Intelligent Support for Radiological Reporting of Lung Neoplasms

NCT07360145 | Active Not Recruiting

• 1 other identifier: ASO.Rad.23.01
• Study Type: observational
• Target: 329
• Locations: 1 country
• Sites: 1

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.

Conditions

Pulmonary Nodules

Outcome Measures

Primary Outcomes (1)

• 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.

  Through study completion, an average of 18 months

Secondary Outcomes (1)

• Automatic generation of results by the AI model

  Through study completion, an average of 18 months

Study Arms (1)

Patients with pulmonary nodules

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

Interventions

Collection of variables identified for the study OTHER

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.

Patients with pulmonary nodules

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients who have pulmonary nodules on computed tomography (CT) evaluation and who undergo biopsy are expected to be enrolled.

You may qualify if:

• Age ≥18 years
• Evidence of pulmonary nodule documented radiologically by chest CT scan
• Presence of CT scan report
• Presence of histological report (pulmonary nodule biopsy)
• Presence of written informed consent, signed

You may not qualify if:

• Previous cancer
• Previous lung surgery
• Previous radiation therapy and/or chemotherapy

Sponsors & Collaborators

• Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria: lead

Study Sites (1)

SSD Laboratori di Ricerca (DAIRI) - AOU Alessandria

Alessandria, Piedmont, 15121, Italy

Location

MeSH Terms

Conditions

Multiple Pulmonary Nodules

Condition Hierarchy (Ancestors)

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

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 14, 2026
• First Posted: January 22, 2026
• Study Start: March 23, 2024
• Primary Completion: March 23, 2025
• Study Completion: February 15, 2026
• Last Updated: January 22, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)