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Imaging-based PRediction of Eligibility for ChemoImmunotherapy in reSEctable NSCLC, iPRECISE (iPRECISE)

2026년 5월 5일 업데이트: Ho Yun Lee, Samsung Medical Center

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.

연구 개요

상태

모병

정황

개입 / 치료

상세 설명

This is a prospective observational study designed to evaluate imaging biomarkers in patients with resectable non-small cell lung cancer who receive neoadjuvant chemoimmunotherapy before planned surgery.

Neoadjuvant chemoimmunotherapy has become an important treatment option for resectable non-small cell lung cancer. Although pathologic complete response and major pathologic response are associated with favorable outcomes, reliable noninvasive methods for predicting these responses remain limited. Imaging biomarkers derived from computed tomography may provide a quantitative and repeatable way to assess tumor burden, nodal disease, treatment response, and treatment-related lung toxicity over time.

In this study, participants will undergo chest computed tomography before starting neoadjuvant chemoimmunotherapy and again after completion of neoadjuvant treatment before surgery. If disease progression is clinically suspected before planned surgery, an additional computed tomography scan may be performed and used for response assessment. Computed tomography scans will be acquired using a standardized imaging protocol. In addition to conventional computed tomography reconstruction, high-resolution deep learning-based computed tomography reconstruction will be applied after image acquisition.

The study will evaluate imaging features of the primary tumor, lymph nodes, suspected extranodal extension, adjacent structure involvement, radiologic treatment response, and possible immune-related pneumonitis. Imaging findings will be compared with clinical data, molecular and immunohistochemical findings, and surgical pathology findings, including pathologic complete response, major pathologic response, final pathologic stage, extranodal extension, and spread through air spaces when available.

The study will also explore whether imaging biomarkers are associated with progression-free survival, overall survival, treatment-related pneumonitis, and whether high-resolution deep learning-based CT reconstruction improves imaging biomarker development compared with conventional reconstruction. The anticipated enrollment is 150 participants.

연구 유형

관찰

등록 (추정된)

150

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 장소

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

아니

샘플링 방법

비확률 샘플

연구 인구

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.

설명

Inclusion Criteria:

  • 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.
  • Performance Status: Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1.
  • Informed Consent: Able and willing to provide written informed consent after receiving a detailed explanation of the study.

Exclusion Criteria:

  • 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.
  • Renal Impairment: Acute renal failure or moderate to severe renal impairment (CrCl < 45 mL/min/1.73 m² or serum creatinine > 1.5x upper limit of normal).
  • Recent Surgery: Major surgery within 4 weeks of enrollment or incomplete recovery from major surgery.
  • Pregnancy/Nursing: Currently pregnant or breastfeeding; women of childbearing potential without a negative baseline pregnancy test.
  • Contraception: Men or women of childbearing potential unwilling to use appropriate contraception during the study.

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
Predictive performance of computed tomography-based imaging biomarkers for pathologic complete response
기간: From baseline computed tomography before neoadjuvant chemoimmunotherapy to surgical pathology assessment after surgery, up to 6 months after enrollment.
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.
From baseline computed tomography before neoadjuvant chemoimmunotherapy to surgical pathology assessment after surgery, up to 6 months after enrollment.

2차 결과 측정

결과 측정
측정값 설명
기간
Association between computed tomography-based imaging biomarkers and progression-free survival
기간: From initiation of neoadjuvant chemoimmunotherapy through disease progression, recurrence, death, or last follow-up, up to study completion.
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.
From initiation of neoadjuvant chemoimmunotherapy through disease progression, recurrence, death, or last follow-up, up to study completion.

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

Samsung Medical Center

간행물 및 유용한 링크

연구에 대한 정보 입력을 담당하는 사람이 자발적으로 이러한 간행물을 제공합니다. 이것은 연구와 관련된 모든 것에 관한 것일 수 있습니다.

일반 간행물

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (실제)

2026년 2월 1일

기본 완료 (추정된)

2028년 12월 1일

연구 완료 (추정된)

2028년 12월 1일

연구 등록 날짜

최초 제출

2026년 4월 23일

QC 기준을 충족하는 최초 제출

2026년 4월 23일

처음 게시됨 (실제)

2026년 4월 30일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 5월 8일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 5월 5일

마지막으로 확인됨

2026년 5월 1일

추가 정보

이 연구와 관련된 용어

기타 연구 ID 번호

  • 2024-12-118

개별 참가자 데이터(IPD) 계획

개별 참가자 데이터(IPD)를 공유할 계획입니까?

아니요

IPD 계획 설명

Individual participant data will not be shared. This study includes clinical data, imaging-derived data, molecular and pathologic information, and follow-up outcome data from patients with resectable non-small cell lung cancer. Because of the potential risk of participant re-identification and the absence of a pre-specified external IPD sharing plan in the study protocol and informed consent process, individual participant-level data will not be made available to other researchers.

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .

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