ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2020
Development and validation of a deep learning system for ascites cytopathology interpretation.
Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.Briefings in functional genomics · 2025Pooled it
- Artificial intelligence in gastric cancer: a systematic review.Journal of cancer research and clinical oncology · 2020Pooled it
- Deep learning for malignancy and tumor origin prediction using cytology or histopathology whole slide images.NPJ digital medicine · 2026Article
- Deep learning neural network of adenocarcinoma detection in effusion cytology.American journal of clinical pathology · 2025Article
- Deep learning radiomics based on MRI for differentiating tongue cancer T - staging.BMC cancer · 2025Article
- Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology.BMC cancer · 2025Article
- Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis.Heliyon · 2024Review
- Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid.Cancers · 2024Article
- Deep learning-driven diagnosis of multi-type vertebra diseases based on computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning algorithms for classification and detection of recurrent aphthous ulcerations using oral clinical photographic images.Journal of dental sciences · 2024Article
- Artificial intelligence in theranostics of gastric cancer, a review.Medical review (2021) · 2023Review
- Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning.Neuroscience bulletin · 2023Article
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- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning.Laboratory investigation; a journal of technical methods and pathology · 2022Article
- Accurate preoperative staging and HER2 status prediction of gastric cancer by the deep learning system based on enhanced computed tomography.Frontiers in oncology · 2022Article
- Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2021Article
- Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer.Frontiers in oncology · 2021Article
- Machine Learning of Urine Cytology Highlights Increased Neutrophil Count in Muscle-Invasive Urothelial Carcinoma.Journal of cytologyArticle
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8 authors at 4 institutions in 1 country.
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Abstract
backgroundEarly diagnosis of Peritoneal metastasis (PM) is clinically significant regarding optimal treatment selection and avoidance of unnecessary surgical procedures. Cytopathology plays an important role in early screening of PM. We aimed to develop a deep learning (DL) system to achieve intelligent cytopathology interpretation, especially in ascites cytopathology.
methodsThe original ascites cytopathology image dataset consists of 139 patients' original hematoxylin-eosin (HE) and Papanicolaou (PAP) Staining images. DL system was developed using transfer learning (TL) to achieve cell detection and classification. Pre-trained alexnet, vgg16, goolenet, resnet18 and resnet50 models were studied. Cell detection dataset consists of 176 cropped images with 6573 annotated cell bounding boxes. Cell classification data set consists of 487 cropped images with 18,558 and 6089 annotated malignant and benign cells in total, respectively.
resultsWe established a novel ascites cytopathology image dataset and achieved automatically cell detection and classification. DetectionNet based on Faster R-CNN using pre-trained resnet18 achieved cell detection with 87.22% of cells' Intersection of Union (IoU) bigger than the threshold of 0.5. The mean average precision (mAP) was 0.8316. The ClassificationNet based on resnet50 achieved the greatest performance in cell classification with AUC = 0.8851, Precision = 96.80%, FNR = 4.73%. The DL system integrating the separately trained DetectionNet and Classificationnet showed great performance in the cytopathology image interpretation.
conclusionsWe demonstrate that the integration of DL can improve the efficiency of healthcare. The DL system we developed using TL techniques achieved accurate cytopathology interpretation, and had great potential to be integrated into clinician workflow.
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