Trial Title:
Combing a Deep Learning-Based Radiomics With Liquid Biopsy for Preoperative and Non-invasive Diagnosis of Glioma
NCT ID:
NCT05536024
Condition:
Glioma (Diagnosis)
Liquid Biopsy
Deep Learning
Conditions: Official terms:
Glioma
Disease
Conditions: Keywords:
Glioma
Diagnostic model
radiomic
liquid biopsy
Study type:
Observational [Patient Registry]
Overall status:
Unknown status
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
Prediction of glioma grading and molecular subtype
Description:
Prediction of WHO grading(II/III/IV), IDH gene mutation status, ATRX deletion status,
1p/19q deletion status, CDKN2A/B homozygous deletion status, TERT gene mutation status,
epidermal growth factor receptor (EGFR) mutation status, chromosome 7gain and chromosome
10 less status, H3F3A G34 (H3.3 G34) mutation status, H3 K27M mutation status
Arm group label:
glioma patients
Summary:
This registry has the following objectives. First, according to the guidance of 2021 WHO
of CNS classification, we constructed and externally tested a multi-task DL model for
simultaneous diagnosis of tumor segmentation, glioma classification and more extensive
molecular subtype, including IDH mutation, ATRX deletion status, 1p19q co-deletion, TERT
gene mutation status, etc. Second, based on the same ultimate purpose of liquid biopsy
and radiomics, we innovatively put forward the concept and idea of combining radiomics
and liquid biopsy technology to improve the diagnosis of glioma. And through our study,
it will provide some clinical validation for this concept, hoping to supply some new
ideas for subsequent research and supporting clinical decision-making.
Detailed description:
Gliomas are the most common primary intracranial malignancies, accounting for 27% of all
primary brain tumors, and approximately 100,000 people are diagnosed with diffuse gliomas
worldwide each year. To date, "integrated diagnosis" was considered the gold standard for
glioma diagnosis, which combines histopathology, molecular pathology, and World Health
Organization (WHO) grade. Previous glioma diagnostic criteria have primarily relied on
histopathological biopsies, while histological classification has traditionally been
determined based on tumor morphology, resulting in intra-observer variability due to
intra-tumor spatial heterogeneity and sampling errors. In addition, Traditional
histopathology is somewhat difficult to explain why patients with the same pathology have
significantly different survival. Over the past decade, advances in molecular pathology
and histopathology detection techniques have deepened our understanding in the molecular
features and biology of gliomas. Increasing evidence revealed the important role of
molecular status in the " integrated diagnosis" of glioma. In particular, after the
concept of molecular diagnosis was proposed by the 2016 WHO Central Nervous System (CNS)
classification, the 2021 CNS classification (CNS5) reemphasized the importance of several
molecular biomarkers in gliomas diagnosis and treatment guidance, including isocitrate
dehydrogenase gene (IDH ) mutation status, alpha-thalassemia/mental retardation syndrome
X-linked (ATRX) deletion status, 1p19q deletion status, telomerase reverse transcriptase
(TERT) promoter mutation, etc. Which the objective is to classify the tumor subtypes more
systematically, and categorize the glioma patients with similar efficacy and prognosis
into a subgroup.
The current standard of therapy for gliomas is surgical resection followed by
radiotherapy and/or chemotherapy based on clinical and tumor grade and molecular
characteristics. Preoperatively non-invasive and accurate early " integrated diagnosis"
will bring great benefits to the treatment and prognosis of patients, especially for
those with special tumor location that cannot receive craniotomy or needle biopsy. Such
special patients can take experimental radiotherapy and chemotherapy based on
non-invasive diagnosis results. Although diagnostic criteria for molecular information in
gliomas are often based on tissue biopsy, other techniques, such as radiomics,
radiogenomics, and liquid biopsy, have shown promise. At present, conventional magnetic
resonance imaging (MRI) scans are still the main method to assist in the diagnosis of
gliomas, including pre- and post- contrast T1w, T2w, and T2w-FLAIR. Multimodal radiomics
based on deep learning (DL) can analyze patterns of intratumor heterogeneity and tumor
imaging features that are imperceptible by the human eye, so as to conduct " integrated
prediction" of gliomas18. Up to now, most studies have focused on using ML algorithms to
construct novel radiomic model to predict glioma, R van der Voort et al. developed the
multi-task conventional neural network (CNN) model and achieved a glioma grade
(II/III/IV) with AUC of 0.81, IDH-AUC of 90%, 1p19q co-deletion AUC of 0.85 in the test
set. The best DL model developed by Matsui et al. achieved an overall accuracy of 65.9%
in predicting IDH mutation and 1p/19q co-deletion. Also, the multi-task CNN model
constructed by Decuyper et al. achieved 94%, 86%, and 87% accuracy in predicting grades,
IDH mutations, and 1p/19q co-deletion states in external validation. The model
constructed by Luo et al. achieved 83.9% and 80.4% in external tests for histological and
molecular subtype diagnosis. In addition to the "integrated prediction", there exists
many models that only predicting glioma grading or single molecular markers. Meanwhile,
previous studies were based on the 2016 CNS classification for glioma grading and
molecular subtypes prediction. Therefore, a multi-task DL radiomics model for
preoperatively and non-invasively predicting glioma grading and more extensive molecular
markers is urgently needed according to the latest 2021 CNS classification.
