Ignite Creation Date:
2024-10-26 @ 3:43 PM
Last Modification Date:
2024-10-26 @ 3:43 PM
Study NCT ID:
NCT06649565
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-06-24
Brief Title:
Prospective Validation and Application of an Artificial Intelligence-based Model for Evaluating the Efficacy of Breast Cancer Patients After Neoadjuvant Therapy
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Prospective Validation and Application of an Artificial Intelligence-based Model for Evaluating the Efficacy of Breast Cancer Patients After Neoadjuvant Therapy
Status:
RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Breast cancer has become the worlds number one cancer While its therapeutic efficacy is increasing how to achieve non-invasive evaluation of the efficacy of neoadjuvant therapy NAT for breast cancer patients and thus avoid surgery has become a bottleneck problem that needs to be broken through in clinical diagnosis and treatment Existing non-invasive evaluation strategies are limited to single-center single-modality modeling and have problems such as low performance and poor versatility Therefore in the early stage of this study multi-modality breast cancer patient data from multiple centers across the country were collected and the establishment of an artificial intelligence AI efficacy prediction model was preliminarily completed On this basis this project intends to further improve the multi-center prospective validation study of the prediction model The research results will help solve the scientific problem of non-invasive judgment of NAT efficacy in breast cancer patients and provide a new paradigm for the research of high-performance AI diagnosis and treatment auxiliary systems applicable to multiple centers
Detailed Description:
1 Prospectively collect breast MRI original images DCE and ADC sequences and corresponding clinical and surgical pathological data of multi-center breast cancer patients before and after neoadjuvant treatment store and transport them via mobile hard disks and input the processed data into the established efficacy determination model stored in a dedicated cloud server 2 Use artificial intelligence to automatically delineate the ROI area and extract the imaging genomics and deep learning features therein and combine the clinical pathological characteristics of the patients to further prospectively verify the effectiveness of the established pCR efficacy determination model
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None