AI showdown: three computer models battle to predict lung cancer from CT scans
NCT ID NCT07727122
First seen Jul 27, 2026 · Last updated Jul 28, 2026 · Updated 1 time
Summary
This study will compare three different artificial intelligence (AI) models that analyze chest CT scans to predict whether a lung nodule is cancerous. The trial will enroll 3,000 adults with small lung nodules (≤3 cm) who have a definitive diagnosis from biopsy or surgery. Each AI model will generate a cancer-risk score based only on the CT images, and the results will be checked against the actual pathology report to see which model is most accurate.
What this could mean
Our plain-language read of the trial. This is informational only, not medical advice or a prediction.
- Active substance
- three artificial intelligence models (MVCS, LungDoc, and a United Imaging AI model) that analyze chest CT images to predict malignancy risk
- What this could lead to
- If one AI model proves clearly more accurate, it could become a standard tool to help doctors decide which lung nodules need biopsy or surgery, reducing unnecessary procedures.
- What could go wrong
- The AI models are tested only on nodules that already have a pathology result, which may not reflect real-world screening populations. Also, the best model may still have limited accuracy for certain nodule types.
This is an AI summary of the original study and may miss details. Read our disclaimer.
Study facts
What this study's own registry entry says, in plain language.
- Participants
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About 3,000 people
The number the study aims to enrol. It can still change while the study runs.
- Expected to start
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Jul 2026
An estimate. Start dates often move.
- Expected to finish
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Dec 2028
An estimate. End dates often move.
- Lead sponsor
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Other sponsor
The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.
Who can take part
This study's own entry requirements. Only the study team can say for certain whether you qualify.
Who is studied
Adults (≥18 years) undergoing routine clinical care at five tertiary hospitals in China who have at least one pulmonary nodule (≤3 cm) detected on chest CT and receive surgical or biopsy pathology with a definitive benign or malignant diagnosis. All participants have adequate CT image quality and complete clinicopathologic information for AI model validation
- Ages
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18 years and older
- Sex
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Anyone
- Healthy volunteers
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Not accepted
This study is not open to healthy volunteers. The entry requirements below say who it is open to.
Show the full entry requirements Hide the full entry requirements
Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.
Inclusion Criteria: * Age ≥ 18 years, any sex. * At least one pulmonary nodule detected on chest CT, with initial nodule diameter ≤ 3 cm. * The nodule undergoes surgical resection or biopsy with a definitive benign or malignant pathological diagnosis. * Time interval between CT examination and pathological examination ≤ 6 months. * Availability of complete CT imaging data in DICOM format with adequate image quality (no severe artifacts), meeting input requirements of all three AI models. Availability of complete clinicopathologic information including histologic type and grade, with clear pathological diagnosis suitable as gold standard labels for AI validation. -The patient (or legally authorized representative) is willing and able to sign written informed consent. Exclusion Criteria: * Pathological results are unclear, inconclusive, or disputed; nodule nature or grade cannot be reliably determined. * The patient receives treatments between CT and pathology that may significantly alter nodule appearance (e.g., chemotherapy, radiotherapy, targeted therapy). * CT imaging data are incomplete (missing essential series) or have severe motion, metal, or other artifacts preventing accurate AI analysis. * Required metadata for any AI model are missing and cannot be imputed. History of other malignant tumors (malignancies other than the index non-small cell lung cancer). * Severe psychiatric illness, cognitive impairment, or other conditions that prevent cooperation with study-related procedures and follow-up. Participation in another clinical study that may interfere with the results of this research. -The patient or legal representative refuses participation. Exclusion (Post-Enrollment / Removal from Analysis) Participants already enrolled may be excluded from the analysis set if: * They are later found not to meet inclusion criteria or to meet exclusion criteria. * No usable data are available after enrollment. * Required AI model assessments are not completed (e.g., technical failure to generate outputs). * Critical data are missing, preventing contribution to primary analysis. * The interval between CT and pathology exceeds 6 months.
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Conditions
The condition(s) this trial relates to.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.
How to take part
Only the study team decides who joins. These are the ways to reach them.
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The places running it
5 sites. The list below names each one and where it is.
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The official record
ClinicalTrials.gov lists the study team's own contact details, including names and phone numbers. We don't republish those.
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A doctor treating you
A doctor who knows your case can contact a study site on your behalf, and can tell you whether this study is worth pursuing at all.
Contacts and locations
Locations
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Affiliated Hospital of Xuzhou Medical University
Xuzhou, Jiangsu, 221006, China
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Guangdong Provincial People's Hospital
Guangzhou, Guangdong, 510000, China
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Second Affiliated Hospital of Army Medical University (Xinqiao Hospital)
Chongqing, Chongqing Municipality, 400037, China
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Zhejiang University
Hangzhou, Zhejiang, 310003, China
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Zhujiang Hospital, Southern Medical University
Guangzhou, Guangdong, 510000, China
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Other studies related to the condition(s) this trial covers.
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