Indeterminate Pulmonary NodulesAI-SONARNCT05375591
AI & Radiomics for Stratification of Lung Nodules After Radically Treated Cancer
An observational study of Indeterminate Pulmonary Nodules and Lung Metastases, run by Royal Marsden NHS Foundation Trust at 2 sites in United Kingdom.
Trial summary
This study will assess the utility of radiomics and artificial intelligence approaches to new lung nodules in patients who have undergone radical treatment for a previous cancer. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Indeterminate Pulmonary Nodules, Lung Metastases, Second Primary Cancer
- What is being tested?
- Non-Interventional Study
- How many people
- 1,000 participants
- Where
- 2 sitesUnited Kingdom
- Study dates
- 13 Oct 2021to 1 Nov 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
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
- Adult, Older adult
Study population: Retrospective cohorts of patient cases with a new pulmonary nodule finding on thoracic CT imaging and previous diagnosis of cancer (treated radically) within the past 10 years (or less).
Who may be able to join
- Confirmed history of previous radically or curative-intent treated solid organ cancer within 10 years of new index CT thoracic scan demonstrating a new pulmonary nodule and either of the following:
- Biopsy confirming previous malignancy with MDT consensus and successful cancer resolution/remission following anti-cancer treatment on interval imaging or blood assay analysis
- Where biopsy was not possible/confirmed for previous malignancy, MDT consensus outcome confirming cancer (+/- calculated Herder score >80% if applicable) and decision to treat as malignancy with subsequent resolution/remission following anti-cancer treatment on interval imaging or blood assay analysis
- Radical treatment for previous cancer defined as either of the following:
- Surgical resection
- Radical radiotherapy or stereotactic beam radiotherapy
Show all 15Show fewer
- Radical chemotherapy
- Radical chemo-radiotherapy
- Multi-modality treatment with any of the above
- New pulmonary nodule ground truth known
- Scan data showing 2-year stability (based on diameter or volumetry) or resolution in cases of benign disease
- Scan data showing progressive nodule enlargement or increase in nodule number on interval imaging with MDT consensus (+/- PET with Herder score >80% if applicable) determining metastatic disease or new primary malignancy
- Biopsy sampling confirming benign disease or malignancy and in cases of malignancy, metastasis or new primary lung cancer
- CT scan slice thickness ≤ 2.5mm
- Nodule size ≥ 5mm
Who cannot join
- CT Imaging > 10 years old
- Non-solid haematological malignancies including leukaemia
- Cases of radically treated primary cancer disease with early oligometastatic recurrence treated radically
Trial locations and contacts
2 sites in 1 country.
United Kingdom2 sites, 2 recruiting
- Recruiting
The Royal Marsden NHS Foundation Trust (Chelsea Site)
London, SW3 6JJ
Sejal Jain, 02078082603, sejal.jain@rmh.nhs.uk
Laura Boddy, 07414643915, laura.boddy@rmh.nhs.uk
- Recruiting
Royal Brompton Hospital
London, SW3 6NP
Hardeep Kalsi, 02078082603, hardeep.kalsi@rmh.nhs.uk
Anand Deveraj
About this study
Official title: Artificial Intelligence & Radiomics for Stratification Of Lung Nodules After Radically Treated Cancer (AI-SONAR)
This study will assess the utility of radiomics and artificial intelligence approaches to new lung nodules in patients who have undergone radical treatment for a previous cancer.
Read the detailed description
Improvements in cancer detection and diagnosis have led to increasing numbers of patients being diagnosed with early stage cancer and potentially receiving curative therapy with improved survival outcomes. Recent retrospective studies in cancer survivors have demonstrated such patients possess an increased risk of further cancer in their lifetime compared to the general population, in part potentially due to shared lifestyle risk factors (e.g. smoking), genetic cancer pre-disposition or downstream oncogenic side effects of anti-cancer therapies (eg. radiotherapy). Lung cancer remains the leading cause of cancer related deaths worldwide and the lungs also represent a common site for metastatic disease in patients with non-pulmonary malignancy. Furthermore, lung cancer is one of the most common second primary malignancy in patients with a prior history of treated cancer. Therefore, discerning the significance of a pulmonary nodule in the context of a previous cancer remains a clinical challenge given it may possess the potential to represent benign disease, metastatic relapse or new primary malignancy.
This study will assess the utility of radiomics and artificial intelligence approaches to new lung nodules in patients who have undergone radical treatment for a previous cancer. This will entail use of machine learning (ML) approaches and later, exploration of deep-learning/convolutional neural network approaches to nodule interpretation for differentiation of benign, metastatic and new primary lung cancer nodules/lesions. Development of a ML classifier or deep learning based tool may help guide which patients would benefit from earlier investigations including additional imaging, biopsy sampling and lead to earlier cancer diagnosis, leading to better patient outcomes in this unique cohort. This is a retrospective study analysing data already collected routinely as part of patient care. All data will be anonymised prior to any analysis, no patient directed/related interventions will be employed and consent-waiver for study inclusion will be exercised.
- Being tested
- Non-Interventional Study
- Medical subject headings
- Neoplasms, Second Primary, Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Non-Interventional StudyOther
First nodule detection CT scans as per eligibility criteria will be used as input into in-house software to extract multiple radiomic features and used to develop a machine learning based classifier to differentiate nodule aetiology. Scans will also be used as input in to a deep learning/convolutional neural network models to perform automated imaging classification.
| Study group | Role | What they receive |
|---|---|---|
| Benign Nodules | CT scans of patients with a new lung nodule(s) subsequently confirmed to be benign and in the context of a previous history of radically treated cancer, will be identified at participating NHS sites and recruited. Receives: Other: Non-Interventional Study | |
| Metastatic Nodules | CT scans of patients with a new lung nodule(s) subsequently confirmed to be metastatic in nature and in the context of a previous history of radically treated cancer, will be identified at participating NHS sites and recruited. Receives: Other: Non-Interventional Study | |
| Second Primary Lung Cancers | CT scans of patients with a new lung nodule(s) subsequently confirmed to be a new second primary lung cancer and in the context of a previous history of radically treated cancer, will be identified at participating NHS sites and recruited. Receives: Other: Non-Interventional Study |
What is being measured
The primary outcome is the main question the study is designed to answer.
Development of a CT-thorax based radiomics ML classifier model to predict cancer risk in new lung nodules after previous radically treated cancer.
The study aims to identify distinct clusters of radiomics variables to generate a radiomics predictive vector (RPV), which can be used to stratify benign vs malignant nodules in patients who have previously received radical treatment for a malignancy. The RPV will be used in multivariate analysis and compared to existing risk models used in clinical practice.
Measured: 2 years
Development of the CT-thorax based ML classifier model to predict whether a new malignant nodule represents metastatic lung disease (new cancer vs previous cancer recurrence) or a new primary lung malignancy.
The study aims to identify distinct clusters of radiomic variables to generate a radiomics predictive vector (RPV) which is able to differentiate metastatic lung nodules from new primary lung cancer in patients who have previously received radical treatment for a cancer. No current models exist in clinical practice which address this diagnostic challenge.
Measured: 2 years
Secondary outcomes (1)
To evaluate performance the developed CT-thorax based ML classifier model in an independent external validation cohort.
The investigators aim to assess performance of the derived radiomics predictive vector (RPV) on an external independent post-cancer lung nodule dataset to evaluate generalisability and potential real-world performance.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 May 2022
- Study start13 Oct 2021
- Primary completion1 Nov 2022 (estimated)
- Study completion1 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Indeterminate Pulmonary NodulesStudies of the same condition, open and under way.
- More trials run by Royal Marsden NHS Foundation TrustThe sponsor's other registered studies.
- 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
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Royal Marsden NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Collaborators
- Institute of Cancer Research, United Kingdom, National Institute for Health Research, United Kingdom, Royal Brompton & Harefield NHS Foundation Trust, Royal Marsden Partners Cancer Alliance, Imperial College London, Oxford University Hospitals NHS Trust, National Heart and Lung Institute
- Responsible party
- Sponsor
- Investigators
- Richard Lee, Principal investigator, The Royal Marsden Hospitals NHS Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05375591
- Sponsor's protocol ID
- CCR5502
- Acronym
- AI-SONAR
- Status last verified
- May 2022
- First submitted
- 27 Jan 2022
- Last updated
- 24 May 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Tabuchi T, Ito Y, Ioka A, Miyashiro I, Tsukuma H. Incidence of metachronous second primary cancers in Osaka, Japan: update of analyses using population-based cancer registry data. Cancer Sci. 2012 Jun;103(6):1111-20. doi: 10.1111/j.1349-7006.2012.02254.x. Epub 2012 Apr 11.
PubMed 22364479Youlden DR, Baade PD. The relative risk of second primary cancers in Queensland, Australia: a retrospective cohort study. BMC Cancer. 2011 Feb 23;11:83. doi: 10.1186/1471-2407-11-83.
PubMed 21342533Stella GM, Kolling S, Benvenuti S, Bortolotto C. Lung-Seeking Metastases. Cancers (Basel). 2019 Jul 19;11(7):1010. doi: 10.3390/cancers11071010.
PubMed 31330946Deng L, Harethardottir H, Song H, Xiao Z, Jiang C, Wang Q, Valdimarsdottir U, Cheng H, Loo BW, Lu D. Mortality of lung cancer as a second primary malignancy: A population-based cohort study. Cancer Med. 2019 Jun;8(6):3269-3277. doi: 10.1002/cam4.2172. Epub 2019 Apr 16.
PubMed 30993899Mery CM, Pappas AN, Bueno R, Mentzer SJ, Lukanich JM, Sugarbaker DJ, Jaklitsch MT. Relationship between a history of antecedent cancer and the probability of malignancy for a solitary pulmonary nodule. Chest. 2004 Jun;125(6):2175-81. doi: 10.1378/chest.125.6.2175.
PubMed 15189939Johnson BE. Second lung cancers in patients after treatment for an initial lung cancer. J Natl Cancer Inst. 1998 Sep 16;90(18):1335-45. doi: 10.1093/jnci/90.18.1335.
PubMed 9747865Travis LB. The epidemiology of second primary cancers. Cancer Epidemiol Biomarkers Prev. 2006 Nov;15(11):2020-6. doi: 10.1158/1055-9965.EPI-06-0414. Epub 2006 Oct 20.
PubMed 17057028Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res. 2017 Feb;6(1):86-91. doi: 10.21037/tlcr.2017.01.04.
PubMed 28331828Baldwin DR, Gustafson J, Pickup L, Arteta C, Novotny P, Declerck J, Kadir T, Figueiras C, Sterba A, Exell A, Potesil V, Holland P, Spence H, Clubley A, O'Dowd E, Clark M, Ashford-Turner V, Callister ME, Gleeson FV. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax. 2020 Apr;75(4):306-312. doi: 10.1136/thoraxjnl-2019-214104. Epub 2020 Mar 5.
PubMed 32139611
Study at a glance
From the registry record, last updated by the sponsor on 24 May 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence & Radiomics for Stratification Of Lung Nodules After Radically Treated Cancer (AI-SONAR)
- ClinicalTrials.gov ID
- NCT05375591
- Acronym / sponsor ID
- AI-SONAR / CCR5502
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Royal Marsden NHS Foundation TrustUniversity, hospital or other
- Collaborators
- Institute of Cancer Research, United Kingdom, National Institute for Health Research, United Kingdom, Royal Brompton & Harefield NHS Foundation Trust, Royal Marsden Partners Cancer Alliance, Imperial College London, Oxford University Hospitals NHS Trust, National Heart and Lung Institute
- Study start
- 13 Oct 2021 (actual)
- Primary completion
- 1 Nov 2022 (estimated)
- Study completion
- 1 Nov 2026 (estimated)
- First posted
- 16 May 2022 (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.
This study will assess the utility of radiomics and artificial intelligence approaches to new lung nodules in patients who have undergone radical treatment for a previous cancer.
