Efficiency of Contemporary Off-line Adaptive Radiotherapy for Lung Cancer

NCT07259447 | Recruiting

• 1 other identifier: S68182
• Study Type: interventional
• Target: 30
• Locations: 1 country
• Sites: 1

Brief Summary

Locally advanced non-small cell lung cancer (LA-NSCLC) patients could benefit in overall and progression-free survival from regular dosimetric treatment plan adaptations during radiotherapy. This is known as adaptive radiotherapy (ART). However, implementing an adaptive radiotherapy workflow presents a highly cumbersome process. First, repeated planning-CT imaging during treatment is required, which results in additional radiation dose for patients. Second, an ART workflow includes the repetition of various manual and semi-automated tasks such as target and organ-at-risk contouring on the images and dosimetric treatment planning. These obstacles hinder widespread ART implementation. To avoid repeated planning-CT imaging, position-verification imaging can be utilized. Modern cone-beam CT (CBCT) imaging, integrated into the treatment unit, assists radiation therapists (RTTs) in administering the dose. Recent improvements in CBCT imaging sources and detectors have enhanced image quality. Moreover, it may be possible to calculate radiation dose directly on these CBCTs. Utilizing CBCT imaging for plan adaptation could also eliminate the need for an additional CT procedure, thereby increasing patient comfort. To address the labor-intensive contouring and treatment planning steps, CE-marked and validated commercial AI applications are already being used to support organ contouring and accelerate the treatment-planning process. These tools are currently applied to pre-treatment planning CTs. The time efficiency of these contemporary tools in a prospective ART workflow has yet to be studied, as has the feasibility of applying these applications within a CBCT-based ART workflow.

Conditions

NSCLC (Advanced Non-small Cell Lung Cancer); Adaptive Radiotherapy

Keywords

CBCT-based Adaptive Radiotherapy

Outcome Measures

Primary Outcomes (4)

• Time required for each step of the prospective adaptive radiotherapy workflow

  The total time required to complete each component of the prospective adaptive radiotherapy workflow will be quantified. Time measurements will be recorded using standardized digital workflow time-logging templates in the department's hospital document management system. The following steps will be assessed: * Acquisition of the repeated 4D planning CT * Automated organ-at-risk contouring (Syngo.via®) * Physician approval of organ-at-risk contouring * Manual tumor delineation * Knowledge-based treatment planning (RapidPlan®) * Medical physics plan review and approval * Physician plan review and approval The primary variable will be the total cumulative duration (minutes) and step-specific durations (minutes).

  From initiation of the mid-treatment 4DCT scan until final approval of the adapted treatment plan, assessed for each participant, up to 7 weeks of radiotherapy

• Time required for each step of the retrospectively tested CBCT-based adaptive radiotherapy workflow

  The total time required to complete each component of the retrospective CBCT-based adaptive radiotherapy workflow will be quantified. Time measurements will be recorded using standardized digital workflow time-logging templates in the department's hospital document management system. The following steps will be assessed: * CBCT acquisition (retrospective use of routine daily CBCT) * Preparation of the CBCT for accurate dose calculation (enhancing image quality, applying correct density to Hounsfield unit curve) * Automated organ-at-risk contouring (Syngo.via® and MIM®) * Manual tumor delineation * Knowledge-based treatment planning (RapidPlan®) The primary variable will be the total cumulative duration (minutes) and step-specific durations (minutes).

  Assessed retrospectively for each participant from the first available mid-treatment CBCT to completion of the simulated treatment plan, up to 6 months

• Plan quality comparison using dose-volume histogram (DVH) metrics

  Quality of CBCT-based or synthetic-CT-based adaptive treatment plans will be compared with reference prospective 4DCT-based plans. Metrics include: * Target coverage (i.e. iCTV, PTV) (%) * Organ-at-risk doses (i.e. lungs, spinal canal, esophagus, brachial plexi, mediastinal envelope, heart, contralateral lung) (Gy and %) * Dose deviations between CBCT-based/synthetic CT-based and 4DCT-based plans Evaluation will be performed using standard treatment planning system DVH tools.

