ArticleProceedings of the National Academy of Sciences of the United States of America2018
Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: Detection of bladder cancer.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- High-Throughput Nanorheology of Living Cells Powered by Supervised Machine Learning.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2025Article
- Diagnosis of early-stage bladder cancer via unprocessed urine samples at the point of care.Nature biomedical engineering · 2025Article
- Perceiving and Countering Marine Biofouling: Structure, Forces, and Processes at Surfaces in Sea Water Across the Length Scales.Langmuir : the ACS journal of surfaces and colloids · 2025Review
- Data-driven analysis for the evaluation of cortical mechanics of non-adherent cells.Scientific reports · 2025Article
- Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study.Journal of medical Internet research · 2025Article
- Recent Advances and Emerging Innovations in Transurethral Resection of Bladder Tumor (TURBT) for Non-Muscle Invasive Bladder Cancer: A Comprehensive Review of Current Literature.Research and reports in urology · 2025Review
- Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning.Cells · 2024Article
- New Challenges in Bladder Cancer Diagnosis: How Biosensing Tools Can Lead to Population Screening Opportunities.Sensors (Basel, Switzerland) · 2024Review
- Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis.Computational and structural biotechnology journal · 2024Review
- Nanomechanical mapping of PLA hydroxyapatite composite scaffolds links surface homogeneity to stem cell differentiation.Scientific reports · 2024Article
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition.Physical chemistry chemical physics : PCCP · 2024Review
- The Revolution in Breast Cancer Diagnostics: From Visual Inspection of Histopathology Slides to Using Desktop Tissue Analysers for Automated Nanomechanical Profiling of Tumours.Bioengineering (Basel, Switzerland) · 2024Review
- Cell Surface Parameters for Accessing Neutrophil Activation Level with Atomic Force Microscopy.Cells · 2024Article
- A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection.Scientific data · 2024Article
- Intracellular remodeling associated with endoplasmic reticulum stress modifies biomechanical compliance of bladder cells.Cell communication and signaling : CCS · 2023Article
- Article
- Quick Detection ofSensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We report an approach in diagnostic imaging based on nanoscale-resolution scanning of surfaces of cells collected from body fluids using a recent modality of atomic force microscopy (AFM), subresonance tapping, and machine-leaning analysis. The surface parameters, which are typically used in engineering to describe surfaces, are used to classify cells. The method is applied to the detection of bladder cancer, which is one of the most common human malignancies and the most expensive cancer to treat. The frequent visual examinations of bladder (cytoscopy) required for follow-up are not only uncomfortable for the patient but a serious cost for the health care system. Our method addresses an unmet need in noninvasive and accurate detection of bladder cancer, which may eliminate unnecessary and expensive cystoscopies. The method, which evaluates cells collected from urine, shows 94% diagnostic accuracy when examining five cells per patient's urine sample. It is a statistically significant improvement (
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Registered trials
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.