Acute Promyelocytic Leukemia (APL)LEAPNCT07203885
AI-Assisted Acute Myeloid Leukemia Evaluation With the Leukemia End-to-End Analysis Platform (LEAP) Versus Clinician-Only Assessment
A study testing Unaided Review First, Then AI-Assisted Review and AI-Assisted Review First, Then Unaided Review in people with Acute Promyelocytic Leukemia (APL) and Acute Myeloid Leukaemia (AML), run by Harvard Medical School (HMS and HSDM) at 1 site in United States.
Trial summary
This study will test whether artificial intelligence (AI) can help doctors diagnose a rare blood cancer called acute promyelocytic leukemia (APL) more quickly and accurately. Doctors usually examine bone marrow samples under a microscope to make this diagnosis, but it can be challenging and time-consuming. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Acute Promyelocytic Leukemia (APL), Acute Myeloid Leukaemia (AML)
- What is being tested?
- Unaided Review First, Then AI-Assisted Review and AI-Assisted Review First, Then Unaided Review
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 10 participants
- Where
- 1 siteUnited States
- Study dates
- 9 Sep 2025to 10 Oct 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Wright-Giemsa-stained bone marrow aspirate smears
- Final diagnosis confirmed through molecular testing in conjunction with expert pathology evaluation
- Board-certified or board-eligible pathologists, or board-certified/board-eligible hematologists who routinely make hematopathology diagnoses in their clinical practice
- Willingness to complete both unaided and AI-assisted review sessions
Who cannot join
- Poor-quality or unreadable slides
- Cases used in AI training
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Prospective Studies on Artificial Intelligence for Cancer Pathology Evaluation
This study will test whether artificial intelligence (AI) can help doctors diagnose a rare blood cancer called acute promyelocytic leukemia (APL) more quickly and accurately. Doctors usually examine bone marrow samples under a microscope to make this diagnosis, but it can be challenging and time-consuming.
In this study, doctors will review bone marrow samples under three different conditions:
Read more
- Unaided Review: Without AI assistance.
- AI as Double-Check: AI-generated evaluation shown after the doctor makes an initial decision.
- AI as First Look: AI-generated evaluation shown at the start of the review.
Doctors will be randomly assigned to different orders of these three conditions. This design will allow us to compare how AI support affects diagnostic accuracy, speed, and confidence.
Read the detailed description
This study aims to evaluate the effect of artificial intelligence (AI) assistance on clinicians' diagnostic performance in detecting acute promyelocytic leukemia (APL) using Wright-Giemsa-stained bone marrow whole-slide images (WSIs). The Leukemia End-to-End Analysis Platform (LEAP) will serve as the AI model under assessment.
This is a single-session, within-reader study. Participants will be randomly assigned to one of two study arms, which differ in the order of diagnostic blocks:
- Arm 1 (X -> Y): Block X (Unaided Review): Clinicians review WSIs without AI support. Diagnostic accuracy, time to decision, and confidence will be recorded.
Block Y (AI-Assisted Review): Comprising two sub-blocks presented in randomized order:
Y1 (AI as Double-Check): Clinicians provide an initial diagnosis and confidence score without the aid of AI. AI predictions are then revealed, and clinicians may revise their diagnosis. Both pre-AI and post-AI decisions will be recorded.
Y2 (AI as First Look): Clinicians review WSIs with AI-predicted diagnoses visible from the beginning.
- Arm 2 (Y -> X): Block Y (AI-Assisted Review): Sub-blocks Y1 and Y2 presented in randomized order.
Block X (Unaided Review): As described above.
Each clinician will review 102 de-identified WSIs. For each reader, slides will be randomly divided into three disjoint subsets (e.g. 34/34/34), stratified by APL status, and assigned to Block X (Unaided), Block Y1 (AI as Double-Check), or Block Y2 (AI as First Look). No slide will be shown to the same reader in more than one block.
In addition, the AI system will independently generate diagnostic predictions for all WSIs to enable benchmarking; however, this does not constitute a participant arm.
