AI-Assisted 3D Reconstruction for Lung Resection Planning

NCT07853157 | Not Yet Recruiting

• 1 other identifier: SANTANDREA-3D-LUNG-01
• Study Type: observational
• Target: 50
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Surgery for Primary Lung Cancer; Lung Cancer; Artifical Intelligence

Keywords

Artificial Intelligence; Three-Dimensional Reconstruction; Lung Resection; Thoracic Surgery; Preoperative Planning; Pulmonary Anatomy; Image Segmentation

Outcome Measures

Primary Outcomes (1)

• 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.

  From preoperative CT imaging through completion of the surgical procedure

Study Arms (1)

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.

Other: AI-Based 3D Reconstruction

Interventions

AI-Based 3D Reconstruction OTHER

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.

Prospective Validation Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• S. Andrea Hospital: lead

Study Sites (1)

Azienda Ospedaliera Universitaria Sant'Andrea

Rome, Lazio, 00189, Italy

Location

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Thoracic Surgeon

Study Record Dates

• First Submitted: September 23, 2026
• First Posted: October 1, 2026
• Study Start (Estimated): November 2, 2026
• Primary Completion (Estimated): May 1, 2027
• Study Completion (Estimated): August 1, 2027
• Last Updated: October 1, 2026

Record last verified: 2026-09

Locations

IT (1)