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.
Studienübersicht
Status
Bedingungen
Intervention / Behandlung
- Verhalten: Unaided Review First, Then AI as Double-Check, Then AI as First-Look.
- Verhalten: Unaided Review First, Then AI as First-Look, Then AI as Double-Check.
- Verhalten: AI as Double-Check First, Then AI as First-Look, Then Unaided Review.
- Verhalten: AI as First-Look First, Then AI as Double-Check, Then Unaided Review.
Detaillierte Beschreibung
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.
Studientyp
Einschreibung (Geschätzt)
Phase
- Unzutreffend
Kontakte und Standorte
Studienorte
-
-
Massachusetts
-
Boston, Massachusetts, Vereinigte Staaten, 02115
- Harvard Medical School,
-
-
Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
- Kind
- Erwachsene
- Älterer Erwachsener
Akzeptiert gesunde Freiwillige
Beschreibung
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
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
- Hauptzweck: Diagnose
- Zuteilung: Zufällig
- Interventionsmodell: Crossover-Aufgabe
- Maskierung: Vervierfachen
Waffen und Interventionen
Teilnehmergruppe / Arm |
Intervention / Behandlung |
|---|---|
|
Aktiver 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.
|
|
Aktiver 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.
|
|
Aktiver 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.
|
|
Aktiver 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.
|
Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
Diagnostic performance of cancers
Zeitfenster: 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)
|
Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
Zeit der Diagnose
Zeitfenster: Periprocedural (zum Zeitpunkt der Folienüberprüfung)
|
Durchschnittliche Zeit (Sekunden pro Fall), die zur Abschluss einer Diagnose erforderlich ist.
|
Periprocedural (zum Zeitpunkt der Folienüberprüfung)
|
|
Inter-Beobachtungsvariabilität
Zeitfenster: Periprocedural (zum Zeitpunkt der Folienüberprüfung)
|
Übereinstimmung zwischen den Klinikern über die Bedingungen hinweg, gemessen unter Verwendung von Metriken zwischen den Rater zuverlässigen (z. B. Kappa-Statistik).
|
Periprocedural (zum Zeitpunkt der Folienüberprüfung)
|
|
Netto -Nutzen nach KI -Exposition
Zeitfenster: Periprocedural (zum Zeitpunkt der Folienüberprüfung)
|
Die allgemeine Änderung der diagnostischen Genauigkeit, die auf KI -Unterstützung zurückzuführen ist.
|
Periprocedural (zum Zeitpunkt der Folienüberprüfung)
|
|
Clinician confidence level
Zeitfenster: 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)
|
Mitarbeiter und Ermittler
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
- ENLIGHT Study
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