Central Nervous System NeoplasmsNCT04671368
Diagnostic Efficiency of Artificial Intelligence for Surgical Neuropathology
A study testing Artificial Intelligence, Practicing Pathologists and Gold Standard in people with Central Nervous System Neoplasms, run by Jinsong Wu.
Trial summary
This is a multi-center, prospective, self-controlled, diagnostic accuracy comparative study of Artificial Intelligence Diagnostic System for Surgical Neuropathology. The investigators will compare the diagnostic efficiency of Artificial Intelligence with that of practicing pathologists, and suppose that the diagnostic… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Central Nervous System Neoplasms
- What is being tested?
- Artificial Intelligence, Practicing Pathologists and Gold Standard
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 141 participants
- Where
- No sites listed yet
- Study dates
- Feb 2021to Feb 2022
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients or their guardians understand the research process, agree to use their data, and sign the informed consent form;
- Aged >=18 years;
- MRI shows intracranial spaceoccupying lesions;
- The clinical diagnosis is glioma, metastasis or lymphoma thus requiring surgical treatment;
- The patient is willing to accept the surgery.
Who cannot join
- The patient has serious underlying diseases thus is not suitable for surgery;
- After further clinical evaluation, surgical treatment was not the best choice;
- The patient participate in clinical research of other drugs or devices;
- The researchers believe that there are other factors that will make the patients unable to complete the study.
Trial locations and contacts
No sites are listed yet.
About this study
Official title: A Multi-center, Prospective, Self-Controlled Diagnostic Accuracy Comparative Studies of Artificial Intelligence Diagnostic System for Surgical Neuropathology
This is a multi-center, prospective, self-controlled, diagnostic accuracy comparative study of Artificial Intelligence Diagnostic System for Surgical Neuropathology. The investigators will compare the diagnostic efficiency of Artificial Intelligence with that of practicing pathologists, and suppose that the diagnostic efficiency of artificial intelligence in prospective clinical data is no less than that of pathologists.
Read the detailed description
In this study, 141 patients will be recruited. After being enrolled, the patients will accept surgery and specimens for pathological analysis will be taken according to the routine treatment process.
The histopathologic slides will then be digitized by a whole-slide scanner. The images will be reviewed by gold standard committee for evaluation of ground truth. And then be separately diagnosed by Artificial Intelligence Diagnostic System and practicing pathologists. So the investigators can compare the diagnostic efficiency of Artificial Intelligence with that of pathologists, thus understand the gap between artificial intelligence and actual clinical practice.
- Conditions
- Central Nervous System Neoplasms
- Being tested
- Artificial Intelligence, Practicing Pathologists, Gold Standard
- Keywords
- Artificial Intelligence, CNS Tumor, Surgical Pathology, Diagnostic Accuracy Study
- Medical subject headings
- Central Nervous System Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial IntelligenceDiagnostic test
The investigators will use the Artificial Intelligence Diagnostic System to review the H＆E stained slide of each patient and then report the classification of the tumor on a 10-type scale.
Practicing PathologistsDiagnostic test
The ordinary pathologist will review the H＆E stained slide of each patient(without additional informations such as: Immunohistochemistry et al.) and then report the classification of the tumor on a 10-type scale only bases on the slide images
Gold StandardDiagnostic test
Firstly, the two expert pathologist(>=10 years of experience) will review the H＆E stained slide of each patient on their own (with additional informations such as: Immunohistochemistry et al.) and then report the classification of the tumor on a 10-type scale.If they report the same opinion, that opinion will perform as the ground truth; while if their opinion clash with each other, the expert pathologist(>=15 years of experience) will get involved and the agreement of three experts will perform as the ground truth
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence | Experimental | A deep learning based artificial intelligence diagnostic system(DOI:10.1093/neuonc/noaa163) Receives: Diagnostic Test: Artificial Intelligence |
| Practicing Pathologists | Active comparator | One pathologist who has at least 5 years of experience Receives: Diagnostic Test: Practicing Pathologists |
| Gold Standard | Other | A committee composed of two expert pathologists who has at least 10 years of experience and one expert pathologist who has at least 15 years of experience Receives: Diagnostic Test: Gold Standard |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of Study Arms
The number of correctly diagnosed participants by study arms divided by the total number of participants
Measured: 1 week after the last patient's diagnosis is completed
Secondary outcomes (2)
Sensitivity and specificity of Study Arms
Sensitivity and specificity of study arms for each type calculated by 2x2 tables
Measured: 1 week after the last patient's diagnosis is completed
Spearman Coefficient of Study Arms related to Gold Standard
Spearman Correlation Analysis between Study Arms and Gold Standard
Measured: 1 week after the last patient's diagnosis is completed
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Dec 2020
- Study startFeb 2021 (estimated)
- Primary completionFeb 2022 (estimated)
- Study completionFeb 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Central Nervous System NeoplasmsStudies 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
- Non-randomised
- Intervention model
- Parallel groups
- Model description
- All patients will be diagnosed by both AI and ordinary pathologists, thus performing a self-controlled study
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 141 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Jinsong Wu
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Jinsong Wu, Huashan Hospital
- Investigators
- Cuiyun Wu, Ph.D, Study director, Huashan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04671368
- Sponsor's protocol ID
- PAAI2020
- Status last verified
- Dec 2020
- First submitted
- 4 Dec 2020
- Last updated
- 17 Dec 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Dec 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-center, Prospective, Self-Controlled Diagnostic Accuracy Comparative Studies of Artificial Intelligence Diagnostic System for Surgical Neuropathology
- ClinicalTrials.gov ID
- NCT04671368
- Acronym / sponsor ID
- PAAI2020
- Conditions
- Central Nervous System Neoplasms
- Keywords
- Artificial Intelligence, CNS Tumor, Surgical Pathology, Diagnostic Accuracy Study
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 141 participants (estimated)
- Lead sponsor
- Jinsong WuUniversity, hospital or other
- Study start
- Feb 2021 (estimated)
- Primary completion
- Feb 2022 (estimated)
- Study completion
- Feb 2022 (estimated)
- First posted
- 17 Dec 2020 (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 is a multi-center, prospective, self-controlled, diagnostic accuracy comparative study of Artificial Intelligence Diagnostic System for Surgical Neuropathology. The investigators will compare the diagnostic efficiency of Artificial Intelligence with that of practicing pathologists, and suppose that the diagnostic efficiency of artificial intelligence in prospective clinical data is no less than that of pathologists.
