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February 9, 2026Physica Medica0 citationsOpen Access

Comparative Analysis of HistoQC and PathProfiler for Artefact Detection

Ensuring reliable digital pathology: a comparative analysis of HistoQC and PathProfiler for artefacts detection in prostate whole-slide images

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Authors

DRDaniele RavanelliERErich RobbiSCSara Citter

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Overview

Comparative analysis reveals how HistoQC and PathProfiler enhance artefact detection in prostate cancer, indicating improved diagnostic methods.

Key Points

  • The central aim is to compare HistoQC and PathProfiler for detecting artefacts in whole-slide images of prostate cancer.
  • Conducted a comparative analysis of HistoQC and PathProfiler.
  • Evaluated their reliability in assessing whole-slide image quality.
  • Assessed adaptability of HistoQC using machine learning techniques.
  • Both tools reliably assess whole-slide image quality in prostate cancer.
  • PathProfiler shows greater efficiency for clinical use compared to HistoQC.
  • HistoQC offers adaptable scoring which enhances diagnostic accuracy.
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Cite This Study

Ravanelli et al. (2026) studied this question.

synapsesocial.com/papers/69897983f0ec2af6756e73c1https://doi.org/10.1016/j.ejmp.2026.105745
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Application of an open-source AI tool for quantitative quality control in whole slide images of prostate needle core biopsies - a retrospective study2026
  2. 2Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting2024 · 6 citations
  3. 3GrandQC adaptation as an artificial intelligence tool for quantitative artifact detection in hematoxylin and eosin whole-slide images—Simulation of quality control biopsies day2026
  4. 4Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology2024 · 25 citations
  5. 5Abstract 3511: iQC: machine-learning-driven prediction of surgery reveals systematic confounds in cancer whole slide images from hospitals by protocol2024