Lung CancerNCT07463300
A Hierarchical Multi-modal AI Framework for Pathological and Genetic Subtyping of Lung Cancer Based on PET/CT Imaging
An observational study of Lung Cancer, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 9 sites in China.
Trial summary
PET/CT imaging and clinical information (age, gender, smoking history, family history of cancer, history of present illness, and several tumor biomarkers, etc.) were used to establish a hierarchical multi-modal AI framework for pathological and genetic subtyping of lung cancer Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer
- What is being tested?
- PET imaging analysis, data mining, and AI model developing
- How many people
- 5,500 participants
- Where
- 9 sitesChina
- Study dates
- 1 Aug 2024to 1 Aug 2027
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The target population of this study is patients with lung cancer who have undergone pre-treatment 18F-FDG PET/CT. The study will assess the relationships between PET/CT-based imaging features and clinical variables with the pathological subtypes.
Who may be able to join
- Newly diagnosed NSCLC confirmed pathologically
- Age ≥18 y
- Underwent pre-treatment 18F-FDG PET/CT scan
- No prior anti-tumor treatments
- No history of other malignancies
Who cannot join
- Pure ground-glass nodules with no FDG uptake
Trial locations and contacts
9 sites in 1 country. Contact the study team or a site near you to ask about taking part.
9 sites in 1 country
China9 sites, 9 recruiting
- Recruiting
West China Hospital
Chengdu, Sichuan, 610041
Rong Tian, 0086-028-85422114, rongtiannuclear@126.com
- Recruiting
Guangdong Second Provincial General Hospital
Guangzhou, Guangdong, 510403
Xiaofen Ma, 0086-020-89168066, xiaofenma12@163.com
- Recruiting
The First Affiliated Hospital of Zhejiang Chinese Medical University
Hangzhou, Zhejiang, 310006
Lixia Zhang, 0086-571-87068001, 353826107@qq.com
- Recruiting
Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, 310009
Xiaohui Zhang, 0086-571-87767138, zhanghui4127@zju.edu.cn
- Recruiting
Zhejiang Cancer Hospital
Hangzhou, Zhejiang, 310022
Heqing Yi, 0085-571-88122222, yihq@zjcc.org.cn
- Recruiting
First Hospital of China Medical University
Shenyang, Liaoning, 110801
Xuena Li, 0086-024-961200, lixuenacmunm@163.com
- Recruiting
Wuhan Tongji Hospital
Wuhan, Hubei, 430030
Xiaohua Zhu, 0086-027-83662640, evazhu@tjh.tjmu.edu.cn
- Recruiting
Zhongnan Hospital
Wuhan, Hubei, 430071
Yong He, 0086-027-67812888, heyong@whu.edu.cn
- Recruiting
Northern Jiangsu People's Hospital
Yangzhou, Jiangsu, 225001
Xia Lu, 0086-0514-87373012, lxgf2222@163.com
About this study
PET/CT imaging and clinical information (age, gender, smoking history, family history of cancer, history of present illness, and several tumor biomarkers, etc.) were used to establish a hierarchical multi-modal AI framework for pathological and genetic subtyping of lung cancer
Read the detailed description
The multi-modal AI framework is developed to facilitate a hierarchical and precise stratification process. The first level involves the accurate differentiation between small cell lung cancer and non-small cell lung cancer (NSCLC) in patients diagnosed with lung cancer. The second level entails the further categorization of NSCLC patients into adenocarcinoma, squamous cell carcinoma, and other less prevalent subtypes. The third level involves predicting the mutation status of the EGFR driver gene, which is most-commonly observed in patients with lung adenocarcinoma. The whole cohort was divided into the training cohort (retrospective), validation cohort (retrospective), test cohort (retrospective), and prospective cohort.
