Engineering the future of 3D pathology
Abstract In recent years, technological advances in tissue preparation, high‐throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high‐resolution histologic datasets are obtained from thick tissue specimens, such...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | The Journal of Pathology: Clinical Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/cjp2.347 |
| _version_ | 1797372456699166720 |
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| author | Jonathan TC Liu Sarah SL Chow Richard Colling Michelle R Downes Xavier Farré Peter Humphrey Andrew Janowczyk Tuomas Mirtti Clare Verrill Inti Zlobec Lawrence D True |
| author_facet | Jonathan TC Liu Sarah SL Chow Richard Colling Michelle R Downes Xavier Farré Peter Humphrey Andrew Janowczyk Tuomas Mirtti Clare Verrill Inti Zlobec Lawrence D True |
| author_sort | Jonathan TC Liu |
| collection | DOAJ |
| description | Abstract In recent years, technological advances in tissue preparation, high‐throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high‐resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets. |
| first_indexed | 2024-03-08T18:36:00Z |
| format | Article |
| id | doaj.art-ae2382569a7a4659b6ce5953678b78d0 |
| institution | Directory Open Access Journal |
| issn | 2056-4538 |
| language | English |
| last_indexed | 2024-03-08T18:36:00Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Pathology: Clinical Research |
| spelling | doaj.art-ae2382569a7a4659b6ce5953678b78d02023-12-29T12:56:36ZengWileyThe Journal of Pathology: Clinical Research2056-45382024-01-01101n/an/a10.1002/cjp2.347Engineering the future of 3D pathologyJonathan TC Liu0Sarah SL Chow1Richard Colling2Michelle R Downes3Xavier Farré4Peter Humphrey5Andrew Janowczyk6Tuomas Mirtti7Clare Verrill8Inti Zlobec9Lawrence D True10Department of Mechanical Engineering University of Washington Seattle WA USADepartment of Mechanical Engineering University of Washington Seattle WA USAJohn Radcliffe Hospital University of Oxford Oxford UKSunnybrook Health Sciences Centre University of Toronto Toronto CanadaPublic Health Agency of Catalonia Lleida SpainDepartment of Urology Yale School of Medicine New Haven CT USAWallace H Coulter Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta GA USAHelsinki University Hospital and University of Helsinki Helsinki FinlandJohn Radcliffe Hospital University of Oxford Oxford UKInstitute for Tissue Medicine and Pathology University of Bern Bern SwitzerlandDepartment of Laboratory Medicine & Pathology University of Washington School of Medicine Seattle USAAbstract In recent years, technological advances in tissue preparation, high‐throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high‐resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.https://doi.org/10.1002/cjp2.347light‐sheet microscopynondestructive 3D pathologydigital pathologyprostate cancerprognosis |
| spellingShingle | Jonathan TC Liu Sarah SL Chow Richard Colling Michelle R Downes Xavier Farré Peter Humphrey Andrew Janowczyk Tuomas Mirtti Clare Verrill Inti Zlobec Lawrence D True Engineering the future of 3D pathology The Journal of Pathology: Clinical Research light‐sheet microscopy nondestructive 3D pathology digital pathology prostate cancer prognosis |
| title | Engineering the future of 3D pathology |
| title_full | Engineering the future of 3D pathology |
| title_fullStr | Engineering the future of 3D pathology |
| title_full_unstemmed | Engineering the future of 3D pathology |
| title_short | Engineering the future of 3D pathology |
| title_sort | engineering the future of 3d pathology |
| topic | light‐sheet microscopy nondestructive 3D pathology digital pathology prostate cancer prognosis |
| url | https://doi.org/10.1002/cjp2.347 |
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