Electrodes Recognition AbilityLOC-ECOGNCT04479410
Efficient Automated Localization of ECoG Electrodes in CT Images Via Shape Analysis
An observational study of Electrodes Recognition Ability, run by Neuromed IRCCS at 1 site in Italy.
Trial summary
People with drug epilepsy (PwE) refractory to anti-seizure medications may be evaluated for surgery. In several cases non invasive presurgical work-up is not sufficient for localization of the Epileptogenic Zone and its correct delineation requires intracranial investigations by means of intraparenchymal or subdural… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Electrodes Recognition Ability
- How many people
- 24 participants
- Where
- 1 siteItaly
- Study dates
- 1 Aug 2020to 7 Sep 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Child, Adult, Older adult
Study population: 24 people with epilepsy (PwE) refractory to anti seizure medications (ASM) who underwent epilepsy surgery at IRCCS Neuromed.
Who may be able to join
- Patients implanted with subdural ECoG electrodes underwent epilepsy surgery
- Availability of a post-operative CT scan with acceptable image quality
Who cannot join
- Patients having CT scans with low image quality
Trial locations and contacts
1 site in 1 country.
About this study
People with drug epilepsy (PwE) refractory to anti-seizure medications may be evaluated for surgery. In several cases non invasive presurgical work-up is not sufficient for localization of the Epileptogenic Zone and its correct delineation requires intracranial investigations by means of intraparenchymal or subdural electrodes.The methodological approach with subdural electrodes allows to obtain electrocorticography (ECoG) covering large cortical regions and to map eloquent areas.
To delineate the seizure onset zone it is mandatory to precisely localize the electrode position on the cortical surface. Electrodes are usually recognized by processing patients' computed tomography (CT) images using simple image processing (e.g. thresholding) that isolates metal objects. However, also wires, stitches, clips and other metal objects are actually recognized and need to be removed by manual intervention. A new automated method, based on shape analysis, will be retrospectively tested in a group of subjects with refractory focal epilepsy previously investigated with subdural electrodes for diagnostic purposes to provide advanced ECoG subdural electrodes recognition. A total of 24 CT scans with a large number (> 1700) of round platinum electrodes arrays will be recruited for testing.
Read the detailed description
For people with epilepsy (PwE) refractory to anti-seizure medication sometimes the non-invasive presurgical evaluation using ElectroEncephaloGram (EEG) recorded directly from the scalp is not sufficient to delineate the epileptogenic zone and to identificate the eloquent cortex. In these cases, an invasive approach using intracranial electroencephalography (iEEG) is needed Subdural electrodes are used frequently in the presurgical evaluation of patients who are candidates for epilepsy surgery. Electrodes placed directly on the surface of the cortex provide a signal with a much higher resolution than that provided from scalp electrodes, and have a much clear view of small loci of activity which is difficult to see on the scalp.
Subdural electrodes allow not only the localization of abnormal epileptic tissue but also the localization of adjacent normal functions. Therefore, the precise anatomical localization of the electrodes on the patient's brain plays a key role in the definition of the epileptogenic zone or in the mapping of eloquent cortex.
From a clinical point of view, the accurate localization of the anatomical boundaries of the epileptogenic zone allows to exclude eloquent areas, avoid deficits to patient and minimize brain volume resection.
The localization of these electrodes is generally obtained by matching the locations of the electrodes with the brain anatomy of the patient. Commonly, a pre-implant magnetic resonance image (MRI) is co-registered to a post-implant computed tomography scan (CT) because MRI offers higher brain tissue contrast, while CT supports electrodes localization , even if CT images are affected by metal artifacts.
Various dedicated software tools that support pre-surgical evaluation are currently available as Matlab-based packages or open source softwares, also with graphical user interfaces. They mainly provide MRI-CT co-registration and offer only basic features for recognition of ECoG electrodes from CT scans. Most dedicated softwares segment the electrodes via simple image thresholding and allow manual interaction to correct the data. Manual methods are very time consuming,user-dependent and prone to inaccuracy. On the other hand, the mere CT image thresholding method is not able to recognize all the electrodes and to completely exclude other metallic objects, such as wires, tooth filings, intracranial clips, splinters, stitches, hearing aids or intracranial stents. Hence, manual intervention is often required to adjust the data. For example, the ALICE tool considers the volume of segmented clusters to identify the electrodes, but turned out to be unable to exclude other objects with comparable volumes (e.g. wire clusters).
