Pulmonary Nodules
Conditions
Brief summary
Lung cancer is one of the most common cancers and has one of the worst prognoses, mainly due to the difficulty of early diagnosis. In Italy, there are an estimated 41,000 new cases each year, and in 2021, the disease was responsible for approximately 34,000 deaths. The social impact is significant, as the disease is often diagnosed at an advanced stage, when the chances of survival are reduced: the 5-year survival rate is around 18% in advanced stages, while it can reach 90% if diagnosed at an early stage. Early-stage lung cancer mainly manifests itself in the form of pulmonary nodules, which can be detected by computed tomography (CT). However, the diagnosis of these nodules often requires invasive procedures, such as bronchoscopy, CT-guided needle biopsy, or surgical biopsies, which affect patients' quality of life and healthcare costs. For this reason, the ability to accurately distinguish between benign and malignant nodules is a central theme in clinical research. In recent years, artificial intelligence, particularly deep learning techniques, has shown considerable potential in supporting CT screening. Results show that AI can achieve performance superior to that of individual radiologists and comparable to that of a multidisciplinary team, using histological reports as a diagnostic reference. This confirms the value of AI as a tool to support clinical decision-making. Considering the multimodal nature of clinical data (images, text reports, diagnostic tests), there is growing interest in models capable of integrating multiple sources of information. In this context, the research project aims to develop a system capable of automatically recognizing pulmonary nodules and generating natural language text descriptions of the findings.
Interventions
OTHERCollection of variables identified for the study
The intervention involves enrolling patients with lung nodules and collecting clinical data, anonymizing it, pre-process CT images and prepare them for use in training artificial intelligence models, ensuring clinical validation and ethical compliance.
Sponsors
Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Age ≥18 years 2. Evidence of pulmonary nodule documented radiologically by chest CT scan 3. Presence of CT scan report 4. Presence of histological report (pulmonary nodule biopsy) 5. Presence of written informed consent, signed
Exclusion criteria
1. Previous cancer 2. Previous lung surgery 3. Previous radiation therapy and/or chemotherapy
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Development of a AI computer model | Through study completion, an average of 18 months | Development of a computer model that, through the application of artificial intelligence, is capable of recognizing and differentiating pulmonary nodules. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Automatic generation of results by the AI model | Through study completion, an average of 18 months | Automatically generate natural language text describing the results that the AI model has recognized from the data provided to it |
Countries
Italy
Outcome results
None listed