The Quantitative Study of the Habenula Based on Multi-channel Cascaded

Status Recruiting

Clinical Trial Summary

Depression is the second leading cause of disease burden in our country. It has serious effects on the physical and mental health of human beings, and about 30% of patients with depression are unresponsive or respond poorly to antidepressant treatment. Clinical practice is in a tough position of wanting objective measures of assessing depression. The applicant and her team have devoted many years to the basic and clinical research on habenular nucleus (Hb) accumulating a significant amount of experience from animal experiments and patients' magnetic resonance (MR) studies. These studies have demonstrated that the habenular nucleus is the key target area that is responsible for the pathophysiological changes in depression as well as its treatment. Small volumes and unsatisfactory contrast have been knotty problems in the MR imaging of Hb. In addition, time-consuming manual segmentation and lack of quantitative standards in conventional studies has impeded the advancement of Hb research. Fortunately, the development of high-resolution multi-parametric quantitative MR imaging and the extensive use of artificial intelligence (AI) technology in medical imaging can just provide powerful support for the imaging, segmentation and quantification of Hb. This project proposes to use high resolution MR anatomy of Hb combined with multimodal fusion to 1) construct a model for automatic 3D segmentation of Hb MR images based on the densely connected multichannel dilated convolutional neural networks; 2) sift out the quantitative imaging signatures related to the antidepressants' efficacy using the radiomics methodology, and in combination with clinical information, construct an individualized prediction model for treatment efficacy. Overall, this study focuses on the translation of basic research to clinical application in the hope of providing quantifiable objective imaging markers in clinical practice, facilitating clinical decision-making and bringing about individualized precise diagnosis and treatment.

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT05872607
Study type	Observational
Source	The First Hospital of Jilin University
Contact	Lei Zhang, doctor
Phone	15804300150
Email	zlei99@jlu.edu.cn
Status	Recruiting
Phase
Start date	January 1, 2022
Completion date	December 31, 2024

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Clinical trials are conducted in a series of steps, called phases - each phase is designed to answer a separate research question.

Phase 1: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase 2: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase 3: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase 4: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.

The Quantitative Study of the Habenula Based on Multi-channel Cascaded Neural Network and the Establishment of the Prediction Model of the Curative Effect in Patients With Depression

Clinical Trial Summary

Clinical Trial Description

Study Design

Related Conditions & MeSH terms