NCT05375591

Brief 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.

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
1,000

participants targeted

Target at P75+ for all trials

Timeline
6mo left

Started Oct 2021

Longer than P75 for all trials

Geographic Reach
1 country

2 active sites

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress90%
Oct 2021Nov 2026

Study Start

First participant enrolled

October 13, 2021

Completed
4 months until next milestone

First Submitted

Initial submission to the registry

January 27, 2022

Completed
4 months until next milestone

First Posted

Study publicly available on registry

May 16, 2022

Completed
6 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

November 1, 2022

Completed
4 years until next milestone

Study Completion

Last participant's last visit for all outcomes

November 1, 2026

Expected
Last Updated

May 24, 2022

Status Verified

May 1, 2022

Enrollment Period

1.1 years

First QC Date

January 27, 2022

Last Update Submit

May 17, 2022

Conditions

Indeterminate Pulmonary NodulesLung MetastasesSecond Primary CancerLung Cancer

Outcome Measures

Primary Outcomes (2)

  • 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

  • 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 Outcomes (1)

  • To evaluate performance the developed CT-thorax based ML classifier model in an independent external validation cohort.

    2 years

Study Arms (3)

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

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

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

Interventions

Non-Interventional StudyOTHER

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.

Benign NodulesMetastatic NodulesSecond Primary Lung Cancers

Eligibility Criteria

Age18 Years+
Sexall
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-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).

You may qualify if:

  • 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

You may not qualify if:

  • CT Imaging \> 10 years old
  • Non-solid haematological malignancies including leukaemia
  • Cases of radically treated primary cancer disease with early oligometastatic recurrence treated radically

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (2)

The Royal Marsden NHS Foundation Trust (Chelsea Site)

London, SW3 6JJ, United Kingdom

RECRUITING

Royal Brompton Hospital

London, SW3 6NP, United Kingdom

RECRUITING

Related Publications (9)

  • 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.

    PMID: 22364479BACKGROUND

  • 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.

    PMID: 21342533BACKGROUND

  • Stella GM, Kolling S, Benvenuti S, Bortolotto C. Lung-Seeking Metastases. Cancers (Basel). 2019 Jul 19;11(7):1010. doi: 10.3390/cancers11071010.

    PMID: 31330946BACKGROUND

  • Deng L, Harethardottir H, Song H, Xiao Z, Jiang C, Wang Q, Valdimarsdottir 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.

    PMID: 30993899BACKGROUND

  • 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. doi: 10.1378/chest.125.6.2175.

    PMID: 15189939BACKGROUND

  • 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. doi: 10.1093/jnci/90.18.1335.

    PMID: 9747865BACKGROUND

  • Travis LB. The epidemiology of second primary cancers. Cancer Epidemiol Biomarkers Prev. 2006 Nov;15(11):2020-6. doi: 10.1158/1055-9965.EPI-06-0414. Epub 2006 Oct 20.

    PMID: 17057028BACKGROUND

  • 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.

    PMID: 28331828BACKGROUND

  • 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.

    PMID: 32139611BACKGROUND

MeSH Terms

Conditions

Neoplasms, Second PrimaryLung Neoplasms

Condition Hierarchy (Ancestors)

NeoplasmsRespiratory Tract NeoplasmsThoracic NeoplasmsNeoplasms by SiteLung DiseasesRespiratory Tract Diseases

Study Officials

  • Richard Lee

    The Royal Marsden Hospitals NHS Trust

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Sejal Jain

CONTACT

020 7808 2603sejal.jain@rmh.nhs.uk

Laura Boddy

CONTACT

020 7808 2603laura.boddy@rmh.nhs.uk

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

January 27, 2022

First Posted

May 16, 2022

Study Start

October 13, 2021

Primary Completion

November 1, 2022

Study Completion (Estimated)

November 1, 2026

Last Updated

May 24, 2022

Record last verified: 2022-05

Data Sharing

IPD Sharing
Will not share

Locations

GB(2)