Clinical trial NCT03305380: 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
Official title: 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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Stage IV Non-small Cell Lung Cancer
Keywords: anti-PD1
anti-PD-L1
Lead sponsor: Maastricht Radiation Oncology
Sponsor class: OTHER
Collaborators: name: Maastricht University Medical Center
name: Zuyderland Medical Centre
First posted: 2017-10-10
Enrollment: 637
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: OTHER
Sampling method: PROBABILITY_SAMPLE
Study population: 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

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.

Eligibility criteria: 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

Primary outcomes:
measure: Cause of pneumonitis
timeFrame: 6 months
Secondary outcomes:
measure: Predictive accuracy of radiomics for determining the cause of pneumonitis
timeFrame: 6 months
Other outcomes:
Locations:
city: Heerlen
country: Netherlands
facility: Zuyderland Medical Center
city: Maastricht
country: Netherlands
facility: MUMC+
Study officials:
affiliation: Maastro Clinic, The Netherlands
name: Dirk De Ruysscher, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: