Evidence map›Paper›PMID 34976305›Full record

ArticleComputational and structural biotechnology journal2021

DeepFoci: Deep learning-based algorithm for fast automatic analysis of DNA double-strand break ionizing radiation-induced foci.

Tomas Vicar, Jaromir Gumulec, Radim Kolar, Olga Kopecna, Eva Pagacova, Iva Falkova, Martin Falk

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Review
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  4. Article
  5. Article
  6. Review
  7. Clinical and translational radiation oncology · 2024
    Article
  8. Review
  9. Spatial-Temporal Genome Regulation in Stress-Response and Cell-Fate Change.International journal of molecular sciences · 2023
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. DNA double-strand break repair in cancer: A path to achieving precision medicine.International review of cell and molecular biology · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Tomas VicarDepartment of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3058/10, Brno, Czech Republic.
Jaromir GumulecDepartment of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, Czech Republic.
Radim KolarDepartment of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3058/10, Brno, Czech Republic.
Olga KopecnaCzech Academy of Sciences, Institute of Biophysics, v.v.i, Department of Cell Biology and Radiobiology, Kralovopolska 135, Brno, Czech Republic.
Eva PagacovaCzech Academy of Sciences, Institute of Biophysics, v.v.i, Department of Cell Biology and Radiobiology, Kralovopolska 135, Brno, Czech Republic.
Iva FalkovaCzech Academy of Sciences, Institute of Biophysics, v.v.i, Department of Cell Biology and Radiobiology, Kralovopolska 135, Brno, Czech Republic.
Martin FalkCzech Academy of Sciences, Institute of Biophysics, v.v.i, Department of Cell Biology and Radiobiology, Kralovopolska 135, Brno, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA double-strand breaks (DSBs), marked by ionizing radiation-induced (repair) foci (IRIFs), are the most serious DNA lesions and are dangerous to human health. IRIF quantification based on confocal microscopy represents the most sensitive and gold-standard method in radiation biodosimetry and allows research on DSB induction and repair at the molecular and single-cell levels. In this study, we introduce DeepFoci - a deep learning-based fully automatic method for IRIF counting and morphometric analysis. DeepFoci is designed to work with 3D multichannel data (trained for 53BP1 and γH2AX) and uses U-Net for nucleus segmentation and IRIF detection, together with maximally stable extremal region-based IRIF segmentation. The proposed method was trained and tested on challenging datasets consisting of mixtures of nonirradiated and irradiated cells of different types and IRIF characteristics - permanent cell lines (NHDFs, U-87) and primary cell cultures prepared from tumors and adjacent normal tissues of head and neck cancer patients. The cells were dosed with 0.5-8 Gy γ-rays and fixed at multiple (0-24 h) postirradiation times. Under all circumstances, DeepFoci quantified the number of IRIFs with the highest accuracy among current advanced algorithms. Moreover, while the detection error of DeepFoci remained comparable to the variability between two experienced experts, the software maintained its sensitivity and fidelity across dramatically different IRIF counts per nucleus. In addition, information was extracted on IRIF 3D morphometric features and repair protein colocalization within IRIFs. This approach allowed multiparameter IRIF categorization of single- or multichannel data, thereby refining the analysis of DSB repair processes and classification of patient tumors, with the potential to identify specific cell subclones. The developed software improves IRIF quantification for various practical applications (radiotherapy monitoring, biodosimetry, etc.) and opens the door to advanced DSB focus analysis and, in turn, a better understanding of (radiation-induced) DNA damage and repair.

Indexed as

53BP1, P53-binding protein 1BiodosimetryCNN, convolutional neural networkConfocal MicroscopyConvolutional Neural NetworkDeep LearningDNA Damage and RepairDSB, DNA double-strand breakFOV, field of viewGUI, graphical user interfaceImage AnalysisIonizing Radiation-Induced Foci (IRIFs)IRIF, ionizing radiation-induced (repair) fociMorphometryMSER, maximally stable extremal region (algorithm)NHDFs, normal human dermal fibroblastsRAD51, DNA repair protein RAD51 homolog 1U-87, U-87 glioblastoma cell lineγH2AX, histone H2AX phosphorylated at serine 139

Identifiers

PMID34976305
PMCPMC8668444

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.