Investigating the Correlation Between Pre-Treatment Imaging-Derived Body Composition, Chemotherapy Dose Adjustment, and Treatment Efficacy in Gynecological Cancer Patients "

Not Applicable

Not yet recruiting

• Conditions: Search MeSH

• Registration Number: NCT07144618
• Lead Sponsor: National Cheng-Kung University Hospital

Brief Summary

The dosage of paclitaxel, an adjuvant chemotherapy agent for endometrial and ovarian cancer, is typically calculated based on the patient's body surface area (BSA). However, cancer patients with the same BSA may exhibit significant differences in body composition, which could influence the distribution pattern of paclitaxel in the body. These variations may lead to individual differences in drug tolerance and adverse effects. Such variability not only impacts the patient's treatment experience and quality of life but may also increase medical costs, including hospitalization, emergency department visits, and additional treatments required to manage chemotherapy-induced toxicities.

Our preliminary study results indicate that skeletal muscle area (SMA) and skeletal muscle index (SMI), as assessed through computed tomography (CT) imaging, are significantly associated with the incidence of Grade 3 or higher leukopenia or neutropenia following the first two cycles of chemotherapy in patients with endometrial cancer. Furthermore, the predictive accuracy of these CT-derived muscle measurements surpasses the clinical judgment made by physicians based on conventional treatment guidelines. Patients who develop Grade 3 or higher leukopenia or neutropenia during the first two cycles are more likely to experience more frequent occurrences of Grade 3 or higher chemotherapy-related adverse effects in subsequent treatment cycles.

However, no study has comprehensively investigated the relationship between body composition, chemotherapy dosing, and adverse effects. Therefore, this trial aims to examine the impact of body composition on chemotherapy dose adjustments and adverse effects. By utilizing body composition data extracted from abdominal CT imaging through this product, this study seeks to establish a risk stratification tool to assist physicians in treating patients with endometrial and ovarian cancer by providing a reference for chemotherapy dose reduction.

It is expected that through a precision chemotherapy strategy, the incidence of chemotherapy-related adverse effects can be reduced, thereby lowering medical resource expenditures incurred from managing these adverse effects, such as emergency department visits, hospitalizations, additional diagnostic tests, and supportive medication costs. Furthermore, this approach aims to improve patients' health-related quality of life and achieve a dual benefit of medical economic efficiency and clinical effectiveness.

Detailed Description

Endometrial cancer ranks sixth in incidence among women worldwide and is rising, while ovarian cancer, though less common, remains the leading cause of gynecologic cancer mortality. Standard adjuvant chemotherapy for these malignancies includes paclitaxel (dose based on body surface area, BSA) and carboplatin (dose based on AUC). However, BSA-based dosing does not account for variations in body composition, which can significantly influence drug distribution, toxicity, and treatment efficacy.

Retrospective analysis of 124 endometrial cancer patients at National Cheng Kung University Hospital demonstrated that CT-derived skeletal muscle parameters (SMA, SMI) are significantly associated with grade ≥3 hematologic toxicities and outperform traditional clinical judgment for risk prediction. Patients experiencing severe hematologic toxicity within the first two cycles are more likely to experience repeated toxicities in subsequent cycles.

This trial integrates prospective randomized controlled trial (RCT) and retrospective target trial emulation.

Prospective RCT: 294 patients will be stratified by cancer stage and randomized 1:1 to intervention vs. control groups. The intervention group will undergo AI-assisted risk stratification based on preoperative CT body composition analysis, with high-risk patients receiving a 15% paclitaxel dose reduction from cycle 1. The control group will receive standard-of-care dosing adjustments based on clinical judgment.

Retrospective analysis: Patients diagnosed from 2012-2024 with available CT images and complete treatment records will be analyzed using propensity score methods to control for confounders.

Primary endpoint: incidence of grade ≥3 leukopenia or neutropenia within the first two cycles.

Secondary endpoints: chemotherapy cycle delay (\>7 days), hospitalization rate, HRQoL (CIPN, EORTC-QoL), two-year PFS and OS, cost-effectiveness (ICER, VBP).

The trial hypothesizes that AI-guided dosing adjustments will reduce severe hematologic toxicity rates from 75% to 60% without compromising relative dose intensity (\>85%), thereby achieving both clinical and economic benefits.

Study Timeline

• Study First Submitted: 2025-06-15
• Status Verified: 2025-07
• Study First Submitted QC: 2025-08-20
• Study First Posted: 2025-08-27
• Study Start: 2025-09-01
• Last Update Submitted: 2025-09-16
• Last Update Posted: 2025-09-22
• Primary Completion: 2028-12-31
• Study Completion: 2028-12-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: Female
• Target Recruitment: 294

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
The accuracy of predicting the risk of Grade 3 or higher leukopenia or neutropenia as an adverse effect in endometrial and ovarian cancer patients undergoing postoperative adjuvant chemotherapy (including Paclitaxel) during the first two treatment cycles	6 weeks	Statistical methods: 1. The primary endpoint is a categorical variable, representing the incidence rate of patients experiencing adverse effects. Chi-square test or Fisher's exact test will be used to compare the incidence of Grade 3 or higher chemotherapy-related adverse effects between the experimental group (AI-assisted dose adjustment) and the control group (dose adjustment based on standard clinical guidelines).; 2. Considering the impact of adverse effects across treatment cycles, a Generalized Estimating Equation (GEE) analysis will be performed to assess the overall differences between groups.; Analysis approach: An intent-to-treat (ITT) analysis will be conducted, including all randomized participants to minimize selection bias.

Secondary Outcome Measures

Name	Time	Method
Secondary endpoints include: Chemotherapy cycle delay, admission rate, health-related quality of life, two-year survival rates (PFS, OS), and cost-effectiveness.	2 years	Chemotherapy Cycle Delay Description: % of patients with \>7-day delay in any chemo cycle (21 days/cycle). Time Frame: First two cycles Unit: % of participants Hospitalization Rate Description: % of patients hospitalized at least once during chemo. Time Frame: Up to 2 years Unit: % of participants HRQoL - CIPN Score Description: Change in CIPN score from baseline. Time Frame: Baseline, during chemo, 1-year follow-up Unit: Scale units (CIPN) HRQoL - EORTC-QoL Score Description: Change in EORTC-QoL global health status score. Time Frame: Baseline, during chemo, 1-year follow-up Unit: Scale units (EORTC-QoL) Progression-Free Survival (PFS) Description: Time from first chemo to progression or death. Time Frame: Up to 24 months Unit: Months Overall Survival (OS) Description: Time from first chemo to death from any cause. Time Frame: Up to 24 months Unit: Months Incremental Cost-Effectiveness Ratio (ICER) Description: Cost/QALY for AI-assisted vs. standard dosing. Time Frame: Up to 2 year

Trial Locations

Locations (1)

National Cheng Kung University Hospital; 🇨🇳 Tainan City, Taiwan