, Ma J. Metastatic patterns of level II-V cervical lymph nodes assessed per vertebral levels in nasopharyngeal carcinoma. Radiother Oncol. 2023 Feb;179:109447. doi: 10.1016/j.radonc.2022.109447. Epub 2022 Dec 19.

    PubMed 36549338

  • Meng Z, Li P, Yang D, Huang H, Dong H, Qin Y, Bin Y, Li R, Wang S, Chen X, Kang M. The feasibility of level Ib-sparing intensity-modulated radiation therapy in patients with nasopharyngeal carcinoma and high-risk factors classified based on the International Guideline. Radiother Oncol. 2024 Feb;191:110027. doi: 10.1016/j.radonc.2023.110027. Epub 2023 Nov 24.

    PubMed 38008418

  • Yao JJ, Qi ZY, Liu ZG, Jiang GM, Xu XW, Chen SY, Zhu FT, Zhang WJ, Lawrence WR, Ma J, Zhou GQ, Sun Y. Clinical features and survival outcomes between ascending and descending types of nasopharyngeal carcinoma in the intensity-modulated radiotherapy era: A big-data intelligence platform-based analysis. Radiother Oncol. 2019 Aug;137:137-144. doi: 10.1016/j.radonc.2019.04.025. Epub 2019 May 15.

    PubMed 31102988

  • Fang J, Wang J, Li A, Yan Y, Liu H, Li J, Yang H, Hou Y, Yang X, Yang M, Liu J. Parameterized Gompertz-Guided Morphological AutoEncoder for Predicting Pulmonary Nodule Growth. IEEE Trans Med Imaging. 2023 Dec;42(12):3602-3613. doi: 10.1109/TMI.2023.3297209. Epub 2023 Nov 30.

    PubMed 37471191

  • Venkadesh KV, Aleef TA, Scholten ET, Saghir Z, Silva M, Sverzellati N, Pastorino U, van Ginneken B, Prokop M, Jacobs C. Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules. Radiology. 2023 Aug;308(2):e223308. doi: 10.1148/radiol.223308.

    PubMed 37526548

  • Kolk MZH, Ruiperez-Campillo S, Alvarez-Florez L, Deb B, Bekkers EJ, Allaart CP, Van Der Lingen ACJ, Clopton P, Isgum I, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator. EBioMedicine. 2024 Jan;99:104937. doi: 10.1016/j.ebiom.2023.104937. Epub 2023 Dec 19.

    PubMed 38118401

Questions about this trial

What is this clinical trial studying?

An observational study of Nasopharyngeal Cancinoma (NPC) and Lymph Node Metastasis, run by Sun Yat-sen University at 1 site 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 4 inclusion and 2 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 17 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 1 site 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?

Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

AUC (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 NCT06829147, which also lists 24 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 17 Feb 2025.

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