1
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Song AH, Williams M, Williamson DFK, Chow SSL, Jaume G, Gao G, Zhang A, Chen B, Baras AS, Serafin R, Colling R, Downes MR, Farré X, Humphrey P, Verrill C, True LD, Parwani AV, Liu JTC, Mahmood F. Analysis of 3D pathology samples using weakly supervised AI. Cell 2024; 187:2502-2520.e17. [PMID: 38729110 DOI: 10.1016/j.cell.2024.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 01/15/2024] [Accepted: 03/25/2024] [Indexed: 05/12/2024]
Abstract
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.
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Affiliation(s)
- Andrew H Song
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mane Williams
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Drew F K Williamson
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sarah S L Chow
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Guillaume Jaume
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gan Gao
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Andrew Zhang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bowen Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alexander S Baras
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert Serafin
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Richard Colling
- Nuffield Department of Surgical Sciences, University of Oxford, UK; Department of Cellular Pathology, Oxford University Hospitals NHS Foundations Trust, John Radcliffe Hospital, Oxford, UK
| | - Michelle R Downes
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Xavier Farré
- Public Health Agency of Catalonia, Lleida, Spain
| | - Peter Humphrey
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Clare Verrill
- Nuffield Department of Surgical Sciences, University of Oxford, UK; Department of Cellular Pathology, Oxford University Hospitals NHS Foundations Trust, John Radcliffe Hospital, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Lawrence D True
- Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Jonathan T C Liu
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
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2
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Zheng J, Wu YC, Phillips EH, Cai X, Wang X, Seung-Young Lee S. Increased Multiplexity in Optical Tissue Clearing-Based Three-Dimensional Immunofluorescence Microscopy of the Tumor Microenvironment by Light-Emitting Diode Photobleaching. J Transl Med 2024; 104:102072. [PMID: 38679160 DOI: 10.1016/j.labinv.2024.102072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/29/2024] [Accepted: 04/19/2024] [Indexed: 05/01/2024] Open
Abstract
Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy is transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only 3 or 4 cellular and noncellular TME components can be localized in cleared tumor tissue. Here we report a light-emitting diode (LED) photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through 3 work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.
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Affiliation(s)
- Jingtian Zheng
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Yi-Chien Wu
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Evan H Phillips
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Xiaoying Cai
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Xu Wang
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Steve Seung-Young Lee
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois; University of Illinois Cancer Center, University of Illinois Chicago, Chicago, Illinois.
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3
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Wang L, Li M, Hwang TH. The 3D Revolution in Cancer Discovery. Cancer Discov 2024; 14:625-629. [PMID: 38571426 DOI: 10.1158/2159-8290.cd-23-1499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
SUMMARY The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.
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Affiliation(s)
- Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tae Hyun Hwang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, Florida
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4
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Bishop KW, Erion Barner LA, Han Q, Baraznenok E, Lan L, Poudel C, Gao G, Serafin RB, Chow SSL, Glaser AK, Janowczyk A, Brenes D, Huang H, Miyasato D, True LD, Kang S, Vaughan JC, Liu JTC. An end-to-end workflow for nondestructive 3D pathology. Nat Protoc 2024; 19:1122-1148. [PMID: 38263522 DOI: 10.1038/s41596-023-00934-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/23/2023] [Indexed: 01/25/2024]
Abstract
Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3-6 d to complete, bearing in mind that data analysis may take longer.
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Affiliation(s)
- Kevin W Bishop
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | | | - Qinghua Han
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Elena Baraznenok
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Lydia Lan
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Biology, University of Washington, Seattle, WA, USA
| | - Chetan Poudel
- Department of Chemistry, University of Washington, Seattle, WA, USA
| | - Gan Gao
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Robert B Serafin
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Sarah S L Chow
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Adam K Glaser
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
- Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland
- Department of Diagnostics, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - David Brenes
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Hongyi Huang
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Dominie Miyasato
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Lawrence D True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Soyoung Kang
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Joshua C Vaughan
- Department of Chemistry, University of Washington, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
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5
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Abraham TM, Levenson R. Current Landscape of Advanced Imaging Tools for Pathology Diagnostics. Mod Pathol 2024; 37:100443. [PMID: 38311312 DOI: 10.1016/j.modpat.2024.100443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/13/2023] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
Histopathology relies on century-old workflows of formalin fixation, paraffin embedding, sectioning, and staining tissue specimens on glass slides. Despite being robust, this conventional process is slow, labor-intensive, and limited to providing two-dimensional views. Emerging technologies promise to enhance and accelerate histopathology. Slide-free microscopy allows rapid imaging of fresh, unsectioned specimens, overcoming slide preparation delays. Methods such as fluorescence confocal microscopy, multiphoton microscopy, along with more recent innovations including microscopy with UV surface excitation and fluorescence-imitating brightfield imaging can generate images resembling conventional histology directly from the surface of tissue specimens. Slide-free microscopy enable applications such as rapid intraoperative margin assessment and, with appropriate technology, three-dimensional histopathology. Multiomics profiling techniques, including imaging mass spectrometry and Raman spectroscopy, provide highly multiplexed molecular maps of tissues, although clinical translation remains challenging. Artificial intelligence is aiding the adoption of new imaging modalities via virtual staining, which converts methods such as slide-free microscopy into synthetic brightfield-like or even molecularly informed images. Although not yet commonplace, these emerging technologies collectively demonstrate the potential to modernize histopathology. Artificial intelligence-assisted workflows will ease the transition to new imaging modalities. With further validation, these advances may transform the century-old conventional histopathology pipeline to better serve 21st-century medicine. This review provides an overview of these enabling technology platforms and discusses their potential impact.
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Affiliation(s)
- Tanishq Mathew Abraham
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Richard Levenson
- Department of Pathology and Laboratory Medicine, UC Davis Health, Sacramento, California.
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Yoshikawa AL, Omura T, Takahashi-Kanemitsu A, Susaki EA. Blueprints from plane to space: outlook of next-generation three-dimensional histopathology. Cancer Sci 2024; 115:1029-1038. [PMID: 38316137 PMCID: PMC11006986 DOI: 10.1111/cas.16095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/02/2024] [Accepted: 01/16/2024] [Indexed: 02/07/2024] Open
Abstract
Here, we summarize the literature relevant to recent advances in three-dimensional (3D) histopathology in relation to clinical oncology, highlighting serial sectioning, tissue clearing, light-sheet microscopy, and digital image analysis with artificial intelligence. We look forward to a future where 3D histopathology expands our understanding of human pathophysiology and improves patient care through cross-disciplinary collaboration and innovation.
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Affiliation(s)
- Akira Leon Yoshikawa
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
- Department of Pathology, Kameda Medical Center, Chiba, Japan
| | - Takaki Omura
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - Atsushi Takahashi-Kanemitsu
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Etsuo A Susaki
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
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Park WY, Yun J, Shin J, Oh BH, Yoon G, Hong SM, Kim KH. Open-top Bessel beam two-photon light sheet microscopy for three-dimensional pathology. eLife 2024; 12:RP92614. [PMID: 38488831 PMCID: PMC10942781 DOI: 10.7554/elife.92614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024] Open
Abstract
Nondestructive pathology based on three-dimensional (3D) optical microscopy holds promise as a complement to traditional destructive hematoxylin and eosin (H&E) stained slide-based pathology by providing cellular information in high throughput manner. However, conventional techniques provided superficial information only due to shallow imaging depths. Herein, we developed open-top two-photon light sheet microscopy (OT-TP-LSM) for intraoperative 3D pathology. An extended depth of field two-photon excitation light sheet was generated by scanning a nondiffractive Bessel beam, and selective planar imaging was conducted with cameras at 400 frames/s max during the lateral translation of tissue specimens. Intrinsic second harmonic generation was collected for additional extracellular matrix (ECM) visualization. OT-TP-LSM was tested in various human cancer specimens including skin, pancreas, and prostate. High imaging depths were achieved owing to long excitation wavelengths and long wavelength fluorophores. 3D visualization of both cells and ECM enhanced the ability of cancer detection. Furthermore, an unsupervised deep learning network was employed for the style transfer of OT-TP-LSM images to virtual H&E images. The virtual H&E images exhibited comparable histological characteristics to real ones. OT-TP-LSM may have the potential for histopathological examination in surgical and biopsy applications by rapidly providing 3D information.
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Affiliation(s)
- Won Yeong Park
- Department of Mechanical Engineering, Pohang University of Science and TechnologyPohangRepublic of Korea
| | - Jieun Yun
- Department of Mechanical Engineering, Pohang University of Science and TechnologyPohangRepublic of Korea
| | - Jinho Shin
- Department of Medicine, University of Ulsan College of Medicine, SeoulSeoulRepublic of Korea
| | - Byung Ho Oh
- Department of Dermatology, College of Medicine, Yonsei UniversitySeoulRepublic of Korea
| | - Gilsuk Yoon
- Department of Pathology, School of Medicine, Kyungpook National UniversityDaeguRepublic of Korea
| | - Seung-Mo Hong
- Department of Pathology, Asan Medical Center, University of Ulsan College of MedicineSeoulRepublic of Korea
| | - Ki Hean Kim
- Department of Mechanical Engineering, Pohang University of Science and TechnologyPohangRepublic of Korea
- Medical Science and Engineering Program, School of Convergence Science and Technology, Pohang University of Science and TechnologyPohangRepublic of Korea
- Institute for Convergence Research and Education in Advanced Technology, Yonsei UniversitySeoulRepublic of Korea
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8
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Wang R, Chow SSL, Serafin RB, Xie W, Han Q, Baraznenok E, Lan L, Bishop KW, Liu JTC. Direct three-dimensional segmentation of prostate glands with nnU-Net. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:036001. [PMID: 38434772 PMCID: PMC10905031 DOI: 10.1117/1.jbo.29.3.036001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/06/2024] [Accepted: 02/09/2024] [Indexed: 03/05/2024]
Abstract
Significance In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance. Aim To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E). Approach For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue. Results nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs. Conclusions Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.