The aim of this project is to develop a novel, robust, automated method to recognize ECoG electrodes in CT volumes. It consists of metal artifacts removal from CT volumes, identification of groups/arrays of metal objects within the skull and shape analysis of detected objects to achieve ECoG electrodes localization.The proposed approach could be easily integrated in existing tools.
- Conditions
- Electrodes Recognition Ability
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Drug resistant epilepsy patients | Patients with drug resistant epilepsy underwent epilepsy surgery |
What is being measured
The primary outcome is the main question the study is designed to answer.
Classification accuracy of a Linear Discriminant Analysis classifier in detecting electrodes
A distinct database will be created for each patient, with rows corresponding to potential electrode objects within the CT volume, and composed by a collection of the extracted geometrical features and the assigned class. Two classes will be considered: "electrode" and "non-electrode". The "electrode" class is assigned to the actual electrodes, while the non-electrode class is assigned to all the other detected metal objects.
A Linear Discriminant Analysis (LDA) algorithm will be used for model training and data classification. Classification performances will be assessed by applying a 10-fold cross validation on each of the 24 patients' databases. In 10-fold cross-validation, the dataset will be randomly divided into 10 subsets of equal size, and then each subset will be tested using the classifier trained on the remaining nine subsets. Then, the obtained 10 classification accuracies will be averaged to provide an overall classification accuracy.
Measured: September 2020
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Jul 2020
- Study start1 Aug 2020
- Primary completion25 Aug 2020
- Study completion7 Sep 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Electrodes Recognition AbilityStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 24 participants (actual)
Sponsor and investigators
- Lead sponsor
- Neuromed IRCCS
- Sponsor type
- University, hospital or other
- Collaborators
- Federico II University
- Responsible party
- Principal investigator, Luigi Pavone, Neuromed IRCCS
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04479410
- Sponsor's protocol ID
- BIOING_01
- Acronym
- LOC-ECOG
- Status last verified
- Sep 2020
- First submitted
- 15 Jul 2020
- Last updated
- 10 Sep 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Taimouri V, Akhondi-Asl A, Tomas-Fernandez X, Peters JM, Prabhu SP, Poduri A, Takeoka M, Loddenkemper T, Bergin AM, Harini C, Madsen JR, Warfield SK. Electrode localization for planning surgical resection of the epileptogenic zone in pediatric epilepsy. Int J Comput Assist Radiol Surg. 2014 Jan;9(1):91-105. doi: 10.1007/s11548-013-0915-6. Epub 2013 Jun 23.
PubMed 23793723Brunner P, Ritaccio AL, Lynch TM, Emrich JF, Wilson JA, Williams JC, Aarnoutse EJ, Ramsey NF, Leuthardt EC, Bischof H, Schalk G. A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans. Epilepsy Behav. 2009 Jul;15(3):278-86. doi: 10.1016/j.yebeh.2009.04.001. Epub 2009 Jun 17.
PubMed 19366638Arnulfo G, Narizzano M, Cardinale F, Fato MM, Palva JM. Automatic segmentation of deep intracerebral electrodes in computed tomography scans. BMC Bioinformatics. 2015 Mar 25;16:99. doi: 10.1186/s12859-015-0511-6.
PubMed 25887573Dykstra AR, Chan AM, Quinn BT, Zepeda R, Keller CJ, Cormier J, Madsen JR, Eskandar EN, Cash SS. Individualized localization and cortical surface-based registration of intracranial electrodes. Neuroimage. 2012 Feb 15;59(4):3563-70. doi: 10.1016/j.neuroimage.2011.11.046. Epub 2011 Nov 28.
PubMed 22155045Hermes D, Miller KJ, Noordmans HJ, Vansteensel MJ, Ramsey NF. Automated electrocorticographic electrode localization on individually rendered brain surfaces. J Neurosci Methods. 2010 Jan 15;185(2):293-8. doi: 10.1016/j.jneumeth.2009.10.005. Epub 2009 Oct 27.
PubMed 19836416Lachaux JP, Rudrauf D, Kahane P. Intracranial EEG and human brain mapping. J Physiol Paris. 2003 Jul-Nov;97(4-6):613-28. doi: 10.1016/j.jphysparis.2004.01.018.
