AI & Radiomics for Stratification of Lung Nodules After Radically Treated Cancer

Lung Cancer Clinical Trial

— AI-SONAR  

Official title:

Artificial Intelligence & Radiomics for Stratification Of Lung Nodules After Radically Treated Cancer (AI-SONAR)

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05375591
• Other study ID #: CCR5502
• Status: Recruiting
• Start date: October 13, 2021
• Est. completion date: November 1, 2026

Study information

• Verified date: May 2022
• Source: Royal Marsden NHS Foundation Trust
• Contact: Sejal Jain
• Phone: 020 7808 2603
• Email: sejal.jain[@]rmh.nhs.uk
• Is FDA regulated: No
• Study type: Observational

Clinical 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: November 1, 2026
• Est. primary completion date: November 1, 2022
• 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

Related Conditions & MeSH terms

• Lung Cancer
• Lung Metastases
• Multiple Pulmonary Nodules
• Neoplasms, Second Primary

Intervention

Other:
• 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.

Locations

Country	Name	City	State
United Kingdom	Royal Brompton Hospital	London
United Kingdom	The Royal Marsden NHS Foundation Trust (Chelsea Site)	London

Sponsors (8)

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

Country where clinical trial is conducted

United Kingdom, 

References & Publications (9)

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

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Travis LB. The epidemiology of second primary cancers. Cancer Epidemiol Biomarkers Prev. 2006 Nov;15(11):2020-6. Epub 2006 Oct 20. Review. — View Citation

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. — View Citation

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. — View Citation

Outcome

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