Ground-truth diagnoses will be determined by molecular confirmation and expert consensus.
- Being tested
- Unaided Review First, Then AI-Assisted Review, AI-Assisted Review First, Then Unaided Review
- Keywords
- AI-Assisted Detection, Acute Promyelocytic Leukemia, Bone Marrow Slide, Whole-slide images, Acute Myeloid Leukemia
- Medical subject headings
- Leukemia, Promyelocytic, Acute, Leukemia, Myeloid, Acute
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Unaided Review First, Then AI-Assisted ReviewBehavioural
Readers first complete Block X (Unaided) on their assigned subset SX (34 slides). They then complete Block Y (AI-Assisted) on two separate subsets: SY1 (34 slides; AI as Double-Check) and SY2 (34 slides; AI as First Look). Within Block Y, the order of Y1 and Y2 is randomized. For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status.
AI-Assisted Review First, Then Unaided ReviewBehavioural
Readers first complete Block Y (AI-Assisted) on two assigned subsets: SY1 (up to 40 slides; AI as Double-Check) and SY2 (up to 40 slides; AI as First Look), with the order of Y1 and Y2 randomized. They then complete Block X (Unaided) on subset SX (up to 40 slides). For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status.
| Study group | Role | What they receive |
|---|---|---|
| Unaided Review First, Then AI-Assisted Review | Active comparator | Readers first complete Block X (Unaided) on their assigned subset SX (34 slides). They then complete Block Y (AI-Assisted) on two separate subsets: SY1 (34 slides; AI as Double-Check) and SY2 (34 slides; AI as First Look). Within Block Y, the order of Y1 and Y2 is randomized. For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status. Receives: Behavioral: Unaided Review First, Then AI-Assisted Review |
| AI-Assisted Review First, Then Unaided Review | Active comparator | Readers first complete Block Y (AI-Assisted) on two assigned subsets: SY1 (34 slides; AI as Double-Check) and SY2 (34 slides; AI as First Look), with the order of Y1 and Y2 randomized. They then complete Block X (Unaided) on subset SX (34 slides). For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status. Receives: Behavioral: AI-Assisted Review First, Then Unaided Review |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of APL detection
Performance of clinicians (unaided and AI-assisted) in detecting APL, measured in accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.
Measured: Periprocedural (at the time of slide review)
Secondary outcomes (6)
Time to diagnosis
Average time (seconds per case) required to finalize a diagnosis.
Measured: Periprocedural (at the time of slide review)
Inter-observer variability
Agreement among clinicians across conditions, measured using inter-rater reliability metrics (e.g., kappa statistics).
Measured: Periprocedural (at the time of slide review)
Concordance between AI predictions and clinicians' diagnoses
The proportion of cases in which AI predictions match clinicians' decisions in each study condition.
Measured: Periprocedural (at the time of slide review)
Decision-change rates
The proportion of cases in which a clinician's initial diagnosis is revised after exposure to AI assistance.
Measured: Periprocedural (at the time of slide review)
Net benefit after AI exposure
The overall change in diagnostic accuracy attributable to AI assistance.
Measured: Periprocedural (at the time of slide review)
Clinician confidence level
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.
