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Updated: Mar 28, 2026
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
Published on: April 8, 2016
Prostate cancer tissue mapping and stratification using DRAQ5 and Eosin fluorescent labels integrated with AI
Michail Georgios Papachristos1, Emiliano Spezi2, Carolina Fuentes3
1School of Medicine, Division of Cancer and Genetics, Cardiff University, Cardiff, United Kingdom.
Background:
Fluorescent microscopy using the DRAQ5 and Eosin probes has been shown in the literature to be capable of producing rapid tissue characterization through synthetic H&E-like pseudoimages, which can be potentially utilized in the clinic. This study focuses on developing deep learning models for classification and segmentation of prostate tissue labeled with DRAQ5&Eosin. The fluorophores provide highly specific features of nuclear and cytoplasmic content that allows for enhanced spatial resolution and multi-parametric analytics. The inter-dependencies of image acquisition and configuration variability on AI predictive accuracy is systematically interrogated. We are thus able to establish limits on experimental and analytical robustness in automated Gleason Grading (1-5) tissue samples of prostate cancer.
Materials And Methods:
A labeling technique based on a far-red DNA probe DRAQ5, and Eosin allowed us to generate a two-channel fluorescent readout of prostatic tissue samples. Deep learning networks were employed to classify and segment DRAQ5 and Eosin fluorescent image regions into healthy and high/low grade cancerous tissue. A subset of images were acquired with variable microscopy configurations (focus, noise, zoom, lens) to evaluate the robustness of the proposed experimental-analytical pipeline and reproducibility of predictions.
Results:
Machine Leaning classifiers of High Grade Cancer (Gleason pattern 4 or 5) vs Healthy, Low Grade Cancer (Gleason pattern 3) vs Healthy, and High Grade Cancer vs Low Grade Cancer achieved an area under the curve of 0.9314, 0.8398, and 0.7715 respectively. Pixel wide cancer segmentation attained DICE scores of 0.8436, 0.5138, and 0.705 for background, healthy, and cancerous tissue respectively. The segmentation model also displayed robustness against a broad range of induced acquisition variability.
Conclusion:
Overall, DRAQ5 and Eosin labeling in combination with AI tools demonstrate a potential pipeline used in diagnostic clinical application when employing fluorescent imaging. Future research could expand and bring this combined fluorescent biomarker and AI methodology to the clinic.
