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.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
TREATMENT
Masking
NONE
Enrollment
3,000
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.
Cancer Institute and Hospital, Chinese Academy of Medical Sciences (Langfang Branch)
Langfang, Hebei, China
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.
Time frame: Every 6 weeks, up to 2 years since enrollment
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.
Time frame: Every 6 weeks, up to 2 years since enrollment
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: Every 6 weeks, up to 2 years since enrollment
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