AI-Driven Tumor Response Evaluation for Solid Tumors
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorShanghai Zhongshan Hospital
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial population
This multi-center retrospective-prospective cohort study includes adult patients (≥18 years) with radiologically or pathologically confirmed solid tumors who have received or are receiving non-surgical treatment. The retrospective cohort comprises approximately 6,000 patients treated between January 2015 and December 2025, with complete imaging, clinical, laboratory, and molecular data from multiple centers across China. The prospective cohort consists of approximately 120 patients consecutively enrolled from 2026 onward, with data collected in real-world clinical settings. Participants are excluded if imaging quality is insufficient for AI-based analysis, key clinical or follow-up data are missing, or they have concurrent malignancies that cannot be distinguished from the primary tumor. The study population reflects the diversity of solid tumor types and treatment patterns in routine clinical practice.
Trial design
Design model
Cohort
Time perspective
Other
Treatments tested in this trial
Observational Data Collection
Other interventionThis is an observational study. No interventions are assigned. Data are collected from routine clinical imaging (CT/MRI), medical records, and laboratory tests as part of standard clinical care.
Treatment groups
6,120 Participants
are divided into 2 treatment groupsGroup A: Retrospective Cohort1 intervention
Group B: Prospective Cohort1 intervention
Trial outcomes
Primary outcomes
1
Overall Survival (OS)
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.
Time frame
From treatment initiation until death or last follow-up, assessed up to 36 monthsSecondary outcomes
1
Progression-Free Survival (PFS)
Time from treatment initiation to first documented disease progression or death from any cause.
Time frame
From treatment initiation until disease progression or death, assessed up to 36 months2
Tumor Volume Change Rate
Percentage change in total tumor volume from baseline, as measured by AI-based automated segmentation on CT/MRI imaging.
Time frame
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 months3
Change in Number of Lesions
Change in the total number of tumor lesions from baseline, as identified by AI-based detection on CT/MRI imaging.
Time frame
Baseline and at each follow-up imaging time point, assessed up to 36 months4
Appearance of New Lesions
Presence or absence of new tumor lesions identified on follow-up CT/MRI imaging compared to baseline.
Time frame
At each follow-up imaging time point, assessed up to 36 monthsOther outcomes
1
AI Model Performance - Concordance Index (C-index)
Concordance index of the AI-driven prognostic model for predicting overall survival, evaluated in the prospective validation cohort.
Time frame
At study completion, assessed up to 36 months2
AI Model Performance - Time-Dependent ROC AUC
Time-dependent Area Under the Receiver Operating Characteristic Curve (AUC) of the AI-driven prognostic model for predicting survival at specific time points (e.g., 12, 24 months).
Time frame
At study completion, assessed up to 36 months3
Physician Workflow Efficiency - Reading Time
Time required for radiologists or clinicians to complete tumor response assessment, comparing AI-assisted reading versus independent manual reading.
Time frame
During the human-machine collaboration evaluation, assessed up to 36 monthsLocations
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Shanghai Zhongshan Hospital
Lead sponsor
West China Hospital
Collaborator
Peking University Cancer Hospital & Institute
Collaborator
Sun Yat-Sen University Cancer Center
Collaborator
Fudan University
Collaborator
Source ClinicalTrials.gov
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