Lung CancerNCT07853157
AI-Assisted 3D Reconstruction for Lung Resection Planning
An observational study of Lung Cancer and Artifical Intelligence, run by S. Andrea Hospital at 1 site in Italy.
Trial summary
This prospective observational study aims to validate an artificial intelligence (AI)-based system for automated three-dimensional (3D) reconstruction of pulmonary anatomy from preoperative chest computed tomography (CT) scans in patients undergoing lung resection. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer, Artifical Intelligence, Surgery for Primary Lung Cancer
- What is being tested?
- AI-Based 3D Reconstruction
- How many people
- 50 participants
- Where
- 1 siteItaly
- Study dates
- 2 Nov 2026to 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: Adult patients undergoing anatomical lung resection at the Thoracic Surgery Unit of Sant'Andrea University Hospital who have a preoperative contrast-enhanced chest CT scan suitable for AI-based 3D reconstruction.
Who may be able to join
- Age ≥18 years
- Scheduled for anatomical lung resection (segmentectomy, lobectomy, bilobectomy, or pneumonectomy)
- Availability of a preoperative contrast-enhanced chest CT scan suitable for 3D reconstruction, with slice thickness ≤1.5 mm.
- Ability to provide informed consent.
Who cannot join
- Preoperative CT images of insufficient quality or technical characteristics for 3D reconstruction.
- Cancellation of the planned surgical procedure before surgery.
- Inability or unwillingness to provide informed consent.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Italy1 site
Azienda Ospedaliera Universitaria Sant'Andrea
Rome, Lazio, 00189
Giacomo Argento, MD, +390633775773, giargento@ospedalesantandrea.it
About this study
Official title: Prospective Validation of an Artificial Intelligence-Based 3D Reconstruction Model for Preoperative Planning of Lung Resection.
This prospective observational study aims to validate an artificial intelligence (AI)-based system for automated three-dimensional (3D) reconstruction of pulmonary anatomy from preoperative chest computed tomography (CT) scans in patients undergoing lung resection.
The AI system will automatically identify and reconstruct relevant pulmonary anatomical structures, including pulmonary arteries, veins, bronchi, lobes, and segments. AI-generated 3D reconstructions will be compared with expert manual 3D reconstructions and with anatomical findings documented during surgery.
Read more
The primary objective is to assess the anatomical accuracy of AI-generated 3D reconstructions in identifying surgically relevant bronchovascular anatomy and anatomical variants. Secondary objectives include evaluating accuracy for individual anatomical structures, discrepancies between automated and manual reconstructions, concordance with intraoperative findings, and the time required to generate and review the automated reconstruction.
The study is observational and will not modify the planned surgical procedure or standard patient care.
Read the detailed description
Patients scheduled for anatomical lung resection who meet the study eligibility criteria will be prospectively enrolled. Preoperative contrast-enhanced chest CT images acquired as part of routine clinical care will be used for 3D reconstruction.
For each patient, an AI-based segmentation system will generate a 3D reconstruction of the relevant pulmonary anatomy, including pulmonary arterial, venous and bronchial structures, as well as lobar and segmental anatomy when applicable. The automated reconstruction will be reviewed without modifying the planned surgical treatment.
AI-generated reconstructions will be compared with expert manual 3D reconstructions and with anatomical findings documented intraoperatively. Particular attention will be given to the identification of surgically relevant bronchovascular branches and anatomical variants.
The primary analysis will assess the proportion of patients in whom the AI-generated reconstruction accurately represents all predefined surgically relevant anatomical structures for the planned resection, without clinically relevant omissions or incorrect structures.
Secondary analyses will assess performance at the individual anatomical-structure level, including arterial, venous and bronchial structures; characterize errors and discrepancies between automated and manual reconstruction; evaluate concordance with intraoperative anatomy; and assess the time required for automated reconstruction and expert review.
The study is a prospective, single-center observational validation study. All diagnostic and therapeutic decisions, including the indication and type of lung resection, will be made according to standard clinical practice independently of study participation.
- Being tested
- AI-Based 3D Reconstruction
- Keywords
- Artificial Intelligence, Three-Dimensional Reconstruction, Lung Resection, Thoracic Surgery, Preoperative Planning, Pulmonary Anatomy, Image Segmentation
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based 3D ReconstructionOther
Preoperative contrast-enhanced chest CT images will be processed using an artificial intelligence-based segmentation system to automatically generate three-dimensional reconstructions of pulmonary anatomy, including pulmonary arteries, veins, bronchi, lobes, and segments when applicable. The AI-generated reconstructions will be evaluated against expert manual 3D reconstruction and intraoperative anatomical findings. The AI output will not determine or modify the planned surgical treatment.
| Study group | Role | What they receive |
|---|---|---|
| Prospective Validation Cohort | Patients undergoing lung resection who are prospectively enrolled for validation of the AI-based 3D reconstruction system. Preoperative CT images will be processed by the AI system, and the resulting 3D reconstructions will be compared with expert manual reconstruction and intraoperative anatomical findings. Receives: Other: AI-Based 3D Reconstruction |
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient-Level Anatomical Accuracy of AI-Generated 3D Reconstruction
Proportion of patients in whom the AI-generated 3D reconstruction correctly represents all predefined surgically relevant pulmonary arterial, venous, and bronchial structures and anatomical variants for the planned lung resection, without clinically relevant omissions or incorrectly represented structures. Accuracy will be assessed by comparison with expert manual 3D reconstruction and intraoperative anatomical findings.
Measured: From preoperative CT imaging through completion of the surgical procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Oct 2026
- Study start2 Nov 2026 (estimated)
- Primary completion1 May 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.
