# Biomechanics‐Driven 3D Architecture Inference from Histology Using CellSqueeze3D

## TL;DR

CellSqueeze3D uses biomechanics to infer 3D cell structures from 2D histology images, improving cancer diagnostics and gene mutation predictions.

## Contribution

CellSqueeze3D introduces a biomechanics-driven computational framework to infer 3D cell architecture from single 2D H&E-stained sections.

## Key findings

- CellSqueeze3D's nuclear-to-cytoplasmic ratio distribution differs significantly from random assignments (p = 1.39e-80).
- 3D-informed cellular classifier outperforms traditional methods with AUC increases of 0.136 and 0.069.
- Cellular and nuclear size indices predict 21 gene mutation statuses with median AUROC above 0.65.

## Abstract

Conventional 2D analysis of hematoxylin and eosin (H&E)‐stained images is fundamentally limited by the tissue thickness, as cellular overlap and morphological changes in the compressed perspective obscure distinct cell boundaries. To address this, it develops CellSqueeze3D, a computational framework that reconstructs the 3D spatial distribution and size of individual cells from a single H&E‐stained section. Founded on the principle that 2D cell compression preserves 3D geometry, the method employs a hybrid Particle Swarm Optimization (PSO) approach with biomechanical constraints to infer biologically plausible reconstructions. Validation shows that the nuclear‐to‐cytoplasmic (N/C) ratio distribution derived from the predicted cell radii differs significantly from random assignments (p = 1.39e‐80). By employing projected cell boundaries, the 3D‐informed cellular classifier surpassed traditional methods (AUC increases of 0.136 and 0.069). The resulting morphological metrics also revealed strong associations with key gene expression patterns, providing prognostic insights. Furthermore, cellular and nuclear size indices from CellSqueeze3D significantly predict the mutation status of 21 genes in TCGA cohorts, achieving a median AUROC above 0.65 in fivefold cross‐validation. This study demonstrates that fully utilizing the previously untapped 3D spatial information from a single slice significantly enhances computational pathology and quantitative tissue phenotyping.

CellSqueeze3D reconstructs 3D cellular architecture from standard 2D histology images using biomechanical constraints and optimization. Validated on clinical datasets, it enables accurate tissue phenotyping, predicts gene mutations, and reveals significant correlations between nuclear‐cytoplasmic ratio entropy and tumor progression. This demonstrates the value of extracting 3D spatial information from routine pathology to advance cancer diagnostics.