Radiomics to Identify Patients at Risk for Developing Pneumonitis, Differentiate Immune Checkpoint Inhibitor-induced Pneumonitis From Other Lung Inflammation and Distinguish Tumour Pseudo-progression From Real Tumour Growth

Radiomics to 1. Identify Patients at Risk for Developing Pneumonitis, 2. Differentiate Immune Checkpoint Inhibitor-induced Pneumonitis From Other Lung Inflammation and 3. Distinguish Tumour Pseudo-progression From Real Tumour Growth, in Patients With Non-small Cell Lung Cancer Treated With Anti-PD1 or Anti-PD-L1

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT03305380

Enrollment

637

Registered

2017-10-10

Start date

2017-09-01

Completion date

2021-04-01

Last updated

2021-09-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Stage IV Non-small Cell Lung Cancer

Keywords

anti-PD1, anti-PD-L1

Brief summary

The investigators will develop a radiomics signature for immune checkpoint-induced pneumonitis in 40 patients with a pulmonary event under anti-PD1 or anti-PD-L1 (cases) and 40 patients without a pulmonary event under anti-PD1 or anti-PD-L1 (controls). On the basis of the case-control study of patients treated with anti-PD1 or anti-PD-L1, they will further optimise the model using reinforcement machine learning. The model will then be validated in 300 prospective patients.

Detailed description

Preliminary analyses on a dataset showed a clear distinction in radiomics features for patients with and without pneumonitis from anti-PD1 or anti-PD-L1. Prior experience of the investigators of training and validating radiomics signatures combined with their preliminary exploratory results presented here, will be used to develop a radiomics signature for immune checkpoint-induced pneumonitis in 40 patients with a pulmonary event under anti-PD1 or anti-PD-L1 (cases) and 40 patients without a pulmonary event under anti-PD1 or anti-PD-L1 (controls). On the basis of the case-control study of patients treated with anti-PD1 or anti-PD-L1, the investigators will be able to further optimise the model using reinforcement machine learning. The model will then be validated in 300 prospective patients.

Interventions

OTHERNo interventions

As this is a patient registry, there are no interventions.

Study design

Observational model

OTHER

Time perspective

OTHER

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

* Patients who receive standard anti-PD1 or anti-PD-L1 treatment in routine clinical practice for first or second line stage IV non-small cell lung cancer

Exclusion criteria

* The opposite of the above

Design outcomes

Primary

Measure	Time frame	Description
Cause of pneumonitis	6 months	Determining cause of the pneumonitis by medical status of the patient

Secondary

Measure	Time frame	Description
Predictive accuracy of radiomics for determining the cause of pneumonitis	6 months	Three subgroups of immune checkpoint induced pneumonitis: 1. Immune checkpoint-induced pneumonitis from tumour progression 2. Immune checkpoint-induced pneumonitis from other types of pneumonitis 3. Patients with interstitial lung disease that are at risk to develop immune checkpoint-induced pneumonitis and those who are not. Radiomics will be used to predict the cause of pneumonitis

Countries

Netherlands

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026