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Analysis of 3D pathology samples using weakly supervised AI.

Andrew H Song¹, Mane Williams², Drew F K Williamson¹

¹Department 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.

|May 10, 2024

Summary

This summary is machine-generated.

TriPath, a deep-learning platform, uses 3D tissue imaging to predict prostate cancer recurrence. This 3D approach surpasses traditional 2D methods, reducing sampling bias and improving risk prediction accuracy.

Keywords:

3D deep learning 3D microscopy 3D pathology computational pathology deep learning intratumoral heterogeneity microCT patient prognosis slide-free microscopy

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Area of Science:

Computational pathology
Digital pathology
Oncology imaging

Background:

Human tissue analysis traditionally relies on 2D histopathology, which can miss crucial 3D structural information due to sampling bias.
Existing 3D imaging methods face challenges in clinical translation due to manual evaluation complexity and lack of computational tools for large datasets.

Purpose of the Study:

To introduce TriPath, a deep-learning platform designed for processing 3D tissue volumes.
To develop and validate 3D morphological feature-based models for predicting clinical outcomes, specifically recurrence risk in prostate cancer.

Main Methods:

Prostate cancer specimens were imaged using open-top light-sheet microscopy and microcomputed tomography.
Deep learning models were trained on these 3D datasets to predict recurrence risk.
Performance was compared against traditional 2D slice-based methods and expert pathologist assessments.

Main Results:

3D volume-based prognostication demonstrated superior performance compared to traditional 2D approaches.
The platform effectively predicted recurrence risk, outperforming baseline models including expert pathologist evaluations.
Utilizing larger tissue volumes improved prognostic accuracy and reduced variability caused by sampling bias.

Conclusions:

TriPath offers an efficient computational platform for analyzing 3D tissue data.
3D morphological analysis provides more accurate and reliable clinical outcome predictions than 2D methods.
The study highlights the significant value of comprehensive 3D tissue characterization in overcoming limitations of traditional histopathology.