Development of an Artificial Intelligence-Driven Novel Response Evaluation Framework and Its Biological Characterization
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 6,120
- 主要终点
- Overall Survival (OS)
研究概览
简要总结
Purpose: This study is developing and validating an artificial intelligence (AI)-driven system to evaluate tumor response using changes in total tumor volume. The goal is to determine whether this AI-based approach can better predict patient survival compared with the current standard method (RECIST), which relies on linear measurements of a few selected tumors.
Participants: The study includes both retrospective and prospective cohorts. The retrospective cohort includes approximately 6,000 patients with solid tumors who received non-surgical treatment between 2015 and 2025. The prospective cohort will enroll approximately 120 patients starting in mid-2026.
Study details include:
Study Duration: Approximately 3 years
Participation Duration: Up to 6 months for prospective participants; retrospective participants contribute existing medical records only
Visit Frequency: For prospective participants, follow-up visits occur every 3 months (up to 6 months) aligned with routine clinical care
Intervention: None. This is an observational study using routine clinical imaging (CT/MRI) and medical records
Primary endpoints: Overall survival (OS) and progression-free survival (PFS). The study will also evaluate the feasibility and impact of AI-assisted tumor response reporting on clinical workflow and patient understanding.
Participants in the prospective cohort will receive either a standard RECIST report or an AI-assisted dynamic tumor response report. This comparison is for research purposes only and does not alter standard medical care.
详细描述
Background: Current tumor response evaluation relies primarily on RECIST 1.1 and its variants, which measure changes in the longest diameter of a limited number of target lesions. While standardized and widely used, these criteria have limitations: they may not fully reflect total tumor burden changes, fail to capture spatial and temporal heterogeneity across lesions, and are subject to inter-observer variability. Advances in artificial intelligence, particularly in medical image analysis, now enable automated tumor segmentation and volumetric quantification, offering a more comprehensive assessment of tumor burden dynamics. However, systematic validation of AI-driven volume-based response criteria against traditional methods remains limited.
Study Design: This is a multi-center, retrospective-prospective cohort study designed to develop and validate an AI-driven prognostic model based on total tumor volume changes and multi-dimensional features. The study is being conducted across approximately 40 participating sites in China.
Data Sources:
Training Set (Retrospective): Approximately 6,000 patients with solid tumors who received non-surgical treatment between January 2015 and December 2025, including the Hepatorch cohort (~300 patients). Data include imaging (CT/MRI), clinical characteristics, laboratory tests, and molecular markers.
Validation Set: Approximately 20% of the training set data randomly extracted for hyperparameter tuning and internal validation.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ≥ 18 years, any sex.
- •Radiologically or pathologically confirmed diagnosis of solid tumor.
- •Received non-surgical treatment with a clearly defined treatment start date.
- •Availability of baseline and at least one follow-up imaging study (CT/MRI) of sufficient quality for AI-based segmentation and volumetric analysis.
- •Availability of key clinical data and follow-up outcome information.
- •For retrospective cohort: prior signed informed consent for biobank donation, agreeing to donate samples and data for medical research.
- •For prospective cohort: planned to receive or currently receiving non-surgical treatment, and able to provide written informed consent.
排除标准
- •Imaging data incomplete or of insufficient quality for accurate segmentation or volumetric calculation.
- •Key clinical information or follow-up outcome data missing.
- •Treatment start or baseline time point cannot be clearly determined.
- •Concurrent other malignancy that cannot be distinguished from the primary study tumor.
- •Severe underlying diseases (e.g., cardiac, pulmonary, renal insufficiency) that may significantly affect survival outcome assessment.
- •Cognitive impairment or other conditions that prevent cooperation with study procedures.
- •For prospective cohort: expected inability to complete follow-up.
- •Other conditions judged by the investigator as unsuitable for study inclusion. -
研究组 & 干预措施
Retrospective Cohort
Approximately 6,000 patients with solid tumors who received non-surgical treatment between January 2015 and December 2025. Data are collected from existing medical records, imaging archives (CT/MRI), and laboratory databases, with no additional interventions or procedures. This cohort is used for AI model training and internal validation.
干预措施: Observational Data Collection (Other)
Prospective Cohort
Approximately 120 patients with solid tumors consecutively enrolled from 2026 onward. Data are collected prospectively in real-world clinical settings using an EDC system, including imaging, clinical, laboratory, and molecular data. Participants undergo standard-of-care imaging and follow-up; no study-specific interventions are assigned. This cohort is used for independent external validation of the AI model and assessment of clinical feasibility and patient experience.
干预措施: Observational Data Collection (Other)
结局指标
主要结局
Overall Survival (OS)
时间窗: From treatment initiation until death or last follow-up, assessed up to 36 months
Time from treatment initiation to death from any cause or last follow-up. OS is an objective, clinically meaningful endpoint that directly reflects treatment efficacy and patient prognosis.
次要结局
- Progression-Free Survival (PFS)(From treatment initiation until disease progression or death, assessed up to 36 months)
- Tumor Volume Change Rate(Baseline and at each follow-up imaging time point (e.g., 4-8 weeks, 3 months, 6 months, 12 months post-treatment), assessed up to 36 months)
- Change in Number of Lesions(Baseline and at each follow-up imaging time point, assessed up to 36 months)
- Appearance of New Lesions(At each follow-up imaging time point, assessed up to 36 months)