Brain Tumor Intraoperative Ultrasound Database

NCT ID: NCT05062772

Last Updated: 2024-11-15

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 150 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-05-25
• Study Completion Date: 2024-11-14

Brief Summary

Predicting the survival of patients diagnosed with glioblastoma (GBM) is essential to guide surgical strategy and subsequent adjuvant therapies. Intraoperative ultrasound (ioUS) is a low-cost, versatile technique available in most neurosurgical departments. The images from ioUS contain biological information possibly correlated to the tumor's behavior, aggressiveness, and oncological outcomes. Today's advanced image processing techniques require a large amount of data. Therefore, the investigators propose creating an international database aimed to share intraoperative ultrasound images of brain tumors. The acquired data must be processed to extract radiomic or texture characteristics from ioUS images. The rationale is that ultrasound images contain much more information than the human eye can process. Our main objective is to find a relationship between these imaging characteristics and overall survival (OS) in GBM. The predictive models elaborated from this imaging technique will complement those already based on other sources such as magnetic resonance imaging (MRI), genetic and molecular analysis, etc. Predicting survival using an intraoperative imaging technique affordable for most hospitals would greatly benefit the patients' management.

Detailed Description

The investigators plan to carry out a multicentre retrospective study of patients operated with GBM diagnosis between January 2018 and January 2020, in order to set the base for future prospective collection of patients. All cases with an ioUS study will be included. All patients must count with B-mode modality. After an pseudonymization process, the images will be uploaded to a private cloud server. Demographic, clinical, conventional radiological, and molecular variables (IDH, MGMT) will also be collected. OS will be defined as the time elapsed between the histopathological diagnosis and the patient's death. The acquired data must be processed to obtain a series of radiomic markers to perform the study. A pre-processing stage will be necessary (noise cleaning, despeckling, intensity normalization, filtering) to calculate radiomics measurements (histogram, volumetric, shape, texture, etc.). In the previous stage, a very high number of radiological features per subject will be calculated. Because the number of features is much higher than the data set, to avoid the curse of dimensionality, it will be necessary to reduce their number using feature selection and extraction techniques (standard in pattern recognition and radiomics) that allow choosing those characteristics (or transformations of them) that have greater discriminating power. A predictive model of survival will then be elaborated based on the features selected.

Hypotheses

Intraoperative ultrasound images in B-mode harbour tumor texture features correlated with overall survival in glioblastomas.

Objectives:

• To determine the relationship between the radiomic features of intraoperative ultrasound B-mode and overall survival in glioblastomas.
• Develop a predictive survival model using the texture features with the highest discriminatory power.
• Validate the model against an external dataset and compare it with currently available predictive models.
• Build a data set that allows exploring various image harmonization techniques that allow the reproducibility of our predictions.
• Establish an international cooperation network (BraTioUS-DB) whose objective will be to interchange ultrasound images and clinical data of patients operated on for a brain tumor prospectively from its creation and start-up.

Conditions

Brain Tumor; Brain Neoplasms; Brain Cancer; Glioma; Glioblastoma

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Study Groups

Glioblastoma

Ultrasound

Intervention Type: DIAGNOSTIC_TEST

Intraoperative ultrasound imaging

Interventions

Ultrasound

Intraoperative ultrasound imaging

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Adult patients operated between January 2018 and January 2020 with a pathological diagnosis of WHO grade IV astrocytoma (Glioblastoma).
• Intraoperative ultrasound study that includes B-mode images

Exclusion Criteria

• Other histopathological diagnoses. Even though the international database will be established in such a way that other tumor types can be included prospectively.
• Artifacts in ultrasound images that make their analysis impossible
• Stereotactic biopsies.

• Minimum Eligible Age: 15 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Hospital del Rio Hortega

OTHER

Sponsor Role: lead

Responsible Party

Santiago Cepeda

Principal investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Santiago Cepeda, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Department of Neurosurgery University Hospital Río Hortega

Locations

Massachusetts General Hospital

Boston, Massachusetts, United States

Hôpital Bretonneau, CHRU de Tours

Tours, , France

Tata Memorial Centre

Mumbai, Parel, India

Fondazione Irccs Istituto Neurologico "Carlo Besta"

Milan, , Italy

Unit of Neurosurgery, Department of Biomedicine Neurosciences and Advanced Diagnsotics, University of Palermo

Palermo, , Italy

University Hospital Rio Hortega

Valladolid, , Spain

Countries

United States; France; India; Italy; Spain

References

Cepeda S, Sarabia R. Letter to the Editor. Intraoperative ultrasound elastography applied in meningioma surgery. Neurosurg Focus. 2021 May;50(5):E23. doi: 10.3171/2021.1.FOCUS2115. No abstract available.

Reference Type: BACKGROUND

PMID: 33932930 (View on PubMed)

Cepeda S, Garcia-Garcia S, Velasco-Casares M, Fernandez-Perez G, Zamora T, Arrese I, Sarabia R. Is There a Relationship between the Elasticity of Brain Tumors, Changes in Diffusion Tensor Imaging, and Histological Findings? A Pilot Study Using Intraoperative Ultrasound Elastography. Brain Sci. 2021 Feb 21;11(2):271. doi: 10.3390/brainsci11020271.

Reference Type: BACKGROUND

PMID: 33669989 (View on PubMed)

Cepeda S, Garcia-Garcia S, Arrese I, Velasco-Casares M, Sarabia R. Acute changes in diffusion tensor-derived metrics and its correlation with the motor outcome in gliomas adjacent to the corticospinal tract. Surg Neurol Int. 2021 Feb 10;12:51. doi: 10.25259/SNI_862_2020. eCollection 2021.

Reference Type: BACKGROUND

PMID: 33654554 (View on PubMed)

Cepeda S, Garcia-Garcia S, Arrese I, Fernandez-Perez G, Velasco-Casares M, Fajardo-Puentes M, Zamora T, Sarabia R. Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis. Front Oncol. 2021 Feb 2;10:590756. doi: 10.3389/fonc.2020.590756. eCollection 2020.

Reference Type: BACKGROUND

PMID: 33604286 (View on PubMed)

Cepeda S, Garcia-Garcia S, Arrese I, Velasco-Casares M, Sarabia R. Relationship between the overall survival in glioblastomas and the radiomic features of intraoperative ultrasound: a feasibility study. J Ultrasound. 2022 Mar;25(1):121-128. doi: 10.1007/s40477-021-00569-9. Epub 2021 Feb 16.

Reference Type: BACKGROUND

PMID: 33594589 (View on PubMed)

Cepeda S, Arrese I, Garcia-Garcia S, Velasco-Casares M, Escudero-Caro T, Zamora T, Sarabia R. Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers. World Neurosurg. 2021 Feb;146:e1147-e1159. doi: 10.1016/j.wneu.2020.11.113. Epub 2020 Nov 28.

Reference Type: BACKGROUND

PMID: 33259973 (View on PubMed)

Cepeda S, Barrena C, Arrese I, Fernandez-Perez G, Sarabia R. Intraoperative Ultrasonographic Elastography: A Semi-Quantitative Analysis of Brain Tumor Elasticity Patterns and Peritumoral Region. World Neurosurg. 2020 Mar;135:e258-e270. doi: 10.1016/j.wneu.2019.11.133. Epub 2019 Nov 30.

Reference Type: BACKGROUND

PMID: 31790843 (View on PubMed)

Provided Documents

Document Type: Study Protocol

View Document

Related Links

https://www.neurocirugiariohortega.es

Institution Website

Other Identifiers

21-PI085

Identifier Type: -

Identifier Source: org_study_id