An observational study in Pulmonary Nodules, sponsored by Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria. Active, not recruiting at 1 site in Italy. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-01-22.
Sponsored by Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria · Observational
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.
188 studies on the registry are indexed under Multiple Pulmonary Nodules; 60 are open to participants now.
This study's enrollment of 329 is above the median of 255 across 87 observational studies indexed under Multiple Pulmonary Nodules.
Browse Multiple Pulmonary Nodules studies →Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria is the lead sponsor of 36 studies on the registry; 13 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Patients who have pulmonary nodules on computed tomography (CT) evaluation and who undergo biopsy are expected to be enrolled.
Exclusion Criteria:
Patients who have pulmonary nodules on computed tomography (CT) evaluation and who undergo biopsy will be enrolled.
Other: Collection 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.
Development of a AI computer model
Development of a computer model that, through the application of artificial intelligence, is capable of recognizing and differentiating pulmonary nodules.
Time frame: Through study completion, an average of 18 months
Automatic generation of results by the AI model
Automatically generate natural language text describing the results that the AI model has recognized from the data provided to it
Time frame: Through study completion, an average of 18 months
Plan to share: No
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This study is active, not recruiting, as verified in Jan 2026. You cannot join it, but the record below documents what was studied.
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Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria