Ignite Creation Date:
2024-10-26 @ 3:38 PM
Last Modification Date:
2024-10-26 @ 3:38 PM
Study NCT ID:
NCT06573307
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-08-26
Brief Title:
Hematological Dynamic Scores for Predicting Survival and Treatment Response for Advanced Gastric Cancer After Neoadjuvant Therapy
Sponsor:
None
Organization:
None
Study Overview
Official Title:
To Develop a Prognostic Model for Predicting Survival and Treatment Response for Advanced Gastric Cancer Patients After Neoadjuvant Therapy by Analyzing Hematological Markers Dynamic Load
Status:
COMPLETED
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:
HMDLS based on hematological markers could effectively distinguish the long-term efficacy of AGC patients after NAT The predictive performance of nomogram-HMDLS was better than ypTNM stage achieving better prognostic stratification and tumor treatment response prediction
Detailed Description:
In this research we incorporated a total of 320 patients from the Union Hospital of Fujian Medical University to form the training cohort TC Additionally we included 122 patients from four distinct medical centers to serve as the external validation cohort EVC The Hematological Marker Dynamic Load ΔHMDL was determined using the following formula ΔHMDL HMDL pre-surgery - HMDL pre-NAT HMDL pre-NAT where HMDL represents the hematological marker levels before surgery and before the initiation of Neoadjuvant Therapy NAT respectively
Employing LASSO regression analysis we identified the most influential and statistically significant ΔHMDL indicators These were then utilized to compute the Hematological Marker Dynamic Load Score HMDLS defined as HMDLS ΣLASSO coefficient ΔHMDL where the summation encompasses the products of the LASSO-estimated coefficients and the corresponding ΔHMDL values
Further leveraging the outcomes of a multivariate COX regression analysis we integrated clinical parameters with the HMDLS to formulate a predictive model termed the Nomogram-HMDLS model The efficacy of this model in terms of predictive accuracy clinical utility and calibration was meticulously assessed and confirmed through several metrics including the concordance index C-index Receiver Operating Characteristic ROC curve analysis decision curve analysis DCA and calibration curves
Employing LASSO regression analysis we identified the most influential and statistically significant ΔHMDL indicators These were then utilized to compute the Hematological Marker Dynamic Load Score HMDLS defined as HMDLS ΣLASSO coefficient ΔHMDL where the summation encompasses the products of the LASSO-estimated coefficients and the corresponding ΔHMDL values
Further leveraging the outcomes of a multivariate COX regression analysis we integrated clinical parameters with the HMDLS to formulate a predictive model termed the Nomogram-HMDLS model The efficacy of this model in terms of predictive accuracy clinical utility and calibration was meticulously assessed and confirmed through several metrics including the concordance index C-index Receiver Operating Characteristic ROC curve analysis decision curve analysis DCA and calibration curves
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