Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis
Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. Traditional pathology practices have limitations, including interobserver variability and time-consuming evaluations. In recent years, digital patholog...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/11/11/2875 |
| _version_ | 1797460113156472832 |
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| author | Elena Ivanova Alexey Fayzullin Victor Grinin Dmitry Ermilov Alexander Arutyunyan Peter Timashev Anatoly Shekhter |
| author_facet | Elena Ivanova Alexey Fayzullin Victor Grinin Dmitry Ermilov Alexander Arutyunyan Peter Timashev Anatoly Shekhter |
| author_sort | Elena Ivanova |
| collection | DOAJ |
| description | Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. Traditional pathology practices have limitations, including interobserver variability and time-consuming evaluations. In recent years, digital pathology tools emerged as a promising solution to enhance the diagnosis and management of renal cancer. This review aims to provide a comprehensive overview of the current state and potential of digital pathology in the context of renal cell carcinoma. Through advanced image analysis algorithms, artificial intelligence (AI) technologies facilitate quantification of cellular and molecular markers, leading to improved accuracy and reproducibility in renal cancer diagnosis. Digital pathology platforms empower remote collaboration between pathologists and help with the creation of comprehensive databases for further research and machine learning applications. The integration of digital pathology tools with other diagnostic modalities, such as radiology and genomics, enables a novel multimodal characterization of different types of renal cell carcinoma. With continuous advancements and refinement, AI technologies are expected to play an integral role in diagnostics and clinical decision-making, improving patient outcomes. In this article, we explored the digital pathology instruments available for clear cell, papillary and chromophobe renal cancers from pathologist and data analyst perspectives. |
| first_indexed | 2024-03-09T17:00:30Z |
| format | Article |
| id | doaj.art-56de7da251b24072ac2b8e846e288a36 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-09T17:00:30Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-56de7da251b24072ac2b8e846e288a362023-11-24T14:30:32ZengMDPI AGBiomedicines2227-90592023-10-011111287510.3390/biomedicines11112875Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and PrognosisElena Ivanova0Alexey Fayzullin1Victor Grinin2Dmitry Ermilov3Alexander Arutyunyan4Peter Timashev5Anatoly Shekhter6Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., Moscow 119991, RussiaInstitute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., Moscow 119991, RussiaPJSC VimpelCom, 10, 8th March Street, Moscow 127083, RussiaPJSC VimpelCom, 10, 8th March Street, Moscow 127083, RussiaPJSC VimpelCom, 10, 8th March Street, Moscow 127083, RussiaInstitute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., Moscow 119991, RussiaInstitute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., Moscow 119991, RussiaRenal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. Traditional pathology practices have limitations, including interobserver variability and time-consuming evaluations. In recent years, digital pathology tools emerged as a promising solution to enhance the diagnosis and management of renal cancer. This review aims to provide a comprehensive overview of the current state and potential of digital pathology in the context of renal cell carcinoma. Through advanced image analysis algorithms, artificial intelligence (AI) technologies facilitate quantification of cellular and molecular markers, leading to improved accuracy and reproducibility in renal cancer diagnosis. Digital pathology platforms empower remote collaboration between pathologists and help with the creation of comprehensive databases for further research and machine learning applications. The integration of digital pathology tools with other diagnostic modalities, such as radiology and genomics, enables a novel multimodal characterization of different types of renal cell carcinoma. With continuous advancements and refinement, AI technologies are expected to play an integral role in diagnostics and clinical decision-making, improving patient outcomes. In this article, we explored the digital pathology instruments available for clear cell, papillary and chromophobe renal cancers from pathologist and data analyst perspectives.https://www.mdpi.com/2227-9059/11/11/2875digital pathologydeep learninghistological biomarkersartificial intelligencerenal cell carcinoma |
| spellingShingle | Elena Ivanova Alexey Fayzullin Victor Grinin Dmitry Ermilov Alexander Arutyunyan Peter Timashev Anatoly Shekhter Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis Biomedicines digital pathology deep learning histological biomarkers artificial intelligence renal cell carcinoma |
| title | Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis |
| title_full | Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis |
| title_fullStr | Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis |
| title_full_unstemmed | Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis |
| title_short | Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis |
| title_sort | empowering renal cancer management with ai and digital pathology pathology diagnostics and prognosis |
| topic | digital pathology deep learning histological biomarkers artificial intelligence renal cell carcinoma |
| url | https://www.mdpi.com/2227-9059/11/11/2875 |
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