Predictive Multimodal Signatures Associated With Response to Treatment and Prognosis of Patients With Stage IV Non-small Cell Lung Cancer

Study Overview
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Brief Summary
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Predicting response to therapy and disease progression in stage IV NSCLC patients treated with pembrolizumab monotherapy, chemotherapy-pembrolizumab combination therapy or chemotherapy alone in the first-line setting.

Official Title
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Deep Learning-Enabled Exploration of Predictive Signatures in a Multicenter Retrospective and Prospective Observational Study Allowing the Analysis of the Aggregation of Multimodal Clinical, Biological, Genomic and Radiomics Data Associated With the Response to Treatment and Prognosis of Patients With Stage IV Non-small Cell Lung Cancer

Conditions
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Non-small Cell Lung Cancer Metastatic

Intervention / Treatment
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* Other: Predictive models (data collection)


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Adult ≥18 years old Patient diagnosed with Stage IV NSCLC (de novo or earlier stage progression to stage IV) Absence of oncogene activating mutations eligible patients to targeted therapy (EGFR, ALK) Cohort A: Received first line treatment with pembrolizumab monotherapy Cohort B: Received first line treatment with chemotherapy and pembrolizumab combination therapy Cohort C: Received first line treatment with chemotherapy doublet Exclusion Criteria: Prior anti-cancer therapy for actual stage IV NSCLC Critical data missing (e.g., PD-L1 status, baseline millimetric imaging, first evaluation millimetric imaging) Patients participating in other clinical trials that modify the standard of care

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Pembrolizumab monotherapy<br> | Other: Predictive models (data collection)<br>* Machine learning predictive models<br>|
| Chemotherapy and pembrolizumab combination therapy<br> | Other: Predictive models (data collection)<br>* Machine learning predictive models<br>|
| Chemotherapy doublet<br> | Other: Predictive models (data collection)<br>* Machine learning predictive models<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Treatment response at first evaluation | Predict treatment response at first evaluation using baseline data | 6-12 weeks after treatment start | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Progression-Free Survival | Predict Progression-Free Survival (PFS) using data at baseline and first evaluation | Through study completion, expected 6-14 months contingent on cohort | 
| Overall Survival | Predict Overall Survival (OS) using data at baseline and first evaluation | Through study completion, expected 8-20 months contingent on cohort | 
| Duration of Response | Predict Duration of Response (DoR) using data at baseline and first evaluation | Through study completion, expected 6-14 months contingent on cohort | 
| Time-To-Progression | Predict Time-To-Progression (TTP) using data at baseline and first evaluation | Through study completion, expected 6-14 months contingent on cohort | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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NSCLC, Immunotherapy, Chemotherapy, Predictive models, Radiomics, Multimodal, Genomics, Machine learning