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Clinical Trial Details — Status: Terminated

Administrative data

NCT number NCT04589884
Other study ID # 20-005
Secondary ID
Status Terminated
Phase
First received
Last updated
Start date September 22, 2020
Est. completion date October 15, 2021

Study information

Verified date January 2024
Source IHU Strasbourg
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

The intraoperative recognition of target structures, which need to be preserved or selectively removed, is of paramount importance during surgical procedures. This task relies mainly on the anatomical knowledge and experience of the operator. Misperception of the anatomy can have devastating consequences. Hyperspectral imaging (HSI) represents a promising technology that is able to perform a real-time optical scanning over a large area, providing both spatial and spectral information. HSI is an already established method of objectively classifying image information in a number of scientific fields (e.g. remote sensing). Our group recently employed HSI as intraoperative tool in the porcine model to quantify perfusion of the organs of the gastrointestinal tract against robust biological markers. Results showed that this technology is able to quantify bowel blood supply with a high degree of precision. Hyperspectral signatures have been successfully used, coupled to machine learning algorithms, to discriminate fine anatomical structures such as nerves or ureters intraoperatively (unpublished data). The i-EX-MACHYNA3 study aims at translating the HSI technology in combination with several deep learning algorithms to differentiate among different classes of human tissues (including key anatomical structures such as BD, nerves and ureters).


Description:

The intraoperative recognition of target structures, which need to be preserved or selectively removed, is of paramount importance during surgical procedures. This task relies mainly on the anatomical knowledge and experience of the operator. In the setting of minimally invasive surgery, there is a reduced tactile feedback and the surgeon's vision is the only clue to discriminate the tissues. Misperception of the anatomy, due to patient-specific pathologic conditions and/or to the surgeon's inexperience, can lead to an increased risk of iatrogenic injury of critical anatomical structures and can have devastating consequences. Hyperspectral imaging (HSI) represents a promising technology that combines a photo camera to a spectrometer and that is able to perform a real-time optical scanning over a large area, in a contrast-free manner, providing both spatial and spectral information, generated by the tissue/light interaction. The technology is based on the use of reflectance spectroscopic imaging measurements. The measurement consists in the irradiation of white light on the area (normal halogen lamps, in non-harmful intensity) and the recording of the remitted spectral intensities from the area in the form of remission spectra. The optical interaction (scattering, absorption) of the incident light with the various components (including the depth) of the target material (e.g. biological tissues) alters the spectral distribution of light so that the remitted light carries information about the current material or tissue composition and physiology (e.g. perfusion). HSI is an already established method of objectively classifying image information in a number of scientific fields (e.g. remote sensing), which was first applied in the area of human medicine about 15 years ago. Because of the intrinsic advantages of non-destructive sample collection, interfacing possibilities with common optical modalities (microscope, endoscope) and quantitative, examiner independent results, various approaches have been developed in the meantime to harness the potential of hyperspectral imaging in medicine. Its usefulness in the biomedical field has been already extensively prove. It has been previously applied in digestive surgery to quantify intestinal oxygenated hemoglobin during several procedures, or in case of mesenteric ischemia. A number of previous works focused successfully on the ability of HSI to discriminate between normal and tumor tissue, in prostate cancer, colorectal cancer, gastric cancer, glioblastoma, head and neck cancers. In the oncological field, the advances in hyperspectral features classification have been remarkable and lead to the successful use of sophisticated deep learning algorithms. In surgery, the usefulness of HSI camera has been studied to visualize the operative field under difficult bleeding or to detect tumor presence within the resection margins after surgical excision. A japanese group used an HSI system as additional visualization tool to detect intestinal ischemia and also to classify the intraabdominal anatomy. They identified a particular wavelength (756-830 nm) for the differentiation between healthy and less perfused bowel. They also demonstrated that the spleen, colon, small intestine, urinary bladder and peritoneum have different spectral features. This finding might enable in the future HSI-based navigation of the operation field. Our group recently employed HSI as intraoperative tool in the porcine model to quantify perfusion of the organs of the gastrointestinal tract against robust biological markers. Results showed that this technology is able to quantify bowel blood supply with a high degree of precision. Other groups previously attempted to discriminate bile duct from the vessels, esophagus from tracheal tissue, thyroid from parathyroid gland, nerve and ureter from the surrounding tissue. However, those previous works directed on recognizing key anatomical structures were conducted using either simple feature discrimination algorithms or band selection methods. The amount of information obtained after each acquisition, varies according to the camera resolution, but is quite large, therefore machine and deep learning techniques for data classification and feature extraction are required. In a set of controlled experiments in the porcine model, hyperspectral signatures have been successfully used, coupled to machine learning algorithms, to discriminate fine anatomical structures such as nerves or ureters intraoperatively (unpublished data). The i-EX-MACHYNA3 study aims at translating the HSI technology in combination with several deep learning algorithms to differentiate among different classes of human tissues (including key anatomical structures such as BD, nerves and ureters).


