# DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set

arXiv: https://arxiv.org/abs/2009.08123 · HTML: https://mlsanity.com/paper/2009.08123/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology](https://mlsanity.com/paper/2408.00738/) (2024)
- [What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2201.08550/) (2022)
- [A Unified Low-level Foundation Model for Enhancing Pathology Image Quality](https://mlsanity.com/paper/2509.01071/) (2025)
- [A Multicenter Benchmark of Multiple Instance Learning Models for Lymphoma Subtyping from HE-stained Whole Slide Images](https://mlsanity.com/paper/2512.14640/) (2025)
- [A Deployment-Friendly Foundational Framework for Efficient Computational Pathology](https://mlsanity.com/paper/2602.14010/) (2026)
- [PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection](https://mlsanity.com/paper/2307.03211/) (2023)
- [LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype](https://mlsanity.com/paper/2311.09574/) (2023)
- [Classification and Morphological Analysis of DLBCL Subtypes in H&E-Stained Slides](https://mlsanity.com/paper/2411.08531/) (2024)

## Abstract (arXiv)

Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistently demonstrated to correlate with prognosis. We present a morphologic analysis of histology sections from 209 DLBCL cases with associated clinical and cytogenetic data. Duplicate tissue core sections were arranged in tissue microarrays (TMAs), and replicate sections were stained with H&E and immunohistochemical stains for CD10, BCL6, MUM1, BCL2, and MYC. The TMAs are accompanied by pathologist-annotated regions-of-interest (ROIs) that identify areas of tissue representative of DLBCL. We used a deep learning model to segment all tumor nuclei in the ROIs, and computed several geometric features for each segmented nucleus. We fit a Cox proportional hazards model to demonstrate the utility of these geometric features in predicting survival outcome, and found that it achieved a C-index (95% CI) of 0.635 (0.574,0.691). Our finding suggests that geometric features computed from tumor nuclei are of prognostic importance, and should be validated in prospective studies.