Clinical trial NCT05739500: MRI-based Computer Aided Diagnosis Software (V1) for Glioma
Official title: The Clinical Trial 01 to Evaluate the Effectiveness of MRI-based Computer Aided Diagnosis Software (V1) for Glioma Segmentation, Gene Prediction and Tumor Grading
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Glioma
Primary Brain Tumor
Lead sponsor: Mingge LLC
Sponsor class: INDUSTRY
Collaborators: name: Huashan Hospital
name: Fudan University
First posted: 2023-02-22
Enrollment: 250
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CROSSOVER
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Preliminary diagnosis of glioma patients and patients who plan to undergo surgical treatment

Brief summary: The goal of this multi-center clinical trial is to evaluate the effectiveness of MRI-based computer-aided diagnosis software (V1) for glioma segmentation, gene prediction, and tumor grading. Machine learning methods such as high-precision tumor segmentation and classification and discrimination modeling can further optimize the non-invasive molecular diagnosis and prognosis prediction. The main question it aims to answer is whether the software can predict the molecular type and the prognosis quickly and correctly. The results will be compared with the real-world clinical data double-blindly. Finally, form a set of user-friendly automatic glioma diagnosis and treatment systems for clinics.

Detailed description: BACKGROUND:

The molecular type is crucial for surgical planning and post-operative treatment of glioma. MRI-based radiomics is an emerging technique that extracts unrevealed information including pathology, biomarkers, and genomics by using automated high-throughput extraction of a large number of quantitative features. With the help of artificial intelligence, MRI-based radiomics could be a promising noninvasive method to reveal molecular type by using a quantitative radiomics approach for glioma.

AIM:

MRI-based computer-aided diagnosis software (V1) is an MRI-based radiomics tool with machine learning methods such as high-precision tumor segmentation and classification and discrimination modeling that can further optimize the non-invasive molecular diagnosis and prognosis prediction. The main question it aims to answer is whether the software can predict the molecular type and the prognosis quickly and correctly.

PROCESS:

Participants will read an informed consent agreement before surgery and voluntarily decide whether or not to join the experimental group. They will undergo preoperative multimodal magnetic resonance imaging, which is the routine neuro-images of preoperative evaluation. After surgery, the patient's tumor tissue samples will undergo specialist genetic testing to obtain multiple molecular diagnostic results, such as isocitrate dehydrogenase (IDH), telomerase reverse transcriptase promoter (TERTp), the short arm chromosome 1 and the long arm of chromosome 19 (1p/19q), et al. The participants need to be followed up for 1-year after surgery. Also, their imaging data, genotype data, clinical history data, pathology data, and clinical follow-up data will be analyzed for the study.

The preoperative Multimodality imaging will be input to the software (V1), and glioma segmentation, gene prediction, tumor grading, and lifetime will be analyzed by the software. The results will be compared with the real-world clinical data double-blindly. In order to evaluate the estimation performance of the software, several indexes will be calculated including accuracy (ACC), sensitivity (SENS), and specificity (SPEC). Finally, form a promising set of user-friendly automatic glioma diagnosis and treatment systems for clinics.

Eligibility criteria: Inclusion Criteria:

1. Age front 18 to 70 years old (not including threshold), gender is not limited;
2. Preliminary diagnosis of glioma patients and patients who plan to undergo surgical treatment;
3. Preoperative cranial MRI (T1, T2, T2 Flair, T1 enhanced GE company magnetic resonance package), tumor pathological examination (H\&E section, Kuoran Gene Company package), acceptable follow-up and brain MRI scan;
4. The patient himself voluntarily participated and signed the informed consent in writing.

Exclusion Criteria:

1. Patients who only underwent biopsy rather than surgical tumor resection;
2. Postoperative pathologically confirmed non-glioma patients;
3. Patients with multiple glioma metastases or multiple gliomas;
4. Patients who died of complications in the early postoperative period;
5. The researcher believes that this researcher should not be included.

Primary outcomes:
measure: Accuracy rate
timeFrame: end of the study (one year after the surgery of the last participants).
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Zhen Fan
state: Shanghai
Study officials:
affiliation: Huashan Hospital
name: Zhifeng Shi, MD.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Garcia CR, Slone SA, Dolecek TA, Huang B, Neltner JH, Villano JL. Primary central nervous system tumor treatment and survival in the United States, 2004-2015. J Neurooncol. 2019 Aug;144(1):179-191. doi: 10.1007/s11060-019-03218-8. Epub 2019 Jun 28.
citation: Reardon DA, Wen PY. Glioma in 2014: unravelling tumour heterogeneity-implications for therapy. Nat Rev Clin Oncol. 2015 Feb;12(2):69-70. doi: 10.1038/nrclinonc.2014.223. Epub 2015 Jan 6.
citation: Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169. Epub 2015 Nov 18.
citation: Yu J, Shi Z, Lian Y, Li Z, Liu T, Gao Y, Wang Y, Chen L, Mao Y. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol. 2017 Aug;27(8):3509-3522. doi: 10.1007/s00330-016-4653-3. Epub 2016 Dec 21.