Although radiomics has showed some feasibility in predicting tumor molecular pathology,
it is ridiculous to administer precision targeted therapy solely on the basis of this
prediction. Therefore, we hope to provide more clinical evidence for the molecular
pathological diagnosis of gliomas patients by using liquid biopsy technique as an
important complement of radiomics. Circulating tumor cell (CTC), as one of the liquid
biopsy techniques, shares the same final objective to preoperatively non-invasive and
accurate diagnosis of gliomas.
Based on the several limitations of the current diagnostic models of glioma, and the
combined methods of radiomics and liquid biopsy have great potential to non-invasive
diagnose glioma grading and molecular markers since they are both easy to perform.
Furthermore, to our knowledge, there has been no study of preoperative non-invasive
diagnosis of glioma in the context of liquid biopsy-assisted radiomics.
Therefore, this study has the following objectives. First, according to the guidance of
2021 WHO of CNS classification, we constructed and externally tested a multi-task DL
model for simultaneous diagnosis of tumor segmentation, glioma classification and more
extensive molecular subtype, including IDH mutation, ATRX deletion status, 1p19q
co-deletion, TERT gene mutation status, etc. Second, based on the same ultimate purpose
of liquid biopsy and radiomics, we innovatively put forward the concept and idea of
combining radiomics and liquid biopsy technology to improve the diagnosis of glioma. And
through our study, it will provide some clinical validation for this concept, hoping to
supply some new ideas for subsequent research and supporting clinical decision-making.
Criteria for eligibility:
Study pop:
Patients with newly diagnosed glioma that receiving surgical resection or needle biopsy
accoding to 2021 WHO of CNS classfication
Sampling method:
Probability Sample
Criteria:
Inclusion Criteria:
- glioma patients with postoperative pathological examination
- age >18 years old
- without any radiotherapy and/or chemotherapy prior to preoperative MRI scan
- receiving surgical resection or needle biopsy for the first diagnosis
- Signed informed consent
Exclusion Criteria:
- Non gliomas
- Without any preoperatiev MRI scan in Imaging Record System
- Or receiving radiotherapy and/or chemotherapy prior to preoperative MRI scan
- Rejecting surgical resection or needle biopsy
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
Accepts Healthy Volunteers
Locations:
Facility:
Name:
Renmin Hospital of Wuhan University
Address:
City:
Wuhan
Zip:
430060
Country:
China
Status:
Recruiting
Contact:
Last name:
Qianxue Chen, Prof
Phone:
13607141618
Email:
chenqx666@whu.edu.cn
Facility:
Name:
The Second Affiliated Hospital of Nanchang University
Address:
City:
Nanchang
Zip:
330000
Country:
China
Status:
Recruiting
Contact:
Last name:
Xingen Zhu, Prof
Phone:
138 0354 6020
Email:
zxg2008vip@163.com
Start date:
May 1, 2022
Completion date:
August 30, 2023
Lead sponsor:
Agency:
Second Affiliated Hospital of Nanchang University
Agency class:
Other
Collaborator:
Agency:
Renmin Hospital of Wuhan University
Agency class:
Other
Collaborator:
Agency:
Wuhan University
Agency class:
Other
Source:
Second Affiliated Hospital of Nanchang University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05536024
