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Intelligent Support for Radiological Reporting of Lung Neoplasms

NCT07360145 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 329

Last updated 2026-01-22

No results posted yet for this study

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

Interventions

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.

Sponsors & Collaborators

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

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-03-23
Primary Completion
2025-03-23
Completion
2026-02-15

Countries

  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07360145 on ClinicalTrials.gov