Precise Recognition With Enhanced Vision of Endocrine Neck Targets (PREVENT)

January 5, 2024 updated by: IHU Strasbourg

Iatrogenic injuries to the parathyroid glands during thyroid surgery or to the recurrent laryngeal nerve (RLN) do still occur, requiring often specialized management.

Recently, it has been demonstrated that the parathyroid gland shows a significant autofluorescence. Using a commercially available Near-InfraRed (NIR) camera (Fluobeam®, Fluoptics©, France), the parathyroid glands can be clearly visualized by contrast-free fluorescence imaging. However it lacks real-time quantification of the fluorescence intensity.

The hyperspectral imaging (HSI), which is a technology that combines a spectrometer to a camera system, examines the optical properties of a large area in a wavelength range from NIR to visual light (VIS). It provides spatial information real time, in a contact-free, non-ionizing manner. The HSI technology would add the spatial information, thus enormously enhancing the intraoperative performance.

The aim of the proposed study is to identify the spectral features of the important neck target structures, in particular the parathyroid glands, using an appropriate deep learning algorithm, to perform an automated parathyroid recognition. Additionally, this study proposes to compare the detection rate of the hyperspectral based parathyroid recognition with the already existing NIR autofluorescence based recognition.

Study Overview

Status

Terminated

Conditions

Intervention / Treatment

Detailed Description

The major challenge in thyroid and parathyroid procedures, is the safe identification of the recurrent laryngeal nerve (RLN) and the localization of the parathyroid glands (to be preserved or to be selectively removed). Iatrogenic injuries to the parathyroid glands during thyroid surgery (resulting in transient or permanent hypocalcemia) or to the RLN (resulting in hoarseness, dysphonia, dyspnea) do still occur, requiring often specialized management.

The percentage of incidental parathyroidectomies, in specialized endocrine centers, is around 16%. In these cases, it is more likely to observe clinical relevant hypocalcemia than after planned parathyroidectomy for hyperparathyroidism. Therefore, there is a critical need for an intra-operative method enabling a precise, real-time parathyroid identification.

Recently, it has been demonstrated that the parathyroid gland shows a significant autofluorescence, which is caused by the optical properties of a still unknown intrinsic fluorophore. When the gland is excited by a light source with a wavelength ranging from 750-785 nm, it emits a fluorescence peak around 820 nm. Taking advantage of this property, Falco et al., using a commercially available NIR camera (Fluobeam®, Fluoptics©, France), could clearly visualize the parathyroid glands by contrast-free fluorescence imaging and could easily discriminate them from the thyroid and the surrounding tissue. The drawback with this autofluorescence-based imaging is that it lacks real-time quantification of the fluorescence intensity.

The hyperspectral imaging (HSI), which is a technology that combines a spectrometer to a camera system, examines the optical properties of a large area in a wavelength range from near infrared (NIR) to visual light (VIS). It provides diagnostic information about the tissue physiology, composition and perfusion. The fact that the HSI produces pictures, thus providing spatial information real time, in a contact-free, non-ionizing manner, makes it potentially a very valuable tool for the intraoperative use.

HSI has exhibited its great potential in the medical field especially in the diagnosis of various neoplasia (e.g. of the cervix, breast, colon, brain), in the detection of perfusion pattern in patients with peripheral arterial disease and in the area of wound diagnostic.

As previously shown, it is possible to discriminate the thyroid from the parathyroid glands according to the spectral characteristics, but the HSI technology would add the spatial information, thus enormously enhancing the intraoperative performance.

In collaboration with the University of Leipzig, Germany, the investigators performed a clinical pilot trial on 8 patients, which showed promising results. Hyperspectral images during benign endocrine surgery procedures were able to demonstrate that thyroid and parathyroid have specific hyperspectral signatures. Furthermore, the parathyroid glands showed usually less oxygenated than the thyroid. A discrimination of the parathyroid glands based on these characteristics is proven to be possible.

The aim of the proposed study is to identify the spectral features of the important neck target structures, in particular the parathyroid glands, using an appropriate deep learning algorithm, to perform an automated parathyroid recognition. Additionally, this study proposes to compare the detection rate of the hyperspectral based parathyroid recognition with the already existing NIR autofluorescence based recognition.

Study Type

Observational

Enrollment (Actual)

54

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • France
      • Strasbourg, France, 67000
        • Service de Chirurgie Digestive et Endocrinienne, NHC

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Adult patients of both sexes for whom a total or partial thyroid or parathyroid resection for a benign or malignant pathology is programmed

Description

Inclusion Criteria:

  • Man or woman over 18 years old.
  • Patient with benign or malignant pathology of the thyroid or parathyroid gland
  • Patient with no contraindication to anesthesia and surgery
  • Patient able to receive and understand information related to the study
  • Patient affiliated to the French social security system.

