ArticleCancers2020
Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Integration of Histologic Pattern Classifiers and Graph Convolutional Networks for Prognostic Prediction in Lung Adenocarcinoma.JTO clinical and research reports · 2026Article
- Comparative evaluation of CNN models for nasopharyngeal carcinoma classification on pathology data.Scientific reports · 2026Article
- PATHOS: Pathology attention framework for treatment response stratification in ovarian high-grade serous carcinomas following neoadjuvant chemotherapy on H&E images.Journal of pathology informatics · 2026Article
- Assessing the influence of two deep-learning assistance modes on pathologists in cancer identification.Diagnostic pathology · 2026Article
- Nasopharyngeal Carcinoma at the Virology Precision Oncology Nexus: Decoding Molecular Alterations for Early Intervention and Therapeutic Innovation.Biomolecules · 2026Review
- Dual-stage deep learning framework for neuroblastoma differentiation by integrating cell segmentation and multiscale modeling.World journal of pediatric surgery · 2026Article
- Early diagnosis of nasopharyngeal carcinoma based on machine learning modelling and blood plasma metallomics analysis.Scientific reports · 2025Article
- Annotation-free deep learning for predicting gene mutations from whole slide images of acute myeloid leukemia.NPJ precision oncology · 2025Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia.Tissue engineering. Part A · 2024Review
- Advancing precise diagnosis of nasopharyngeal carcinoma through endoscopy-based radiomics analysis.iScience · 2024Article
- Automatic Annotation Diagnostic Framework for Nasopharyngeal Carcinoma via Pathology-Fidelity GAN and Prior-Driven Classification.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility.Veterinary sciences · 2024Article
- Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance.Journal of imaging informatics in medicine · 2024Article
- Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.Molecular cancer research : MCR · 2024Article
- Immunohistochemical Stain-Aided Annotation Accelerates Machine Learning and Deep Learning Model Development in the Pathologic Diagnosis of Nasopharyngeal Carcinoma.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.Journal of clinical medicine · 2023Review
- Nasopharyngeal carcinoma ecology theory: cancer as multidimensional spatiotemporal "unity of ecology and evolution" pathological ecosystem.Theranostics · 2023Review
- Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging.Combinatorial chemistry & high throughput screening · 2023Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most cases are nonkeratinizing carcinoma with little differentiation and many admixed lymphocytes. Our aim was to evaluate the possibility to identify NPC in nasopharyngeal biopsies using deep learning. A total of 726 nasopharyngeal biopsies were included. Among them, 100 cases were randomly selected as the testing set, 20 cases as the validation set, and all other 606 cases as the training set. All three datasets had equal numbers of NPC cases and benign cases. Manual annotation was performed. Cropped square image patches of 256 × 256 pixels were used for patch-level training, validation, and testing. The final patch-level algorithm effectively identified NPC patches, with an area under the receiver operator characteristic curve (AUC) of 0.9900. Using gradient-weighted class activation mapping, we demonstrated that the identification of NPC patches was based on morphologic features of tumor cells. At the second stage, whole-slide images were sequentially cropped into patches, inferred with the patch-level algorithm, and reconstructed into images with a smaller size for training, validation, and testing. Finally, the AUC was 0.9848 for slide-level identification of NPC. Our result shows for the first time that deep learning algorithms can identify NPC.
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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.