Detailed description
In this study, 141 patients will be recruited. After being enrolled, the patients will accept surgery and specimens for pathological analysis will be taken according to the routine treatment process.
The histopathologic slides will then be digitized by a whole-slide scanner. The images will be reviewed by gold standard committee for evaluation of ground truth. And then be separately diagnosed by Artificial Intelligence Diagnostic System and practicing pathologists. So the investigators can compare the diagnostic efficiency of Artificial Intelligence with that of pathologists, thus understand the gap between artificial intelligence and actual clinical practice.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 3
- Model description
- All patients will be diagnosed by both AI and ordinary pathologists, thus performing a self-controlled study
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence | Experimental | Diagnostic Test: Artificial IntelligenceA deep learning based artificial intelligence diagnostic system(DOI:10.1093/neuonc/noaa163) |
| Practicing Pathologists | Active comparator | Diagnostic Test: Practicing PathologistsOne pathologist who has at least 5 years of experience |
| Gold Standard | Other | Diagnostic Test: Gold StandardA committee composed of two expert pathologists who has at least 10 years of experience and one expert pathologist who has at least 15 years of experience |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Patients or their guardians understand the research process, agree to use their data, and sign the informed consent form;
- Aged >=18 years;
- MRI shows intracranial spaceoccupying lesions;
- The clinical diagnosis is glioma, metastasis or lymphoma thus requiring surgical treatment;
- The patient is willing to accept the surgery.
Key exclusion criteria (4 listed)
- The patient has serious underlying diseases thus is not suitable for surgery;
- After further clinical evaluation, surgical treatment was not the best choice;
- The patient participate in clinical research of other drugs or devices;
- The researchers believe that there are other factors that will make the patients unable to complete the study.
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 |
|---|---|---|
| Artificial IntelligenceThe investigators will use the Artificial Intelligence Diagnostic System to review the H＆E stained slide of each patient and then report the classification of the tumor on a 10-type scale. | Diagnostic test | Artificial Intelligence |
| Practicing PathologistsThe ordinary pathologist will review the H＆E stained slide of each patient(without additional informations such as: Immunohistochemistry et al.) and then report the classification of the tumor on a 10-type scale only bases on the slide images | Diagnostic test | Practicing Pathologists |
| Gold StandardFirstly, the two expert pathologist(>=10 years of experience) will review the H＆E stained slide of each patient on their own (with additional informations such as: Immunohistochemistry et al.) and then report the classification of the tumor on a 10-type scale.If they report the same opinion, that opinion will perform as the ground truth; while if their opinion clash with each other, the expert pathologist(>=15 years of experience) will get involved and the agreement of three experts will perform as the ground truth | Diagnostic test | Gold Standard |
Primary outcome
Diagnostic Accuracy of Study Arms
Time frame: 1 week after the last patient's diagnosis is completed
The number of correctly diagnosed participants by study arms divided by the total number of participants
2 secondary outcomes
Sensitivity and specificity of Study Arms
Time frame: 1 week after the last patient's diagnosis is completed
Spearman Coefficient of Study Arms related to Gold Standard
Time frame: 1 week after the last patient's diagnosis is completed
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Dec 2020.
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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.
Cuiyun Wu, Ph.D study director
Huashan Hospital
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence, Practicing Pathologists and Gold Standard in people with Central Nervous System Neoplasms, run by Jinsong Wu.
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 5 inclusion and 4 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: status not confirmed, as of the sponsor's last update on 17 Dec 2020. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Lei Jin, DR (0086-13817841756, ozlei91@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of Study Arms (time frame: 1 week after the last patient's diagnosis is completed). 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 17 Dec 2020; AzTrials last refreshed its copy on 6 Nov 2026.