Non-oncological Pulmonary Pathologies, Pulmonary Nodules, Thoracic Neoplasm
Conditions
Keywords
Lung, Artificial Intelligence, observational, Photon Counting Computed Tomography, thoracic malignancies
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.
Interventions
None listed
Sponsors
Casa di Cura Dott. Pederzoli
Study design
Observational model
CASE_ONLY
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Describe the frequency of patients performing chest photon counting CT without mdc with pulmonary nodules | through study completion, an average of 1 year | Number of pulmonary nodules |
| Describe the frequency of thoracic malignancies in subjects performing chest photon counting CT without mdc. | through study completion, an average of 1 year | Number of thoracic malignancies |
| Describe the frequency of non-oncological pulmonary diseases of subjects performing chest photon counting CT without mdc. | through study completion, an average of 1 year | Number of non-oncological pulmonary diseases |
Secondary
| 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 |
Countries
Italy
Outcome results
None listed