Evidence map›Paper›PMID 38729110›Full record

ArticleCell2024

Analysis of 3D pathology samples using weakly supervised AI.

Andrew H Song, Mane Williams, Drew F K Williamson, Sarah S L Chow, Guillaume Jaume, Gan Gao, Andrew Zhang, Bowen Chen, Alexander S Baras, Robert Serafin and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

60 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Assessing the Effects of a 3-Dimensional (3D) Pathology Tissue-Processing Workflow on Downstream Molecular Analyses.Laboratory investigation; a journal of technical methods and pathology · 2026
    Article
  5. Article
  6. Prototype-based AI triage for 3D pathology.bioRxiv : the preprint server for biology · 2026
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
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  15. Review
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  17. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Andrew H SongDepartment 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 WilliamsDepartment 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 WilliamsonDepartment 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 ChowDepartment of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
Guillaume JaumeDepartment 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 GaoDepartment of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
Andrew ZhangDepartment 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 ChenDepartment 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 BarasDepartment 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 SerafinDepartment of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
Richard CollingNuffield 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 DownesSunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
Xavier FarréPublic Health Agency of Catalonia, Lleida, Spain.
Peter HumphreyDepartment of Pathology, Yale School of Medicine, New Haven, CT, USA.
Clare VerrillNuffield 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 TrueDepartment of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, WA, USA.
Anil V ParwaniDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Jonathan T C LiuDepartment of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA. Electronic address: jonliu@uw.edu.
Faisal MahmoodDepartment 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. Electronic address: faisalmahmood@bwh.harvard.edu.

Funding

Translational Therapeutics Research Program (TT)P30CA016058 · NCI · OHIO STATE UNIVERSITY · PI Jennifer A. Woyach · 1985 to 2026
$132.3M
Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Multimodal and Generative AI for PathologyR35GM138216 · NIGMS · BROAD INSTITUTE, INC. · PI Faisal Mahmood · 2020 to 2026
$3.4M
Prostate cancer risk stratification via computational 3D pathologyR01CA268207 · NCI · UNIVERSITY OF WASHINGTON · PI Jonathan T.C. Liu, Anant Madabhushi · 2022 to 2026
$3.1M
Oncopathology Training ProgramT32CA251062 · NCI · DANA-FARBER CANCER INST · PI JON C. ASTER, KATHLEEN H BURNS · 2020 to 2026
$2.4M
Instrumentation platform for 3D pathology with open-top light-sheet microscopyR01EB031002 · NIBIB · UNIVERSITY OF WASHINGTON · PI LIU, JONATHAN T.C. · 2021 to 2024
$1.9M
Acquisition of a Sub-micron High-Resolution 3D X-Ray Imaging SystemS10OD023519 · OD · HARVARD UNIVERSITY · PI BELL, DAVID CHARLES · 2018 to 2018
$1.8M
BLRD VA IK6 BX006185NCATS NIH HHS UL1 TR001863NCI NIH HHS P30 CA016058NCI NIH HHS R01 CA268207NCI NIH HHS T32 CA251062NHGRI NIH HHS T32 HG002295NIBIB NIH HHS R01 EB031002NIGMS NIH HHS R35 GM138216NIH HHS S10 OD023519
6 · The paper itself

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.

Indexed as

Imaging, Three-DimensionalProstatic NeoplasmsSupervised Machine LearningDeep LearningHumansMalePrognosisX-Ray Microtomography3D deep learning3D microscopy3D pathologycomputational pathologydeep learningintratumoral heterogeneitymicroCTpatient prognosisslide-free microscopy

Identifiers

PMID38729110
PMCPMC11168832

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.