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

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест High-resolution deep learning-based CT reconstruction
  High-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.

Первичные конечные точки

• Predictive performance of computed tomography-based imaging biomarkers for pathologic complete response [Срок оценки: From baseline computed tomography before neoadjuvant chemoimmunotherapy to surgical pathology assessment after surgery, up to 6 months after enrollment.]

Вторичные конечные точки (1)

• Association between computed tomography-based imaging biomarkers and progression-free survival [Срок оценки: From initiation of neoadjuvant chemoimmunotherapy through disease progression, recurrence, death, or last follow-up, up to study completion.]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

South Korea · 1 центр

• Samsung Medical Center — Seoul

Публикации

• She Y, He B, Wang F, Zhong Y, Wang T, Liu Z, Yang M, Yu B, Deng J, Sun X, Wu C, Hou L, Zhu Y, Yang Y, Hu H, Dong D, Chen C, Tian J. Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study. EBioMedicine. 2022 Dec;86:104364. doi: 10.1016/j.ebiom.2022.104364. Epub 2022 Nov 14. PMID 36395737
• Greffier J, Pastor M, Si-Mohamed S, Goutain-Majorel C, Peudon-Balas A, Bensalah MZ, Frandon J, Beregi JP, Dabli D. Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study. Diagn Interv Imaging. 2024 Mar;105(3):110-117. doi: 10.1016/j.diii.2023.10.004. Epub 2023 Nov 8. PMID 37949769
• Marinelli D, Nuccio A, Di Federico A, Ambrosi F, Bertoglio P, Faccioli E, Ferrara R, Ferro A, Giusti R, Guerrera F, Mammana M, Pittaro A, Sepulcri M, Viscardi G, Gallina FT. Improved Event-Free Survival After Complete or Major Pathologic Response in Patients With Resectable NSCLC Treated With Neoadjuvant Chemoimmunotherapy Regardless of Adjuvant Treatment: A Systematic Review and Individual Patien PMID 39389220
• Sorin M, Prosty C, Ghaleb L, Nie K, Katergi K, Shahzad MH, Dube LR, Atallah A, Swaby A, Dankner M, Crump T, Walsh LA, Fiset PO, Sepesi B, Forde PM, Cascone T, Provencio M, Spicer JD. Neoadjuvant Chemoimmunotherapy for NSCLC: A Systematic Review and Meta-Analysis. JAMA Oncol. 2024 May 1;10(5):621-633. doi: 10.1001/jamaoncol.2024.0057. PMID 38512301
• Forde PM, Spicer J, Lu S, Provencio M, Mitsudomi T, Awad MM, Felip E, Broderick SR, Brahmer JR, Swanson SJ, Kerr K, Wang C, Ciuleanu TE, Saylors GB, Tanaka F, Ito H, Chen KN, Liberman M, Vokes EE, Taube JM, Dorange C, Cai J, Fiore J, Jarkowski A, Balli D, Sausen M, Pandya D, Calvet CY, Girard N; CheckMate 816 Investigators. Neoadjuvant Nivolumab plus Chemotherapy in Resectable Lung Cancer. N Engl PMID 35403841

Идентификаторы

NCT: NCT07559123 · 2024-12-118

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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