Imaging-based PRediction of Eligibility for ChemoImmunotherapy in reSEctable NSCLC, iPRECISE
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorSamsung Medical Center
About this trial
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 scans that may help predict how well a patient's cancer responds to neoadjuvant chemoimmunotherapy. The study will compare computed tomography 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 computed tomography-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 computed tomography scans will be obtained before treatment and before surgery as part of the study protocol. These computed tomography images will also be reconstructed using a high-resolution deep learning-based computed tomography 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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Age: 18 years or older.
Diagnosis: Histologically or cytologically confirmed non-small cell lung cancer (NSCLC).
Staging: Resectable NSCLC of stage IIIA or lower.
Treatment Plan: Planned to receive neoadjuvant chemoimmunotherapy before surgery according to standard clinical practice.
Disqualifiers
No Measurable Lesion: Participants must have at least one measurable lesion (>= 10 mm on spiral/multidetector computed tomography).
Prior Malignancy: History of another malignancy within 5 years before enrollment (excluding adequately treated basal cell skin carcinoma or cervical carcinoma in situ).
Neurological/Psychiatric Conditions: History of clinically significant uncontrolled seizures, CNS disease, or psychiatric disorders that may interfere with study participation or consent.
Contrast Allergy: History of severe allergic reaction to iodinated CT contrast media.
Trial population
The study population will include adult patients with resectable non-small cell lung cancer of stage IIIA or lower who are scheduled to receive neoadjuvant chemoimmunotherapy before surgery at Samsung Medical Center. Participants will be enrolled prospectively after providing written informed consent. Treatment decisions, including the use of neoadjuvant chemoimmunotherapy and surgery, will be made according to standard clinical practice and will not be assigned by the investigators. Participants will undergo chest computed tomography before neoadjuvant chemoimmunotherapy and before surgery as part of the study protocol. Imaging, clinical, molecular, and pathologic data will be collected to evaluate computed tomography-based imaging biomarkers associated with treatment response, pathologic response, treatment-related pneumonitis, and clinical outcomes.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
High-resolution deep learning-based CT reconstruction
Diagnostic testHigh-resolution deep learning-based computed tomography reconstruction will be applied after computed tomography image acquisition to generate additional reconstructed images. These images will be compared with conventional computed tomography reconstruction images to evaluate their usefulness for imaging biomarker development and assessment of extranodal extension.
Treatment groups
No treatment groups listed
Trial outcomes
Primary outcomes
1
Predictive performance of computed tomography-based imaging biomarkers for pathologic complete response
The predictive performance of imaging biomarkers derived from contrast-enhanced chest computed tomography scans obtained before and after neoadjuvant chemoimmunotherapy will be evaluated for pathologic complete response. Pathologic complete response will be assessed using surgical pathology findings after resection. Predictive performance will be measured using model discrimination, including the area under the receiver operating characteristic curve.
Time frame
From baseline computed tomography before neoadjuvant chemoimmunotherapy to surgical pathology assessment after surgery, up to 6 months after enrollment.Secondary outcomes
1
Association between computed tomography-based imaging biomarkers and progression-free survival
The association between computed tomography-based imaging biomarkers and progression-free survival will be evaluated. Progression-free survival is defined as the time from initiation of neoadjuvant chemoimmunotherapy to disease progression, recurrence, or death, whichever occurs first.
Time frame
From initiation of neoadjuvant chemoimmunotherapy through disease progression, recurrence, death, or last follow-up, up to study completion.Sponsors and contacts
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Source ClinicalTrials.gov