ArticleScientific reports2021
A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 26 papers.
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, 59 citations in OpenAlex.
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Harnessing Next-Generation 3D Cancer Models to Elucidate Tumor-Microbiome Crosstalk.Advanced healthcare materials · 2026Review
- Advances in machine learning-enhanced microfluidic cell sorting.Science advances · 2025Review
- Article
- Cancer-on-a-chip for precision cancer medicine.Lab on a chip · 2025Review
- Emerging Trends in Microfluidic Biomaterials: From Functional Design to Applications.Journal of functional biomaterials · 2025Review
- The Latest Advances in Microfluidic DLD Cell Sorting Technology: The Optimization of Channel Design.Biosensors · 2025Review
- Machine Learning-Driven Innovations in Microfluidics.Biosensors · 2024Review
- Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.ACS sensors · 2024Review
- scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information.Communications biology · 2024Article
- Microfluidics-based patient-derived disease detection tool for deep learning-assisted precision medicine.Biomicrofluidics · 2024Article
- Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis.Biosensors · 2023Review
- Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems.Micromachines · 2023Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Deep learning with microfluidics for on-chip droplet generation, control, and analysis.Frontiers in bioengineering and biotechnology · 2023Review
- A review on deep learning applications in highly multiplexed tissue imaging data analysis.Frontiers in bioinformatics · 2023Review
- Patient-derived organoids of lung cancer based on organoids-on-a-chip: enhancing clinical and translational applications.Frontiers in bioengineering and biotechnology · 2023Review
- Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis.Frontiers in bioengineering and biotechnology · 2023Article
- Prediction of Hemorrhagic Complication after Thrombolytic Therapy Based on Multimodal Data from Multiple Centers: An Approach to Machine Learning and System Implementation.Journal of personalized medicine · 2022Article
Corrections and comments
- Retraction · 2025-07-07Computer-Aided Content or Computer-Generated Content · Concerns/Issues about Image · Euphemisms for Plagiarism · Investigation by Journal/Publisher · Investigation by Third Party · Objections by Author(s) · Original Data and/or Images not Provided and/or not Available · Plagiarism of Text · · see also: https://pubpeer.com/publications/A6F22085325622D08AB15B9946180B
- Retracted
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung cancer detection and its sub-types classification from cell's images play a crucial role toward an early-stage cancer prognosis and more individualized therapy. The rapid development of machine learning techniques, especially deep learning algorithms, has attracted much interest in its application to medical image problems. In this study, to develop a reliable Computer-Aided Diagnosis (CAD) system for accurately distinguishing between cancer and healthy cells, we grew popular Non-Small Lung Cancer lines in a microfluidic chip followed by staining with Phalloidin and images were obtained by using an IX-81 inverted Olympus fluorescence microscope. We designed and tested a deep learning image analysis workflow for classification of lung cancer cell-line images into six classes, including five different cancer cell-lines (P-C9, SK-LU-1, H-1975, A-427, and A-549) and normal cell-line (16-HBE). Our results demonstrate that ResNet18, a residual learning convolutional neural network, is an efficient and promising method for lung cancer cell-lines categorization with a classification accuracy of 98.37% and F1-score of 97.29%. Our proposed workflow is also able to successfully distinguish normal versus cancerous cell-lines with a remarkable average accuracy of 99.77% and F1-score of 99.87%. The proposed CAD system completely eliminates the need for extensive user intervention, enabling the processing of large amounts of image data with robust and highly accurate results.
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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.