- 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
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- S. Andrea Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Giacomo Argento, S. Andrea Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07853157
- Sponsor's protocol ID
- SANTANDREA-3D-LUNG-01
- Status last verified
- Sep 2026
- First submitted
- 23 Sep 2026
- Last updated
- 1 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 1 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Validation of an Artificial Intelligence-Based 3D Reconstruction Model for Preoperative Planning of Lung Resection.
- ClinicalTrials.gov ID
- NCT07853157
- Acronym / sponsor ID
- SANTANDREA-3D-LUNG-01
- Keywords
- Artificial Intelligence, Three-Dimensional Reconstruction, Lung Resection, Thoracic Surgery, Preoperative Planning, Pulmonary Anatomy, Image Segmentation
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- S. Andrea HospitalUniversity, hospital or other
- Study start
- 2 Nov 2026 (estimated)
- Primary completion
- 1 May 2027 (estimated)
- Study completion
- 1 Aug 2027 (estimated)
- First posted
- 1 Oct 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.
This prospective observational study aims to validate an artificial intelligence (AI)-based system for automated three-dimensional (3D) reconstruction of pulmonary anatomy from preoperative chest computed tomography (CT) scans in patients undergoing lung resection.
The AI system will automatically identify and reconstruct relevant pulmonary anatomical structures, including pulmonary arteries, veins, bronchi, lobes, and segments. AI-generated 3D reconstructions will be compared with expert manual 3D reconstructions and with anatomical findings documented during surgery.
The primary objective is to assess the anatomical accuracy of AI-generated 3D reconstructions in identifying surgically relevant bronchovascular anatomy and anatomical variants. Secondary objectives include evaluating accuracy for individual anatomical structures, discrepancies between automated and manual reconstructions, concordance with intraoperative findings, and the time required to generate and review the automated reconstruction.
The study is observational and will not modify the planned surgical procedure or standard patient care.
Detailed description
Patients scheduled for anatomical lung resection who meet the study eligibility criteria will be prospectively enrolled. Preoperative contrast-enhanced chest CT images acquired as part of routine clinical care will be used for 3D reconstruction.
For each patient, an AI-based segmentation system will generate a 3D reconstruction of the relevant pulmonary anatomy, including pulmonary arterial, venous and bronchial structures, as well as lobar and segmental anatomy when applicable. The automated reconstruction will be reviewed without modifying the planned surgical treatment.
AI-generated reconstructions will be compared with expert manual 3D reconstructions and with anatomical findings documented intraoperatively. Particular attention will be given to the identification of surgically relevant bronchovascular branches and anatomical variants.
The primary analysis will assess the proportion of patients in whom the AI-generated reconstruction accurately represents all predefined surgically relevant anatomical structures for the planned resection, without clinically relevant omissions or incorrect structures.
Secondary analyses will assess performance at the individual anatomical-structure level, including arterial, venous and bronchial structures; characterize errors and discrepancies between automated and manual reconstruction; evaluate concordance with intraoperative anatomy; and assess the time required for automated reconstruction and expert review.
The study is a prospective, single-center observational validation study. All diagnostic and therapeutic decisions, including the indication and type of lung resection, will be made according to standard clinical practice independently of study participation.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prospective Validation Cohort | – | Other: AI-Based 3D ReconstructionPatients undergoing lung resection who are prospectively enrolled for validation of the AI-based 3D reconstruction system. Preoperative CT images will be processed by the AI system, and the resulting 3D reconstructions will be compared with expert manual reconstruction and intraoperative anatomical findings. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients undergoing anatomical lung resection at the Thoracic Surgery Unit of Sant'Andrea University Hospital who have a preoperative contrast-enhanced chest CT scan suitable for AI-based 3D reconstruction.
Key inclusion criteria (4 listed)
- Age ≥18 years
- Scheduled for anatomical lung resection (segmentectomy, lobectomy, bilobectomy, or pneumonectomy)
- Availability of a preoperative contrast-enhanced chest CT scan suitable for 3D reconstruction, with slice thickness ≤1.5 mm.
- Ability to provide informed consent.
Key exclusion criteria (3 listed)
- Preoperative CT images of insufficient quality or technical characteristics for 3D reconstruction.
- Cancellation of the planned surgical procedure before surgery.
- Inability or unwillingness to provide informed consent.
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 |
|---|---|---|
| AI-Based 3D ReconstructionPreoperative contrast-enhanced chest CT images will be processed using an artificial intelligence-based segmentation system to automatically generate three-dimensional reconstructions of pulmonary anatomy, including pulmonary arteries, veins, bronchi, lobes, and segments when applicable. The AI-generated reconstructions will be evaluated against expert manual 3D reconstruction and intraoperative anatomical findings. The AI output will not determine or modify the planned surgical treatment. | Other | Prospective Validation Cohort |
Primary outcome
Patient-Level Anatomical Accuracy of AI-Generated 3D Reconstruction
Time frame: From preoperative CT imaging through completion of the surgical procedure
Proportion of patients in whom the AI-generated 3D reconstruction correctly represents all predefined surgically relevant pulmonary arterial, venous, and bronchial structures and anatomical variants for the planned lung resection, without clinically relevant omissions or incorrectly represented structures. Accuracy will be assessed by comparison with expert manual 3D reconstruction and intraoperative anatomical findings.
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 1 Oct 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.
Azienda Ospedaliera Universitaria Sant'Andrea
Rome, Italy
1 site 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 and Artifical Intelligence, run by S. Andrea Hospital at 1 site in Italy.
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 3 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: opening soon, as of the sponsor's last update on 1 Oct 2026. 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 Italy. 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?
Patient-Level Anatomical Accuracy of AI-Generated 3D Reconstruction (time frame: From preoperative CT imaging through completion of the surgical procedure). 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 1 Oct 2026.