ArticleCancer medicine2023
Prediction of cervical lymph node metastasis from immunostained specimens of tongue cancer using a multilayer perceptron neural network.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Review
- Deep learning radiomics based on multimodal MRI for preoperative prediction of N stage in tongue squamous cell carcinoma: a multicenter study.BMC oral health · 2026Article
- Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.The Laryngoscope · 2025Article
- Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Prediction of cervical lymph node metastasis from immunostained specimens of tongue cancer using a multilayer perceptron neural network.Cancer medicine · 2023Article
- Comparison of Efficacy and Safety of Atezolizumab Plus Bevacizumab and Lenvatinib as First-Line Therapy for Unresectable Hepatocellular Carcinoma: A Propensity Score Matching Analysis.Targeted oncology · 2022Article
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough cervical lymph node metastasis is an important prognostic factor for oral cancer, occult metastases remain undetected even by diagnostic imaging. We developed a learning model to predict lymph node metastasis in resected specimens of tongue cancer by classifying the level of immunohistochemical (IHC) staining for angiogenesis- and lymphangiogenesis-related proteins using a multilayer perceptron neural network (MNN).
methodsWe obtained a dataset of 76 patients with squamous cell carcinoma of the tongue who had undergone primary tumor resection. All 76 specimens were IHC stained for the six types shown above (VEGF-C, VEGF-D, NRP1, NRP2, CCR7, and SEMA3E) and 456 slides were prepared. We scored the staining levels visually on all slides. We created virtual slides (4560 images) and the accuracy of the MNN model was verified by comparing it with a hue-saturation (HS) histogram, which quantifies the manually determined visual information.
resultsThe accuracy of the training model with the MNN was 98.6%, and when the training image was converted to grayscale, the accuracy decreased to 52.9%. This indicates that our MNN adequately evaluates the level of staining rather than the morphological features of the IHC images. Multivariate analysis revealed that CCR7 staining level and T classification were independent factors associated with the presence of cervical lymph node metastasis in both HS histograms and MNN.
conclusionThese results suggest that IHC assessment using MNN may be useful for identifying lymph node metastasis in patients with tongue cancer.
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