Diagnostic Efficiency of Artificial Intelligence for Surgical Neuropathology

Sponsor

Jinsong Wu (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT04671368

Collaborator

(none)

141

Enrollment

Arms

Anticipated Duration (Months)

Study Details

Study Description

Brief 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 efficiency of artificial intelligence in prospective clinical data is no less than that of pathologists.

Condition or Disease	Intervention/Treatment	Phase
Central Nervous System Neoplasms	Diagnostic Test: Artificial Intelligence Diagnostic Test: Practicing Pathologists Diagnostic Test: Gold Standard	N/A

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

Study Type:

Interventional

Anticipated Enrollment :

141 participants

Allocation:

Non-Randomized

Intervention Model:

Parallel Assignment

Intervention Model Description:

All patients will be diagnosed by both AI and ordinary pathologists, thus performing a self-controlled studyAll patients will be diagnosed by both AI and ordinary pathologists, thus performing a self-controlled study

Masking:

Single (Outcomes Assessor)

Masking Description:

The AI group, ordinary pathologists and gold standard group will not be informed of each other's results

Primary Purpose:

Diagnostic

Official Title:

A Multi-center, Prospective, Self-Controlled Diagnostic Accuracy Comparative Studies of Artificial Intelligence Diagnostic System for Surgical Neuropathology

Anticipated Study Start Date :

Feb 1, 2021

Anticipated Primary Completion Date :

Feb 1, 2022

Anticipated Study Completion Date :

Feb 1, 2022

Arms and Interventions

Arm	Intervention/Treatment
Experimental: Artificial Intelligence A deep learning based artificial intelligence diagnostic system(DOI:10.1093/neuonc/noaa163)	Diagnostic Test: Artificial Intelligence 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.
Active Comparator: Practicing Pathologists One pathologist who has at least 5 years of experience	Diagnostic Test: Practicing Pathologists 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
Other: Gold Standard 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	Diagnostic Test: Gold Standard 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

Arm

Intervention/Treatment

Experimental: Artificial Intelligence

A deep learning based artificial intelligence diagnostic system(DOI:10.1093/neuonc/noaa163)

Diagnostic Test: Artificial Intelligence

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.

Active Comparator: Practicing Pathologists

One pathologist who has at least 5 years of experience

Diagnostic Test: Practicing Pathologists

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

Other: Gold Standard

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

Diagnostic Test: Gold Standard

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

Outcome Measures

Primary Outcome Measures

Diagnostic Accuracy of Study Arms [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

Secondary Outcome Measures

Sensitivity and specificity of Study Arms [1 week after the last patient's diagnosis is completed]
Sensitivity and specificity of study arms for each type calculated by 2x2 tables
Spearman Coefficient of Study Arms related to Gold Standard [1 week after the last patient's diagnosis is completed]
Spearman Correlation Analysis between Study Arms and Gold Standard

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

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.

Exclusion Criteria:

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.

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

Jinsong Wu

Investigators

Study Director: Cuiyun Wu, Ph.D, Huashan Hospital

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Jinsong Wu, Chief Physician of Neurosurgery Department, Vice-director of Neurosurgery Institute, Member of Ethics Committee, Clinical Professor of Surgery, Huashan Hospital

ClinicalTrials.gov Identifier:

NCT04671368

Other Study ID Numbers:

PAAI2020

First Posted:

Dec 17, 2020

Last Update Posted:

Dec 17, 2020

Last Verified:

Dec 1, 2020

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Keywords provided by Jinsong Wu, Chief Physician of Neurosurgery Department, Vice-director of Neurosurgery Institute, Member of Ethics Committee, Clinical Professor of Surgery, Huashan Hospital

Artificial Intelligence

CNS Tumor

Surgical Pathology

Diagnostic Accuracy Study

Additional relevant MeSH terms:

Nervous System Neoplasms

Central Nervous System Neoplasms

Study Results

No Results Posted as of Dec 17, 2020