Hematological biomarkers for predicting pathologic response to neoadjuvant immunochemotherapy and cycle optimization in locally advanced gastric cancer - Scorecard - MDSpire
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Hematological biomarkers for predicting pathologic response to neoadjuvant immunochemotherapy and cycle optimization in locally advanced gastric cancer
Clinical Scorecard: Hematological Indicators for Anticipating Pathological Outcomes to Neoadjuvant Immunochemotherapy and Treatment Cycle Adjustment in Locally Advanced Gastric Cancer
At a Glance
Category
Detail
Condition
Locally Advanced Gastric Cancer (LAGC)
Key Mechanisms
Neoadjuvant immunochemotherapy (NICT) influences tumor microenvironment and systemic immune responses.
Target Population
Patients with pathologically confirmed locally advanced gastric adenocarcinoma (clinical stage Ib, II, or III).
Care Setting
Single-center retrospective study at The First Hospital of Lanzhou University.
Key Highlights
Pre-treatment neutrophil count and post-treatment albumin are independent predictors of major pathologic response (MPR).
Post-treatment platelet count is an independent risk factor for MPR.
Optimal pre-treatment neutrophil cutoff identified at 3.39×10^9/L.
Extended neoadjuvant therapy cycles (4 vs. 2-3) improve MPR and pathologic complete response rates in patients with low pre-treatment neutrophils.
Guideline-Based Recommendations
Diagnosis
Pathological confirmation of locally advanced gastric adenocarcinoma is required.
Management
Consider neoadjuvant immunochemotherapy (NICT) for LAGC patients.
Monitoring & Follow-up
Monitor hematological parameters pre- and post-treatment to predict pathologic response.
Risks
Patients with high post-treatment platelet counts may have poorer pathologic responses.
Patient & Prescribing Data
246 patients with LAGC receiving NICT.
Patients with low pre-treatment neutrophil levels may benefit from extended treatment cycles.
Clinical Best Practices
Utilize hematological parameters as indicators for treatment response.
Evaluate the number of neoadjuvant therapy cycles based on individual patient profiles.