Recruitment information / eligibility

Status Terminated
Enrollment 112
Est. completion date October 15, 2021
Est. primary completion date October 15, 2021
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - Man or woman over 18 years old. - Scheduled for elective or emergency surgery - Patient able to receive and understand information related to the study. - Patient affiliated to the French social security system. Exclusion Criteria: - Contra-indication for anesthesia - Pregnant or lactating patient. - Patient under guardianship or trusteeship. - Patient under the protection of justice.

Study Design


Intervention

Other:
Hyperspectral Imaging
Hyperspectral images of the operative field will be collected at several time points during the surgical procedure. The device used is the TIVITA® compact Hyperspectral imaging system (Diaspective Vision GmbH, Germany). It is a CE (European Economic Area) mark approved device. The acquisition takes roughly 10 seconds, is contrast-free and contact-free.

Locations

Country Name City State
France Service de Chirurgie Digestive et Endocrinienne, NHC Strasbourg

Sponsors (2)

Lead Sponsor Collaborator
IHU Strasbourg ARC Foundation for Cancer Research

Country where clinical trial is conducted

France, 

References & Publications (22)

Akbari H, Halig LV, Schuster DM, Osunkoya A, Master V, Nieh PT, Chen GZ, Fei B. Hyperspectral imaging and quantitative analysis for prostate cancer detection. J Biomed Opt. 2012 Jul;17(7):076005. doi: 10.1117/1.JBO.17.7.076005. — View Citation

Akbari H, Kosugi Y, Kojima K, Tanaka N. Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging. IEEE Trans Biomed Eng. 2010 Aug;57(8):2011-7. doi: 10.1109/TBME.2010.2049110. Epub 2010 May 10. — View Citation

Baltussen EJM, Kok END, Brouwer de Koning SG, Sanders J, Aalbers AGJ, Kok NFM, Beets GL, Flohil CC, Bruin SC, Kuhlmann KFD, Sterenborg HJCM, Ruers TJM. Hyperspectral imaging for tissue classification, a way toward smart laparoscopic colorectal surgery. J Biomed Opt. 2019 Jan;24(1):1-9. doi: 10.1117/1.JBO.24.1.016002. — View Citation

Barberio M, Felli E, Seyller E, Longo F, Chand M, Gockel I, Geny B, Swanstrom L, Marescaux J, Agnus V, Diana M. Quantitative fluorescence angiography versus hyperspectral imaging to assess bowel ischemia: A comparative study in enhanced reality. Surgery. 2020 Jul;168(1):178-184. doi: 10.1016/j.surg.2020.02.008. Epub 2020 Mar 27. — View Citation

Barberio M, Longo F, Fiorillo C, Seeliger B, Mascagni P, Agnus V, Lindner V, Geny B, Charles AL, Gockel I, Worreth M, Saadi A, Marescaux J, Diana M. HYPerspectral Enhanced Reality (HYPER): a physiology-based surgical guidance tool. Surg Endosc. 2020 Apr;34(4):1736-1744. doi: 10.1007/s00464-019-06959-9. Epub 2019 Jul 15. — View Citation

Fabelo H, Ortega S, Ravi D, Kiran BR, Sosa C, Bulters D, Callico GM, Bulstrode H, Szolna A, Pineiro JF, Kabwama S, Madronal D, Lazcano R, J-O'Shanahan A, Bisshopp S, Hernandez M, Baez A, Yang GZ, Stanciulescu B, Salvador R, Juarez E, Sarmiento R. Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations. PLoS One. 2018 Mar 19;13(3):e0193721. doi: 10.1371/journal.pone.0193721. eCollection 2018. — View Citation

Fei B, Lu G, Wang X, Zhang H, Little JV, Patel MR, Griffith CC, El-Diery MW, Chen AY. Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients. J Biomed Opt. 2017 Aug;22(8):1-7. doi: 10.1117/1.JBO.22.8.086009. — View Citation

Goetz AF, Vane G, Solomon JE, Rock BN. Imaging spectrometry for Earth remote sensing. Science. 1985 Jun 7;228(4704):1147-53. doi: 10.1126/science.228.4704.1147. — View Citation

Halicek M, Dormer JD, Little JV, Chen AY, Fei B. Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning. Biomed Opt Express. 2020 Feb 18;11(3):1383-1400. doi: 10.1364/BOE.381257. eCollection 2020 Mar 1. — View Citation

Halicek M, Lu G, Little JV, Wang X, Patel M, Griffith CC, El-Deiry MW, Chen AY, Fei B. Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J Biomed Opt. 2017 Jun 1;22(6):60503. doi: 10.1117/1.JBO.22.6.060503. — View Citation

