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Using artificial intelligence to help doctors and scientists in the lab that studies diseases in the body, in order to use less of a specific testing method called immunohistochemistry and make the work process smoother and more efficient.

Clinical implementation of artificial intelligence assistance in pathology workflow

Status
Recruiting
Phases
Unknown
Study type
Interventional
Source
ISRCTN
Registry ID
ISRCTN14323711
Enrollment
260
Registered
2023-05-31
Start date
2022-09-19
Completion date
Unknown
Last updated
2023-06-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Reduction of immunohistochemistry used for the diagnosis of lymph node metastases in breast cancer (BCa - CONFIDENT-B), and prostate cancer (PCa - CONFIDENT-P) in prostate needle biopsies. Not Applicable

Interventions

During the study period, all whole slide images (WSI) will be assessed by the same group of pathologists
i.e. two expert urological pathologists for the prostate needle biopsies, and three expert breast pathologists for the lymph node assessment from BCa patients. For both the CONFIDENT-B and CONFIDENT

Sponsors

University Medical Center Utrecht
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Health professionals: 1.1. PCa: dedicated uropathologists 1.2. BCa: dedicated breast pathologists Patients: 1.1. PCa: adult males of any age undergoing prostate needle biopsies at the investigation site will be enrolled in the study and have their diagnosis determined in a prospective, consecutive manner. 1.2. BCa: adult females or males of any age with breast cancer as primary malignancy whose SN-specimen is assessed at the investigation site will be enrolled in the study and have their diagnosis determined in a prospective, consecutive manner. 2. All specimens that are H&E-stained and glass or film cover-slipped will be evaluated. Adjacent unstained slides will available for IHC staining. 3. The WSIs fulfill the quality checks described in the scanner manufacturer’s Instruction for Use and general clinical practice. 4. No diagnostic markings and patient identifiable markings are visible on the slides.

Exclusion criteria

Exclusion criteria: 1. Patients who were referred for a second opinion 2. Patients that have opted out of all medical research 3. Cases in which the associated IHC-stained slides are unavailable/cannot be generated 4. Cases not meeting defined quality criteria for digital clinical primary diagnosis evaluation

Design outcomes

Primary

MeasureTime frame
Relative risk of IHC-use per detected case of SN-metastases and risk of IHC-use per detected tumor in prostate needle biopies (case- and slide-level) measured using number of spent resources, i.e. the number of IHC-stains performed in both groups, measured after the single visit

Secondary

MeasureTime frame
1. Sensitivity and negative-predictive value of the AI-assisted pathologist. We use the assessment of the pathologist on immunohistochemistry stained slides as a reference standard. These slides are always performed in cases where the pathologist is in doubt, or where they think the slides are benign. Obvious malignant cases are spared an IHC staining (as our hypothesis is that these obvious malignant cases will be higher in the AI-assisted arm than in the standard of care-arm). 2. Differences in mean reading time per H&E-slide and per case between study arms, measured during the primary WSI assessment. 3. The number of IHC stains that may have been omitted after AI-implementation by analyzing standalone performances of the AI on the standard-of-care arm retrospectively 4. Pathologists’ evaluation by a questionnaire on the AI-assisted work process after ending of the study enrollment. 5. Stand-alone performance of the algorithm. We measure sensitivity and negative-predictive value using the pathologists’ assessment as a ground truth (with or without IHC, as specified above), performed retrospectively on the control-arm as well as on the AI-assisted arm. Specificity and positive predictive value cannot be calculated because of the study design and are therefore not incorporated in the stand-alone performance nor in the AI-assisted pathologists’ performance. 6. Difference in diagnostic confidence of the pathologists between the study arms, measured on a 5-Likert scale during the primary WSI assessment. Sensitivity and specificity analyses of the algorithm itself have already been well documented, and is therefore outside the scope of the paper, as we focus on the combination of pathologist and AI to explore cost savings.

Countries

Netherlands

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026