- Conditions
- Lung Cancer
- Being tested
- PET imaging analysis, data mining, and AI model developing
- Keywords
- subtype, artificial intelligence, diagnosis
- Medical subject headings
- Lung Neoplasms, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PET imaging analysis, data mining, and AI model developingOther
PET imaging analysis, data mining, and AI model developing
| Study group | Role | What they receive |
|---|---|---|
| Training Cohort | All patients underwent pre-treatment 18F-FDG PET/CT scan. Receives: Other: PET imaging analysis, data mining, and AI model developing | |
| Validation Cohort | All patients underwent pre-treatment 18F-FDG PET/CT scan. Receives: Other: PET imaging analysis, data mining, and AI model developing | |
| Test cohort | All patients underwent pre-treatment 18F-FDG PET/CT scan. Receives: Other: PET imaging analysis, data mining, and AI model developing | |
| Prospective cohort | All patients underwent pre-treatment 18F-FDG PET/CT scan. Receives: Other: PET imaging analysis, data mining, and AI model developing |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accurate differentiation between small cell lung cancer and non-small cell lung cancer
Measured: 1 year
Secondary outcomes (1)
Histological subtyping of NSCLC, including adenocarcinoma, squamous cell carcinoma, and other NSCLC subtypes
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Mar 2026
- Study start1 Aug 2024
- Primary completion1 Aug 2027 (estimated)
- Study completion1 Aug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital, Zhejiang University, School of MedicineThe 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
- Other
- Enrolment
- 5,500 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- First Hospital of China Medical University, West China Hospital, Zhongnan Hospital, Zhejiang Cancer Hospital, Guangdong Second Provincial General Hospital, The First Affiliated Hospital of Zhejiang Chinese Medical University, Wuhan TongJi Hospital, Northern Jiangsu People's Hospital
- Responsible party
- Sponsor
- Investigators
- Hong Zhang, Principal investigator, Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07463300
- Sponsor's protocol ID
- 2024-0689
- Status last verified
- Mar 2026
- First submitted
- 6 Mar 2026
- Last updated
- 11 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Hierarchical Multi-modal AI Framework for Pathological and Genetic Subtyping of Lung Cancer Based on PET/CT Imaging
- ClinicalTrials.gov ID
- NCT07463300
- Acronym / sponsor ID
- 2024-0689
- Conditions
- Lung Cancer
- Keywords
- subtype, artificial intelligence, diagnosis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 5,500 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Collaborators
- First Hospital of China Medical University, West China Hospital, Zhongnan Hospital, Zhejiang Cancer Hospital, Guangdong Second Provincial General Hospital, The First Affiliated Hospital of Zhejiang Chinese Medical University, Wuhan TongJi Hospital, Northern Jiangsu People's Hospital
- Study start
- 1 Aug 2024 (actual)
- Primary completion
- 1 Aug 2027 (estimated)
- Study completion
- 1 Aug 2027 (estimated)
- First posted
- 11 Mar 2026 (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.
PET/CT imaging and clinical information (age, gender, smoking history, family history of cancer, history of present illness, and several tumor biomarkers, etc.) were used to establish a hierarchical multi-modal AI framework for pathological and genetic subtyping of lung cancer
Detailed description
The multi-modal AI framework is developed to facilitate a hierarchical and precise stratification process. The first level involves the accurate differentiation between small cell lung cancer and non-small cell lung cancer (NSCLC) in patients diagnosed with lung cancer. The second level entails the further categorization of NSCLC patients into adenocarcinoma, squamous cell carcinoma, and other less prevalent subtypes. The third level involves predicting the mutation status of the EGFR driver gene, which is most-commonly observed in patients with lung adenocarcinoma. The whole cohort was divided into the training cohort (retrospective), validation cohort (retrospective), test cohort (retrospective), and prospective cohort.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Training Cohort | – | Other: PET imaging analysis, data mining, and AI model developingAll patients underwent pre-treatment 18F-FDG PET/CT scan. |
| Validation Cohort | – | Other: PET imaging analysis, data mining, and AI model developingAll patients underwent pre-treatment 18F-FDG PET/CT scan. |
| Test cohort | – | Other: PET imaging analysis, data mining, and AI model developingAll patients underwent pre-treatment 18F-FDG PET/CT scan. |
| Prospective cohort | – | Other: PET imaging analysis, data mining, and AI model developingAll patients underwent pre-treatment 18F-FDG PET/CT scan. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The target population of this study is patients with lung cancer who have undergone pre-treatment 18F-FDG PET/CT. The study will assess the relationships between PET/CT-based imaging features and clinical variables with the pathological subtypes.
Key inclusion criteria (5 listed)
- Newly diagnosed NSCLC confirmed pathologically
- Age ≥18 y
- Underwent pre-treatment 18F-FDG PET/CT scan
- No prior anti-tumor treatments
- No history of other malignancies
Key exclusion criteria (1 listed)
- Pure ground-glass nodules with no FDG uptake
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| PET imaging analysis, data mining, and AI model developingPET imaging analysis, data mining, and AI model developing | Other | Prospective cohort, Test cohort, Training Cohort, Validation Cohort |
Primary outcome
Accurate differentiation between small cell lung cancer and non-small cell lung cancer
Time frame: 1 year
1 secondary outcome
Histological subtyping of NSCLC, including adenocarcinoma, squamous cell carcinoma, and other NSCLC subtypes
Time frame: 1 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 11 Mar 2026.
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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.
Hong Zhang principal investigator
Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University
9 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 9 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 5 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 11 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 9 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 Hong Zhang (0086-571-87767138, hzhang21@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accurate differentiation between small cell lung cancer and non-small cell lung cancer (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 11 Mar 2026.