Detailed description
Improvements in cancer detection and diagnosis have led to increasing numbers of patients being diagnosed with early stage cancer and potentially receiving curative therapy with improved survival outcomes. Recent retrospective studies in cancer survivors have demonstrated such patients possess an increased risk of further cancer in their lifetime compared to the general population, in part potentially due to shared lifestyle risk factors (e.g. smoking), genetic cancer pre-disposition or downstream oncogenic side effects of anti-cancer therapies (eg. radiotherapy). Lung cancer remains the leading cause of cancer related deaths worldwide and the lungs also represent a common site for metastatic disease in patients with non-pulmonary malignancy. Furthermore, lung cancer is one of the most common second primary malignancy in patients with a prior history of treated cancer. Therefore, discerning the significance of a pulmonary nodule in the context of a previous cancer remains a clinical challenge given it may possess the potential to represent benign disease, metastatic relapse or new primary malignancy.
This study will assess the utility of radiomics and artificial intelligence approaches to new lung nodules in patients who have undergone radical treatment for a previous cancer. This will entail use of machine learning (ML) approaches and later, exploration of deep-learning/convolutional neural network approaches to nodule interpretation for differentiation of benign, metastatic and new primary lung cancer nodules/lesions. Development of a ML classifier or deep learning based tool may help guide which patients would benefit from earlier investigations including additional imaging, biopsy sampling and lead to earlier cancer diagnosis, leading to better patient outcomes in this unique cohort. This is a retrospective study analysing data already collected routinely as part of patient care. All data will be anonymised prior to any analysis, no patient directed/related interventions will be employed and consent-waiver for study inclusion will be exercised.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Benign Nodules | – | Other: Non-Interventional StudyCT scans of patients with a new lung nodule(s) subsequently confirmed to be benign and in the context of a previous history of radically treated cancer, will be identified at participating NHS sites and recruited. |
| Metastatic Nodules | – | Other: Non-Interventional StudyCT scans of patients with a new lung nodule(s) subsequently confirmed to be metastatic in nature and in the context of a previous history of radically treated cancer, will be identified at participating NHS sites and recruited. |
| Second Primary Lung Cancers | – | Other: Non-Interventional StudyCT scans of patients with a new lung nodule(s) subsequently confirmed to be a new second primary lung cancer and in the context of a previous history of radically treated cancer, will be identified at participating NHS sites and recruited. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Retrospective cohorts of patient cases with a new pulmonary nodule finding on thoracic CT imaging and previous diagnosis of cancer (treated radically) within the past 10 years (or less).
Key inclusion criteria (15 listed)
- Confirmed history of previous radically or curative-intent treated solid organ cancer within 10 years of new index CT thoracic scan demonstrating a new pulmonary nodule and either of the following:
- Radical treatment for previous cancer defined as either of the following:
- New pulmonary nodule ground truth known
- CT scan slice thickness ≤ 2.5mm
- Nodule size ≥ 5mm
Key exclusion criteria (3 listed)
- CT Imaging > 10 years old
- Non-solid haematological malignancies including leukaemia
- Cases of radically treated primary cancer disease with early oligometastatic recurrence treated radically
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 |
|---|---|---|
| Non-Interventional StudyFirst nodule detection CT scans as per eligibility criteria will be used as input into in-house software to extract multiple radiomic features and used to develop a machine learning based classifier to differentiate nodule aetiology. Scans will also be used as input in to a deep learning/convolutional neural network models to perform automated imaging classification. | Other | Benign Nodules, Metastatic Nodules, Second Primary Lung Cancers |
Primary outcome
Development of a CT-thorax based radiomics ML classifier model to predict cancer risk in new lung nodules after previous radically treated cancer.
Time frame: 2 years
The study aims to identify distinct clusters of radiomics variables to generate a radiomics predictive vector (RPV), which can be used to stratify benign vs malignant nodules in patients who have previously received radical treatment for a malignancy. The RPV will be used in multivariate analysis and compared to existing risk models used in clinical practice.
