Study of Collection and Analysis of Clinical, Anamnestic, Functional, Biological Data Through the Support of Artificial Intelligence to Evaluate the Possibility of Defining a Digital Twin of the Lung (LUCE)

NCT07517393 | Recruiting DMC

• 1 other identifier: 887CET
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

This research study aims to retrospectively and prospectively analyze the clinical, anamnestic, functional, biological data of patients who have performed or will perform non-contrast chest photon count CT. The use of contrast medium is evaluated by the physician. Main objective The study aims to understand how many people who undergo non-contrast photon-counting chest CT have: lung nodules, chest tumors, and other non-cancerous lung diseases. Secondary objectives

• Analyze the data with the help of artificial intelligence to create a "digital twin" of the lung, that is, a virtual copy of the lung that allows you to study and simulate the behavior of the lung for research and analysis purposes, without directly intervening on the patient.
• Follow any suspicious lung changes over time to understand how they change and whether they are linked to clinical, biological, or laboratory parameters. The enrollment phase will last 12 months and will begin with the approval of the study. At this stage, clinical data already present in the patient's medical record and data relating to the photon count CT examination will be collected. After the enrollment phase, the patient will be observed for a further total duration of 10 years: in particular, a follow-up visit will be arranged at two, five and ten years. More specifically, if there are no radiological elements that require scheduling a visit to the center, the patient will be contacted by telephone for a telematic update of the data. If, however, pulmonary alterations are identified during the study, the patient will be referred to the Reference Operating Unit, as per clinical practice, to continue with the most appropriate personalized diagnostic-therapeutic process. In these cases, the patient will be contacted by telephone and an appointment with the institution will be suggested.

Conditions

Thoracic Neoplasm; Pulmonary Nodules; Non-oncological Pulmonary Pathologies

Keywords

Lung; Artificial Intelligence; observational; Photon Counting Computed Tomography; thoracic malignancies

Outcome Measures

Primary Outcomes (3)

• Describe the frequency of patients performing chest photon counting CT without mdc with pulmonary nodules

  Number of pulmonary nodules

  through study completion, an average of 1 year

• Describe the frequency of thoracic malignancies in subjects performing chest photon counting CT without mdc.

  Number of thoracic malignancies

  through study completion, an average of 1 year

• Describe the frequency of non-oncological pulmonary diseases of subjects performing chest photon counting CT without mdc.

  Number of non-oncological pulmonary diseases

  through study completion, an average of 1 year

Secondary Outcomes (3)

• Accuracy of the AI-based lung digital twin model measured by mean absolute error (MAE) between model-predicted and observed pulmonary function parameters (FEV1, FVC, DLCO)

  through study completion, an average of 1 year

• Change in lung structural abnormalities measured by quantitative CT parameters (lung density, lung volume, lesion size)

  through study completion, an average of 1 year

• Association between lung imaging abnormalities and clinical, biological, and laboratory variables assessed by multivariable regression analysis

  Baseline to 12 months

Study Arms (1)

Patients who performed a non-contrast photon count chest CT scan

The enrollment phase of the subjects will last 12 months and will begin with the approval of the study. During the following year, all the data collected from a statistical, mathematical, and artificial intelligence perspective will be analyzed. The data collected are anamnestic, functional where available, biological and radiological data. Following the enlistment phase, subjects will be observed for a total duration of 10 years: in particular, a follow-up visit will be arranged at two, five and ten years. More specifically, if there are no radiological elements that require specific follow-ups, the subject will be contacted by telephone for a telematic update of the data. If, however, pulmonary alterations are identified during the study, the patient will be referred to the Reference Operating Unit, as per clinical practice, to continue with the most appropriate personalized diagnostic-therapeutic process.

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

The population of this study consists of all patients who perform the Photon Counting Computed Tomography

You may qualify if:

• Men or women aged ≥ 18 years who will perform a smdc photon count chest CT scan
• written informed consent.

You may not qualify if:

• Lack of information regarding anamnestic data
• any reason why the investigator deems the patient unenrollable in the practice.

Sponsors & Collaborators

• Casa di Cura Dott. Pederzoli: lead

Study Sites (1)

Ospedale P. Pederzoli Casa di Cura Privata S.p.A.

Peschiera del Garda, Verona, 37019, Italy

RECRUITING

MeSH Terms

Conditions

Thoracic Neoplasms; Multiple Pulmonary Nodules

Condition Hierarchy (Ancestors)

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

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 26, 2026
• First Posted: April 8, 2026
• Study Start: April 1, 2026
• Primary Completion (Estimated): March 1, 2036
• Study Completion (Estimated): March 1, 2036
• Last Updated: April 8, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)