Measured: Periprocedural (at the time of slide review)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Oct 2025
- Study start9 Sep 2025
- Primary completion10 Oct 2025
- Study completion10 Oct 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Acute Promyelocytic Leukemia (APL)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Triple blind
- Who is masked
- Care provider, Investigator, Outcomes assessor
- Enrolment
- 10 participants (actual)
Sponsor and investigators
- Lead sponsor
- Harvard Medical School (HMS and HSDM)
- Sponsor type
- University, hospital or other
- Collaborators
- National Taiwan University Hospital, Far Eastern Memorial Hospital, Taipei Veterans General Hospital, Taiwan, Brigham and Women's Hospital, Massachusetts General Hospital
- Responsible party
- Principal investigator, Kun-Hsing Yu, Harvard Medical School (HMS and HSDM)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07203885
- Sponsor's protocol ID
- IRB23-0403
- Acronym
- LEAP
- Status last verified
- Oct 2025
- First submitted
- 8 Sep 2025
- Last updated
- 31 Oct 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 31 Oct 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Studies on Artificial Intelligence for Cancer Pathology Evaluation
- ClinicalTrials.gov ID
- NCT07203885
- Acronym / sponsor ID
- LEAP / IRB23-0403
- Keywords
- AI-Assisted Detection, Acute Promyelocytic Leukemia, Bone Marrow Slide, Whole-slide images, Acute Myeloid Leukemia
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 10 participants (actual)
- Lead sponsor
- Harvard Medical School (HMS and HSDM)University, hospital or other
- Collaborators
- National Taiwan University Hospital, Far Eastern Memorial Hospital, Taipei Veterans General Hospital, Taiwan, Brigham and Women's Hospital, Massachusetts General Hospital
- Study start
- 9 Sep 2025 (actual)
- Primary completion
- 10 Oct 2025 (actual)
- Study completion
- 10 Oct 2025 (actual)
- First posted
- 2 Oct 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study will test whether artificial intelligence (AI) can help doctors diagnose a rare blood cancer called acute promyelocytic leukemia (APL) more quickly and accurately. Doctors usually examine bone marrow samples under a microscope to make this diagnosis, but it can be challenging and time-consuming.
In this study, doctors will review bone marrow samples under three different conditions:
- Unaided Review: Without AI assistance.
- AI as Double-Check: AI-generated evaluation shown after the doctor makes an initial decision.
- AI as First Look: AI-generated evaluation shown at the start of the review.
Doctors will be randomly assigned to different orders of these three conditions. This design will allow us to compare how AI support affects diagnostic accuracy, speed, and confidence.
Detailed description
This study aims to evaluate the effect of artificial intelligence (AI) assistance on clinicians' diagnostic performance in detecting acute promyelocytic leukemia (APL) using Wright-Giemsa-stained bone marrow whole-slide images (WSIs). The Leukemia End-to-End Analysis Platform (LEAP) will serve as the AI model under assessment.
This is a single-session, within-reader study. Participants will be randomly assigned to one of two study arms, which differ in the order of diagnostic blocks:
- Arm 1 (X -> Y): Block X (Unaided Review): Clinicians review WSIs without AI support. Diagnostic accuracy, time to decision, and confidence will be recorded.
Block Y (AI-Assisted Review): Comprising two sub-blocks presented in randomized order:
Y1 (AI as Double-Check): Clinicians provide an initial diagnosis and confidence score without the aid of AI. AI predictions are then revealed, and clinicians may revise their diagnosis. Both pre-AI and post-AI decisions will be recorded.
Y2 (AI as First Look): Clinicians review WSIs with AI-predicted diagnoses visible from the beginning.
- Arm 2 (Y -> X): Block Y (AI-Assisted Review): Sub-blocks Y1 and Y2 presented in randomized order.
Block X (Unaided Review): As described above.
Each clinician will review 102 de-identified WSIs. For each reader, slides will be randomly divided into three disjoint subsets (e.g. 34/34/34), stratified by APL status, and assigned to Block X (Unaided), Block Y1 (AI as Double-Check), or Block Y2 (AI as First Look). No slide will be shown to the same reader in more than one block.
In addition, the AI system will independently generate diagnostic predictions for all WSIs to enable benchmarking; however, this does not constitute a participant arm.