PubMed 15242670Branco MP, Gaglianese A, Glen DR, Hermes D, Saad ZS, Petridou N, Ramsey NF. ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids. J Neurosci Methods. 2018 May 1;301:43-51. doi: 10.1016/j.jneumeth.2017.10.022. Epub 2017 Nov 1.
PubMed 29100838
Study at a glance
From the registry record, last updated by the sponsor on 10 Sep 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Efficient Automated Localization of ECoG Electrodes in CT Images Via Shape Analysis
- ClinicalTrials.gov ID
- NCT04479410
- Acronym / sponsor ID
- LOC-ECOG / BIOING_01
- Conditions
- Electrodes Recognition Ability
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 24 participants (actual)
- Lead sponsor
- Neuromed IRCCSUniversity, hospital or other
- Collaborators
- Federico II University
- Study start
- 1 Aug 2020 (actual)
- Primary completion
- 25 Aug 2020 (actual)
- Study completion
- 7 Sep 2020 (actual)
- First posted
- 21 Jul 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
People with drug epilepsy (PwE) refractory to anti-seizure medications may be evaluated for surgery. In several cases non invasive presurgical work-up is not sufficient for localization of the Epileptogenic Zone and its correct delineation requires intracranial investigations by means of intraparenchymal or subdural electrodes.The methodological approach with subdural electrodes allows to obtain electrocorticography (ECoG) covering large cortical regions and to map eloquent areas.
To delineate the seizure onset zone it is mandatory to precisely localize the electrode position on the cortical surface. Electrodes are usually recognized by processing patients' computed tomography (CT) images using simple image processing (e.g. thresholding) that isolates metal objects. However, also wires, stitches, clips and other metal objects are actually recognized and need to be removed by manual intervention. A new automated method, based on shape analysis, will be retrospectively tested in a group of subjects with refractory focal epilepsy previously investigated with subdural electrodes for diagnostic purposes to provide advanced ECoG subdural electrodes recognition. A total of 24 CT scans with a large number (> 1700) of round platinum electrodes arrays will be recruited for testing.
Detailed description
For people with epilepsy (PwE) refractory to anti-seizure medication sometimes the non-invasive presurgical evaluation using ElectroEncephaloGram (EEG) recorded directly from the scalp is not sufficient to delineate the epileptogenic zone and to identificate the eloquent cortex. In these cases, an invasive approach using intracranial electroencephalography (iEEG) is needed Subdural electrodes are used frequently in the presurgical evaluation of patients who are candidates for epilepsy surgery. Electrodes placed directly on the surface of the cortex provide a signal with a much higher resolution than that provided from scalp electrodes, and have a much clear view of small loci of activity which is difficult to see on the scalp.
Subdural electrodes allow not only the localization of abnormal epileptic tissue but also the localization of adjacent normal functions. Therefore, the precise anatomical localization of the electrodes on the patient's brain plays a key role in the definition of the epileptogenic zone or in the mapping of eloquent cortex.
From a clinical point of view, the accurate localization of the anatomical boundaries of the epileptogenic zone allows to exclude eloquent areas, avoid deficits to patient and minimize brain volume resection.
The localization of these electrodes is generally obtained by matching the locations of the electrodes with the brain anatomy of the patient. Commonly, a pre-implant magnetic resonance image (MRI) is co-registered to a post-implant computed tomography scan (CT) because MRI offers higher brain tissue contrast, while CT supports electrodes localization , even if CT images are affected by metal artifacts.
Various dedicated software tools that support pre-surgical evaluation are currently available as Matlab-based packages or open source softwares, also with graphical user interfaces. They mainly provide MRI-CT co-registration and offer only basic features for recognition of ECoG electrodes from CT scans. Most dedicated softwares segment the electrodes via simple image thresholding and allow manual interaction to correct the data. Manual methods are very time consuming,user-dependent and prone to inaccuracy. On the other hand, the mere CT image thresholding method is not able to recognize all the electrodes and to completely exclude other metallic objects, such as wires, tooth filings, intracranial clips, splinters, stitches, hearing aids or intracranial stents. Hence, manual intervention is often required to adjust the data. For example, the ALICE tool considers the volume of segmented clusters to identify the electrodes, but turned out to be unable to exclude other objects with comparable volumes (e.g. wire clusters).
The aim of this project is to develop a novel, robust, automated method to recognize ECoG electrodes in CT volumes. It consists of metal artifacts removal from CT volumes, identification of groups/arrays of metal objects within the skull and shape analysis of detected objects to achieve ECoG electrodes localization.The proposed approach could be easily integrated in existing tools.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Drug resistant epilepsy patients | – | –Patients with drug resistant epilepsy underwent epilepsy surgery |
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- 24 people with epilepsy (PwE) refractory to anti seizure medications (ASM) who underwent epilepsy surgery at IRCCS Neuromed.
Key inclusion criteria (2 listed)
- Patients implanted with subdural ECoG electrodes underwent epilepsy surgery
- Availability of a post-operative CT scan with acceptable image quality
Key exclusion criteria (1 listed)
- Patients having CT scans with low image quality
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Classification accuracy of a Linear Discriminant Analysis classifier in detecting electrodes
Time frame: September 2020
A distinct database will be created for each patient, with rows corresponding to potential electrode objects within the CT volume, and composed by a collection of the extracted geometrical features and the assigned class. Two classes will be considered: "electrode" and "non-electrode". The "electrode" class is assigned to the actual electrodes, while the non-electrode class is assigned to all the other detected metal objects. A Linear Discriminant Analysis (LDA) algorithm will be used for model training and data classification. Classification performances will be assessed by applying a 10-fold cross validation on each of the 24 patients' databases. In 10-fold cross-validation, the dataset will be randomly divided into 10 subsets of equal size, and then each subset will be tested using the classifier trained on the remaining nine subsets. Then, the obtained 10 classification accuracies will be averaged to provide an overall classification accuracy.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Sep 2020.
- Results publication: PubMed 23793723Taimouri V, Akhondi-Asl A, Tomas-Fernandez X, Peters JM, Prabhu SP, Poduri A, Takeoka M, Loddenkemper T, Bergin AM, Harini C, Madsen JR, Warfield SK. Electrode localization for planning surgical resection of the epileptogenic zone in pediatric epilepsy. Int J Comput Assist Radiol Surg. 2014 Jan;9(1):91-105. doi: 10.1007/s11548-013-0915-6. Epub 2013 Jun 23.
- Results publication: PubMed 19366638Brunner P, Ritaccio AL, Lynch TM, Emrich JF, Wilson JA, Williams JC, Aarnoutse EJ, Ramsey NF, Leuthardt EC, Bischof H, Schalk G. A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans. Epilepsy Behav. 2009 Jul;15(3):278-86. doi: 10.1016/j.yebeh.2009.04.001. Epub 2009 Jun 17.
- Results publication: PubMed 25887573Arnulfo G, Narizzano M, Cardinale F, Fato MM, Palva JM. Automatic segmentation of deep intracerebral electrodes in computed tomography scans. BMC Bioinformatics. 2015 Mar 25;16:99. doi: 10.1186/s12859-015-0511-6.
- Results publication: PubMed 22155045Dykstra AR, Chan AM, Quinn BT, Zepeda R, Keller CJ, Cormier J, Madsen JR, Eskandar EN, Cash SS. Individualized localization and cortical surface-based registration of intracranial electrodes. Neuroimage. 2012 Feb 15;59(4):3563-70. doi: 10.1016/j.neuroimage.2011.11.046. Epub 2011 Nov 28.
- Results publication: PubMed 19836416Hermes D, Miller KJ, Noordmans HJ, Vansteensel MJ, Ramsey NF. Automated electrocorticographic electrode localization on individually rendered brain surfaces. J Neurosci Methods. 2010 Jan 15;185(2):293-8. doi: 10.1016/j.jneumeth.2009.10.005. Epub 2009 Oct 27.
- Results publication: PubMed 15242670Lachaux JP, Rudrauf D, Kahane P. Intracranial EEG and human brain mapping. J Physiol Paris. 2003 Jul-Nov;97(4-6):613-28. doi: 10.1016/j.jphysparis.2004.01.018.
- Results publication: PubMed 29100838Branco MP, Gaglianese A, Glen DR, Hermes D, Saad ZS, Petridou N, Ramsey NF. ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids. J Neurosci Methods. 2018 May 1;301:43-51. doi: 10.1016/j.jneumeth.2017.10.022. Epub 2017 Nov 1.
- Individual participant dataSharing planned: No
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Electrodes Recognition Ability, run by Neuromed IRCCS at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 10 Sep 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Classification accuracy of a Linear Discriminant Analysis classifier in detecting electrodes (time frame: September 2020). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04479410, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Sep 2020.