Clinical trial NCT05062772: Brain Tumor Intraoperative Ultrasound Database
Official title: Predicting Overall Survival in Glioblastomas Using Radiomic Features of Intraoperative Ultrasound. A Proposal for the Creation of an International Database of Brain Tumor Ultrasound Images
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Brain Tumor
Brain Neoplasms
Brain Cancer
Glioma
Glioblastoma
Keywords: glioma
glioblastoma
brain tumor
ultrasound
intraoperative ultrasound
Lead sponsor: Hospital del Rio Hortega
Sponsor class: OTHER
First posted: 2021-09-30
Enrollment: 150
Sex: ALL
Minimum age (years): 15
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The cases that meet the inclusion criteria mentioned in the following section will be collected retrospectively to carry out the project's first phase. From January 2018 to January 2020, it is estimated that each center can contribute with a minimum of 20 cases. Therefore, the total sample size for this phase of the study will be approximately 120 patients.

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.

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.

Primary outcomes:
measure: Overall survival
timeFrame: 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Boston
country: United States
facility: Massachusetts General Hospital
state: Massachusetts
city: Tours
country: France
facility: Hôpital Bretonneau, CHRU de Tours
city: Mumbai
country: India
facility: Tata Memorial Centre
state: Parel
city: Milan
country: Italy
facility: Fondazione Irccs Istituto Neurologico "Carlo Besta"
city: Palermo
country: Italy
facility: Unit of Neurosurgery, Department of Biomedicine Neurosciences and Advanced Diagnsotics, University of Palermo
city: Valladolid
country: Spain
facility: University Hospital Rio Hortega
Study officials:
affiliation: Department of Neurosurgery University Hospital Río Hortega
name: Santiago Cepeda, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.