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Imaging-based PRediction of Eligibility for ChemoImmunotherapy in reSEctable NSCLC, iPRECISE
Sponsor: Samsung Medical Center
Summary
This study is for adults with resectable non-small cell lung cancer who are scheduled to receive neoadjuvant chemoimmunotherapy before surgery. Neoadjuvant chemoimmunotherapy can help shrink lung cancer before surgery and may improve treatment outcomes. However, not all patients benefit from this treatment in the same way, and it can sometimes cause side effects, such as immune-related pneumonitis. At present, it is still difficult to predict before or during treatment which patients will have a strong response. The purpose of this study is to find imaging features on chest computed tomography (CT) scans that may help predict how well a patient's cancer responds to neoadjuvant chemoimmunotherapy. The study will compare CT findings before treatment and before surgery with pathologic findings from surgery, including pathologic complete response and major pathologic response. The study will also evaluate whether CT-based imaging features are associated with treatment-related side effects and long-term outcomes such as disease progression and survival. This is an observational study. The investigators will not assign participants to a specific cancer treatment. Participants will receive neoadjuvant chemoimmunotherapy and surgery according to standard clinical practice. Chest CT scans will be obtained before treatment and before surgery as part of the study protocol. These CT images will also be reconstructed using a high-resolution deep learning-based CT reconstruction technique to explore whether this approach can improve the development of imaging biomarkers. The results of this study may help develop a noninvasive imaging-based model to identify patients who are more likely to benefit from neoadjuvant chemoimmunotherapy and to better guide treatment planning for resectable non-small cell lung cancer.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
150
Start Date
2026-02-01
Completion Date
2028-12
Last Updated
2026-04-30
Healthy Volunteers
No
Conditions
Interventions
High-resolution deep learning-based CT reconstruction
High-resolution deep learning-based CT reconstruction will be applied after CT image acquisition to generate additional reconstructed images. These images will be compared with conventional CT reconstruction images to evaluate their usefulness for imaging biomarker development and assessment of extranodal extension.
Locations (1)
Samsung Medical Center
Seoul, South Korea