Development of the CT-thorax based ML classifier model to predict whether a new malignant nodule represents metastatic lung disease (new cancer vs previous cancer recurrence) or a new primary lung malignancy.
Time frame: 2 years
The study aims to identify distinct clusters of radiomic variables to generate a radiomics predictive vector (RPV) which is able to differentiate metastatic lung nodules from new primary lung cancer in patients who have previously received radical treatment for a cancer. No current models exist in clinical practice which address this diagnostic challenge.
1 secondary outcome
To evaluate performance the developed CT-thorax based ML classifier model in an independent external validation cohort.
Time frame: 2 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 May 2022.
- Background: PubMed 22364479Tabuchi T, Ito Y, Ioka A, Miyashiro I, Tsukuma H. Incidence of metachronous second primary cancers in Osaka, Japan: update of analyses using population-based cancer registry data. Cancer Sci. 2012 Jun;103(6):1111-20. doi: 10.1111/j.1349-7006.2012.02254.x. Epub 2012 Apr 11.
- Background: PubMed 21342533Youlden DR, Baade PD. The relative risk of second primary cancers in Queensland, Australia: a retrospective cohort study. BMC Cancer. 2011 Feb 23;11:83. doi: 10.1186/1471-2407-11-83.
- Background: PubMed 31330946Stella GM, Kolling S, Benvenuti S, Bortolotto C. Lung-Seeking Metastases. Cancers (Basel). 2019 Jul 19;11(7):1010. doi: 10.3390/cancers11071010.
- Background: PubMed 30993899Deng L, Harethardottir H, Song H, Xiao Z, Jiang C, Wang Q, Valdimarsdottir U, Cheng H, Loo BW, Lu D. Mortality of lung cancer as a second primary malignancy: A population-based cohort study. Cancer Med. 2019 Jun;8(6):3269-3277. doi: 10.1002/cam4.2172. Epub 2019 Apr 16.
- Background: PubMed 15189939Mery CM, Pappas AN, Bueno R, Mentzer SJ, Lukanich JM, Sugarbaker DJ, Jaklitsch MT. Relationship between a history of antecedent cancer and the probability of malignancy for a solitary pulmonary nodule. Chest. 2004 Jun;125(6):2175-81. doi: 10.1378/chest.125.6.2175.
- Background: PubMed 9747865Johnson BE. Second lung cancers in patients after treatment for an initial lung cancer. J Natl Cancer Inst. 1998 Sep 16;90(18):1335-45. doi: 10.1093/jnci/90.18.1335.
- Background: PubMed 17057028Travis LB. The epidemiology of second primary cancers. Cancer Epidemiol Biomarkers Prev. 2006 Nov;15(11):2020-6. doi: 10.1158/1055-9965.EPI-06-0414. Epub 2006 Oct 20.
- Background: PubMed 28331828Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res. 2017 Feb;6(1):86-91. doi: 10.21037/tlcr.2017.01.04.
- Background: PubMed 32139611Baldwin DR, Gustafson J, Pickup L, Arteta C, Novotny P, Declerck J, Kadir T, Figueiras C, Sterba A, Exell A, Potesil V, Holland P, Spence H, Clubley A, O'Dowd E, Clark M, Ashford-Turner V, Callister ME, Gleeson FV. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax. 2020 Apr;75(4):306-312. doi: 10.1136/thoraxjnl-2019-214104. Epub 2020 Mar 5.
- Individual participant dataSharing planned: No
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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.
Richard Lee principal investigator
The Royal Marsden Hospitals NHS Trust
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Indeterminate Pulmonary Nodules and Lung Metastases, run by Royal Marsden NHS Foundation Trust at 2 sites in United Kingdom.
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 15 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: status not confirmed, as of the sponsor's last update on 24 May 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United Kingdom. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Sejal Jain (020 7808 2603, sejal.jain@rmh.nhs.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of a CT-thorax based radiomics ML classifier model to predict cancer risk in new lung nodules after previous radically treated cancer. (time frame: 2 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05375591, which also lists 9 linked publications.
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 24 May 2022.