AI & Radiomics for Stratification of Lung Nodules After Radically Treated Cancer (AI-SONAR)

May 17, 2022 updated by: Royal Marsden NHS Foundation Trust

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

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.

Study Overview

Status

Recruiting

Conditions

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

Observational

Enrollment (Anticipated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • United Kingdom
      • London, United Kingdom, SW3 6JJ
        • Recruiting
        • The Royal Marsden NHS Foundation Trust (Chelsea Site)
        • Contact:
        • Contact:
      • London, United Kingdom, SW3 6NP
        • Recruiting
        • Royal Brompton Hospital
        • Contact:
        • Principal Investigator:
          • Anand Deveraj

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study 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).

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
Intervention / 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.
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.
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.
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.
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.
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.

What is the study measuring?

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.
2 years

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

This is where you will find people and organizations involved with this study.

Sponsor

Royal Marsden NHS Foundation Trust

Collaborators

Imperial College London
Royal Brompton & Harefield NHS Foundation Trust
Institute of Cancer Research, United Kingdom
National Institute for Health Research, United Kingdom
Oxford University Hospitals NHS Trust
Royal Marsden Partners Cancer Alliance
National Heart and Lung Institute

Investigators

  • Principal Investigator: Richard Lee, The Royal Marsden Hospitals NHS Trust

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 13, 2021

Primary Completion (Anticipated)

November 1, 2022

Study Completion (Anticipated)

November 1, 2026

Study Registration Dates

First Submitted

January 27, 2022

First Submitted That Met QC Criteria

May 10, 2022

First Posted (Actual)

May 16, 2022

Study Record Updates

Last Update Posted (Actual)

May 24, 2022

Last Update Submitted That Met QC Criteria

May 17, 2022

Last Verified

May 1, 2022

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CCR5502

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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