Neoplasms

Respiratory Tract (Lung and Bronchial) Diseases

All Conditions

Symptoms and General Pathology

Lung Neoplasms

Neoplasm Metastasis

Multiple Pulmonary Nodules

Neoplasms, Second Primary

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

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.

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.

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.

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.

Non-Interventional Study

Sejal Jain

Laura Boddy

Richard Lee

Hardeep Kalsi

Anand Deveraj

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.

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.

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

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

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.

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.

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.

Institute of Cancer Research, United Kingdom

National Institute for Health Research, United Kingdom

Royal Brompton & Harefield NHS Foundation Trust

Royal Marsden Partners Cancer Alliance

Imperial College London

Oxford University Hospitals NHS Trust

National Heart and Lung Institute

Royal Marsden NHS Foundation Trust

Participants in this group will be 55-77 years old and currently smoke or were former smokers with 30 pack-years or more (and quit less than 15 years ago)

Participants in this group will be \> 45 years old and currently smoke or were former smokers with 10 pack-years or more (and quit less than 15 years ago)

Institutional standard of care may involve the use of LungRADS (screening setting) or Fleischner criteria (either combined 2005, 2013 or 2017 guidelines)

Institutional standard of care

Identify biomarkers in biosamples from airway and blood for the preclinical detection of lung cancer.

Biosamples of airway and blood

Ehab Billatos, MD

Denise Aberle, MD

George Washko, MD

DECAMP 1 PLUS aims to improve the efficiency of the diagnostic evaluation of patients with indeterminate pulmonary nodules (8-25 mm). Molecular biomarkers for lung cancer diagnosis measured in minimally invasive and non-invasive biospecimens may be able to distinguish between malignant or benign indeterminate pulmonary nodules in high-risk smokers. Ultimately, this study aims to validate molecular as well as clinical and imaging biomarkers of lung cancer in individuals with indeterminate lung nodules.

The Detection of Early lung Cancer Among Military Personnel (DECAMP) consortium is a multidisciplinary and translational research program that previously initiated the DECAMP-1 and DECAMP-2 studies for lung cancer early detection. Now, with the support of Johnson and Johnson, 3 Veterans Administration Hospitals (VAH), 3 Military Treatment Facilities (MTF) and 12 academic hospitals as clinical study sites, several molecular biomarker laboratories, along with Biostatics, Bioinformatics, Pathology and Biorepository cores, we are expanding our work in lung cancer early detection.

Blood, Nasal brushing, Buccal scraping, Stool, Bronchial Biopsy, Bronchial brushing, Lung tissue

DECAMP 1 PLUS: Prediction of Lung Cancer Using Noninvasive Biomarkers

The number of new cases of lung cancer that developed divided by the population at risk.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
AI & Radiomics for Stratification of Lung Nodules After Radically Treated Cancer	NCT05375591	Indeterminate P... Lung Metastases Second Primary ... Lung Cancer	Non-Interventio...	18 Years -	Royal Marsden NHS Foundation Trust
DECAMP 1 PLUS: Prediction of Lung Cancer Using Noninvasive Biomarkers	NCT04165564	Pulmonary Disea...	Institutional s... Biosamples of a...	46 Years -	Boston University

Spots Global Cancer Trial Database for indeterminate pulmonary nodules