AI & Radiomics for Stratification of Lung Nodules After Radically Treated Cancer
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorRoyal Marsden NHS Foundation Trust
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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
Surgical resection
Radical radiotherapy or stereotactic beam radiotherapy
Disqualifiers
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 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).
Trial design
Design model
Cohort
Time perspective
Retrospective
Treatments tested in this trial
Non-Interventional Study
Other interventionFirst 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.
Treatment groups
1,000 Participants
are divided into 3 treatment groupsGroup A: Benign Nodules1 intervention
Group B: Metastatic Nodules1 intervention
Group C: Second Primary Lung Cancers1 intervention
Trial outcomes
Primary outcomes
1
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.
Time frame
2 years2
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.
Time frame
2 yearsSecondary 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.
Time frame
2 yearsSponsors and contacts
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Royal Marsden NHS Foundation Trust
Lead sponsor
Institute of Cancer Research, United Kingdom
Collaborator
National Institute for Health Research, United Kingdom
Collaborator
Royal Brompton & Harefield NHS Foundation Trust
Collaborator
Royal Marsden Partners Cancer Alliance
Collaborator
Imperial College London
Collaborator
Oxford University Hospitals NHS Trust
Collaborator
National Heart and Lung Institute
Collaborator
Source ClinicalTrials.gov