Hematological Dynamic Scores for Predicting Survival and Treatment Response for Advanced Gastric Cancer After Neoadjuvant Therapy

NCT ID: NCT06573307

Last Updated: 2024-08-27

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 442 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-06-10
• Study Completion Date: 2024-08-26

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 = Σ(LASSO 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.

Conditions

Gastric Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Eligibility Criteria

Inclusion Criteria

• (1) AGC with clinical stage T2-4NxM0 (cT2-4NxM0) before NAT, (2) no history of other malignant tumors, distant metastases or invasion of adjacent organs, and (3) patients who underwent radical gastrectomy after receiving NAT.

Exclusion Criteria

• (1) history of upper abdominal surgery (except for the laparoscopic cholecystectomy), (2) history of upper abdominal radiotherapy, (3) emergency surgery, or palliative surgery, (4) continuous use of medications such as anticoagulant, antiplatelet, and leukocyte-boosting drugs that significantly affect hematological markers during therapy, and (5) incomplete clinical and follow-up data.

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Chang-Ming Huang, Prof.

OTHER

Sponsor Role: lead

Responsible Party

Chang-Ming Huang, Prof.

Professor

Responsibility Role: SPONSOR_INVESTIGATOR

Principal Investigators

Changming Huang

Role: PRINCIPAL_INVESTIGATOR

Fujian Medical University Union Hospital

Locations

Department of Gastric Surgery， Fujian Medical University Union Hospital

Fuzhou, Fujian, China

Countries

China

Other Identifiers

FMUUH-0323

Identifier Type: -

Identifier Source: org_study_id