  Assessed retrospectively after completion of radiotherapy, up to 6 months

• Contour accuracy comparison using similarity metrics (DSC, HD, ASSD)

  Contour similarity between CBCT-based (or synthetic-CT-based) contours and reference 4DCT-based contours will be evaluated. Metrics include: * Dice Similarity Coefficient (DSC) (unitless) * Hausdorff Distance (HD) (millimeters) * Average Symmetric Surface Distance (ASSD) (millimeters) Analyses will be performed using MIM®.

  Assessed retrospectively after completion of radiotherapy, up to 6 months

Study Arms (1)

CT-based Adaptive Radiotherapy applied midtreatment. Retrospective CBCT-based Adaptive comparison

EXPERIMENTAL

Device: CBCT-based adaptive radiotherapy

Interventions

CBCT-based adaptive radiotherapy DEVICE

The objective is to temporarily implement and study the efficiency of a prospective ART workflow for LA-NSCLC using repeated planning-CT imaging. This initiative aims to contour and plan in accordance with the contemporary clinical AI-tools already being standard-of-care in the pre-treatment workflow, with a specific focus on evaluating and reporting the time efficiency of the process. Following the prospective part, we want to retrospectively study a CBCT-based ART workflow for LA-NSCLC using CBCT imaging in comparison with the CT-based ART workflow. The contours and treatment plans generated utilizing 4DCT imaging serve as ground truth. These retrospective tests are fully outside the clinical flow. We will evaluate whether it is possible to implement an adaptive workflow without repeated planning-CT imaging. For this objective, we will utilize the same commercial AI tools, with a focus on reporting both the time efficiency and the quality of contours and plans in comparison.

CT-based Adaptive Radiotherapy applied midtreatment. Retrospective CBCT-based Adaptive comparison

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Voluntary written informed consent of the participant or their legally authorized representative has been obtained prior to any screening procedures
• Patients diagnosed with non-small-cell lung cancer
• Locally advanced disease (≥Stage III)
• Treated with sequential or concurrent chemoradiotherapy

You may not qualify if:

• Small-cell lung cancer
• Non-small-cell lung cancer of early stage
• Mesothelioma

Sponsors & Collaborators

• Universitaire Ziekenhuizen KU Leuven: lead

Study Sites (1)

UZ Leuven

Leuven, Vlaams-Brabant, 3000, Belgium

RECRUITING

Related Publications (3)

• Bertholet J, Anastasi G, Noble D, Bel A, van Leeuwen R, Roggen T, Duchateau M, Pilskog S, Garibaldi C, Tilly N, Garcia-Molla R, Bonaque J, Oelfke U, Aznar MC, Heijmen B. Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part II: Offline and online plan adaption for interfractional changes. Radiother Oncol. 2020 Dec;153:88-96. doi: 10.1016/j.radonc.2020.06.017. Epub 2020 Jun 21.

  PMID: 32579998 BACKGROUND

• Moller DS, Lutz CM, Khalil AA, Alber M, Holt MI, Kandi M, Schmidt HH, Tvilum M, Appelt A, Knap MM, Hoffmann L. Survival benefits for non-small cell lung cancer patients treated with adaptive radiotherapy. Radiother Oncol. 2022 Mar;168:234-240. doi: 10.1016/j.radonc.2022.01.039. Epub 2022 Feb 2.

  PMID: 35121030 BACKGROUND

• Berkovic P, Paelinck L, Gulyban A, van Eijkeren M, Surmont V, Lievens Y, Vandecasteele K. Adaptive radiotherapy for locally advanced non-small cell lung cancer: dosimetric gain and treatment outcome prediction. Acta Oncol. 2017 Nov;56(11):1656-1659. doi: 10.1080/0284186X.2017.1352103. Epub 2017 Aug 23. No abstract available.

  PMID: 28835160 BACKGROUND

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung

Condition Hierarchy (Ancestors)

Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

Wouter Crijns, Prof. Dr.

CONTACT

+3216341453; wouter.crijns@uzleuven.be

Maarten Lambrecht, Prof. Dr.

CONTACT

+3216347629; maarten.lambrecht@uzleuven.be

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 14, 2025
• First Posted: December 2, 2025
• Study Start: June 21, 2024
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: December 2, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will not share

Locations

BE (1)