Exclusion Criteria:

  • Patient who needs an emergency surgery
  • Pregnant or lactating patient.
  • Patient under guardianship or trusteeship.
  • Patient under the protection of justice.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Thyroids
The aim is to identify and preserve the parathyroid glands during the total or partial removal of the thyroid. Repeating of the procedure for each lobe
Other: Hyperspectral and Fluobeam acquisition
Once enough exposure of the operative site is achieved, an RGB (Red Green Blue) picture will be taken and the surgeon will depict the parathyroid glands on it, this picture will act as "ground truth". At this point, without changing the surgical exposure, a second surgeon involved in the study will attempt once to detect the parathyroid glands intraoperatively using the HSI system and the Fluobeam® alternatively. The number and the position of the parathyroid glands visualized with each tool will be compared to the number and position of the glands previously visualized by the operating surgeon. The procedure will be repeated every time the surgeon attempts to visualize the parathyroid glands. The order of the detection tools randomized for each case will be preserved in case of repeated visualizations.
Parathyroids
The aim is to selectively remove the pathological parathyroid gland(s). Repeating of the procedure for each removed gland
Other: Hyperspectral and Fluobeam acquisition
Once enough exposure of the operative site is achieved, an RGB (Red Green Blue) picture will be taken and the surgeon will depict the parathyroid glands on it, this picture will act as "ground truth". At this point, without changing the surgical exposure, a second surgeon involved in the study will attempt once to detect the parathyroid glands intraoperatively using the HSI system and the Fluobeam® alternatively. The number and the position of the parathyroid glands visualized with each tool will be compared to the number and position of the glands previously visualized by the operating surgeon. The procedure will be repeated every time the surgeon attempts to visualize the parathyroid glands. The order of the detection tools randomized for each case will be preserved in case of repeated visualizations.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Comparison of the intraoperative detection rate between the automated HSI-based parathyroid recognition against the surgeon's clinical appreciation.
Time Frame: 1 day
Detection rate of the parathyroids by the automated HSI-based parathyroid recognition against the visual identification by the operating surgeon (clinical ground truth) and, if required by the surgeon, against the histopathological examination (extemporaneous anatomopathology = histological ground truth). Also, final pathology will be used as ground truth.
1 day
Comparison of the intraoperative detection rate between the Fluobeam®, against the surgeon's clinical appreciation.
Time Frame: 1 day
Detection rate of the parathyroids by the Fluobeam® against the visual identification by the operating surgeon (clinical ground truth) and, if required by the surgeon, against the histopathological examination (extemporaneous anatomopathology = histological ground truth). Also, final pathology will be used as ground truth.
1 day

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
in vivo collection of HSI spectral features of the parathyroid and thyroid glands to successively enable automated recognition.
Time Frame: 1 day
Collection of clean and consistent datasets of the parathyroid and thyroid glands
1 day
in vivo collection of HSI spectral signatures of other tissues routinely exposed during neck surgery, such as thyroid, fat, muscle, cartilage and nerves will be collected.
Time Frame: 1 day
Collection of clean and consistent datasets of other tissues exposed during neck surgery. The information will be implemented in the machine learning algorithm in order to allow in the future an automated recognition of the different target structures during neck surgery.
1 day
Recognition of possible pathology specific HSI spectral features of pathological parathyroid or thyroid glands.
Time Frame: 1 month
The ability to predict pathological parathyroid or thyroid glands from the spectral tissue information, based on the final histopathological reports.
1 month
Difference in time to recognition between human eye, Fluobeam® and HSI
Time Frame: 1 day
Comparison between the time of recognition using the HSI algorithm and the Fluobeam®
1 day
Detection rate of the recurrent laryngeal nerve against the clinical impression and the intraoperative neuromonitoring.
Time Frame: 1 day
Number of times the recurrent laryngeal nerve is detected by the automated identification against the number of time it is visually identified by the operating surgeon and confirmed with the Intraoperative neuromonitoring (IONM).
1 day

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

IHU Strasbourg

Investigators

  • Principal Investigator: Michele DIANA, MD, PhD, Service de Chirurgie Digestive et Endocrinienne, NHC, Strasbourg

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 20, 2021

Primary Completion (Actual)

October 15, 2021

Study Completion (Actual)

October 15, 2021

Study Registration Dates

First Submitted

February 4, 2021

First Submitted That Met QC Criteria

February 4, 2021

First Posted (Actual)

February 9, 2021

Study Record Updates

Last Update Posted (Actual)

January 9, 2024

Last Update Submitted That Met QC Criteria

January 5, 2024

Last Verified

January 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 20-007

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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