Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Blilie, Anders Mulliqi, Nita Ji, Xiaoyi Szolnoky, Kelvin Boman, Sol Erika Titus, Matteo Gonzalez, Geraldine Martinez Asenjo, José Gambacorta, Marcello Libretti, Paolo Gudlaugsson, Einar Kjosavik, Svein R. Egevad, Lars Janssen, Emiel A. M. Eklund, Martin Kartasalo, Kimmo |
| author_facet | Blilie, Anders Mulliqi, Nita Ji, Xiaoyi Szolnoky, Kelvin Boman, Sol Erika Titus, Matteo Gonzalez, Geraldine Martinez Asenjo, José Gambacorta, Marcello Libretti, Paolo Gudlaugsson, Einar Kjosavik, Svein R. Egevad, Lars Janssen, Emiel A. M. Eklund, Martin Kartasalo, Kimmo |
| contents | Prostate cancer diagnosis heavily relies on histopathological evaluation, which is subject to variability. While immunohistochemical staining (IHC) assists in distinguishing benign from malignant tissue, it involves increased work, higher costs, and diagnostic delays. Artificial intelligence (AI) presents a promising solution to reduce reliance on IHC by accurately classifying atypical glands and borderline morphologies in hematoxylin & eosin (H&E) stained tissue sections. In this study, we evaluated an AI model's ability to minimize IHC use without compromising diagnostic accuracy by retrospectively analyzing prostate core needle biopsies from routine diagnostics at three different pathology sites. These cohorts were composed exclusively of difficult cases where the diagnosing pathologists required IHC to finalize the diagnosis. The AI model demonstrated area under the curve values of 0.951-0.993 for detecting cancer in routine H&E-stained slides. Applying sensitivity-prioritized diagnostic thresholds reduced the need for IHC staining by 44.4%, 42.0%, and 20.7% in the three cohorts investigated, without a single false negative prediction. This AI model shows potential for optimizing IHC use, streamlining decision-making in prostate pathology, and alleviating resource burdens. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry Blilie, Anders Mulliqi, Nita Ji, Xiaoyi Szolnoky, Kelvin Boman, Sol Erika Titus, Matteo Gonzalez, Geraldine Martinez Asenjo, José Gambacorta, Marcello Libretti, Paolo Gudlaugsson, Einar Kjosavik, Svein R. Egevad, Lars Janssen, Emiel A. M. Eklund, Martin Kartasalo, Kimmo Computer Vision and Pattern Recognition Prostate cancer diagnosis heavily relies on histopathological evaluation, which is subject to variability. While immunohistochemical staining (IHC) assists in distinguishing benign from malignant tissue, it involves increased work, higher costs, and diagnostic delays. Artificial intelligence (AI) presents a promising solution to reduce reliance on IHC by accurately classifying atypical glands and borderline morphologies in hematoxylin & eosin (H&E) stained tissue sections. In this study, we evaluated an AI model's ability to minimize IHC use without compromising diagnostic accuracy by retrospectively analyzing prostate core needle biopsies from routine diagnostics at three different pathology sites. These cohorts were composed exclusively of difficult cases where the diagnosing pathologists required IHC to finalize the diagnosis. The AI model demonstrated area under the curve values of 0.951-0.993 for detecting cancer in routine H&E-stained slides. Applying sensitivity-prioritized diagnostic thresholds reduced the need for IHC staining by 44.4%, 42.0%, and 20.7% in the three cohorts investigated, without a single false negative prediction. This AI model shows potential for optimizing IHC use, streamlining decision-making in prostate pathology, and alleviating resource burdens. |
| title | Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.00979 |