Han Z, Zhang A, Wang X, Sun Z, Wang MD, Xie T. In vivo use of hyperspectral imaging to develop a noncontact endoscopic diagnosis support system for malignant colorectal tumors. J Biomed Opt. 2016 Jan;21(1):16001. doi: 10.1117/1.JBO.21.1.016001. No abstract available. — View Citation

Hu B, Du J, Zhang Z, Wang Q. Tumor tissue classification based on micro-hyperspectral technology and deep learning. Biomed Opt Express. 2019 Nov 19;10(12):6370-6389. doi: 10.1364/BOE.10.006370. eCollection 2019 Dec 1. — View Citation

Jansen-Winkeln B, Holfert N, Kohler H, Moulla Y, Takoh JP, Rabe SM, Mehdorn M, Barberio M, Chalopin C, Neumuth T, Gockel I. Determination of the transection margin during colorectal resection with hyperspectral imaging (HSI). Int J Colorectal Dis. 2019 Apr;34(4):731-739. doi: 10.1007/s00384-019-03250-0. Epub 2019 Feb 2. — View Citation

Jansen-Winkeln B, Maktabi M, Takoh JP, Rabe SM, Barberio M, Kohler H, Neumuth T, Melzer A, Chalopin C, Gockel I. [Hyperspectral imaging of gastrointestinal anastomoses]. Chirurg. 2018 Sep;89(9):717-725. doi: 10.1007/s00104-018-0633-2. German. — View Citation

Kohler H, Jansen-Winkeln B, Maktabi M, Barberio M, Takoh J, Holfert N, Moulla Y, Niebisch S, Diana M, Neumuth T, Rabe SM, Chalopin C, Melzer A, Gockel I. Evaluation of hyperspectral imaging (HSI) for the measurement of ischemic conditioning effects of the gastric conduit during esophagectomy. Surg Endosc. 2019 Nov;33(11):3775-3782. doi: 10.1007/s00464-019-06675-4. Epub 2019 Jan 23. — View Citation

Li Y, Deng L, Yang X, Liu Z, Zhao X, Huang F, Zhu S, Chen X, Chen Z, Zhang W. Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method. Biomed Opt Express. 2019 Sep 9;10(10):4999-5014. doi: 10.1364/BOE.10.004999. eCollection 2019 Oct 1. — View Citation

Lu G, Fei B. Medical hyperspectral imaging: a review. J Biomed Opt. 2014 Jan;19(1):10901. doi: 10.1117/1.JBO.19.1.010901. — View Citation

Ma L, Lu G, Wang D, Wang X, Chen ZG, Muller S, Chen A, Fei B. Deep Learning based Classification for Head and Neck Cancer Detection with Hyperspectral Imaging in an Animal Model. Proc SPIE Int Soc Opt Eng. 2017 Feb;10137:101372G. doi: 10.1117/12.2255562. Epub 2017 Mar 13. — View Citation

Nawn CD, Souhan BE, Carter R 3rd, Kneapler C, Fell N, Ye JY. Distinguishing tracheal and esophageal tissues with hyperspectral imaging and fiber-optic sensing. J Biomed Opt. 2016 Nov 1;21(11):117004. doi: 10.1117/1.JBO.21.11.117004. — View Citation

Nouri D, Lucas Y, Treuillet S. Hyperspectral interventional imaging for enhanced tissue visualization and discrimination combining band selection methods. Int J Comput Assist Radiol Surg. 2016 Dec;11(12):2185-2197. doi: 10.1007/s11548-016-1449-5. Epub 2016 Jul 4. — View Citation

Wisotzky EL, Uecker FC, Arens P, Dommerich S, Hilsmann A, Eisert P. Intraoperative hyperspectral determination of human tissue properties. J Biomed Opt. 2018 May;23(9):1-8. doi: 10.1117/1.JBO.23.9.091409. — View Citation

Zuzak KJ, Naik SC, Alexandrakis G, Hawkins D, Behbehani K, Livingston E. Intraoperative bile duct visualization using near-infrared hyperspectral video imaging. Am J Surg. 2008 Apr;195(4):491-7. doi: 10.1016/j.amjsurg.2007.05.044. — View Citation

* Note: There are 22 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary To collect human tissue spectral features to build a spectral tissue library and build successively machine learning algorithm to enable real-time automated tissue recognition To collect clean and consistent datasets and the evaluation of the accuracy based on ground truth evaluations, such as clinical evaluation and pathology reports. 1 day
Secondary To correlate HSI values with biological data obtained as standard of care The ability to predict biological data from the spectral tissue information 1 day
Secondary To correlate HSI values with pathological data obtained as standard of care The ability to predict pathological data from the spectral tissue information 1 day
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