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

Stage IV Non-small Cell Lung Cancer Clinical Trial

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03305380
• Other study ID #: BMS Radiomics
• Status: Completed
• Start date: September 1, 2017
• Est. completion date: April 1, 2021

Study information

• Verified date: September 2021
• Source: Maastricht Radiation Oncology
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 637
• Est. completion date: April 1, 2021
• Est. primary completion date: January 1, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Carcinoma, Non-Small-Cell Lung
• Inflammation
• Lung Neoplasms
• Pneumonia
• Stage IV Non-small Cell Lung Cancer

Intervention

Other:
• No interventions
As this is a patient registry, there are no interventions.

Locations

Country	Name	City	State
Netherlands	Zuyderland Medical Center	Heerlen
Netherlands	MUMC+	Maastricht

Sponsors (3)

Lead Sponsor	Collaborator
Maastricht Radiation Oncology	Maastricht University Medical Center, Zuyderland Medical Centre

Country where clinical trial is conducted

Netherlands, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Cause of pneumonitis	Determining cause of the pneumonitis by medical status of the patient	6 months
Secondary	Predictive accuracy of radiomics for determining the cause of pneumonitis	Three subgroups of immune checkpoint induced pneumonitis: Immune checkpoint-induced pneumonitis from tumour progression; Immune checkpoint-induced pneumonitis from other types of pneumonitis; 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	6 months