Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers

• URL: http://arxiv.org/abs/2601.06360v1
• Date: Sat, 10 Jan 2026 00:03:33 GMT
• Title: Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers
• Authors: Mara Pleasure, Ekaterina Redekop, Dhakshina Ilango, Zichen Wang, Vedrana Ivezic, Kimberly Flores, Israa Laklouk, Jitin Makker, Gregory Fishbein, Anthony Sisk, William Speier, Corey W. Arnold,
• Abstract summary: We present PROTAS, a deep learning framework that quantifies reactive stroma (RS) in routine hematoxylin and eosin slides.<n> PROTAS-defined RS is characterized by nuclear enlargement, collagen disorganization, and transcriptomic enrichment of contractile pathways.
• Score: 17.12439149946061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current histopathological grading of prostate cancer relies primarily on glandular architecture, largely overlooking the tumor microenvironment. Here, we present PROTAS, a deep learning framework that quantifies reactive stroma (RS) in routine hematoxylin and eosin (H&E) slides and links stromal morphology to underlying biology. PROTAS-defined RS is characterized by nuclear enlargement, collagen disorganization, and transcriptomic enrichment of contractile pathways. PROTAS detects RS robustly in the external Prostate, Lung, Colorectal, and Ovarian (PLCO) dataset and, using domain-adversarial training, generalizes to diagnostic biopsies. In head-to-head comparisons, PROTAS outperforms pathologists for RS detection, and spatial RS features predict biochemical recurrence independently of established prognostic variables (c-index 0.80). By capturing subtle stromal phenotypes associated with tumor progression, PROTAS provides an interpretable, scalable biomarker to refine risk stratification.

Related papers

• HistoPrism: Unlocking Functional Pathway Analysis from Pan-Cancer Histology via Gene Expression Prediction [3.240836649620839]
  HistoPrism is an efficient transformer-based architecture for pan-cancer prediction of gene expression from histology.<n> pathway-level benchmark shifts assessment from isolated gene-level variance to coherent functional pathways.
  arXiv  Detail & Related papers  (2026-01-29T11:25:14Z)
• PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology [8.879502752288325]
  We present PEaRL (Pathway Enhanced Representation Learning), a framework that represents transcriptomics through pathway activation scores computed with ssGSEA.<n>Across three cancer ST datasets, PEaRL consistently outperforms SOTA methods, yielding higher accuracy for both gene- and pathway-level expression prediction.
  arXiv  Detail & Related papers  (2025-10-03T19:21:23Z)
• TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs [49.69047720285225]
  We propose a novel topological approach that explicitly extracts multi-scale topological structures to better approximate breast parenchymal structures. We empirically validate emphTopoTxR using the VICTRE phantom breast dataset. Our qualitative and quantitative analyses suggest differential topological behavior of breast tissue in treatment-na"ive imaging.
  arXiv  Detail & Related papers  (2024-11-05T19:35:10Z)
• Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment Response Prediction from Whole Slide Images [1.4999444543328293]
  We leverage the latest histopathology foundation models trained on large-scale whole slide image (WSI) to extract ovarian tumor tissue features for predicting bevacizumab response. Our survival models are able to stratify high- and low-risk cases with statistical significance.
  arXiv  Detail & Related papers  (2024-07-30T07:15:39Z)
• Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses [40.80133767939435]
  Existing AI models face challenges due to noise in transcriptomics data and lack of biological interpretability. We introduce VETE, a novel neural network framework that incorporates a variational component to mitigate noise effects. VETE bridges the gap between AI-driven predictions and biologically meaningful insights in cancer research.
  arXiv  Detail & Related papers  (2024-07-05T13:13:02Z)
• Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology [3.9270231212340354]
  There is not a widely available method to reproducibly measure tumor and immune phenotypes for each patient's tumor. We applied multiple instance learning (MIL) algorithms to assess activity of ten biologically relevant pathways from the hematoxylin and eosin slide of primary breast tumors. Our trained models recognize biologically relevant spatial patterns of cell sub-populations from H&E.
  arXiv  Detail & Related papers  (2024-04-25T08:15:37Z)
• Regression-based Deep-Learning predicts molecular biomarkers from pathology slides [40.24757332810004]
  We developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
  arXiv  Detail & Related papers  (2023-04-11T11:43:51Z)
• hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images [11.822321981275232]
  Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively. We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA-sequencing techniques to predict the expression of 138 genes.
  arXiv  Detail & Related papers  (2023-04-10T10:54:32Z)
• Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging [66.63200823918429]
  We investigate enhancing clinical support for breast cancer with deep learning models. We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
  arXiv  Detail & Related papers  (2022-11-10T03:02:12Z)
• Generating counterfactual explanations of tumor spatial proteomes to discover effective strategies for enhancing immune infiltration [44.99833362998488]
  The tumor microenvironment (TME) significantly impacts cancer prognosis due to its immune composition. Here, we formulate T-cell infiltration prediction as a self-supervised machine learning problem. We apply our framework to melanoma, colorectal cancer liver metastases, and breast tumor data, discovering perturbations predicted to support T-cell infiltration.
  arXiv  Detail & Related papers  (2022-11-08T05:46:02Z)
• A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology [62.997667081978825]
  Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
  arXiv  Detail & Related papers  (2022-09-27T14:08:19Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer [11.724098271782335]
  We propose a novel method to direct a neural network's attention to a dedicated set of voxels surrounding biologically relevant tissue structures. By extracting multi-dimensional topological structures with high saliency, we build a topology-derived biomarker, TopoTxR. We demonstrate the efficacy of TopoTxR in predicting response to neoadjuvant chemotherapy in breast cancer.
  arXiv  Detail & Related papers  (2021-05-13T02:38:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.