AI-Augmented Diagnostic Assessment With ENLIGHT Versus Independent Pathologist Review (ENLIGHT)
This study will evaluate whether artificial intelligence (AI) can enhance clinicians' accuracy, efficiency, and confidence in distinguishing lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC) and kidney renal papillary cell carcinoma (KIRP) from kidney renal clear cell carcinoma (KIRC) using digitized pathology slides. These subtype classifications are routinely performed by pathologists but can be challenging and time-consuming, particularly in difficult cases.
During the study, participating clinicians will review lung and kidney pathology slides under three different conditions:
- Unaided Review: Diagnosis without AI assistance.
- AI as Double-Check: The clinician first makes an independent diagnosis, after which the AI-generated diagnosis (prediction only or prediction with explanation) is revealed for review.
- AI as First-Look: The AI-generated diagnosis (prediction only or prediction with explanation) is presented before the clinician begins the review.
Clinicians will be randomly assigned to different review sequences to minimize potential order effects. This study design will enable us to assess the impact of AI assistance on diagnostic accuracy, interpretation time, and clinician confidence.
Studieoversigt
Status
Status
Betingelser
Betingelser
Intervention / Behandling
Intervention / Behandling
- Adfærdsmæssigt: Unaided Review First, Then AI as Double-Check, Then AI as First-Look.
- Adfærdsmæssigt: Unaided Review First, Then AI as First-Look, Then AI as Double-Check.
- Adfærdsmæssigt: AI as Double-Check First, Then AI as First-Look, Then Unaided Review.
- Adfærdsmæssigt: AI as First-Look First, Then AI as Double-Check, Then Unaided Review.
Detaljeret beskrivelse
This study aims to evaluate the effect of artificial intelligence (AI) assistance on clinicians' diagnostic performance in distinguishing lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC) and kidney renal papillary cell carcinoma (KIRP) from kidney renal clear cell carcinoma (KIRC) using digitized hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). ENLIGHT (Explainable Neoplasm Learning In Grounded Histology Terms) will serve as the AI system under evaluation. This is a single-session, within-reader, between-case study in which each reader evaluates distinct sets of cases under all study conditions.
The study includes three diagnostic blocks: Block X, in which WSIs are reviewed without AI assistance; Block Y1, in which clinicians make an initial diagnosis before viewing the AI output as a double-check; and Block Y2, in which the AI output is displayed before clinicians begin their review as a first-look aid. Within each AI-assisted block, the prediction-only and prediction-with-explanation sub-blocks are presented in randomized order.
Each participating pathologist will review up to 400 de-identified WSIs (up to 200 lung cancer and up to 200 kidney cancer cases). Readers will be randomly assigned to one of four study arms that differ only in the order in which Blocks X, Y1, and Y2 are completed. For each reader, distinct WSIs will be randomly assigned to the diagnostic conditions so that no WSI is reviewed more than once by the same reader.
- Arm 1 (X -> Y1 -> Y2): Clinicians first complete Block X (Unaided Review), followed by Block Y1 (AI as Double-Check) and then Block Y2 (AI as First-Look).
- Arm 2 (X -> Y2 -> Y1): Clinicians first complete Block X (Unaided Review), followed by Block Y2 (AI as First-Look) and then Block Y1 (AI as Double-Check).
- Arm 3 (Y1 -> Y2 -> X): Clinicians first complete Block Y1 (AI as Double-Check), followed by Block Y2 (AI as First-Look), and then Block X (Unaided Review).
- Arm 4 (Y2 -> Y1 -> X): Clinicians first complete Block Y2 (AI as First-Look), followed by Block Y1 (AI as Double-Check), and then Block X (Unaided Review).
For each case, diagnostic accuracy, time to diagnosis, and diagnostic confidence will be recorded. No reader will review the same WSI under more than one condition, thereby eliminating within-reader recall bias. In parallel, the ENLIGHT model will independently generate diagnostic predictions for all WSIs to enable direct benchmarking of AI performance against pathologists and to evaluate the impact of different AI-assisted workflows on diagnostic performance.
Undersøgelsestype
Undersøgelsestype
Tilmelding (Anslået)
Tilmelding
Fase
Fase
- Ikke anvendelig
Kontakter og lokationer
Studiesteder
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-
Massachusetts
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Boston, Massachusetts, Forenede Stater, 02115
- Harvard Medical School,
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-
Deltagelseskriterier
Berettigelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Barn
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Beskrivelse
Inclusion Criteria for Pathology Slides (i.e., Cases):
- Hematoxylin and eosin (H&E)-stained pathology slides
- Final diagnosis confirmed through molecular testing in conjunction with expert pathology evaluation
Exclusion Criteria for Pathology Slides (i.e., Cases):
- Poor-quality or unreadable slides
- Cases used in AI training
Inclusion Criteria for Readers (i.e., Participants):
- Board-certified or board-eligible pathologists
- Willingness to complete both unaided and AI-assisted review sessions
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
- Primært formål: Diagnostisk
- Tildeling: Randomiseret
- Interventionel model: Crossover opgave
- Maskning: Firedobbelt
Antal våben
Våben og indgreb
Deltagergruppe / ArmDeltagergruppe / Arm |
Intervention / BehandlingIntervention / Behandling |
|---|---|
|
Aktiv komparator: Unaided Review First, Then AI as Double-Check, Then AI as First-Look.
Readers first complete Block X (Unaided) on their assigned subset SX.
They then complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check).
Within Block Y1, the order of SY1a and SY1b is randomized.
They then complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look).
Within Block Y2, the order of SY2a and SY2b is randomized.
