Two-component Radiology-guided Autonomous Cascade Engine (TRACE) (TRACE)
Protocol for a Prospective Randomised Crossover Controlled Trial of the Artificial Intelligence-Assisted Decision-Making System for Gastric Cancer T-Staging (TRACE)
This study employed a prospective, randomised crossover trial design to evaluate the clinical utility of the TRACE artificial intelligence system for gastric cancer T-staging. A total of 54 radiologists from tertiary and non-tertiary hospitals, including both senior and junior practitioners, were enrolled. The study aimed to investigate whether AI-assisted diagnosis could improve the diagnostic accuracy of gastric cancer T-staging compared with independent interpretation by radiologists.
All participants were required to interpret 60 contrast-enhanced CT cases sequentially, completing two readings for each case: one without AI assistance and one with AI assistance; The order of the two readings was randomised, and a one-month washout period was observed between readings to eliminate memory bias. All cases were pathologically confirmed gastric cancer cases (stages T1-T4b), and the study simultaneously recorded the physicians' T-staging diagnostic results and the time taken per case. The 60 cases per radiologist were randomly selected from a pool of 1,000 histologically confirmed gastric cancer cases, stratified by pathological T stage T1-T4b. The reference standard was postoperative pathological T stage. The primary outcome was the change in T-staging accuracy between AI-assisted reading and standard (unaided) reading.The term "prospective" in this study refers to the prospective execution of radiologist enrollment, randomization, reading procedures, and data collection.
Studienübersicht
Status
Bedingungen
Intervention / Behandlung
Detaillierte Beschreibung
The TRACE trial is a prospective, randomized, crossover, controlled study evaluating an artificial intelligence (AI)-assisted decision system for T staging of gastric cancer based on CT images.
Background and rationale: Accurate preoperative T staging is critical for treatment planning in gastric cancer, but remains challenging due to reader variability and imaging limitations. The AI system was developed using deep learning with a large multi-center dataset to improve staging accuracy.
Study design: Eligible patients with pathologically confirmed gastric cancer will undergo preoperative contrast-enhanced CT. Each participant will be assessed twice in random order: once with AI assistance (AI arm) and once without (standard arm). A washout period will be applied between the two readings to minimize recall bias. Radiologists involved in the study are blinded to clinical and pathological reference standards.
Objective: To compare the T staging accuracy (primary outcome) between AI-assisted and standard reading, with secondary outcomes including inter-reader agreement, reading time, and diagnostic confidence.
Statistical methods: A crossover design will be used with a sample size calculated to detect a prespecified difference in overall accuracy. The primary analysis will employ a paired McNemar test or generalized estimating equation accounting for period and carryover effects. Subgroup analyses by tumor location, T category, and reader experience will be exploratory.
Data monitoring: No independent Data Monitoring Committee is required due to the low-risk nature of the diagnostic device. Adverse events related to the use of the software (e.g., workflow disruption) will be recorded and reported.
Ethics and dissemination: The protocol has been approved by the Ethics Committee of Liaoning Cancer Hospital & Institute. Written informed consent (online or paper-based) will be obtained from all participants. Results will be submitted for publication in peer-reviewed journals regardless of outcome.
Studientyp
Einschreibung (Geschätzt)
Phase
- Unzutreffend
Kontakte und Standorte
Studienkontakt
- Name: Guoliang Zheng
- Telefonnummer: 13322400728
- E-Mail: zhengboren1@126.com
Studienorte
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Liaoning
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Shenyang, Liaoning, China, 110024
- Rekrutierung
- Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)
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Kontakt:
- Guoliang Zheng, doctor
- Telefonnummer: 13322400728
- E-Mail: zhengboren1@126.com
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Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
- Kind
- Erwachsene
- Älterer Erwachsener
Akzeptiert gesunde Freiwillige
Beschreibung
Inclusion Criteria (Imaging Data)
- Contrast-enhanced CT (CE-CT) images of gastric cancer patients from the Liaoning Cancer Hospital;
- Patients with a definitive postoperative pathological diagnosis of gastric cancer and a clear T-stage classification (T1-T4, including T4a and T4b);
- Imaging data must be complete and of sufficient quality to meet diagnostic and analytical requirements, with no significant artefacts or missing key data;
- Complete clinical and pathological information must be available to establish a diagnostic gold standard for comparison.
Physician Inclusion Criteria (Image Readers)
- Radiologists holding a valid medical licence;
- From the radiology department of a Grade A tertiary hospital or a non-Grade A tertiary hospital;
- Classified as senior or junior physicians based on clinical experience;
- Voluntarily participating in this study and completing both the non-AI-assisted and AI-assisted image interpretation tasks.
