Parathyroid Diseases, Thyroid Diseases
Conditions
Keywords
Hyperspectral Imaging, Deep learning, Autonomous tissue recognition, Tissue spectral signature, Autofluorescence
Brief summary
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.
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.
Interventions
OTHERHyperspectral 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.
Sponsors
IHU Strasbourg
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Comparison of the intraoperative detection rate between the automated HSI-based parathyroid recognition against the surgeon's clinical appreciation. | 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. |
| Comparison of the intraoperative detection rate between the Fluobeam®, against the surgeon's clinical appreciation. | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Recognition of possible pathology specific HSI spectral features of pathological parathyroid or thyroid glands. | 1 month | The ability to predict pathological parathyroid or thyroid glands from the spectral tissue information, based on the final histopathological reports. |
| in vivo collection of HSI spectral features of the parathyroid and thyroid glands to successively enable automated recognition. | 1 day | Collection of clean and consistent datasets of the parathyroid and thyroid glands |
| Detection rate of the recurrent laryngeal nerve against the clinical impression and the intraoperative neuromonitoring. | 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). |
| Difference in time to recognition between human eye, Fluobeam® and HSI | 1 day | Comparison between the time of recognition using the HSI algorithm and the Fluobeam® |
| 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. | 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. |
Countries
France
Outcome results
None listed