ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2022
Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools.Cancer cytopathology · 2026Pooled it
- Artificial intelligence for lung cancer classification in cytology specimens: A systematic review and diagnostic test accuracy meta-analysis of benign-malignant diagnosis and ADC/SCC/SCLC subtyping.Journal of pathology informatics · 2026Review
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- Deep learning for malignancy and tumor origin prediction using cytology or histopathology whole slide images.NPJ digital medicine · 2026Article
- Ensemble based in transfer learning for cytological classification in pleural fluid.Frontiers in digital health · 2026Article
- Deep learning neural network of adenocarcinoma detection in effusion cytology.American journal of clinical pathology · 2025Article
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models.Diagnostics (Basel, Switzerland) · 2025Article
- Innovative Biomarkers for Diagnosing Malignant Ascites in Liver Cancer.OncoTargets and therapy · 2025Article
- Automated recognition and segmentation of lung cancer cytological images based on deep learning.PloS one · 2025Article
- Multiple serous cavity effusion screening based on smear images using vision transformer.Scientific reports · 2024Article
- Article
- Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid.Cancers · 2024Article
- High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos.Nature communications · 2023Article
- PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.BMC cancer · 2023Article
- A deep learning model to triage and predict adenocarcinoma on pancreas cytology whole slide imaging.Scientific reports · 2023Article
- Review
- Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid.Cells · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019-9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.
Indexed as
Identifiers
What OpenQuestion holds
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.