Hierarchical Diagnosis for Adult Diffuse Glioma Based on Deep Learning

Artificial Intelligence Research of Hierarchical Diagnosis for Adult Diffuse Glioma Based on Deep Learning

This is a restrospective study to establish a deep learning model based on multi-parametric magnetic resonance imaging scans to predict Grade, histopathologic type and genotype of adult diffuse Glioma.

Study Overview

• Status: Recruiting
• Conditions: Astrocytoma; Glioblastoma Multiforme; Oligodendroglioma
• Intervention / Treatment: Diagnostic test: multi-parametric magnetic resonance imaging scan; Diagnostic test: Pathology examination

Detailed Description

Glioma is a common kind of tumor in central nervous system. The pre-operative prediction of grade, histopathologic type and genotype is important for treatment and management of Adult diffuse Glioma patients. Right now, most of the diagnostic prediction models on glioma are based on 2016 WHO central nervous system tumor guideline. The goal of this study is to establish a new deep learning model to predict Grade, histopathologic type and genotype of adult diffuse Glioma. We will recruit 500 patients with pathologically confirmed diagnosis of Glioblastoma, Astrocytoma and Oligodendroglioma who received neurologic surgery in our center. Each subject underwent pre-operative multi-parametric magnetic resonance imaging scans including T1WI, T2WI, T1CE, FLAIR and DWI. Pathologic diagnosis of each patient are available in pathology department. A deep learning based hierarchical diagnosis

• Study Type: Observational
• Enrollment (Anticipated): 500

Contacts and Locations

• Study Contact: Name: Xin Zhang, Master; Phone Number: 13814066403; Email: zhangxin@njglyy.com

Study Locations

• China
  • Jiangsu
    • Nanjing, Jiangsu, China, 210093
      • Recruiting
      • Department of Radiology, the Affiliated Drum Tower Hospital of Nanjing University
      • Contact: Xin Zhang, Master; Phone Number: 13814066403; Email: zhangxin@njglyy.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 16 years to 88 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

Patients with diagnosis of adult diffuse glioma who recevived tumor rection surgery in our center

Description

Inclusion Criteria:

1. Patients undergoing surgery in Nanjing DrumTower Hospital between 2010.01 and 2022.05 with post-surgery pathological diagnosis of WHO Grade II to IV Adult Diffuse Glioma.
2. Available pre-surgery T1WI, T2WI, T1CE, FLARI and DWI MR sequences
3. No pre-surgery anti-tumor therapy

Exclusion Criteria:

1. Poor image quality
2. Failed image preprocessing
3. Unavailable pathology data

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Crossover
• Time Perspectives: Retrospective

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Glioblastoma Group; Patients with the diagnosis of Glioblastoma based on 2021 WHO central nervous system tumor guideline.	Diagnostic test: multi-parametric magnetic resonance imaging scan; Pre-operative multi-parametric magnetic resonance imaging scans including T1WI, T2WI, T1CE, FLAIR and DWI were taken for clinical needs.; Diagnostic test: Pathology examination; The tumor specimen obtained from the surgery were sent to the pathology department for histopathologic examination, immunohistochemistry and gene sequencing test
Astrocytoma Group; Patients with the diagnosis of Astrocytoma based on 2021 WHO central nervous system tumor guideline.	Diagnostic test: multi-parametric magnetic resonance imaging scan; Pre-operative multi-parametric magnetic resonance imaging scans including T1WI, T2WI, T1CE, FLAIR and DWI were taken for clinical needs.; Diagnostic test: Pathology examination; The tumor specimen obtained from the surgery were sent to the pathology department for histopathologic examination, immunohistochemistry and gene sequencing test
Oligodendroglioma Group; Patients with the diagnosis of Oligodengroglioma based on 2021 WHO central nervous system tumor guideline.	Diagnostic test: multi-parametric magnetic resonance imaging scan; Pre-operative multi-parametric magnetic resonance imaging scans including T1WI, T2WI, T1CE, FLAIR and DWI were taken for clinical needs.; Diagnostic test: Pathology examination; The tumor specimen obtained from the surgery were sent to the pathology department for histopathologic examination, immunohistochemistry and gene sequencing test

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Tumor Grade	Tumor Grade based on 2021 WHO central nervous system tumor guideline	up to 2 weeks
Tumor Histologic diagnosis	Tumor histologic diagnosis based on 2021 WHO central nervous system tumor guideline	up to 2 weeks
Tumor genotype	Tumor genotype based on 2021 WHO central nervous system tumor guideline	up to 2 weeks

Collaborators and Investigators

• Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

Study record dates

Study Major Dates

• Study Start (Anticipated): November 20, 2022
• Primary Completion (Anticipated): May 1, 2023
• Study Completion (Anticipated): May 1, 2025

Study Registration Dates

• First Submitted: November 13, 2022
• First Submitted That Met QC Criteria: November 21, 2022
• First Posted (Actual): November 22, 2022

Study Record Updates

• Last Update Posted (Actual): November 22, 2022
• Last Update Submitted That Met QC Criteria: November 21, 2022
• Last Verified: July 1, 2022

More Information

Terms related to this study

• Keywords: artificial intelligence; Glioblastoma; deep learning; Astrocytoma; Oligodendroglioma
• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Glioma; Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Glioblastoma; Astrocytoma; Oligodendroglioma
• Other Study ID Numbers: 2022-LCYJ-MS-25

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No
• IPD Plan Description: We don't plan to share IPD with other researches

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No