# Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction

arXiv: https://arxiv.org/abs/2410.00945 · HTML: https://mlsanity.com/paper/2410.00945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 67 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology](https://mlsanity.com/paper/2408.00738/) (2024)
- [RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank](https://mlsanity.com/paper/2210.02885/) (2022)
- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning](https://mlsanity.com/paper/2306.01859/) (2023)
- [A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models](https://mlsanity.com/paper/2407.06508/) (2024)
- [Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge](https://mlsanity.com/paper/1807.08284/) (2018)
- [Transcriptomics-guided Slide Representation Learning in Computational Pathology](https://mlsanity.com/paper/2405.11618/) (2024)
- [Benchmarking foundation models as feature extractors for weakly-supervised computational pathology](https://mlsanity.com/paper/2408.15823/) (2024)
- [A Comprehensive Analysis of Deep Regression](https://mlsanity.com/paper/1803.08450/) (2018)
- [Multimodal contrastive learning for spatial gene expression prediction using histology images](https://mlsanity.com/paper/2407.08216/) (2024)
- [Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer](https://mlsanity.com/paper/2009.08917/) (2020)
- [A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification](https://mlsanity.com/paper/2405.09990/) (2024)
- [hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images](https://mlsanity.com/paper/2304.04507/) (2023)
- [Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks](https://mlsanity.com/paper/2104.09310/) (2021)

## Abstract (arXiv)

Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally accessible. Recent advances in computational pathology enable prediction of transcriptomic profiles directly from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs), although optimal modeling strategies and clinical relevance remain unclear. In this study, we systematically evaluate deep regression models for WSI-based gene-expression prediction across multiple regression formulations and pathology foundation models (PFMs), and assess whether the resulting predicted transcriptomic signals retain prognostic utility. Across four TCGA datasets, we find that direct regression using attention-based multiple instance learning together with PFM feature extractors provides a strong and computationally efficient baseline, with no consistent benefit from separately training multiple models on subsets of genes. We then externally validate the selected configuration on an independent cohort of 997 breast cancer patients, demonstrating robust generalization for clinically relevant gene sets such as PAM50. To assess clinical relevance, we further evaluate predicted gene-expression scores in two independent population-representative breast cancer cohorts comprising 4,172 patients with survival endpoints, where predicted scores retain prognostic value in both the full patient cohort and the ER+ & HER2- subgroup. Together, these results demonstrate that WSI-based gene-expression prediction can generalize across independent cohorts and recover biologically and clinically meaningful molecular structure, supporting its potential as a scalable approach for transcriptomic phenotyping and risk stratification.