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HSI for Intersegmental Plane Identification During Sublobar Pulmonary Resections

ConditionsLung NeoplasmPulmonary Metastasis
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorLungenClinic Grosshansdorf

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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

Segmentectomy is oncologically indicated or impaired pulmonary and/or cardiac function prevent anatomical resection

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

Serum creatinine ≤ 1.5 x upper limit of normal or creatinine clearance (CrCl ≥ 50 mL/min, Cockcroft-Gault formula)

Disqualifiers

Requirement of a lobectomy or pneumonectomy to achieve complete resection

Allergy to indocyanine green or iodine

Hyperthyroidism

Current or planned pregnancy, nursing period (if defined as requirement of clinical routine treatment)

Trial design

Design model

Single group

Treatments tested in this trial

  • Hyperspectral Imaging

    Device

    Identification of the intersegmental plane

Treatment groups

50 Participants
are divided into 1 treatment group
Group A: Intersegmental plane identification by HSI and ICGExperimental treatment 1 intervention

Trial outcomes

Primary outcomes

1

Intersegmental Plane identification

Distance between intersegmental plane identification with Hyperspectral Imaging compared to near-infrared indocyanine green fluorescence

Time frame
directly before pulmonary intersegmental plane dissection

Secondary outcomes

1

Tumor distance [mm]

Shortest distance between tumor and HSI and ICG-fluorescence predicted intersegmental plane

Time frame
immediately after surgery
2

7-item binary rating scale for feasibilty of HSI measurement

Evaluation of feasibility of HSI and ICG-fluorescence intersegmental plane identification using a 7-item binary rating scale

Time frame
immediately after surgery

Other outcomes

1

Machine Learning

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.

Time frame
one week after surgery
2

Safety of HSI

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

Time frame
at 10 days and 6 weeks after surgery

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

LungenClinic Grosshansdorf

Lead sponsor

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

Collaborator

Source ClinicalTrials.gov

This trial is not recruiting at the moment. You can still explore other options: