HSI for Intersegmental Plane Identification During Sublobar Pulmonary Resections

Studien-Informationen Einschlusskriterien Studien-Rationale Geprüfte Regime

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NCT-Nummer:
NCT05676788

Studienbeginn:
April 2023

Letztes Update:
09.01.2023

Wirkstoff:
-

Indikation (Clinical Trials):
Lung Neoplasms

Geschlecht:
Alle

Altersgruppe:
Erwachsene (18+)

Phase:
-

Sponsor:
LungenClinic Grosshansdorf

Collaborator:
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, Germany

Kontakt

David B Ellebrecht, MD
Kontakt:
Phone: +4941026012201
E-Mail: d.ellebrecht@lungenclinic.de» Kontaktdaten anzeigen

Studienlocations
(1 von 1)

LungenClinic Großhansdorf
22927 Großhansdorf
(Schleswig-Holstein)
Germany» Google-Maps

Studien-Informationen

Detailed Description:

Lung cancer is the leading cause of cancer-related death worldwide. Due to the generalization

of screening strategies, especially for risk populations, an increasing number of lung cancer

cases are detected in an early stage. In this regard, lung cancer is also increasingly

diagnosed in patients with impaired pulmonary function. For preserving lung function and

reducing complication incidence, pulmonary segmentectomies are currently evaluated in this

cohort. Thus, the latest version of the German guideline for the prevention, diagnosis,

treatment and follow-up of lung cancer recommends segmentectomy for patients with impaired

pulmonary function in tumor stage I/II. However, the identification of the intersegmental

plane - the key step of segmentectomy - remains challenging. Inaccurate recognition of the

intersegmental plane may lead to dysfunction of the remaining lung tissue, mismatching of

ventilation or blood flow, or long-term air leakage after surgery, which even requires

unplanned secondary surgery. Indocyanine green (ICG) is one the latest evaluated

identification methods and is considered as gold standard. Hyperspectral Imaging (HSI) - a

newly established intraoperative imaging technique - enables a non-invasive evaluation of

tissue perfusion and the discrimination of pulmonary tissue with different tissue perfusion

during segmentectomies.

The purpose of this prospective, single-center, non-inferiority IDEAL Stage 2b study is the

identification of the intersegmental plane and navigation during sublobar pulmonary

resections in lung cancer using Hyperspectral Imaging, the comparison with ICG fluorescence

intersegmental plane identification, and the establishment of automatic intersegmental plane

navigation using machine learning strategies for intraoperative navigation.

To address this, the intersegmental plane will be detected by both HSI and ICG-fluorescence

during pulmonary segmentectomies and the correspondence of the two identification methods

will be compared with one another. Using machine learning strategies, the detection of

perfused and non-perfused pulmonary tissue and intersegmental plane will be analyzed.

Finally, the investigators will study motion tracking for the improvement of future HSI

illustrations during surgery.

The hypothesis of this study is that HSI could improve the intraoperative navigation during

pulmonary segmentectomies providing as reliable intersegmental plane identification as the

gold standard of indocyanine green fluorescence. In this case, an intravenous application of

fluorescent dye would not be required anymore for the intersegmental plane identification.

In the case of complex segment resection, a large amount or repeated use of ICG is necessary

due to its short pulmonary circulation time. Multiple use of ICG may result in ICG entering

the target lung tissue through the bronchial circulation and increases the risk of adverse

drug reactions of ICG. In contrast, the advantages of HSI would be a faster and repetitive

measurement during surgery. There will be a potential for reducing the total measurement time

during intersegmental plane dissection (10 seconds vs. 3 minutes / measurement) and

consequently patient's burden. In this context, several studies of HSI-based perfusion

measurement during esophageal or colorectal surgery showed already an improved patients'

outcome. Furthermore, HSI can be used for surgery on patients with hyperthyroidism or

impaired renal or hepatic function.

