Lung Cancer Clinical Trial
— AI-SONAROfficial title:
Artificial Intelligence & Radiomics for Stratification Of Lung Nodules After Radically Treated Cancer (AI-SONAR)
NCT number | NCT05375591 |
Other study ID # | CCR5502 |
Secondary ID | |
Status | Recruiting |
Phase | |
First received | |
Last updated | |
Start date | October 13, 2021 |
Est. completion date | November 1, 2026 |
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.
Status | Recruiting |
Enrollment | 1000 |
Est. completion date | November 1, 2026 |
Est. primary completion date | November 1, 2022 |
Accepts healthy volunteers | |
Gender | All |
Age group | 18 Years and older |
Eligibility | 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 |
Country | Name | City | State |
---|---|---|---|
United Kingdom | Royal Brompton Hospital | London | |
United Kingdom | The Royal Marsden NHS Foundation Trust (Chelsea Site) | London |
Lead Sponsor | Collaborator |
---|---|
Royal Marsden NHS Foundation Trust | Imperial College London, Institute of Cancer Research, United Kingdom, National Heart and Lung Institute, National Institute for Health Research, United Kingdom, Oxford University Hospitals NHS Trust, Royal Brompton & Harefield NHS Foundation Trust, Royal Marsden Partners Cancer Alliance |
United Kingdom,
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. — View Citation
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Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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. | 2 years | |
Primary | 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. | 2 years | |
Secondary | 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. | 2 years |
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