For each reader, each of the five subsets (SX, SY1a, SY1b, SY2a, and SY2b) comprises up to 80 slides: up to 40 slides from LUAD-LUSC and up to 40 slides from KIRP-KIRC.
|
Readers first complete Block X (Unaided) on their assigned subset SX.
They then complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check).
Within Block Y1, the order of SY1a and SY1b is randomized.
They then complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look).
Within Block Y2, the order of SY2a and SY2b is randomized.
For each reader, SX, SY1a, SY1b, SY2a, and SY2b are disjoint.
|
|
Aktiv komparator: Unaided Review First, Then AI as First-Look, Then AI as Double-Check.
Readers first complete Block X (Unaided) on their assigned subset SX.
They then complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look).
Within Block Y2, the order of SY2a and SY2b is randomized.
They then complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check).
Within Block Y1, the order of SY1a and SY1b is randomized.
For each reader, each of the five subsets (SX, SY1a, SY1b, SY2a, and SY2b) comprises up to 80 slides: up to 40 slides from LUAD-LUSC and up to 40 slides from KIRP-KIRC.
|
Readers first complete Block X (Unaided) on their assigned subset SX.
They then complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look).
Within Block Y2, the order of SY2a and SY2b is randomized.
They then complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check).
Within Block Y1, the order of SY1a and SY1b is randomized.
For each reader, SX, SY1a, SY1b, SY2a, and SY2b are disjoint.
|
|
Aktiv komparator: AI as Double-Check Review First, Then AI as First-Look, Then Unaided Review.
Readers first complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check).
Within Block Y1, the order of SY1a and SY1b is randomized.
They then complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look).
Within Block Y2, the order of SY2a and SY2b is randomized.
Then readers complete Block X (Unaided) on their assigned subset SX.
For each reader, each of the five subsets (SX, SY1a, SY1b, SY2a, and SY2b) comprises up to 80 slides: up to 40 slides from LUAD-LUSC and up to 40 slides from KIRP-KIRC.
|
Readers first complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check).
Within Block Y1, the order of SY1a and SY1b is randomized.
They then complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look).
Within Block Y2, the order of SY2a and SY2b is randomized.
Then readers complete Block X (Unaided) on their assigned subset SX.
For each reader, SX, SY1a, SY1b, SY2a, and SY2b are disjoint.
|
|
Aktiv komparator: AI as First-Look Review First, Then AI as Double-Check, Then Unaided Review.
Readers first complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look).
Within Block Y2, the order of SY2a and SY2b is randomized.
They then complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check).
Within Block Y1, the order of SY1a and SY1b is randomized.
Then readers complete Block X (Unaided) on their assigned subset SX.
For each reader, each of the five subsets (SX, SY1a, SY1b, SY2a, and SY2b) comprises up to 80 slides: up to 40 slides from LUAD-LUSC and up to 40 slides from KIRP-KIRC.
|
Readers first complete Block Y2 (AI as First-Look) on two separate subsets: SY2a (AI prediction-only as First-Look) and SY2b (AI prediction-with-explanation as First-Look).
Within Block Y2, the order of SY2a and SY2b is randomized.
They then complete Block Y1 (AI as Double-Check) on two separate subsets: SY1a (AI prediction-only as Double-Check) and SY1b (AI prediction-with-explanation as Double-Check).
Within Block Y1, the order of SY1a and SY1b is randomized.
Then readers complete Block X (Unaided) on their assigned subset SX.
For each reader, SX, SY1a, SY1b, SY2a, and SY2b are disjoint.
|
Hvad måler undersøgelsen?
Primære resultatmål
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Diagnostic performance of cancers
Tidsramme: Periprocedural (at the time of slide review)
|
Performance of clinicians (unaided and AI-assisted) for distinguishing LUAD- LUSC and distinguishing KIRP-KIRC, measured in accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1.
|
Periprocedural (at the time of slide review)
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Sekundære resultatmål
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Tid til diagnose
Tidsramme: Periprocedural (på tidspunktet for Slide Review)
|
Gennemsnitlig tid (sekunder pr. Sag), der kræves for at afslutte en diagnose.
|
Periprocedural (på tidspunktet for Slide Review)
|
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Inter-observer-variation
Tidsramme: Periprocedural (på tidspunktet for Slide Review)
|
Aftale mellem klinikere på tværs af forhold, målt ved hjælp af pålidelighed mellem rater (f.eks. Kappa-statistik).
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Periprocedural (på tidspunktet for Slide Review)
|
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Nettofordel efter AI -eksponering
Tidsramme: Periprocedural (på tidspunktet for Slide Review)
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Den samlede ændring i diagnostisk nøjagtighed, der kan henføres til AI -hjælp.
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Periprocedural (på tidspunktet for Slide Review)
|
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Clinician confidence level
Tidsramme: Periprocedural (at the time of slide review)
|
Self-reported diagnostic confidence recorded for each case.
Scale: 5 - Absolutely Certain; 4 - Mostly Certain; 3 - Unsure; 2 - Very Doubtful; 1 - Random Guess; With 5 being the highest confidence score and 1 being the lowest.
|
Periprocedural (at the time of slide review)
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Samarbejdspartnere og efterforskere
Sponsor
Sponsor
Datoer for undersøgelser
Studer store datoer
Studiestart (Anslået)
Studiestart
Primær færdiggørelse (Anslået)
Primær færdiggørelse
Studieafslutning (Anslået)
Studieafslutning
Datoer for studieregistrering
Først indsendt
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Først opslået
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering sendt
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Nøgleord
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
Andre undersøgelses-id-numre
- ENLIGHT Study
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