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NCT05872607

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

Status unknown Last updated 24 May 2023
What this trial tests

trial in Depression in 300 participants. Status unknown.

Timeline
1 January 2022
Primary endpoint
31 December 2024
31 December 2024

Quick facts

Lead sponsorThe First Hospital of Jilin University
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment300
Start date1 January 2022
Primary completion31 December 2024
Estimated completion31 December 2024
Sites1 location across China

Conditions studied

Sponsor

The First Hospital of Jilin University

Who can join

Eligibility, any sex, with Depression. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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