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Affiliation(s)
- Rui Wang
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
| | - Sarah S. L. Chow
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
| | - Robert B. Serafin
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
| | - Weisi Xie
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
| | - Qinghua Han
- University of Washington, Department of Bioengineering, Seattle, Washington, United States
| | - Elena Baraznenok
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
- University of Washington, Department of Bioengineering, Seattle, Washington, United States
| | - Lydia Lan
- University of Washington, Department of Bioengineering, Seattle, Washington, United States
- University of Washington, Department of Biology, Seattle, Washington, United States
| | - Kevin W. Bishop
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
- University of Washington, Department of Bioengineering, Seattle, Washington, United States
| | - Jonathan T. C. Liu
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
- University of Washington, Department of Bioengineering, Seattle, Washington, United States
- University of Washington, Department of Laboratory Medicine and Pathology, Seattle, Washington, United States
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9
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Beitlitum I, Rayyan F, Pokhojaev A, Tal H, Sarig R. A novel micro-CT analysis for evaluating the regenerative potential of bone augmentation xenografts in rabbit calvarias. Sci Rep 2024; 14:4321. [PMID: 38383533 PMCID: PMC10881464 DOI: 10.1038/s41598-024-54313-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 02/11/2024] [Indexed: 02/23/2024] Open
Abstract
Guided Bone Regeneration is a common procedure, yet, as new grafting materials are being introduced into the market, a reliable evaluation method is required. Critical size defect in animal models provides an accurate simulation, followed by histological sections to evaluate the new bone formation. However, histology is destructive, two-dimensional and technique-sensitive. In this study we developed a novel volumetric Micro-CT analysis to quantify new bone formation characteristics. Eight adult female New Zealand white rabbits were subjected to calvarial critical-size defects. Four 8 mm in diameter circular defects were preformed in each animal, to allow random allocation of four treatment modalities. All calvarias were scanned using Micro-CT. Each defect was segmented into four equal parts: pristine bone, outer, middle, and inner. Amira software (v. 6.3, www.fei.com ) was used to calculate the new bone volume in each region and compare it to that of the pristine bone. All grafting materials demonstrated that new bone formation decreased as it moved inward. Only the inner region differed across grafting materials (p = 0.001). The new Micro-CT analysis allowed us to divide each defect into 3D regions providing better understanding of the bone formation process. Amongst the various advantages of the Micro-CT, it enables us to quantify the graft materials and the newly formed bone independently, and to describe the defect morphology in 3D (bi- vs. uni-cortical defects). Providing an insight into the inner region of the defect can better predict the regenerative potential of the bone augmentation graft material. Therefore, the suggested Micro-CT analysis is beneficial for further developing of clinical approaches.
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Affiliation(s)
- Ilan Beitlitum
- Department of Periodontology and Dental Implantology, The Maurice and Gabriela Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Fatma Rayyan
- Department of Periodontology and Dental Implantology, The Maurice and Gabriela Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Oral Biology, The Maurice and Gabriela Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Ariel Pokhojaev
- Department of Oral Biology, The Maurice and Gabriela Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Haim Tal
- Department of Periodontology and Dental Implantology, The Maurice and Gabriela Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Rachel Sarig
- Department of Oral Biology, The Maurice and Gabriela Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel.
- Shmunis Family Anthropology Institute, the Dan David Center for Human Evolution and Biohistory Research, Faculty of Medicine, Tel-Aviv University, Tel Aviv, 6997801, Israel.
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10
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Azadi Moghadam P, Bashashati A, Goldenberg SL. Artificial Intelligence and Pathomics: Prostate Cancer. Urol Clin North Am 2024; 51:15-26. [PMID: 37945099 DOI: 10.1016/j.ucl.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Artificial intelligence (AI) has the potential to transform pathologic diagnosis and cancer patient management as a predictive and prognostic biomarker. AI-based systems can be used to examine digitally scanned histopathology slides and differentiate benign from malignant cells and low from high grade. Deep learning models can analyze patient data from individual or multimodal combinations and identify patterns to be used to predict the response to different therapeutic options, the risk of recurrence or progression, and the prognosis of the newly diagnosed patient. AI-based models will improve treatment planning for patients with prostate cancer and improve the efficiency and cost-effectiveness of the pathology laboratory.
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Affiliation(s)
- Puria Azadi Moghadam
- Department of Electrical and Computer Engineering, University of British Columbia, 2332 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada
| | - Ali Bashashati
- School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, British Columbia V6T 1Z3, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T 1Z7, Canada
| | - S Larry Goldenberg
- Department of Urologic Sciences, University of British Columbia, 2775 Laurel Street, Vancouver British Columbia V5Z 1M9, Canada.
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11
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Lee YH, Huang CY, Hsieh YH, Yang CH, Hung YL, Chen YA, Lin YC, Lin CH, Lee JH, Wang MY, Kuo WH, Lin YY, Lu YS. A novel computer-assisted tool for 3D imaging of programmed death-ligand 1 expression in immunofluorescence-stained and optically cleared breast cancer specimens. BMC Cancer 2024; 24:121. [PMID: 38267903 PMCID: PMC10807239 DOI: 10.1186/s12885-023-11748-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 12/12/2023] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) are the two most common immune checkpoints targeted in triple-negative breast cancer (BC). Refining patient selection for immunotherapy is non-trivial and finding an appropriate digital pathology framework for spatial analysis of theranostic biomarkers for PD-1/PD-L1 inhibitors remains an unmet clinical need. METHODS We describe a novel computer-assisted tool for three-dimensional (3D) imaging of PD-L1 expression in immunofluorescence-stained and optically cleared BC specimens (n = 20). The proposed 3D framework appeared to be feasible and showed a high overall agreement with traditional, clinical-grade two-dimensional (2D) staining techniques. Additionally, the results obtained for automated immune cell detection and analysis of PD-L1 expression were satisfactory. RESULTS The spatial distribution of PD-L1 expression was heterogeneous across various BC tissue layers in the 3D space. Notably, there were six cases (30%) wherein PD-L1 expression levels along different layers crossed the 1% threshold for admitting patients to PD-1/PD-L1 inhibitors. The average PD-L1 expression in 3D space was different from that of traditional immunohistochemistry (IHC) in eight cases (40%). Pending further standardization and optimization, we expect that our technology will become a valuable addition for assessing PD-L1 expression in patients with BC. CONCLUSION Via a single round of immunofluorescence imaging, our approach may provide a considerable improvement in patient stratification for cancer immunotherapy as compared with standard techniques.
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Affiliation(s)
- Yi-Hsuan Lee
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | - Chung-Yen Huang
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | | | | | | | | | | | - Ching-Hung Lin
- Department of Medical Oncology, Cancer Center Branch, National Taiwan University Hospital, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jih-Hsiang Lee
- Department of Oncology, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu, Taiwan
| | - Ming-Yang Wang
- Department of Surgical Oncology, Cancer Center Branch, National Taiwan University Hospital, Taipei, Taiwan
| | - Wen-Hung Kuo
- Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Yen-Shen Lu
- Department of Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan.
- Department of Oncology, National Taiwan University Hospital, No.7, Chung Shan S. Rd., Zhongzheng Dist, Taipei, 100225, Taiwan.
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12
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Liu JTC, Chow SSL, Colling R, Downes MR, Farré X, Humphrey P, Janowczyk A, Mirtti T, Verrill C, Zlobec I, True LD. Engineering the future of 3D pathology. J Pathol Clin Res 2024; 10:e347. [PMID: 37919231 PMCID: PMC10807588 DOI: 10.1002/cjp2.347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/06/2023] [Accepted: 10/15/2023] [Indexed: 11/04/2023]
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.
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Affiliation(s)
- Jonathan TC Liu
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
- Department of BioengineeringUniversity of WashingtonSeattleUSA
| | - Sarah SL Chow
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
| | | | | | | | - Peter Humphrey
- Department of UrologyYale School of MedicineNew HavenCTUSA
| | - Andrew Janowczyk
- Wallace H Coulter Department of Biomedical EngineeringEmory University and Georgia Institute of TechnologyAtlantaGAUSA
- Geneva University HospitalsGenevaSwitzerland
| | - Tuomas Mirtti
- Helsinki University Hospital and University of HelsinkiHelsinkiFinland
- Emory University School of MedicineAtlantaGAUSA
| | - Clare Verrill
- John Radcliffe HospitalUniversity of OxfordOxfordUK
- NIHR Oxford Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Inti Zlobec
- Institute for Tissue Medicine and PathologyUniversity of BernBernSwitzerland
| | - Lawrence D True
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
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13
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Yamada H, Makino SI, Okunaga I, Miyake T, Yamamoto-Nonaka K, Oliva Trejo JA, Tominaga T, Empitu MA, Kadariswantiningsih IN, Kerever A, Komiya A, Ichikawa T, Arikawa-Hirasawa E, Yanagita M, Asanuma K. Beyond 2D: A scalable and highly sensitive method for a comprehensive 3D analysis of kidney biopsy tissue. PNAS NEXUS 2024; 3:pgad433. [PMID: 38193136 PMCID: PMC10772983 DOI: 10.1093/pnasnexus/pgad433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 11/06/2023] [Indexed: 01/10/2024]
Abstract
The spatial organization of various cell populations is critical for the major physiological and pathological processes in the kidneys. Most evaluation of these processes typically comes from a conventional 2D tissue cross-section, visualizing a limited amount of cell organization. Therefore, the 2D analysis of kidney biopsy introduces selection bias. The 2D analysis potentially omits key pathological findings outside a 1- to 10-μm thin-sectioned area and lacks information on tissue organization, especially in a particular irregular structure such as crescentic glomeruli. In this study, we introduce an easy-to-use and scalable method for obtaining high-quality images of molecules of interest in a large tissue volume, enabling a comprehensive evaluation of the 3D organization and cellular composition of kidney tissue, especially the glomerular structure. We show that CUBIC and ScaleS clearing protocols could allow a 3D analysis of the kidney tissues in human and animal models of kidney disease. We also demonstrate that the paraffin-embedded human biopsy specimens previously examined via 2D evaluation could be applicable to 3D analysis, showing a potential utilization of this method in kidney biopsy tissue collected in the past. In summary, the 3D analysis of kidney biopsy provides a more comprehensive analysis and a minimized selection bias than 2D tissue analysis. Additionally, this method enables a quantitative evaluation of particular kidney structures and their surrounding tissues, with the potential utilization from basic science investigation to applied diagnostics in nephrology.
