Diagnostic Efficiency of Artificial Intelligence for Surgical Neuropathology

Not Applicable

• Conditions: Central Nervous System Neoplasms
• Interventions: Diagnostic Test: Artificial Intelligence; Diagnostic Test: Practicing Pathologists; Diagnostic Test: Gold Standard

• Registration Number: NCT04671368
• Lead Sponsor: Jinsong Wu

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.

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 Timeline

• Status Verified: 2020-12
• Study First Submitted: 2020-12-04
• Study First Submitted QC: 2020-12-15
• Last Update Submitted: 2020-12-15
• Study First Posted: 2020-12-17
• Last Update Posted: 2020-12-17
• Study Start: 2021-02
• Primary Completion: 2022-02
• Study Completion: 2022-02

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 141

Inclusion Criteria

1. Patients or their guardians understand the research process, agree to use their data, and sign the informed consent form;
2. Aged >=18 years;
3. MRI shows intracranial spaceoccupying lesions;
4. The clinical diagnosis is glioma, metastasis or lymphoma thus requiring surgical treatment;
5. The patient is willing to accept the surgery.

Exclusion Criteria

1. The patient has serious underlying diseases thus is not suitable for surgery;
2. After further clinical evaluation, surgical treatment was not the best choice;
3. The patient participate in clinical research of other drugs or devices;
4. The researchers believe that there are other factors that will make the patients unable to complete the study.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Artificial Intelligence	Artificial Intelligence	A deep learning based artificial intelligence diagnostic system(DOI:10.1093/neuonc/noaa163)
Practicing Pathologists	Practicing Pathologists	One pathologist who has at least 5 years of experience
Gold Standard	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

Primary Outcome Measures

Name	Time	Method
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

Name	Time	Method
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