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Kiemen AL, Dequiedt L, Shen Y, Zhu Y, Matos-Romero V, Forjaz A, Campbell K, Dhana W, Cornish T, Braxton AM, Wu P, Fishman EK, Wood LD, Wirtz D, Hruban RH. PanIN or IPMN? Redefining Lesion Size in 3 Dimensions. Am J Surg Pathol 2024; 48:839-845. [PMID: 38764379 PMCID: PMC11189722 DOI: 10.1097/pas.0000000000002245] [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: 05/21/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) develops from 2 known precursor lesions: a majority (∼85%) develops from pancreatic intraepithelial neoplasia (PanIN), and a minority develops from intraductal papillary mucinous neoplasms (IPMNs). Clinical classification of PanIN and IPMN relies on a combination of low-resolution, 3-dimensional (D) imaging (computed tomography, CT), and high-resolution, 2D imaging (histology). The definitions of PanIN and IPMN currently rely heavily on size. IPMNs are defined as macroscopic: generally >1.0 cm and visible in CT, and PanINs are defined as microscopic: generally <0.5 cm and not identifiable in CT. As 2D evaluation fails to take into account 3D structures, we hypothesized that this classification would fail in evaluation of high-resolution, 3D images. To characterize the size and prevalence of PanINs in 3D, 47 thick slabs of pancreas were harvested from grossly normal areas of pancreatic resections, excluding samples from individuals with a diagnosis of an IPMN. All patients but one underwent preoperative CT scans. Through construction of cellular resolution 3D maps, we identified >1400 ductal precursor lesions that met the 2D histologic size criteria of PanINs. We show that, when 3D space is considered, 25 of these lesions can be digitally sectioned to meet the 2D histologic size criterion of IPMN. Re-evaluation of the preoperative CT images of individuals found to possess these large precursor lesions showed that nearly half are visible on imaging. These findings demonstrate that the clinical classification of PanIN and IPMN fails in evaluation of high-resolution, 3D images, emphasizing the need for re-evaluation of classification guidelines that place significant weight on 2D assessment of 3D structures.
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Affiliation(s)
- Ashley L. Kiemen
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Lucie Dequiedt
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Yu Shen
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Yutong Zhu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Valentina Matos-Romero
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Kurtis Campbell
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Will Dhana
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Toby Cornish
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO
| | - Alicia M. Braxton
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - PeiHsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Elliot K. Fishman
- Department of Radiology and Radiological Science, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Laura D. Wood
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Denis Wirtz
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Ralph H. Hruban
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
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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 PMCID: PMC11168832 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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Forjaz A, Vaz E, Romero VM, Joshi S, Braxton AM, Jiang AC, Fujikura K, Cornish T, Hong SM, Hruban RH, Wu PH, Wood LD, Kiemen AL, Wirtz D. Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.04.569986. [PMID: 38106231 PMCID: PMC10723352 DOI: 10.1101/2023.12.04.569986] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter- and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.
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Affiliation(s)
- André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Eduarda Vaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Valentina Matos Romero
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Saurabh Joshi
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Alicia M. Braxton
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Ann C. Jiang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Kohei Fujikura
- Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Toby Cornish
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO
| | - Seung-Mo Hong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ralph H. Hruban
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Laura D. Wood
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Ashley L. Kiemen
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Denis Wirtz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
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