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Affiliation(s)
- Hiroyuki Yamada
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
- The Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- Department of Primary Care and Emergency, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Shin-ichi Makino
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
- The Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Issei Okunaga
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
| | - Takafumi Miyake
- The Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Kanae Yamamoto-Nonaka
- The Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Juan Alejandro Oliva Trejo
- The Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan
| | - Takahiro Tominaga
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
| | - Maulana A Empitu
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
| | | | - Aurelien Kerever
- Research Institute for Diseases of Old Age, Graduate School of Medicine, Juntendo University, Tokyo 113-8421, Japan
| | - Akira Komiya
- Department of Urology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
| | - Tomohiko Ichikawa
- Department of Urology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
| | - Eri Arikawa-Hirasawa
- Research Institute for Diseases of Old Age, Graduate School of Medicine, Juntendo University, Tokyo 113-8421, Japan
| | - Motoko Yanagita
- The Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8303, Japan
| | - Katsuhiko Asanuma
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
- The Laboratory for Kidney Research (TMK Project), Medical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto 606-8397, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
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14
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Zheng J, Wu YC, Phillips EH, Wang X, Lee SSY. Increased multiplexity in optical tissue clearing-based 3D immunofluorescence microscopy of the tumor microenvironment by LED photobleaching. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569277. [PMID: 38076864 PMCID: PMC10705380 DOI: 10.1101/2023.11.29.569277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy have been transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only three or four cellular and non-cellular TME components can be localized in a cleared tumor tissue. Here we report a LED photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through three work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.
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15
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Koyuncu C, Janowczyk A, Farre X, Pathak T, Mirtti T, Fernandez PL, Pons L, Reder NP, Serafin R, Chow SSL, Viswanathan VS, Glaser AK, True LD, Liu JTC, Madabhushi A. Visual Assessment of 2-Dimensional Levels Within 3-Dimensional Pathology Data Sets of Prostate Needle Biopsies Reveals Substantial Spatial Heterogeneity. J Transl Med 2023; 103:100265. [PMID: 37858679 PMCID: PMC10926776 DOI: 10.1016/j.labinv.2023.100265] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 10/05/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023] Open
Abstract
Prostate cancer prognostication largely relies on visual assessment of a few thinly sectioned biopsy specimens under a microscope to assign a Gleason grade group (GG). Unfortunately, the assigned GG is not always associated with a patient's outcome in part because of the limited sampling of spatially heterogeneous tumors achieved by 2-dimensional histopathology. In this study, open-top light-sheet microscopy was used to obtain 3-dimensional pathology data sets that were assessed by 4 human readers. Intrabiopsy variability was assessed by asking readers to perform Gleason grading of 5 different levels per biopsy for a total of 20 core needle biopsies (ie, 100 total images). Intrabiopsy variability (Cohen κ) was calculated as the worst pairwise agreement in GG between individual levels within each biopsy and found to be 0.34, 0.34, 0.38, and 0.43 for the 4 pathologists. These preliminary results reveal that even within a 1-mm-diameter needle core, GG based on 2-dimensional images can vary dramatically depending on the location within a biopsy being analyzed. We believe that morphologic assessment of whole biopsies in 3 dimension has the potential to enable more reliable and consistent tumor grading.
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Affiliation(s)
- Can Koyuncu
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia; Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland; Department of Clinical Pathology, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - Xavier Farre
- Public Health Agency of Catalonia, Lleida, Catalonia, Spain
| | - Tilak Pathak
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
| | - Tuomas Mirtti
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia; Department of Pathology, University of Helsinki and Helsinki University, Hospital, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Pedro L Fernandez
- Department of Pathology, Hospital Germans Trias i Pujol, IGTP, Universidad Autonoma de Barcelona, Barcelona, Spain
| | - Laura Pons
- Department of Pathology, Hospital Germans Trias i Pujol, IGTP, Barcelona, Spain
| | - Nicholas P Reder
- Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Laboratory Medicine & Pathology, University of Washington, Seattle, Washington
| | - Robert Serafin
- Department of Mechanical Engineering, University of Washington, Seattle, Washington
| | - Sarah S L Chow
- Department of Mechanical Engineering, University of Washington, Seattle, Washington
| | - Vidya S Viswanathan
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
| | - Adam K Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, Washington
| | - Lawrence D True
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, Washington; Department of Urology, University of Washington, Seattle, Washington
| | - Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Laboratory Medicine & Pathology, University of Washington, Seattle, Washington; Department of Bioengineering, University of Washington, Seattle, Washington
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia; Atlanta VA Medical Center, Atlanta, Georgia.
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16
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Vonk J, Kruijff S, Slart RHJA, Szymanski W, Witjes MJH, Glaudemans AWJM. Towards molecular imaging-guided intervention theatres in oncology. Eur J Nucl Med Mol Imaging 2023:10.1007/s00259-023-06545-1. [PMID: 38012447 DOI: 10.1007/s00259-023-06545-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Affiliation(s)
- J Vonk
- Medical Imaging Center, Department of Nuclear Medicine & Molecular Imaging, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, the Netherlands.
| | - S Kruijff
- Medical Imaging Center, Department of Nuclear Medicine & Molecular Imaging, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, the Netherlands
- Department of Surgery, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - R H J A Slart
- Medical Imaging Center, Department of Nuclear Medicine & Molecular Imaging, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, the Netherlands
- Biomedical Photonic Imaging Group, Faculty of Science and Technology, University of Twente, Enschede, The Netherlands
| | - W Szymanski
- Medical Imaging Center, Department of Nuclear Medicine & Molecular Imaging, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, the Netherlands
- Department of Medicinal Chemistry, Photopharmacology and Imaging, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands
| | - M J H Witjes
- Department of Oral and Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - A W J M Glaudemans
- Medical Imaging Center, Department of Nuclear Medicine & Molecular Imaging, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, the Netherlands
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17
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Tadesse K, Mandracchia B, Yoon K, Han K, Jia S. Three-dimensional multifocal scanning microscopy for super-resolution cell and tissue imaging. OPTICS EXPRESS 2023; 31:38550-38559. [PMID: 38017958 DOI: 10.1364/oe.501100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/24/2023] [Indexed: 11/30/2023]
Abstract
Recent advancements in image-scanning microscopy have significantly enriched super-resolution biological research, providing deeper insights into cellular structures and processes. However, current image-scanning techniques often require complex instrumentation and alignment, constraining their broader applicability in cell biological discovery and convenient, cost-effective integration into commonly used frameworks like epi-fluorescence microscopes. Here, we introduce three-dimensional multifocal scanning microscopy (3D-MSM) for super-resolution imaging of cells and tissue with substantially reduced instrumental complexity. This method harnesses the inherent 3D movement of specimens to achieve stationary, multi-focal excitation and super-resolution microscopy through a standard epi-fluorescence platform. We validated the system using a range of phantom, single-cell, and tissue specimens. The combined strengths of structured illumination, confocal detection, and epi-fluorescence setup result in two-fold resolution improvement in all three dimensions, effective optical sectioning, scalable volume acquisition, and compatibility with general imaging and sample protocols. We anticipate that 3D-MSM will pave a promising path for future super-resolution investigations in cell and tissue biology.
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18
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Burlingame E, Ternes L, Lin JR, Chen YA, Kim EN, Gray JW, Chang YH. 3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding. FRONTIERS IN BIOINFORMATICS 2023; 3:1275402. [PMID: 37928169 PMCID: PMC10620917 DOI: 10.3389/fbinf.2023.1275402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction: Tissue-based sampling and diagnosis are defined as the extraction of information from certain limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually only presents a small fraction of the tumor. Many multiplex tissue imaging platforms (MTIs) make the assumption that tissue microarrays (TMAs) containing small core samples of 2-dimensional (2D) tissue sections are a good approximation of bulk tumors although tumors are not 2D. However, emerging whole slide imaging (WSI) or 3D tumor atlases that use MTIs like cyclic immunofluorescence (CyCIF) strongly challenge this assumption. In spite of the additional insight gathered by measuring the tumor microenvironment in WSI or 3D, it can be prohibitively expensive and time-consuming to process tens or hundreds of tissue sections with CyCIF. Even when resources are not limited, the criteria for region of interest (ROI) selection in tissues for downstream analysis remain largely qualitative and subjective as stratified sampling requires the knowledge of objects and evaluates their features. Despite the fact TMAs fail to adequately approximate whole tissue features, a theoretical subsampling of tissue exists that can best represent the tumor in the whole slide image. Methods: To address these challenges, we propose deep learning approaches to learn multi-modal image translation tasks from two aspects: 1) generative modeling approach to reconstruct 3D CyCIF representation and 2) co-embedding CyCIF image and Hematoxylin and Eosin (H&E) section to learn multi-modal mappings by a cross-domain translation for minimum representative ROI selection. Results and discussion: We demonstrate that generative modeling enables a 3D virtual CyCIF reconstruction of a colorectal cancer specimen given a small subset of the imaging data at training time. By co-embedding histology and MTI features, we propose a simple convex optimization for objective ROI selection. We demonstrate the potential application of ROI selection and the efficiency of its performance with respect to cellular heterogeneity.
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Affiliation(s)
- Erik Burlingame
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
| | - Luke Ternes
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
| | - Jia-Ren Lin
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Yu-An Chen
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Eun Na Kim
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
| | - Joe W. Gray
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Young Hwan Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
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19
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Wang N, Zhang C, Wei X, Yan T, Zhou W, Zhang J, Kang H, Yuan Z, Chen X. Harnessing the power of optical microscopy for visualization and analysis of histopathological images. BIOMEDICAL OPTICS EXPRESS 2023; 14:5451-5465. [PMID: 37854561 PMCID: PMC10581782 DOI: 10.1364/boe.501893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/29/2023] [Accepted: 09/01/2023] [Indexed: 10/20/2023]
Abstract
Histopathology is the foundation and gold standard for identifying diseases, and precise quantification of histopathological images can provide the pathologist with objective clues to make a more convincing diagnosis. Optical microscopy (OM), an important branch of optical imaging technology that provides high-resolution images of tissue cytology and structural morphology, has been used in the diagnosis of histopathology and evolved into a new disciplinary direction of optical microscopic histopathology (OMH). There are a number of ex-vivo studies providing applicability of different OMH approaches, and a transfer of these techniques toward in vivo diagnosis is currently in progress. Furthermore, combined with advanced artificial intelligence algorithms, OMH allows for improved diagnostic reliability and convenience due to the complementarity of retrieval information. In this review, we cover recent advances in OMH, including the exploration of new techniques in OMH as well as their applications, and look ahead to new challenges in OMH. These typical application examples well demonstrate the application potential and clinical value of OMH techniques in histopathological diagnosis.
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Affiliation(s)
- Nan Wang
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Chang Zhang
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
| | - Xinyu Wei
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
| | - Tianyu Yan
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Wangting Zhou
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Jiaojiao Zhang
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Huan Kang
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Zhen Yuan
- Faculty of Health Sciences, University of Macau, Macau, 999078, China
| | - Xueli Chen
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
- Inovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong 510555, China
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20
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Reddi DM, Barner LA, Burke W, Gao G, Grady WM, Liu JTC. Nondestructive 3D Pathology Image Atlas of Barrett Esophagus With Open-Top Light-Sheet Microscopy. Arch Pathol Lab Med 2023; 147:1164-1171. [PMID: 36596255 DOI: 10.5858/arpa.2022-0133-oa] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2022] [Indexed: 01/04/2023]
Abstract
CONTEXT.— Anatomic pathologists render diagnosis on tissue samples sectioned onto glass slides and viewed under a bright-field microscope. This approach is destructive to the sample, which can limit its use for ancillary assays that can inform patient management. Furthermore, the subjective interpretation of a relatively small number of 2D tissue sections per sample contributes to low interobserver agreement among pathologists for the assessment (diagnosis and grading) of various lesions. OBJECTIVE.— To evaluate 3D pathology data sets of thick formalin-fixed Barrett esophagus specimens imaged nondestructively with open-top light-sheet (OTLS) microscopy. DESIGN.— Formalin-fixed, paraffin-embedded Barrett esophagus samples (N = 15) were deparaffinized, stained with a fluorescent analog of hematoxylin-eosin, optically cleared, and imaged nondestructively with OTLS microscopy. The OTLS microscopy images were subsequently compared with archived hematoxylin-eosin histology sections from each sample. RESULTS.— Barrett esophagus samples, both small endoscopic forceps biopsies and endoscopic mucosal resections, exhibited similar resolvable structures between OTLS microscopy and conventional light microscopy with up to a ×20 objective (×200 overall magnification). The 3D histologic images generated by OTLS microscopy can enable improved discrimination of cribriform and well-formed gland morphologies. In addition, a much larger amount of tissue is visualized with OTLS microscopy, which enables improved assessment of clinical specimens exhibiting high spatial heterogeneity. CONCLUSIONS.— In esophageal specimens, OTLS microscopy can generate images comparable in quality to conventional light microscopy, with the advantages of providing 3D information for enhanced evaluation of glandular morphologies and enabling much more of the tissue specimen to be visualized nondestructively.
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Affiliation(s)
- Deepti M Reddi
- From the Department of Laboratory Medicine and Pathology (Reddi, Liu), University of Washington, Seattle
| | - Lindsey A Barner
- Department of Mechanical Engineering (Barner, Gao, Liu), University of Washington, Seattle
| | - Wynn Burke
- Department of Medicine (Burke, Grady), University of Washington, Seattle
- The Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (Burke, Grady)
| | - Gan Gao
- Department of Mechanical Engineering (Barner, Gao, Liu), University of Washington, Seattle
| | - William M Grady
- Department of Medicine (Burke, Grady), University of Washington, Seattle
- The Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (Burke, Grady)
| | - Jonathan T C Liu
- From the Department of Laboratory Medicine and Pathology (Reddi, Liu), University of Washington, Seattle
- Department of Mechanical Engineering (Barner, Gao, Liu), University of Washington, Seattle
- Department of Bioengineering (Liu), University of Washington, Seattle
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21
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Falahkheirkhah K, Mukherjee SS, Gupta S, Herrera-Hernandez L, McCarthy MR, Jimenez RE, Cheville JC, Bhargava R. Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning. CANCER RESEARCH COMMUNICATIONS 2023; 3:1875-1887. [PMID: 37772992 PMCID: PMC10506535 DOI: 10.1158/2767-9764.crc-23-0226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/21/2023] [Accepted: 08/21/2023] [Indexed: 09/30/2023]
Abstract
Histopathology has remained a cornerstone for biomedical tissue assessment for over a century, with a resource-intensive workflow involving biopsy or excision, gross examination, sampling, tissue processing to snap frozen or formalin-fixed paraffin-embedded blocks, sectioning, staining, optical imaging, and microscopic assessment. Emerging chemical imaging approaches, including stimulated Raman scattering (SRS) microscopy, can directly measure inherent molecular composition in tissue (thereby dispensing with the need for tissue processing, sectioning, and using dyes) and can use artificial intelligence (AI) algorithms to provide high-quality images. Here we show the integration of SRS microscopy in a pathology workflow to rapidly record chemical information from minimally processed fresh-frozen prostate tissue. Instead of using thin sections, we record data from intact thick tissues and use optical sectioning to generate images from multiple planes. We use a deep learning–based processing pipeline to generate virtual hematoxylin and eosin images. Next, we extend the computational method to generate archival-quality images in minutes, which are equivalent to those obtained from hours/days-long formalin-fixed, paraffin-embedded processing. We assessed the quality of images from the perspective of enabling pathologists to make decisions, demonstrating that the virtual stained image quality was diagnostically useful and the interpathologist agreement on prostate cancer grade was not impacted. Finally, because this method does not wash away lipids and small molecules, we assessed the utility of lipid chemical composition in determining grade. Together, the combination of chemical imaging and AI provides novel capabilities for rapid assessments in pathology by reducing the complexity and burden of current workflows. SIGNIFICANCE Archival-quality (formalin-fixed paraffin-embedded), thin-section diagnostic images are obtained from thick-cut, fresh-frozen prostate tissues without dyes or stains to expedite cancer histopathology by combining SRS microscopy and machine learning.
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Affiliation(s)
- Kianoush Falahkheirkhah
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois
| | - Sudipta S. Mukherjee
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois
| | - Sounak Gupta
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | | | | | - Rafael E. Jimenez
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - John C. Cheville
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Rohit Bhargava
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois
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22
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Zhan H, Chen S, Gao F, Wang G, Chen SD, Xi G, Yuan HY, Li X, Liu WY, Byrne CD, Targher G, Chen MY, Yang YF, Chen J, Fan Z, Sun X, Cai G, Zheng MH, Zhuo S. AutoFibroNet: A deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD. Aliment Pharmacol Ther 2023; 58:573-584. [PMID: 37403450 DOI: 10.1111/apt.17635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/05/2023] [Accepted: 06/23/2023] [Indexed: 07/06/2023]
Abstract
BACKGROUND Liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated fatty liver disease (MAFLD). Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) is a powerful tool for label-free two-dimensional and three-dimensional tissue visualisation that shows promise in liver fibrosis assessment. AIM To investigate combining multi-photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD. METHODS AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy-confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre-processed images and test data sets. Multi-layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts. RESULTS AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3-4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3-4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts. CONCLUSION AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.
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Affiliation(s)
- Huiling Zhan
- School of Science, Jimei University, Xiamen, China
| | - Siyu Chen
- College of Computer Engineering, Jimei University, Xiamen, China
| | - Feng Gao
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | | | - Sui-Dan Chen
- Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Gangqin Xi
- School of Science, Jimei University, Xiamen, China
| | - Hai-Yang Yuan
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaolu Li
- School of Science, Jimei University, Xiamen, China
| | - Wen-Yue Liu
- Department of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Christopher D Byrne
- Southampton National Institute for Health and Care Research, Biomedical Research Centre, University Hospital Southampton and University of Southampton, Southampton General Hospital, Southampton, UK
| | - Giovanni Targher
- Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Verona, Verona, Italy
| | - Miao-Yang Chen
- Department of Liver Diseases, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China
| | - Yong-Feng Yang
- Department of Liver Diseases, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China
| | - Jun Chen
- Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University, Medical School, Nanjing, China
| | - Zhiwen Fan
- Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University, Medical School, Nanjing, China
| | - Xitai Sun
- Department of Metabolic and Bariatric Surgery, The Affiliated Drum Tower Hospital of Nanjing University, Medical School, Nanjing, China
| | - Guorong Cai
- College of Computer Engineering, Jimei University, Xiamen, China
| | - Ming-Hua Zheng
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
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23
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Bishop KW, Barner LAE, Han Q, Baraznenok E, Lan L, Poudel C, Gao G, Serafin RB, Chow SS, Glaser AK, Janowczyk A, Brenes D, Huang H, Miyasato D, True LD, Kang S, Vaughan JC, Liu JT. An end-to-end workflow for non-destructive 3D pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551845. [PMID: 37577615 PMCID: PMC10418226 DOI: 10.1101/2023.08.03.551845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Recent advances in 3D pathology offer the ability to image orders-of-magnitude more tissue than conventional pathology while providing a volumetric context that is lacking with 2D tissue sections, all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis is non-trivial, requiring careful attention to many details regarding tissue preparation, imaging, and data/image processing in an iterative process. Here we provide an end-to-end protocol covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. While 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol will focus on a fluorescent analog of hematoxylin and eosin (H&E), which remains the most common stain for gold-standard diagnostic determinations. We present our guidelines for a broad range of end-users (e.g., biologists, clinical researchers, and engineers) in a simple tutorial format.
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Affiliation(s)
- Kevin W. Bishop
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | | | - Qinghua Han
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Elena Baraznenok
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Lydia Lan
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Biology, University of Washington, Seattle, Washington, USA
| | - Chetan Poudel
- Department of Chemistry, University of Washington, Seattle, Washington, USA
| | - Gan Gao
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Robert B. Serafin
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Sarah S.L. Chow
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Adam K. Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland
- Department of Clinical Pathology, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - David Brenes
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Hongyi Huang
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Dominie Miyasato
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Lawrence D. True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | - Soyoung Kang
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Joshua C. Vaughan
- Department of Chemistry, University of Washington, Seattle, Washington, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, Washington, USA
| | - Jonathan T.C. Liu
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
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24
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Serafin R, Koyuncu C, Xie W, Huang H, Glaser AK, Reder NP, Janowczyk A, True LD, Madabhushi A, Liu JT. Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment. J Pathol 2023; 260:390-401. [PMID: 37232213 PMCID: PMC10524574 DOI: 10.1002/path.6090] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/16/2023] [Accepted: 04/12/2023] [Indexed: 05/27/2023]
Abstract
Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Robert Serafin
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Can Koyuncu
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Weisi Xie
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Hongyi Huang
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Adam K Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Nicholas P Reder
- Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Andrew Janowczyk
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Precision Oncology Center Institute of Pathology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Department of Clinical Pathology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Lawrence D True
- Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Anant Madabhushi
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Atlanta Veterans Affairs Medical Center, Decatur, GA, USA
| | - Jonathan Tc Liu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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25
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Song AH, Williams M, Williamson DFK, Jaume G, Zhang A, Chen B, Serafin R, Liu JTC, Baras A, Parwani AV, Mahmood F. Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples. ARXIV 2023:arXiv:2307.14907v1. [PMID: 37547660 PMCID: PMC10402184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3-6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7-18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12-14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, MAMBA provides a general and efficient framework for 3D weakly supervised learning for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.
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Affiliation(s)
- Andrew H. Song
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mane Williams
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Drew F. K. Williamson
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Guillaume Jaume
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Andrew Zhang
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bowen Chen
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert Serafin
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Jonathan T. C. Liu
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Alex Baras
- Department of Pathology, Johns Hopkins Hospital, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Anil V. Parwani
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
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26
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Gao G, Miyasato D, Barner LA, Serafin R, Bishop KW, Xie W, Glaser AK, Rosenthal EL, True LD, Liu JT. Comprehensive Surface Histology of Fresh Resection Margins With Rapid Open-Top Light-Sheet (OTLS) Microscopy. IEEE Trans Biomed Eng 2023; 70:2160-2171. [PMID: 37021859 PMCID: PMC10324671 DOI: 10.1109/tbme.2023.3237267] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
OBJECTIVE For tumor resections, margin status typically correlates with patient survival but positive margin rates are generally high (up to 45% for head and neck cancer). Frozen section analysis (FSA) is often used to intraoperatively assess the margins of excised tissue, but suffers from severe under-sampling of the actual margin surface, inferior image quality, slow turnaround, and tissue destructiveness. METHODS Here, we have developed an imaging workflow to generate en face histologic images of freshly excised surgical margin surfaces based on open-top light-sheet (OTLS) microscopy. Key innovations include (1) the ability to generate false-colored H&E-mimicking images of tissue surfaces stained for < 1 min with a single fluorophore, (2) rapid OTLS surface imaging at a rate of 15 min/cm2 followed by real-time post-processing of datasets within RAM at a rate of 5 min/cm2, and (3) rapid digital surface extraction to account for topological irregularities at the tissue surface. RESULTS In addition to the performance metrics listed above, we show that the image quality generated by our rapid surface-histology method approaches that of gold-standard archival histology. CONCLUSION OTLS microscopy has the feasibility to provide intraoperative guidance of surgical oncology procedures. SIGNIFICANCE The reported methods can potentially improve tumor-resection procedures, thereby improving patient outcomes and quality of life.
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Affiliation(s)
- Gan Gao
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Dominie Miyasato
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Lindsey A. Barner
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Robert Serafin
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Kevin W. Bishop
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Weisi Xie
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Adam K. Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Eben L. Rosenthal
- Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lawrence D. True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Department of Urology, University of Washington, Seattle, WA, USA
| | - Jonathan T.C. Liu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
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27
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Bhargava R. Digital Histopathology by Infrared Spectroscopic Imaging. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:205-230. [PMID: 37068745 PMCID: PMC10408309 DOI: 10.1146/annurev-anchem-101422-090956] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.
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Affiliation(s)
- Rohit Bhargava
- Department of Bioengineering; Department of Electrical and Computer Engineering; Department of Mechanical Science and Engineering; Department of Chemical and Biomolecular Engineering; Department of Chemistry; Cancer Center at Illinois; and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA;
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28
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Liu JTC, Glaser AK, Poudel C, Vaughan JC. Nondestructive 3D Pathology with Light-Sheet Fluorescence Microscopy for Translational Research and Clinical Assays. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:231-252. [PMID: 36854208 DOI: 10.1146/annurev-anchem-091222-092734] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In recent years, there has been a revived appreciation for the importance of spatial context and morphological phenotypes for both understanding disease progression and guiding treatment decisions. Compared with conventional 2D histopathology, which is the current gold standard of medical diagnostics, nondestructive 3D pathology offers researchers and clinicians the ability to visualize orders of magnitude more tissue within their natural volumetric context. This has been enabled by rapid advances in tissue-preparation methods, high-throughput 3D microscopy instrumentation, and computational tools for processing these massive feature-rich data sets. Here, we provide a brief overview of many of these technical advances along with remaining challenges to be overcome. We also speculate on the future of 3D pathology as applied in translational investigations, preclinical drug development, and clinical decision-support assays.
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Affiliation(s)
- Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA;
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Adam K Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA;
- Allen Institute for Neural Dynamics, Seattle, Washington, USA
| | - Chetan Poudel
- Department of Chemistry, University of Washington, Seattle, Washington, USA
| | - Joshua C Vaughan
- Department of Chemistry, University of Washington, Seattle, Washington, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, Washington, USA
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29
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Lapierre-Landry M, Liu Y, Bayat M, Wilson DL, Jenkins MW. Digital labeling for 3D histology: segmenting blood vessels without a vascular contrast agent using deep learning. BIOMEDICAL OPTICS EXPRESS 2023; 14:2416-2431. [PMID: 37342724 PMCID: PMC10278624 DOI: 10.1364/boe.480230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/12/2023] [Accepted: 02/20/2023] [Indexed: 06/23/2023]
Abstract
Recent advances in optical tissue clearing and three-dimensional (3D) fluorescence microscopy have enabled high resolution in situ imaging of intact tissues. Using simply prepared samples, we demonstrate here "digital labeling," a method to segment blood vessels in 3D volumes solely based on the autofluorescence signal and a nuclei stain (DAPI). We trained a deep-learning neural network based on the U-net architecture using a regression loss instead of a commonly used segmentation loss to achieve better detection of small vessels. We achieved high vessel detection accuracy and obtained accurate vascular morphometrics such as vessel length density and orientation. In the future, such digital labeling approach could easily be transferred to other biological structures.
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Affiliation(s)
| | - Yehe Liu
- Department of Biomedical Engineering, Case Western Reserve University, USA
| | - Mahdi Bayat
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, USA
| | - David L. Wilson
- Department of Biomedical Engineering, Case Western Reserve University, USA
- Department of Radiology, Case Western Reserve University, USA
| | - Michael W. Jenkins
- Department of Biomedical Engineering, Case Western Reserve University, USA
- Department of Pediatrics, School of
Medicine, Case Western Reserve University, USA
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30
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Hutten SJ, de Bruijn R, Lutz C, Badoux M, Eijkman T, Chao X, Ciwinska M, Sheinman M, Messal H, Herencia-Ropero A, Kristel P, Mulder L, van der Waal R, Sanders J, Almekinders MM, Llop-Guevara A, Davies HR, van Haren MJ, Martin NI, Behbod F, Nik-Zainal S, Serra V, van Rheenen J, Lips EH, Wessels LFA, Wesseling J, Scheele CLGJ, Jonkers J. A living biobank of patient-derived ductal carcinoma in situ mouse-intraductal xenografts identifies risk factors for invasive progression. Cancer Cell 2023; 41:986-1002.e9. [PMID: 37116492 PMCID: PMC10171335 DOI: 10.1016/j.ccell.2023.04.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 02/21/2023] [Accepted: 04/04/2023] [Indexed: 04/30/2023]
Abstract
Ductal carcinoma in situ (DCIS) is a non-obligate precursor of invasive breast cancer (IBC). Due to a lack of biomarkers able to distinguish high- from low-risk cases, DCIS is treated similar to early IBC even though the minority of untreated cases eventually become invasive. Here, we characterized 115 patient-derived mouse-intraductal (MIND) DCIS models reflecting the full spectrum of DCIS observed in patients. Utilizing the possibility to follow the natural progression of DCIS combined with omics and imaging data, we reveal multiple prognostic factors for high-risk DCIS including high grade, HER2 amplification, expansive 3D growth, and high burden of copy number aberrations. In addition, sequential transplantation of xenografts showed minimal phenotypic and genotypic changes over time, indicating that invasive behavior is an intrinsic phenotype of DCIS and supporting a multiclonal evolution model. Moreover, this study provides a collection of 19 distributable DCIS-MIND models spanning all molecular subtypes.
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Affiliation(s)
- Stefan J Hutten
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands
| | - Roebi de Bruijn
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands; Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Catrin Lutz
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands
| | - Madelon Badoux
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands
| | - Timo Eijkman
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands
| | - Xue Chao
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands
| | - Marta Ciwinska
- Center for Cancer Biology, VIB, Department of Oncology, KU Leuven, 3000 Leuven, Belgium
| | - Michael Sheinman
- Oncode Institute, Amsterdam, the Netherlands; Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Hendrik Messal
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands
| | - Andrea Herencia-Ropero
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, 08035 Barcelona, Spain; Department of Biochemistry and Molecular Biology, Autonomous University of Barcelona, Barcelona, Spain
| | - Petra Kristel
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Lennart Mulder
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Rens van der Waal
- Core Facility Molecular Pathology & Biobanking, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Joyce Sanders
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Mathilde M Almekinders
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Alba Llop-Guevara
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, 08035 Barcelona, Spain
| | - Helen R Davies
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, CB2 0QQ Cambridge, UK; Early Cancer Institute, University of Cambridge, CB2 0XZ Cambridge, UK
| | - Matthijs J van Haren
- Biological Chemistry Group, Institute of Biology Leiden, Leiden University, 2302 BH Leiden, the Netherlands
| | - Nathaniel I Martin
- Biological Chemistry Group, Institute of Biology Leiden, Leiden University, 2302 BH Leiden, the Netherlands
| | - Fariba Behbod
- Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS 66103, USA
| | - Serena Nik-Zainal
- Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, CB2 0QQ Cambridge, UK; Early Cancer Institute, University of Cambridge, CB2 0XZ Cambridge, UK
| | - Violeta Serra
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, 08035 Barcelona, Spain
| | - Jacco van Rheenen
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands
| | - Esther H Lips
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Lodewyk F A Wessels
- Oncode Institute, Amsterdam, the Netherlands; Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Division of Diagnostic Oncology, Netherlands Cancer Institute - Antonie van Leeuwenhoek Hospital, 1066 CX Amsterdam, the Netherlands; Department of Pathology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
| | - Colinda L G J Scheele
- Center for Cancer Biology, VIB, Department of Oncology, KU Leuven, 3000 Leuven, Belgium
| | - Jos Jonkers
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands.
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31
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Caldonazzi N, Rizzo PC, Eccher A, Girolami I, Fanelli GN, Naccarato AG, Bonizzi G, Fusco N, d'Amati G, Scarpa A, Pantanowitz L, Marletta S. Value of Artificial Intelligence in Evaluating Lymph Node Metastases. Cancers (Basel) 2023; 15:cancers15092491. [PMID: 37173958 PMCID: PMC10177013 DOI: 10.3390/cancers15092491] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/23/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.
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Affiliation(s)
- Nicolò Caldonazzi
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy
| | - Paola Chiara Rizzo
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, 37126 Verona, Italy
| | - Ilaria Girolami
- Department of Pathology, Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität, Provincial Hospital of Bolzano (SABES-ASDAA), 39100 Bolzano-Bozen, Italy
| | - Giuseppe Nicolò Fanelli
- Division of Pathology, Department of Translational Research, New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
| | - Antonio Giuseppe Naccarato
- Division of Pathology, Department of Translational Research, New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
| | - Giuseppina Bonizzi
- Division of Pathology, IEO, Europefan Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
| | - Nicola Fusco
- Division of Pathology, IEO, Europefan Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giulia d'Amati
- Department of Radiology, Oncology and Pathology, Sapienza, University of Rome, 00185 Rome, Italy
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy
| | - Liron Pantanowitz
- Department of Pathology, University of Michigan, Ann Arbor, MI 48104, USA
| | - Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy
- Department of Pathology, Pederzoli Hospital, 37019 Peschiera del Garda, Italy
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32
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Laurino A, Franceschini A, Pesce L, Cinci L, Montalbano A, Mazzamuto G, Sancataldo G, Nesi G, Costantini I, Silvestri L, Pavone FS. A Guide to Perform 3D Histology of Biological Tissues with Fluorescence Microscopy. Int J Mol Sci 2023; 24:ijms24076747. [PMID: 37047724 PMCID: PMC10094801 DOI: 10.3390/ijms24076747] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 04/09/2023] Open
Abstract
The analysis of histological alterations in all types of tissue is of primary importance in pathology for highly accurate and robust diagnosis. Recent advances in tissue clearing and fluorescence microscopy made the study of the anatomy of biological tissue possible in three dimensions. The combination of these techniques with classical hematoxylin and eosin (H&E) staining has led to the birth of three-dimensional (3D) histology. Here, we present an overview of the state-of-the-art methods, highlighting the optimal combinations of different clearing methods and advanced fluorescence microscopy techniques for the investigation of all types of biological tissues. We employed fluorescence nuclear and eosin Y staining that enabled us to obtain hematoxylin and eosin pseudo-coloring comparable with the gold standard H&E analysis. The computational reconstructions obtained with 3D optical imaging can be analyzed by a pathologist without any specific training in volumetric microscopy, paving the way for new biomedical applications in clinical pathology.
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Affiliation(s)
- Annunziatina Laurino
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
| | - Alessandra Franceschini
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
| | - Luca Pesce
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
| | - Lorenzo Cinci
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, Careggi University Hospital, 50134 Florence, Italy
| | - Alberto Montalbano
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Neurofarba Section of Pharmacology and Toxicology, University of Florence, 50139 Florence, Italy
| | - Giacomo Mazzamuto
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
- National Research Council—National Institute of Optics (CNR-INO), 50125 Sesto Fiorentino, Italy
| | - Giuseppe Sancataldo
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
| | - Gabriella Nesi
- Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Irene Costantini
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- National Research Council—National Institute of Optics (CNR-INO), 50125 Sesto Fiorentino, Italy
- Department of Biology, University of Florence, 50019 Florence, Italy
| | - Ludovico Silvestri
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
- National Research Council—National Institute of Optics (CNR-INO), 50125 Sesto Fiorentino, Italy
| | - Francesco Saverio Pavone
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
- National Research Council—National Institute of Optics (CNR-INO), 50125 Sesto Fiorentino, Italy
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33
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Timin G, Milinkovitch MC. High-resolution confocal and light-sheet imaging of collagen 3D network architecture in very large samples. iScience 2023; 26:106452. [PMID: 37020961 PMCID: PMC10067766 DOI: 10.1016/j.isci.2023.106452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/06/2023] [Accepted: 03/15/2023] [Indexed: 04/03/2023] Open
Abstract
Although notoriously difficult, imaging collagen network architecture, a key element affecting tissue mechanical properties, is of paramount importance in developmental and cancer biology. Here, we introduce a simple and robust method of whole-mount collagen staining with the 'Fast Green' dye that provides unmatched visualization of collagen 3D network architecture, via confocal or light-sheet microscopy, compatible with solvent-based tissue clearing and immunostaining.
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34
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Gallagher BR, Zhao Y. Imaging pathology goes nanoscale with a low-cost strategy. NATURE NANOTECHNOLOGY 2023; 18:324-325. [PMID: 37037894 DOI: 10.1038/s41565-023-01318-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Affiliation(s)
- Brendan R Gallagher
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Yongxin Zhao
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
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35
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Nelson MS, Liu Y, Wilson HM, Li B, Rosado-Mendez IM, Rogers JD, Block WF, Eliceiri KW. Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment. Methods Mol Biol 2023; 2614:187-235. [PMID: 36587127 DOI: 10.1007/978-1-0716-2914-7_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.
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Affiliation(s)
- Michael S Nelson
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Yuming Liu
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA
| | - Helen M Wilson
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Bin Li
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.,Morgridge Institute for Research, Madison, WI, USA
| | - Ivan M Rosado-Mendez
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Jeremy D Rogers
- Morgridge Institute for Research, Madison, WI, USA.,McPherson Eye Research Institute, University of Wisconsin-Madison, Madison, WI, USA
| | - Walter F Block
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Kevin W Eliceiri
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA. .,Morgridge Institute for Research, Madison, WI, USA. .,Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA. .,McPherson Eye Research Institute, University of Wisconsin-Madison, Madison, WI, USA.
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36
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Space in cancer biology: its role and implications. Trends Cancer 2022; 8:1019-1032. [PMID: 35995681 DOI: 10.1016/j.trecan.2022.07.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/14/2022] [Accepted: 07/25/2022] [Indexed: 12/24/2022]
Abstract
Tumor cells present complex behaviors in their interactions with other cells. This intricate behavior is driving the need to develop new tools to understand these ecosystems. The surge of spatial technologies allows evaluation of the complexity of relationships between cells present in a tumor, giving insights about tumor heterogeneity and the tumor microenvironment while providing clinically relevant metrics for tumor classification. In this review, we describe key results obtained using spatial techniques, present recent advances in methods to uncover spatially relevant biological significance, and summarize their main characteristics. We expect spatial technologies to significantly broaden our understanding of tumor biology and to generate clinically relevant tools that will ultimately impact personalized medicine.
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37
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Pac J, Koo DJ, Cho H, Jung D, Choi MH, Choi Y, Kim B, Park JU, Kim SY, Lee Y. Three-dimensional imaging and analysis of pathological tissue samples with de novo generation of citrate-based fluorophores. SCIENCE ADVANCES 2022; 8:eadd9419. [PMID: 36383671 PMCID: PMC9668299 DOI: 10.1126/sciadv.add9419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Two-dimensional (2D) histopathology based on the observation of thin tissue slides is the current paradigm in diagnosis and prognosis. However, labeling strategies in conventional histopathology are limited in compatibility with 3D imaging combined with tissue clearing techniques. Here, we present a rapid and efficient volumetric imaging technique of pathological tissues called 3D tissue imaging through de novo formation of fluorophores, or 3DNFC, which is the integration of citrate-based fluorogenic reaction DNFC and tissue clearing techniques. 3DNFC markedly increases the fluorescence intensity of tissues by generating fluorophores on nonfluorescent amino-terminal cysteine and visualizes the 3D structure of the tissues to provide their anatomical morphology and volumetric information. Furthermore, the application of 3DNFC to pathological tissue achieves the 3D reconstruction for the unbiased analysis of diverse features of the disorders in their natural context. We suggest that 3DNFC is a promising volumetric imaging method for the prognosis and diagnosis of pathological tissues.
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Affiliation(s)
- Jinyoung Pac
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
| | - Dong-Jun Koo
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, South Korea
| | - Hyeongjun Cho
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, South Korea
| | - Dongwook Jung
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
| | - Min-ha Choi
- Department of Plastic and Reconstructive Surgery, Seoul National University Boramae Hospital, Seoul National University College of Medicine, 5 Gil 20, Boramae Road, Dongjak-Gu, Seoul 07061, South Korea
| | - Yunjung Choi
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
| | - Bohyun Kim
- Department of Pathology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, South Korea
| | - Ji-Ung Park
- Department of Plastic and Reconstructive Surgery, Seoul National University Boramae Hospital, Seoul National University College of Medicine, 5 Gil 20, Boramae Road, Dongjak-Gu, Seoul 07061, South Korea
| | - Sung-Yon Kim
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, South Korea
| | - Yan Lee
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
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38
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Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer. Cancers (Basel) 2022; 14:cancers14225595. [PMID: 36428686 PMCID: PMC9688370 DOI: 10.3390/cancers14225595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/29/2022] [Accepted: 11/01/2022] [Indexed: 11/16/2022] Open
Abstract
As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.
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39
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Kiemen AL, Braxton AM, Grahn MP, Han KS, Babu JM, Reichel R, Jiang AC, Kim B, Hsu J, Amoa F, Reddy S, Hong SM, Cornish TC, Thompson ED, Huang P, Wood LD, Hruban RH, Wirtz D, Wu PH. CODA: quantitative 3D reconstruction of large tissues at cellular resolution. Nat Methods 2022; 19:1490-1499. [PMID: 36280719 PMCID: PMC10500590 DOI: 10.1038/s41592-022-01650-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/14/2022] [Indexed: 12/15/2022]
Abstract
A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA's ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues. CODA allows creation of readily quantifiable tissue volumes amenable to biological research. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labeling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. We show that pancreatic precancerous lesions develop into distinct 3D morphological phenotypes and that pancreatic cancer tends to spread far from the bulk tumor along collagen fibers that are highly aligned to the 3D curves of ductal, lobular, vascular and neural structures. Thus, CODA establishes a means to transform broadly the structural study of human diseases through exploration of exhaustively labeled 3D microarchitecture.
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Affiliation(s)
- Ashley L Kiemen
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alicia M Braxton
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mia P Grahn
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Kyu Sang Han
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Jaanvi Mahesh Babu
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rebecca Reichel
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ann C Jiang
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Bridgette Kim
- Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Jocelyn Hsu
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Falone Amoa
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sashank Reddy
- Department of Plastic and Reconstructive Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Seung-Mo Hong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Toby C Cornish
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Elizabeth D Thompson
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peng Huang
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, USA
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Laura D Wood
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ralph H Hruban
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Denis Wirtz
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Materials Science and Engineering, The Johns Hopkins University, Baltimore, MD, USA.
- Johns Hopkins Physical Sciences-Oncology Center, The Johns Hopkins University, Baltimore, MD, USA.
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.
- Johns Hopkins Physical Sciences-Oncology Center, The Johns Hopkins University, Baltimore, MD, USA.
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40
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Hung LH, Straw E, Reddy S, Schmitz R, Colburn Z, Yeung KY. Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using Jupyter notebooks. Sci Rep 2022; 12:14920. [PMID: 36056115 PMCID: PMC9440253 DOI: 10.1038/s41598-022-19173-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Modern biomedical image analyses workflows contain multiple computational processing tasks giving rise to problems in reproducibility. In addition, image datasets can span both spatial and temporal dimensions, with additional channels for fluorescence and other data, resulting in datasets that are too large to be processed locally on a laptop. For omics analyses, software containers have been shown to enhance reproducibility, facilitate installation and provide access to scalable computational resources on the cloud. However, most image analyses contain steps that are graphical and interactive, features that are not supported by most omics execution engines. We present the containerized and cloud-enabled Biodepot-workflow-builder platform that supports graphics from software containers and has been extended for image analyses. We demonstrate the potential of our modular approach with multi-step workflows that incorporate the popular and open-source Fiji suite for image processing. One of our examples integrates fully interactive ImageJ macros with Jupyter notebooks. Our second example illustrates how the complicated cloud setup of an computationally intensive process such as stitching 3D digital pathology datasets using BigStitcher can be automated and simplified. In both examples, users can leverage a form-based graphical interface to execute multi-step workflows with a single click, using the provided sample data and preset input parameters. Alternatively, users can interactively modify the image processing steps in the workflow, apply the workflows to their own data, change the input parameters and macros. By providing interactive graphics support to software containers, our modular platform supports reproducible image analysis workflows, simplified access to cloud resources for analysis of large datasets, and integration across different applications such as Jupyter.
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Affiliation(s)
- Ling-Hong Hung
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA
| | - Evan Straw
- Biodepot LLC, Seattle, 98195, WA, USA
- University of Washington, Seattle, 98195, WA, USA
| | - Shishir Reddy
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA
| | - Robert Schmitz
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA
- Biodepot LLC, Seattle, 98195, WA, USA
| | | | - Ka Yee Yeung
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA.
- Biodepot LLC, Seattle, 98195, WA, USA.
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Chen J, Du Z, Xu C, Xiao X, Gong W, Si K. Ultrafast 3D histological imaging based on a minutes-time scale tissue clearing and multidirectional selective plane illumination microscopy. OPTICS LETTERS 2022; 47:4331-4334. [PMID: 36048646 DOI: 10.1364/ol.463705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
Conventional histopathological examinations are time-consuming and labor-intensive, and are insufficient to depict 3D pathological features intuitively. Here we report an ultrafast 3D histological imaging scheme based on optimized selective plane illumination microscopy (mSPIM), a minutes-time scale clearing method (FOCM), and a deep learning-based image enhancement algorithm (SRACNet) to realize histological preparation and imaging of clinical tissues. Our scheme enables 1-minute clearing and fast imaging (up to 900 mm2/min) of 200 µm-thick mouse kidney slices at micron-level resolution. With hematoxylin and eosin analog, we demonstrated the detailed 3D morphological connections between glomeruli and the surrounding tubules, which is difficult to identify in conventional 2D histology. Further, by the preliminary verification on human kidney tissues, this study will provide new, to the best of our knowledge, feasible histological solutions and inspirations in future 3D digital pathology.
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A Novel Three-Dimensional Imaging System Based on Polysaccharide Staining for Accurate Histopathological Diagnosis of Inflammatory Bowel Diseases. Cell Mol Gastroenterol Hepatol 2022; 14:905-924. [PMID: 35835392 PMCID: PMC9500441 DOI: 10.1016/j.jcmgh.2022.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 12/10/2022]
Abstract
BACKGROUND & AIMS Tissue-clearing and three-dimensional (3D) imaging techniques aid clinical histopathological evaluation; however, further methodological developments are required before use in clinical practice. METHODS We sought to develop a novel fluorescence staining method based on the classical periodic acid-Schiff stain. We further attempted to develop a 3D imaging system based on this staining method and evaluated whether the system can be used for quantitative 3D pathological evaluation and deep learning-based automatic diagnosis of inflammatory bowel diseases. RESULTS We successfully developed a novel periodic acid-FAM hydrazide (PAFhy) staining method for 3D imaging when combined with a tissue-clearing technique (PAFhy-3D). This strategy enabled clear and detailed imaging of the 3D architectures of crypts in human colorectal mucosa. PAFhy-3D imaging also revealed abnormal architectural changes in crypts in ulcerative colitis tissues and identified the distributions of neutrophils in cryptitis and crypt abscesses. PAFhy-3D revealed novel pathological findings including spiral staircase-like crypts specific to inflammatory bowel diseases. Quantitative analysis of crypts based on 3D morphologic changes enabled differential diagnosis of ulcerative colitis, Crohn's disease, and non-inflammatory bowel disease; such discrimination could not be achieved by pathologists. Furthermore, a deep learning-based system using PAFhy-3D images was used to distinguish these diseases The accuracies were excellent (macro-average area under the curve = 0.94; F1 scores = 0.875 for ulcerative colitis, 0.717 for Crohn's disease, and 0.819 for non-inflammatory bowel disease). CONCLUSIONS PAFhy staining and PAFhy-3D imaging are promising approaches for next-generation experimental and clinical histopathology.
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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44
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Mertz L. New Efforts in Biomedical Imaging. IEEE Pulse 2022; 13:2-7. [PMID: 36044471 DOI: 10.1109/mpuls.2022.3191382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Since the invention of the X-ray in 1895, biomedical imaging has come a long way and the pace of advances in the field are only accelerating. Research groups are progressing on many fronts through improvements to existing technologies and the development of novel imaging devices and algorithms that not only deliver better pictures, but also more-and more useful-information from the image data.
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45
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Xian RP, Walsh CL, Verleden SE, Wagner WL, Bellier A, Marussi S, Ackermann M, Jonigk DD, Jacob J, Lee PD, Tafforeau P. A multiscale X-ray phase-contrast tomography dataset of a whole human left lung. Sci Data 2022; 9:264. [PMID: 35654864 PMCID: PMC9163096 DOI: 10.1038/s41597-022-01353-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 05/03/2022] [Indexed: 11/09/2022] Open
Abstract
Technological advancements in X-ray imaging using bright and coherent synchrotron sources now allows the decoupling of sample size and resolution while maintaining high sensitivity to the microstructures of soft, partially dehydrated tissues. The continuous developments in multiscale X-ray imaging resulted in hierarchical phase-contrast tomography, a comprehensive approach to address the challenge of organ-scale (up to tens of centimeters) soft tissue imaging with resolution and sensitivity down to the cellular level. Using this technique, we imaged ex vivo an entire human left lung at an isotropic voxel size of 25.08 μm along with local zooms down to 6.05-6.5 μm and 2.45-2.5 μm in voxel size. The high tissue contrast offered by the fourth-generation synchrotron source at the European Synchrotron Radiation Facility reveals the complex multiscale anatomical constitution of the human lung from the macroscopic (centimeter) down to the microscopic (micrometer) scale. The dataset provides comprehensive organ-scale 3D information of the secondary pulmonary lobules and delineates the microstructure of lung nodules with unprecedented detail.
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Affiliation(s)
- R Patrick Xian
- Department of Mechanical Engineering, University College London, London, UK.
| | - Claire L Walsh
- Department of Mechanical Engineering, University College London, London, UK
| | - Stijn E Verleden
- Antwerp Surgical Training, Anatomy and Research Centre (ASTARC), University of Antwerp, Wilrijk, Belgium
| | - Willi L Wagner
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Centre Heidelberg (TLRC), German Lung Research Centre (DZL), Heidelberg, Germany
| | - Alexandre Bellier
- Laboratoire d'Anatomie des Alpes Françaises (LADAF), Université Grenoble Alpes, Grenoble, France
| | - Sebastian Marussi
- Department of Mechanical Engineering, University College London, London, UK
| | - Maximilian Ackermann
- Institute of Pathology and Molecular Pathology, Helios University Clinic Wuppertal, University of Witten/Herdecke, Wuppertal, Germany
- Institute of Functional and Clinical Anatomy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Danny D Jonigk
- Institute of Pathology, Hannover Medical School, Hannover, Germany
- Biomedical Research in End-stage and Obstructive Lung Disease Hannover (BREATH), German Lung Research Centre (DZL), Hannover, Germany
| | - Joseph Jacob
- Centre for Medical Image Computing, University College London, London, UK
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Peter D Lee
- Department of Mechanical Engineering, University College London, London, UK.
| | - Paul Tafforeau
- European Synchrotron Radiation Facility, Grenoble, France.
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46
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Hansmeier NR, Büschlen IS, Behncke RY, Ulferts S, Bisoendial R, Hägerling R. 3D Visualization of Human Blood Vascular Networks Using Single-Domain Antibodies Directed against Endothelial Cell-Selective Adhesion Molecule (ESAM). Int J Mol Sci 2022; 23:ijms23084369. [PMID: 35457187 PMCID: PMC9028812 DOI: 10.3390/ijms23084369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/07/2022] [Accepted: 04/12/2022] [Indexed: 11/18/2022] Open
Abstract
High-quality three-dimensional (3D) microscopy allows detailed, unrestricted and non-destructive imaging of entire volumetric tissue specimens and can therefore increase the diagnostic accuracy of histopathological tissue analysis. However, commonly used IgG antibodies are oftentimes not applicable to 3D imaging, due to their relatively large size and consequently inadequate tissue penetration and penetration speed. The lack of suitable reagents for 3D histopathology can be overcome by an emerging class of single-domain antibodies, referred to as nanobodies (Nbs), which can facilitate rapid and superior 2D and 3D histological stainings. Here, we report the generation and experimental validation of Nbs directed against the human endothelial cell-selective adhesion molecule (hESAM), which enables spatial visualization of blood vascular networks in whole-mount 3D imaging. After analysis of Nb binding properties and quality, selected Nb clones were validated in 2D and 3D imaging approaches, demonstrating comparable staining qualities to commercially available hESAM antibodies in 2D, as well as rapid and complete staining of entire specimens in 3D. We propose that the presented hESAM-Nbs can serve as novel blood vessel markers in academic research and can potentially improve 3D histopathological diagnostics of entire human tissue specimens, leading to improved treatment and superior patient outcomes.
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Affiliation(s)
- Nils Rouven Hansmeier
- Research Group ‘Lymphovascular Medicine and Translational 3D-Histopathology’, Institute of Medical and Human Genetics, Charité, Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany; (N.R.H.); (I.S.B.); (R.Y.B.); (S.U.)
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Center for Regenerative Therapies, Augustenburger Platz 1, 13353 Berlin, Germany
- Research Group ‘Development and Disease’, Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, 14195 Berlin, Germany
| | - Ina Sophie Büschlen
- Research Group ‘Lymphovascular Medicine and Translational 3D-Histopathology’, Institute of Medical and Human Genetics, Charité, Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany; (N.R.H.); (I.S.B.); (R.Y.B.); (S.U.)
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Center for Regenerative Therapies, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Rose Yinghan Behncke
- Research Group ‘Lymphovascular Medicine and Translational 3D-Histopathology’, Institute of Medical and Human Genetics, Charité, Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany; (N.R.H.); (I.S.B.); (R.Y.B.); (S.U.)
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Center for Regenerative Therapies, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Sascha Ulferts
- Research Group ‘Lymphovascular Medicine and Translational 3D-Histopathology’, Institute of Medical and Human Genetics, Charité, Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany; (N.R.H.); (I.S.B.); (R.Y.B.); (S.U.)
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Center for Regenerative Therapies, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Radjesh Bisoendial
- Department of Rheumatology and Clinical Immunology, Maasstad Hospital, Maasstadweg 21, 3079 DZ Rotterdam, The Netherlands;
- Department of Immunology, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - René Hägerling
- Research Group ‘Lymphovascular Medicine and Translational 3D-Histopathology’, Institute of Medical and Human Genetics, Charité, Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany; (N.R.H.); (I.S.B.); (R.Y.B.); (S.U.)
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Center for Regenerative Therapies, Augustenburger Platz 1, 13353 Berlin, Germany
- Research Group ‘Development and Disease’, Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, 14195 Berlin, Germany
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, BIH Academy, Clinician Scientist Program, Charitéplatz 1, 10117 Berlin, Germany
- Correspondence:
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47
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Bishop KW, Maitland KC, Rajadhyaksha M, Liu JTC. In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220032-PER. [PMID: 35478042 PMCID: PMC9043840 DOI: 10.1117/1.jbo.27.4.040601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/05/2022] [Indexed: 05/05/2023]
Abstract
SIGNIFICANCE There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology). AIM Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption. APPROACH We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations. RESULTS While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow. CONCLUSIONS The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.
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Affiliation(s)
- Kevin W. Bishop
- University of Washington, Department of Bioengineering, Seattle, Washington, United States
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
| | - Kristen C. Maitland
- Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States
| | - Milind Rajadhyaksha
- Memorial Sloan Kettering Cancer Center, Dermatology Service, New York, New York, United States
| | - Jonathan T. C. Liu
- University of Washington, Department of Bioengineering, Seattle, Washington, United States
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
- University of Washington, Department of Laboratory Medicine and Pathology, Seattle, Washington, United States
- Address all correspondence to Jonathan T.C. Liu,
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48
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Lee MY, Mao C, Glaser AK, Woodworth MA, Halpern AR, Ali A, Liu JTC, Vaughan JC. Fluorescent labeling of abundant reactive entities (FLARE) for cleared-tissue and super-resolution microscopy. Nat Protoc 2022; 17:819-846. [PMID: 35110740 DOI: 10.1038/s41596-021-00667-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 09/21/2021] [Indexed: 11/08/2022]
Abstract
Fluorescence microscopy is a vital tool in biomedical research but faces considerable challenges in achieving uniform or bright labeling. For instance, fluorescent proteins are limited to model organisms, and antibody conjugates can be inconsistent and difficult to use with thick specimens. To partly address these challenges, we developed a labeling protocol that can rapidly visualize many well-contrasted key features and landmarks on biological specimens in both thin and thick tissues or cultured cells. This approach uses established reactive fluorophores to label a variety of biological specimens for cleared-tissue microscopy or expansion super-resolution microscopy and is termed FLARE (fluorescent labeling of abundant reactive entities). These fluorophores target chemical groups and reveal their distribution on the specimens; amine-reactive fluorophores such as hydroxysuccinimidyl esters target accessible amines on proteins, while hydrazide fluorophores target oxidized carbohydrates. The resulting stains provide signals analogous to traditional general histology stains such as H&E or periodic acid-Schiff but use fluorescent probes that are compatible with volumetric imaging. In general, the stains for FLARE are performed in the order of carbohydrates, amine and DNA, and the incubation time for the stains varies from 1 h to 1 d depending on the combination of stains and the type and thickness of the biological specimens. FLARE is powerful, robust and easy to implement in laboratories that already routinely do fluorescence microscopy.
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Affiliation(s)
- Min Yen Lee
- Department of Chemistry, University of Washington, Seattle, WA, USA
| | - Chenyi Mao
- Department of Chemistry, University of Washington, Seattle, WA, USA
| | - Adam K Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | | | - Aaron R Halpern
- Department of Chemistry, University of Washington, Seattle, WA, USA
| | - Adilijiang Ali
- Department of Chemistry, University of Washington, Seattle, WA, USA
| | - Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Joshua C Vaughan
- Department of Chemistry, University of Washington, Seattle, WA, USA.
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
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49
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Sangha GS, Hu B, Li G, Fox SE, Sholl AB, Brown JQ, Goergen CJ. Assessment of photoacoustic tomography contrast for breast tissue imaging using 3D correlative virtual histology. Sci Rep 2022; 12:2532. [PMID: 35169198 PMCID: PMC8847353 DOI: 10.1038/s41598-022-06501-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 01/25/2022] [Indexed: 11/12/2022] Open
Abstract
Current breast tumor margin detection methods are destructive, time-consuming, and result in significant reoperative rates. Dual-modality photoacoustic tomography (PAT) and ultrasound has the potential to enhance breast margin characterization by providing clinically relevant compositional information with high sensitivity and tissue penetration. However, quantitative methods that rigorously compare volumetric PAT and ultrasound images with gold-standard histology are lacking, thus limiting clinical validation and translation. Here, we present a quantitative multimodality workflow that uses inverted Selective Plane Illumination Microscopy (iSPIM) to facilitate image co-registration between volumetric PAT-ultrasound datasets with histology in human invasive ductal carcinoma breast tissue samples. Our ultrasound-PAT system consisted of a tunable Nd:YAG laser coupled with a 40 MHz central frequency ultrasound transducer. A linear stepper motor was used to acquire volumetric PAT and ultrasound breast biopsy datasets using 1100 nm light to identify hemoglobin-rich regions and 1210 nm light to identify lipid-rich regions. Our iSPIM system used 488 nm and 647 nm laser excitation combined with Eosin and DRAQ5, a cell-permeant nucleic acid binding dye, to produce high-resolution volumetric datasets comparable to histology. Image thresholding was applied to PAT and iSPIM images to extract, quantify, and topologically visualize breast biopsy lipid, stroma, hemoglobin, and nuclei distribution. Our lipid-weighted PAT and iSPIM images suggest that low lipid regions strongly correlate with malignant breast tissue. Hemoglobin-weighted PAT images, however, correlated poorly with cancerous regions determined by histology and interpreted by a board-certified pathologist. Nuclei-weighted iSPIM images revealed similar cellular content in cancerous and non-cancerous tissues, suggesting malignant cell migration from the breast ducts to the surrounding tissues. We demonstrate the utility of our nondestructive, volumetric, region-based quantitative method for comprehensive validation of 3D tomographic imaging methods suitable for bedside tumor margin detection.
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Affiliation(s)
- Gurneet S Sangha
- Fischell Department of Bioengineering, University of Maryland, 8278 Paint Branch Dr, College Park, MD, 20742, USA.,Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Dr., West Lafayette, IN, 47907, USA
| | - Bihe Hu
- Department of Biomedical Engineering, Tulane University, 547 Lindy Boggs Center, New Orleans, LA, 70118, USA
| | - Guang Li
- Department of Biomedical Engineering, Tulane University, 547 Lindy Boggs Center, New Orleans, LA, 70118, USA
| | - Sharon E Fox
- Department of Pathology, LSU Health Sciences Center, New Orleans, 433 Bolivar St, New Orleans, LA, 70112, USA.,Pathology and Laboratory Medicine Service, Southeast Louisiana Veterans Healthcare System, 2400 Canal Street, New Orleans, LA, 70112, USA
| | - Andrew B Sholl
- Delta Pathology Group, Touro Infirmary, 1401 Foucher St, New Orleans, LA, 70115, USA
| | - J Quincy Brown
- Department of Biomedical Engineering, Tulane University, 547 Lindy Boggs Center, New Orleans, LA, 70118, USA
| | - Craig J Goergen
- Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Dr., West Lafayette, IN, 47907, USA. .,Purdue University Center for Cancer Research, Purdue University, 201 S. University St., West Lafayette, IN, 47907, USA.
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50
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Liu Y, Levenson RM, Jenkins MW. Slide Over: Advances in Slide-Free Optical Microscopy as Drivers of Diagnostic Pathology. THE AMERICAN JOURNAL OF PATHOLOGY 2022; 192:180-194. [PMID: 34774514 PMCID: PMC8883436 DOI: 10.1016/j.ajpath.2021.10.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/12/2021] [Accepted: 10/18/2021] [Indexed: 02/03/2023]
Abstract
Conventional analysis using clinical histopathology is based on bright-field microscopy of thinly sliced tissue specimens. Although bright-field microscopy is a simple and robust method of examining microscope slides, the preparation of the slides needed is a lengthy and labor-intensive process. Slide-free histopathology, however, uses direct imaging of intact, minimally processed tissue samples using advanced optical-imaging systems, bypassing the extended workflow now required for the preparation of tissue sections. This article explains the technical basis of slide-free microscopy, reviews common slide-free optical microscopy techniques, and discusses the opportunities and challenges involved in clinical implementation.
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Affiliation(s)
- Yehe Liu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Richard M. Levenson
- Department of Pathology and Laboratory Medicine, University of California–Davis, Sacramento, California,Address correspondence to Richard M. Levenson, M.D., UC Davis Health, Path Building, 4400 V St., Sacramento, CA 95817; or Michael W. Jenkins, Ph.D., 2109 Adelbert Rd., Wood Bldg., WG28, Cleveland, OH 44106.
| | - Michael W. Jenkins
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio,Address correspondence to Richard M. Levenson, M.D., UC Davis Health, Path Building, 4400 V St., Sacramento, CA 95817; or Michael W. Jenkins, Ph.D., 2109 Adelbert Rd., Wood Bldg., WG28, Cleveland, OH 44106.
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