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)

LUCE: LUng Cancer (r)Evolution

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.

Study Overview

• Status: Recruiting
• Conditions: Thoracic Neoplasm; Pulmonary Nodules; Non-oncological Pulmonary Pathologies

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

Study Locations

• Italy
  • Verona
    • Peschiera del Garda, Verona, Italy, 37019
      • Recruiting
      • Ospedale P. Pederzoli Casa di Cura Privata S.p.A.
      • Contact: Elisa Roca, MD; Phone Number: +39 0456444193; Email: urc@ospedalepederzoli.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Time Frame
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

Collaborators and Investigators

• Sponsor: Casa di Cura Dott. Pederzoli

Study record dates

Study Major Dates

• Study Start (Estimated): April 1, 2026
• Primary Completion (Estimated): March 1, 2036
• Study Completion (Estimated): March 1, 2036

Study Registration Dates

• First Submitted: March 26, 2026
• First Submitted That Met QC Criteria: April 1, 2026
• First Posted (Actual): April 8, 2026

Study Record Updates

• Last Update Posted (Actual): April 8, 2026
• Last Update Submitted That Met QC Criteria: April 1, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Lung; Artificial Intelligence; observational; thoracic malignancies; Photon Counting Computed Tomography
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Lung Neoplasms; Thoracic Neoplasms; Multiple Pulmonary Nodules
• Other Study ID Numbers: 887CET

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No