Ground-truth diagnoses will be determined by molecular confirmation and expert consensus.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Triple blind (care provider, investigator, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Unaided Review First, Then AI-Assisted Review | Active comparator | Behavioral: Unaided Review First, Then AI-Assisted ReviewReaders first complete Block X (Unaided) on their assigned subset SX (34 slides). They then complete Block Y (AI-Assisted) on two separate subsets: SY1 (34 slides; AI as Double-Check) and SY2 (34 slides; AI as First Look). Within Block Y, the order of Y1 and Y2 is randomized. For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status. |
| AI-Assisted Review First, Then Unaided Review | Active comparator | Behavioral: AI-Assisted Review First, Then Unaided ReviewReaders first complete Block Y (AI-Assisted) on two assigned subsets: SY1 (34 slides; AI as Double-Check) and SY2 (34 slides; AI as First Look), with the order of Y1 and Y2 randomized. They then complete Block X (Unaided) on subset SX (34 slides). For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (4 listed)
- Wright-Giemsa-stained bone marrow aspirate smears
- Final diagnosis confirmed through molecular testing in conjunction with expert pathology evaluation
- Board-certified or board-eligible pathologists, or board-certified/board-eligible hematologists who routinely make hematopathology diagnoses in their clinical practice
- Willingness to complete both unaided and AI-assisted review sessions
Key exclusion criteria (2 listed)
- Poor-quality or unreadable slides
- Cases used in AI training
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Unaided Review First, Then AI-Assisted ReviewReaders first complete Block X (Unaided) on their assigned subset SX (34 slides). They then complete Block Y (AI-Assisted) on two separate subsets: SY1 (34 slides; AI as Double-Check) and SY2 (34 slides; AI as First Look). Within Block Y, the order of Y1 and Y2 is randomized. For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status. | Behavioural | Unaided Review First, Then AI-Assisted Review |
| AI-Assisted Review First, Then Unaided ReviewReaders first complete Block Y (AI-Assisted) on two assigned subsets: SY1 (up to 40 slides; AI as Double-Check) and SY2 (up to 40 slides; AI as First Look), with the order of Y1 and Y2 randomized. They then complete Block X (Unaided) on subset SX (up to 40 slides). For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status. | Behavioural | AI-Assisted Review First, Then Unaided Review |
Primary outcome
Diagnostic performance of APL detection
Time frame: Periprocedural (at the time of slide review)
Performance of clinicians (unaided and AI-assisted) in detecting APL, measured in accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.
6 secondary outcomes
Time to diagnosis
Time frame: Periprocedural (at the time of slide review)
Inter-observer variability
Time frame: Periprocedural (at the time of slide review)
Concordance between AI predictions and clinicians' diagnoses
Time frame: Periprocedural (at the time of slide review)
Decision-change rates
Time frame: Periprocedural (at the time of slide review)
Net benefit after AI exposure
Time frame: Periprocedural (at the time of slide review)
Clinician confidence level
Time frame: Periprocedural (at the time of slide review)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Oct 2025.
- Individual participant dataSharing planned: No
Related trials for Acute Promyelocytic Leukemia (APL)
RecruitingPhase 3
Pivotal Open-label Phase 3 Clinical Study of QTX-2101 in Adult Patients With Acute Promyelocytic Leukemia
Acute Promyelocytic Leukemia (APL), Acute Promyelocytic Leukaemia
RecruitingPhase 1
PET/MRI, 18F-FDG PET/CT and Whole Body MRI in Finding Extramedullary Myeloid Leukemia in Patients With Newly Diagnosed Acute Myeloid Leukemia
Acute Myeloid Leukemia, Acute Promyelocytic Leukemia With PML-RARA
Not yet recruiting
Feasibility and Efficiency Study for the Implementation of a New Screening Method for Patients With Acute Myeloid Leukemia, in Laboratory Practice
Acute Myeloid Leukemia, Acute Myeloid Leukemia, Adult
Active, not recruiting
Simplified Patient Care Strategy in Decreasing Early Death in Patients With Acute Promyelocytic Leukemia
Acute Promyelocytic Leukemia With PML-RARA, t(15;17)
Not yet recruitingPhase 2/3
Lisaftoclax for Prevention of Differentiation Syndrom in Acute Promyelocytic Leukemia Patients
Acute Promyelocytic Leukemia (APL)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Unaided Review First, Then AI-Assisted Review and AI-Assisted Review First, Then Unaided Review in people with Acute Promyelocytic Leukemia (APL) and Acute Myeloid Leukaemia (AML), run by Harvard Medical School (HMS and HSDM) at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 31 Oct 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance of APL detection (time frame: Periprocedural (at the time of slide review)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Oct 2025.