Clinical trial NCT06824792: Optimal Standard Treatment Selection for Solid Tumor Patients by Biologically-informed Multi-agent System
Official title: Real-world Study to Investigate Optimal Standard Treatment Selection for Solid Tumor Patients by Guided by Biologically-informed Multi-agent System
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: PHASE4
Conditions: Advanced Solid Tumors
Keywords: Artificial Intelligence
Real World Study
Advanced Solid Tumors
Standard Treatment
Multi-omics
Lead sponsor: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Sponsor class: OTHER
First posted: 2025-02-13
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: TREATMENT

Brief summary: This study is an exploratory cohort study conducted under real-world conditions, aiming to evaluate the feasibility of an artificial intelligence (AI)-guided standard treatment selection model for advanced solid tumors, as well as its superiority compared to clinician-selected treatment plans. A multi-agent system based on multimodal AI models will rank the priority of standard treatment options based on the personalized information of the patients, including including demographics, clinical information, and multi-omics data. The final treatment plan will be jointly selected by the patient and the clinician from the AI-recommended options, thereby delivering a personalized treatment.

Detailed description: This study is an exploratory cohort study conducted under real-world conditions, aiming to evaluate the feasibility of an artificial intelligence (AI)-guided standard treatment selection model for advanced solid tumors, as well as its superiority compared to clinician-selected treatment plans. The study will prospectively collect patient data of multiple dimensions, including demographics, clinical information (pathological classification, tumor staging, imaging findings, previous treatment regimens and their effectiveness, performance status scores), and multi-omics data (DNA gene panel testing, whole-exome sequencing, transcriptome sequencing, etc.). A multi-agent system based on multimodal AI models will rank the priority of standard treatment options based on the personalized information of the patients. The final treatment plan will be jointly selected by the patient and the clinician from the AI-recommended options, thereby delivering a personalized treatment.

Eligibility criteria: Inclusion Criteria:

* Voluntarily participate in the clinical study, fully understand and be informed about the study, sign the informed consent form, and be willing and able to comply with and complete all trial procedures.
* Aged ≥18 years, no gender restrictions.
* Patients with advanced or metastatic malignant tumors confirmed by histology or cytology.
* Able to provide tumor tissue and peripheral blood samples for multi-omics testing, or able to provide qualified whole-exome sequencing and transcriptomics data.

Exclusion Criteria:

* As assessed by the investigator, no standard treatment is available, or the patient is unsuitable for guideline-recommended anti-tumor therapies.
* Other conditions deemed unsuitable for participation in this study by the investigator.

Primary outcomes:
measure: Progression-free survival (PFS)
timeFrame: Every 6 weeks, up to 2 years since enrollment
Secondary outcomes:
measure: Overall response rate (ORR)
timeFrame: Every 6 weeks, up to 2 years since enrollment
measure: Duration of response (DoR)
timeFrame: Every 6 weeks, up to 2 years since enrollment
measure: Time to treatment failure (TTF)
timeFrame: Every 6 weeks, up to 2 years since enrollment
measure: Time to progression (TTP)
timeFrame: Every 6 weeks, up to 2 years since enrollment
measure: Best of response (BoR)
timeFrame: Every 6 weeks, up to 2 years since enrollment
measure: Treatment-emergent adverse events (TEAE)
timeFrame: Every 6 weeks, up to 2 years since enrollment
Other outcomes:
Locations:
city: Langfang
country: China
facility: Cancer Institute and Hospital, Chinese Academy of Medical Sciences (Langfang Branch)
state: Hebei
Study officials:
affiliation: National Cancer Center of China
name: Shuhang Wang, PhD
role: STUDY_DIRECTOR
References: