Pulmonary NodulesSPOILERSNCT07360145
Intelligent Support for Radiological Reporting of Lung Neoplasms
An observational study of Pulmonary Nodules, run by Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pulmonary Nodules
- What is being tested?
- Collection of variables identified for the study
- How many people
- 329 participants
- Where
- 1 siteItaly
- Study dates
- 23 Mar 2024to 15 Feb 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients who have pulmonary nodules on computed tomography (CT) evaluation and who undergo biopsy are expected to be enrolled.
Who may be able to join
- 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
Who cannot join
- Previous cancer
- Previous lung surgery
- Previous radiation therapy and/or chemotherapy
Trial locations and contacts
1 site in 1 country.
Italy1 site
SSD Laboratori di Ricerca (DAIRI) - AOU Alessandria
Alessandria, Piedmont, 15121
About this study
Official title: 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.
Read more
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
- Being tested
- Collection of variables identified for the study
- Medical subject headings
- Multiple Pulmonary Nodules
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Collection of variables identified for the studyOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Patients with pulmonary nodules | Patients who have pulmonary nodules on computed tomography (CT) evaluation and who undergo biopsy will be enrolled. Receives: Other: Collection of variables identified for the study |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, an average of 18 months
Secondary outcomes (1)
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
Measured: Through study completion, an average of 18 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jan 2026
- Study start23 Mar 2024
- Primary completion23 Mar 2025
- Study completion15 Feb 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pulmonary NodulesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 329 participants (actual)
Sponsor and investigators
- Lead sponsor
- Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07360145
- Sponsor's protocol ID
- ASO.Rad.23.01
- Acronym
- SPOILERS
- Status last verified
- Jan 2026
- First submitted
- 14 Jan 2026
- Last updated
- 22 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Intelligent Support for Radiological Reporting of Lung Neoplasms - SPOILERS Study
- ClinicalTrials.gov ID
- NCT07360145
- Acronym / sponsor ID
- SPOILERS / ASO.Rad.23.01
- Conditions
- Pulmonary Nodules
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 329 participants (actual)
- Lead sponsor
- Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di AlessandriaUniversity, hospital or other
- Study start
- 23 Mar 2024 (actual)
- Primary completion
- 23 Mar 2025 (actual)
- Study completion
- 15 Feb 2026 (estimated)
- First posted
- 22 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with pulmonary nodules | – | Other: Collection of variables identified for the studyPatients who have pulmonary nodules on computed tomography (CT) evaluation and who undergo biopsy will be enrolled. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who have pulmonary nodules on computed tomography (CT) evaluation and who undergo biopsy are expected to be enrolled.
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (3 listed)
- Previous cancer
- Previous lung surgery
- Previous radiation therapy and/or chemotherapy
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Collection of variables identified for the studyThe 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. | Other | Patients with pulmonary nodules |
Primary outcome
Development of a AI computer model
Time frame: 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.
1 secondary outcome
Automatic generation of results by the AI model
Time frame: Through study completion, an average of 18 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jan 2026.
- Individual participant dataSharing planned: No
Related trials for Pulmonary Nodules
Active, not recruiting
Molecular Analysis of Endoscopic Cytology Samples Supernatant in Pulmonary Nodules
Lung Cancer, Lung; Node
Recruiting
Longitudinal AI-Assisted Management of Indeterminate Pulmonary Nodules
Pulmonary Nodules, Lung Neoplasms
Active, not recruitingObservational study
Clinical Application of Artificial Intelligence Methods in Diagnostic Imaging of Pulmonary Nodules
Pulmonary Nodules, Lung Neoplasms
RecruitingObservational study
Study of Collection and Analysis of Clinical, Anamnestic, Functional, Biological Data Through the Support of Artificial Intelligence to Evaluate the Possibility of Defining a Digital Twin of the Lung (LUCE)
Thoracic Neoplasm, Pulmonary Nodules
RecruitingObservational study
Lung Cancer Detection in Never Smokers Using Urine Volatile Organic Compound (VOCs)
Lung Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pulmonary Nodules, run by Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo di Alessandria at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 22 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of a AI computer model (time frame: Through study completion, an average of 18 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Jan 2026.