HYPER-Seg: HSI for Intersegmental Plane Identification During Sublobar Pulmonary Resections
Study Details
Study Description
Brief Summary
The purpose of this 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.
| Condition or Disease | Intervention/Treatment | Phase |
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N/A |
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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Intersegmental plane identification by HSI and ICG Hyperspectral Imaging Intersegmental plane identification: Defined as distance between intersegmental plane identification with Hyperspectral Imaging compared to near-infrared indocyanine green fluorescence. |
Device: Hyperspectral Imaging
Identification of the intersegmental plane
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Outcome Measures
Primary Outcome Measures
- Intersegmental Plane identification [directly before pulmonary intersegmental plane dissection]
Distance between intersegmental plane identification with Hyperspectral Imaging compared to near-infrared indocyanine green fluorescence
Secondary Outcome Measures
- Tumor distance [mm] [immediately after surgery]
Shortest distance between tumor and HSI and ICG-fluorescence predicted intersegmental plane
- 7-item binary rating scale for feasibilty of HSI measurement [immediately after surgery]
Evaluation of feasibility of HSI and ICG-fluorescence intersegmental plane identification using a 7-item binary rating scale
Other Outcome Measures
- Machine Learning [one week after surgery]
Intersegmental plane identification with machine learning strategies. To predicted intersegmental plane by the machine learning algorithm developed in this project. The machine learning algorithm is trained on thoracoscopic HSI image and specimen data to predict if the intersegmental plane is displayed correctly during surgery.
- Safety of HSI [at 10 days and 6 weeks after surgery]
Safety will be evaluated by measuring the rates of adverse reactions to HSI and ICG dye, intraoperative complications, and perioperative complications using the Ottawa Thoracic Morbidity and Mortality Classification
Eligibility Criteria
Criteria
Inclusion Criteria:
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Histologically confirmed lung cancer stage I/II or malignancy suspicious nodules
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Segmentectomy is oncologically indicated or impaired pulmonary and/or cardiac function prevent anatomical resection
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Male or female patients aged ≥ 18 years without upper age limit
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Serum creatinine ≤ 1.5 x upper limit of normal or creatinine clearance (CrCl ≥ 50 mL/min, Cockcroft-Gault formula)
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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)
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Aspartate aminotransferase (AST) (serum glutamic-oxaloacetic transaminase)/alanine aminotransferase (ALT) (serum glutamate pyruvate transaminase) ≤ 2.5 x upper limit of normal
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Full legal capacity
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Written informed consent obtained according to international guidelines and local laws
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Ability to understand the nature of the trial and the trial related procedures and to comply with them
Exclusion Criteria:
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Requirement of a lobectomy or pneumonectomy to achieve complete resection
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Allergy to indocyanine green or iodine
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Hyperthyroidism
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Current or planned pregnancy, nursing period (if defined as requirement of clinical routine treatment)
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Medical condition which poses a high risk to undergo surgery as defined by the investigator
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Covid19 / SARS-CoV2-infection at time of screening
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Participation in any other interventional clinical trial within the last 30 days before the start of this trial
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Simultaneous participation in other interventional trials which could interfere with this trial; simultaneous participation in registry and diagnostic trials is allowed
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Known or persistent abuse of medication, drugs or alcohol
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Person who is in a relationship of dependence/employment with the coordinating investigator or the investigator
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | LungenClinic Großhansdorf | Großhansdorf | Germany | 22927 |
Sponsors and Collaborators
- LungenClinic Grosshansdorf
- Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, Germany
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- HSI_01