ArticleScientific reports2017
Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Article
- 3D environment favors persistent changes in cell functions and altered morphology, wrinkling, and biomechanical signature of the nucleus.Cell reports. Physical science · 2026Article
- Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens.Cell systems · 2025Article
- aiSEGcell: User-friendly deep learning-based segmentation of nuclei in transmitted light images.PLoS computational biology · 2024Article
- Imaging and AI based chromatin biomarkers for diagnosis and therapy evaluation from liquid biopsies.NPJ precision oncology · 2023Article
- Detecting radio- and chemoresistant cells in 3D cancer co-cultures using chromatin biomarkers.Scientific reports · 2023Article
- Transcription inhibition suppresses nuclear blebbing and rupture independently of nuclear rigidity.Journal of cell science · 2023Article
- Combined alteration of lamin and nuclear morphology influences the localization of the tumor-associated factor AKTIP.Journal of experimental & clinical cancer research : CR · 2022Article
- Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.Arthritis research & therapy · 2022Review
- Cancer cell mechanobiology: a new frontier for cancer research.Journal of the National Cancer Center · 2022Review
- Artificial intelligence in clinical research of cancers.Briefings in bioinformatics · 2022Article
- A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology.PloS one · 2022Article
- A survey of physical methods for studying nuclear mechanics and mechanobiology.APL bioengineering · 2021Review
- Single cell imaging-based chromatin biomarkers for tumor progression.Scientific reports · 2021Article
- Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas.Cancers · 2021Article
- Label-free microscopy of mitotic chromosomes using the polarization orthogonality breaking technique.Biomedical optics express · 2021Article
- Estimation of three-dimensional chromatin morphology for nuclear classification and characterisation.Scientific reports · 2021Article
- Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides.Frontiers in oncology · 2021Article
- Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm.Frontiers in public health · 2021Article
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current cancer diagnosis employs various nuclear morphometric measures. While these have allowed accurate late-stage prognosis, early diagnosis is still a major challenge. Recent evidence highlights the importance of alterations in mechanical properties of single cells and their nuclei as critical drivers for the onset of cancer. We here present a method to detect subtle changes in nuclear morphometrics at single-cell resolution by combining fluorescence imaging and deep learning. This assay includes a convolutional neural net pipeline and allows us to discriminate between normal and human breast cancer cell lines (fibrocystic and metastatic states) as well as normal and cancer cells in tissue slices with high accuracy. Further, we establish the sensitivity of our pipeline by detecting subtle alterations in normal cells when subjected to small mechano-chemical perturbations that mimic tumor microenvironments. In addition, our assay provides interpretable features that could aid pathological inspections. This pipeline opens new avenues for early disease diagnostics and drug discovery.
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Registered trials
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