Classification of Gliomas-a type of brain tumor using machine learning method an artificial intelligence tool.

CLASSIFICATION OF MOLECULAR SUBTYPES OF GLIOMAS USING MRI RADIOMICS BASED MACHINE LEARNING METHODS

Status

Active, not recruiting

Phases

Phase 2

Study type

Observational

Source

CTRI

Registry ID

CTRI/2022/12/048292

Enrollment

180

Registered

2022-12-20

Start date

Unknown

Completion date

Unknown

Last updated

2023-01-09

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: C719- Malignant neoplasm of brain, unspecified

Interventions

None listed

Eligibility

Inclusion criteria

Inclusion criteria: Patients with confirmed HPE and IHC diagnosis of molecular subtypes of high and low grade gliomas.

Exclusion criteria

Exclusion criteria: Unavailability of IDH1 status Presence of Motion artifacts on MR Images

Design outcomes

Primary

Measure	Time frame
The machine learning methods based on MR Radiomic features can be used as a simple, noninvasive and cost effective diagnostic method for classification of molecular subtypes of gliomas.Timepoint: Single time	—

Secondary

Measure	Time frame
MRI radiomics can be used as an alternative method to identify the recurrence and non-recurrence in gliomas. The study helps in understanding the robustness of MR Radiomic features with change in MR Image Quality Parameters of pulse sequence.Timepoint: 3.5 Years	—

Countries

India

Contacts

Public ContactDr Prakashini K

Kasturba Medical College and Hospital, MAHE, Manipal

prakashini.k@manipal.edu9845053325

Outcome results