Case Exclusion Criteria
- Severe missing imaging data or quality failing to meet analysis requirements (e.g., severe motion artefacts);
- Lack of clear postoperative pathological T-staging results;
- Cases not involving gastric cancer or with incomplete pathological information;
- Cases of duplicate enrolment or inconsistent data recording.
Physician Exclusion Criteria
- Those unable to complete all image review tasks or demonstrating severe non-compliance;
- Those who withdraw during the study period and are unable to provide complete data for both phases of image review;
- Those who fail to complete the AI-assisted and non-AI-assisted interpretation processes as specified.
Withdrawal Criteria
- Physicians who voluntarily withdraw from the study for personal reasons (e.g., time, health or work commitments);
- Physicians who fail to complete the required image review tasks or have data missing in excess of the specified threshold;
- Cases where critical data errors are identified during subsequent verification or where pathological results cannot be traced; Data found during the study to be non-compliant with ethical or quality control requirements must be excluded.
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
- Hauptzweck: Diagnose
- Zuteilung: Zufällig
- Interventionsmodell: Crossover-Aufgabe
- Maskierung: Doppelt
Waffen und Interventionen
Teilnehmergruppe / Arm |
Intervention / Behandlung |
|---|---|
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Experimental: Standard reading 1
Utilizing the TRACE model to assist radiologists in T-staging.
In this arm, participants receive TRACE model assistance in the first reading phase (AI-assisted), followed by independent reading without AI after a 1-month washout period.
The temporal order of the intervention is early application.
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AI-assisted reading: Radiologists interpret preoperative contrast-enhanced CT images for gastric cancer T staging with the support of the TRACE artificial intelligence decision system.
The AI system provides a suggested T stage and relevant imaging features.
The radiologist makes the final staging decision after reviewing the AI output.
This intervention is used only during the AI-assisted reading session.
Participants are required to observe a washout period of at least 30 days between consecutive interventions/assessments.
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Experimental: Standard Reading 2
Utilizing the TRACE model to assist radiologists in T-staging.
In this arm, participants first perform independent reading without AI assistance, and after a 1-month washout period, they receive TRACE model assistance in the second reading phase.
The temporal order of the same intervention is delayed compared to Arm 1.
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AI-assisted reading: Radiologists interpret preoperative contrast-enhanced CT images for gastric cancer T staging with the support of the TRACE artificial intelligence decision system.
The AI system provides a suggested T stage and relevant imaging features.
The radiologist makes the final staging decision after reviewing the AI output.
This intervention is used only during the AI-assisted reading session.
Participants are required to observe a washout period of at least 30 days between consecutive interventions/assessments.
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Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
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Accuracy
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Accuracy of radiologists' interpretation of T staging
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Within 40 days after the first radiologist initiates image reading.
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Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
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Accuracy Change by Physician Experience Level
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Changes in diagnostic accuracy of radiologists with different experience levels before and after AI assistance.
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Within 40 days after the first radiologist initiates image reading.
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Stratified diagnostic accuracy of different T-stages
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Stratified diagnostic accuracy for different T-stages (T1-T4, including T4a and T4b).
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Within 40 days after the first radiologist initiates image reading.
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Agreement between physician diagnosis and pathological gold standard
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Agreement between radiologists' diagnostic results and the pathological gold standard (e.g., Kappa value).
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Within 40 days after the first radiologist initiates image reading.
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Agreement between AI model and physician interpretation
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Agreement analysis between AI model prediction results and radiologists' interpretations.
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Within 40 days after the first radiologist initiates image reading.
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Effect of AI assistance on reading efficiency
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Changes in average reading time for diagnosis with and without AI assistance.
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Within 40 days after the first radiologist initiates image reading.
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Andere Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
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Influence of case characteristics on AI assistance effect
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Influence of different case characteristics (e.g., tumor location, size) on the performance of AI assistance.
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Within 40 days after the first radiologist initiates image reading.
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Impact of individual physician differences on AI assistance effect
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Impact of individual differences among physicians on the performance of AI assistance.
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Within 40 days after the first radiologist initiates image reading.
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Value of AI assistance in reducing diagnostic discrepancy
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Potential value of AI assistance in reducing diagnostic differences and improving reading agreement.
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Within 40 days after the first radiologist initiates image reading.
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Impact of model probability information on physician decisions
Zeitfenster: Within 40 days after the first radiologist initiates image reading.
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Preliminary analysis of the impact of probability output from AI model on physician decision-making behavior.
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Within 40 days after the first radiologist initiates image reading.
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Mitarbeiter und Ermittler
Ermittler
- Hauptermittler: Guoliang Zheng, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute)
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Geschätzt)
Primärer Abschluss (Geschätzt)
Studienabschluss (Geschätzt)
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Schlüsselwörter
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
- KY20260512
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