Intelligent Support for Radiological Reporting of Lung Neoplasms (SPOILERS)

Intelligent Support for Radiological Reporting of Lung Neoplasms - SPOILERS Study

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Pulmonary Nodules
• Intervention / Treatment: Other: Collection of variables identified for the study

• Study Type: Observational
• Enrollment (Actual): 329

Contacts and Locations

Study Locations

• Italy
  • Piedmont
    • Alessandria, Piedmont, Italy, 15121
      • SSD Laboratori di Ricerca (DAIRI) - AOU Alessandria

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	Through study completion, an average of 18 months

Collaborators and Investigators

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

Study record dates

Study Major Dates

• Study Start (Actual): March 23, 2024
• Primary Completion (Actual): March 23, 2025
• Study Completion (Estimated): February 15, 2026

Study Registration Dates

• First Submitted: January 14, 2026
• First Submitted That Met QC Criteria: January 14, 2026
• First Posted (Actual): January 22, 2026

Study Record Updates

• Last Update Posted (Actual): January 22, 2026
• Last Update Submitted That Met QC Criteria: January 14, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Multiple Pulmonary Nodules
• Other Study ID Numbers: ASO.Rad.23.01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No