Patients with locally advanced lung cancer, LA-NSCLC, who are to receive MR-guided adaptive RT with concomitant systemic therapy for clinical indication
Conditions
Interventions
Group 1: Treatment is based on the German S3 guideline for lung cancer, taking into account current evidence-based developments and according to clinical standards. This applies to both radiation dose
Sponsors
Klinik für Radioonkologie und Strahlentherapie, Universität Heidelberg
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Inclusion criteria
Inclusion criteria: • Patient age = 18 years • ECOG score 0-2 (Karnofsky Performance Score > 70%) • Histologically confirmed non-small cell lung cancer (NSCLC) • Tumor stage II with positive lymph nodes (N1) or tumor stage III according to the 9th edition of the Union for International Cancer Control (UICC) - TNM classification • Patients with tumor stage IV are also eligible for inclusion if oligometastasis is present, i.e., both the primary tumor and all metastases are undergoing local ablative therapy • Clinical indication for definitive thoracic radiotherapy using MR-guided adaptive technology (at least weekly, MR-guided adaptation of the radiotherapy plan) • Moderately hypofractionated or normofractionated radiotherapy in 20-35 fractions of 2-3 Gray per single dose according to clinical standards (e.g. 30–35 x 2 Gy, 20 x 2.75 Gy, or similar) • Concomitant sequential or simultaneous systemic therapy (chemoradiotherapy or radioimmunotherapy) according to clinical standards • Patient's capacity to consent • Patient's informed consent (IC)
Exclusion criteria
Exclusion criteria: • Patient refusal to participate in the study • Patient unable to consent • Non-MR-compatible implants (pacemaker, defibrillator, large metal implants, etc.) • Unsuitability for adaptive radiotherapy (ability to remain still for at least 20–40 minutes and, if necessary, follow breathing commands) • Pregnancy
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The primary study objective is to descriptively characterize the feasibility of MR-guided adaptive RT for locally advanced NSCLC in a real-world clinical scenario. The registry is platform-independent, allowing comparison of different state-of-the-art procedures (two different MR-linac systems and a CBCT-based adaptive linac with an MRI shuttle). The corresponding co-primary endpoints are the number of patients treated per year with MR-guided adaptive RT relative to the total number of patients with locally advanced NSCLC treated per center, discontinued adaptive radiation fractions, and adaptive treatment times. | — |
Secondary
| Measure | Time frame |
|---|---|
| The main secondary objective is to further characterize the treated patients and describe the clinical outcomes to identify those patient groups with locally advanced NSCLC who particularly benefit from MR-guided adaptive RT. Key associated secondary endpoints include local and distant tumor control, progression-free and overall survival, investigator-reported toxicity, and patient-reported outcomes (in the form of quality of life questionnaires). Further analyses include the following endpoints: • The frequency of adaptation and the resulting dosimetric benefit are evaluated based on dose-volume parameters (e.g., target volume coverage, minimum/D2, maximum/D98, and mean dose in target volumes and organs at risk) and compared with non-adapted RT plans. • The robustness of plan reoptimization based on the respective optimization specifications. • Retrospective dosimetric comparisons based on the planning MRI and CT images as well as the daily CT/MRI images. • Concepts of treatment planning based exclusively on MRI or cone beam CT (CBCT) and the generation of so-called "pseudo-CTs" • Testing of automatic contouring and image registration tools using daily imaging by comparing them with manually created contours/registrations based on similarity measures (e.g., DICE coefficients, position, and volume of the contours) • Various tracking methods will be analyzed using daily cineMRI images and geometrically compared with the target tracking applied at the MR linac • Various dose accumulation methods are calculated and evaluated for individually adapted treatment fractions. • Training of deep learning models for automatic segmentation and dose accumulation, including transfer learning between different modalities, as well as for predicting clinical outcomes with corresponding quality metrics. • Further medical-physical analyses include the comparison of different imaging modalities to define (volumetry) and annotate the remaining tumor mass during and after compl | — |
Countries
Germany
Contacts
Public ContactSebastian Regnery
Klinik für Radioonkologie und Strahlentherapie Universität Heidelberg
Outcome results
None listed