AI & Radiomics for Stratification of Lung Nodules After Radically Treated Cancer (AI-SONAR)
Artificial Intelligence & Radiomics for Stratification Of Lung Nodules After Radically Treated Cancer (AI-SONAR)
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
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 Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Sejal Jain
- Phone Number: 020 7808 2603
- Email: sejal.jain@rmh.nhs.uk
Study Contact Backup
- Name: Laura Boddy
- Phone Number: 020 7808 2603
- Email: laura.boddy@rmh.nhs.uk
Study Locations
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-
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London, United Kingdom, SW3 6JJ
- Recruiting
- The Royal Marsden NHS Foundation Trust (Chelsea Site)
-
Contact:
- Sejal Jain
- Phone Number: 02078082603
- Email: sejal.jain@rmh.nhs.uk
-
Contact:
- Laura Boddy
- Phone Number: 07414643915
- Email: laura.boddy@rmh.nhs.uk
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London, United Kingdom, SW3 6NP
- Recruiting
- Royal Brompton Hospital
-
Contact:
- Hardeep Kalsi
- Phone Number: 02078082603
- Email: hardeep.kalsi@rmh.nhs.uk
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Principal Investigator:
- Anand Deveraj
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
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
- 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
Exclusion Criteria:
- CT Imaging > 10 years old
- Non-solid haematological malignancies including leukaemia
- Cases of radically treated primary cancer disease with early oligometastatic recurrence treated radically
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Retrospective
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
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.
|
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.
|
|
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.
|
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.
|
|
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.
|
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.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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.
|
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.
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.
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2 years
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
To evaluate performance the developed CT-thorax based ML classifier model in an independent external validation cohort.
Time Frame: 2 years
|
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.
|
2 years
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Richard Lee, The Royal Marsden Hospitals NHS Trust
Publications and helpful links
General 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.
- Youlden 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.
- Stella GM, Kolling S, Benvenuti S, Bortolotto C. Lung-Seeking Metastases. Cancers (Basel). 2019 Jul 19;11(7). pii: E1010. doi: 10.3390/cancers11071010. Review.
- Deng L, Harðardottír H, Song H, Xiao Z, Jiang C, Wang Q, Valdimarsdóttir 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.
- Mery 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.
- Johnson BE. Second lung cancers in patients after treatment for an initial lung cancer. J Natl Cancer Inst. 1998 Sep 16;90(18):1335-45. Review.
- Travis LB. The epidemiology of second primary cancers. Cancer Epidemiol Biomarkers Prev. 2006 Nov;15(11):2020-6. Epub 2006 Oct 20. Review.
- Wilson 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. Review.
- Baldwin 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.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- CCR5502
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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