AI & Radiomics for Stratification of Lung Nodules After Radically Treated Cancer
NCT ID: NCT05375591
Last Updated: 2022-05-24
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
1000 participants
OBSERVATIONAL
2021-10-13
2026-11-01
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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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.
Non-Interventional Study
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.
Non-Interventional Study
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.
Non-Interventional Study
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.
Interventions
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Non-Interventional Study
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.
Eligibility Criteria
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Inclusion Criteria
* 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
* Non-solid haematological malignancies including leukaemia
* Cases of radically treated primary cancer disease with early oligometastatic recurrence treated radically
18 Years
ALL
No
Sponsors
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Institute of Cancer Research, United Kingdom
OTHER
National Institute for Health Research, United Kingdom
OTHER_GOV
Royal Brompton & Harefield NHS Foundation Trust
OTHER
Royal Marsden Partners Cancer Alliance
UNKNOWN
Imperial College London
OTHER
Oxford University Hospitals NHS Trust
OTHER
National Heart and Lung Institute
OTHER
Royal Marsden NHS Foundation Trust
OTHER
Responsible Party
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Principal Investigators
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Richard Lee
Role: PRINCIPAL_INVESTIGATOR
The Royal Marsden Hospitals NHS Trust
Locations
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The Royal Marsden NHS Foundation Trust (Chelsea Site)
London, , United Kingdom
Royal Brompton Hospital
London, , United Kingdom
Countries
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Central Contacts
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Facility Contacts
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References
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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):1010. doi: 10.3390/cancers11071010.
Deng 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.
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. doi: 10.1378/chest.125.6.2175.
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. doi: 10.1093/jnci/90.18.1335.
Travis 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.
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.
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.
Other Identifiers
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CCR5502
Identifier Type: -
Identifier Source: org_study_id
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