In order to support this hypothesis, a prospective non-inferiority trial design will be used

in this study. To ensure the quality of data acquisition and reporting, the study will be

conducted in accordance with the IDEAL reporting guidelines. During pulmonary

segmentectomies, the intersegmental plane will be identified by both HSI and ICG

fluorescence. The determined HSI intersegmental margin will be benchmarked against the ground

truth ICG fluorescence and the feasibility and reproducibility of HSI and ICG mapping will be

studied.

Machine learning methods have greatly improved the interpretation of subtle patterns in

medical image data. Convolutional neural networks (CNNs) can be considered state-of-the-art

for classification and segmentation of medical images. The investigators will extend

CNN-based methods for HSI classification and particularly study patch-based differentiation

between perfused and non-perfused tissue using ICG and HSI data acquired at the same

position. A further challenge is the relatively slow acquisition of HSI (10

seconds/measurement), which makes it prone to motion artifacts, e.g., due to pulsatile

motion. To address this, the investigators will study motion tracking, which is also relevant

for the future illustration of the segment boundary during surgery.

Machine learning approaches and particularly CNNs allow to directly optimize classifiers

based on actual clinical data and the spectral dimension can be handled in a straightforward

fashion. Moreover, as a versatile method for image processing, CNNs can also be used for

localization and motion compensation during intraoperative imaging, e.g., they can be trained

to detect image features and their motion in red/green/blue image streams. This is

interesting for the proposed HSI data acquisition, which is based on a sequence of

measurements which are sensitive to tissue motion.

Ein-/Ausschlusskriterien

Inclusion Criteria:

1. Histologically confirmed lung cancer stage I/II or malignancy suspicious nodules

2. Segmentectomy is oncologically indicated or impaired pulmonary and/or cardiac function

prevent anatomical resection

3. Male or female patients aged ≥ 18 years without upper age limit

4. Serum creatinine ≤ 1.5 x upper limit of normal or creatinine clearance (CrCl ≥ 50

mL/min, Cockcroft-Gault formula)

5. Total bilirubin ≤ 1.5 x upper limit of normal (except patients with Gilbert Syndrome

(Morbus Meulengracht) in whom total bilirubin < 3.0 mg/dL is allowed)

6. Aspartate aminotransferase (AST) (serum glutamic-oxaloacetic transaminase)/alanine

aminotransferase (ALT) (serum glutamate pyruvate transaminase) ≤ 2.5 x upper limit of

normal

7. Full legal capacity

8. Written informed consent obtained according to international guidelines and local laws

9. Ability to understand the nature of the trial and the trial related procedures and to

comply with them

Exclusion Criteria:

1. Requirement of a lobectomy or pneumonectomy to achieve complete resection

2. Allergy to indocyanine green or iodine

3. Hyperthyroidism

4. Current or planned pregnancy, nursing period (if defined as requirement of clinical

routine treatment)

5. Medical condition which poses a high risk to undergo surgery as defined by the

investigator

6. Covid19 / SARS-CoV2-infection at time of screening

7. Participation in any other interventional clinical trial within the last 30 days

before the start of this trial

8. Simultaneous participation in other interventional trials which could interfere with

this trial; simultaneous participation in registry and diagnostic trials is allowed

9. Known or persistent abuse of medication, drugs or alcohol

10. Person who is in a relationship of dependence/employment with the coordinating

investigator or the investigator

Studien-Rationale

Primary outcome:

1. Intersegmental Plane identification (Time Frame - directly before pulmonary intersegmental plane dissection):
Distance between intersegmental plane identification with Hyperspectral Imaging compared to near-infrared indocyanine green fluorescence

Secondary outcome:

1. Tumor distance [mm] (Time Frame - immediately after surgery):
Shortest distance between tumor and HSI and ICG-fluorescence predicted intersegmental plane

2. 7-item binary rating scale for feasibilty of HSI measurement (Time Frame - immediately after surgery):
Evaluation of feasibility of HSI and ICG-fluorescence intersegmental plane identification using a 7-item binary rating scale

Geprüfte Regime

Hyperspectral Imaging:
Identification of the intersegmental plane

Quelle: ClinicalTrials.gov

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