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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Quasar | Experimental | This arm involves the prospective collection of individual patient data, including demographic information, clinical details (such as pathological classification, tumor staging, imaging findings, prior treatments and their efficacy, and performance status scores), and multi-omics data (DNA gene panel testing, whole-exome sequencing, and transcriptome sequencing). An artificial intelligence model (namely, Quasar) integrates this multidimensional information to prioritize standard treatment options and identify the optimal personalized treatment plan for each patient. Based on the AI-recommended treatment list, the final treatment plan is jointly selected by the patient and the physician. If treatment adjustments are required due to tumor progression, intolerance, or other reasons, the AI model will generate a new optimal treatment plan based on updated patient characteristics. This iterative process continues until the patient withdraws from the study. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Biologically-informed multi-agent system (Quasar) including targeted drugs Osimertinib, chemotherapy pemetrexed, immunotherapy pembrolizumab et al. approved by China CDE. | Drug | Quasar is a biologically-informed multi-agent system developed based on multi-omics and multi-modal data. By integrating multidimensional information such as patients' demographic, clinical, and omics data (including DNA genotyping, whole-exome sequencing, transcriptome sequencing, etc.), it prioritizes standard treatment plans and recommends the optimal personalized treatment plan. Including targeted drugs, chemotherapy, immunotherapy approved by China CDE. |
| Measure | Description | Time Frame |
|---|---|---|
| Progression-free survival (PFS) | Defined as the time from enrollment to documented disease progression per RECIST 1.1 or death due to any cause, whichever occurs first. | Every 6 weeks, up to 2 years since enrollment |
| Measure | Description | Time Frame |
|---|---|---|
| Overall response rate (ORR) | Defined as the proportion of cases showing the best response of complete response (CR) or partial response (PR) (i.e., CR+PR) per RECIST 1.1 (based on CT, MRI or PET-CT), during the period from the start of the investigational drug to withdrawal from the trial. | Every 6 weeks, up to 2 years since enrollment |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Ning LI, M.D. | Contact | +86 (010) 8778-8165 | lining@cicams.ac.cn | |
| Yale JIANG, M.D. | Contact | +86 (010) 8778-8713 | yalejiang@cicams.ac.cn |
| Name | Affiliation | Role |
|---|---|---|
| Shuhang Wang, PhD | National Cancer Center of China | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Cancer Institute and Hospital, Chinese Academy of Medical Sciences (Langfang Branch) | Langfang | Hebei | China |
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| Duration of response (DoR) |
Defined as the time from the first documented response, i.e. CR or PR, per RECIST 1.1, to disease progression or death from any cause, whichever occurs first. |
| Every 6 weeks, up to 2 years since enrollment |
| Time to treatment failure (TTF) | Defined as the time from the start of enrollment to the termination of treatment for any reason, including disease progression per RECIST 1.1, treatment toxicity, or death. | Every 6 weeks, up to 2 years since enrollment |
| Time to progression (TTP) | Defined as the time from enrollment to the occurrence of objective tumor progression per RECIST 1.1, excluding death. | Every 6 weeks, up to 2 years since enrollment |
| Best of response (BoR) | Defined as the best therapeutic effect recorded from the start of treatment until disease progression or recurrence, per RECIST 1.1. | Every 6 weeks, up to 2 years since enrollment |
| Treatment-emergent adverse events (TEAE) | Defined as adverse events that emerge or worsen in severity following the initiation of intervention, per CTCAE 5.0. | Every 6 weeks, up to 2 years since enrollment |