ArticleFrontiers in bioengineering and biotechnology2020
A New Method for CTC Images Recognition Based on Machine Learning.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images.Frontiers in medicine · 2024Pooled it
- Circulating tumour cells for guiding antibody‒drug conjugate therapy: Role of artificial intelligence.Clinical and translational medicine · 2026Review
- A multi-task deep learning framework for simultaneous prediction of microsatellite instability and tumor mutational burden in gastric cancer from histopathological images.Frontiers in oncology · 2026Article
- Article
- CTCNet: a fine-grained classification network for fluorescence images of circulating tumor cells.Medical & biological engineering & computing · 2025Article
- A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.Scientific reports · 2025Article
- Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review.PeerJ. Computer science · 2025Article
- Complementary detection strategies for circulating tumor cells in breast cancer: clinical implications of combining immunofluorescence and cytopathological staining.Frontiers in oncology · 2025Article
- Understanding TCR T cell knockout behavior using interpretable machine learning.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Improving Circulating Tumor Cell Detection Using Image Synthesis and Transformer Models in Cancer Diagnostics.Sensors (Basel, Switzerland) · 2024Article
- A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning.Scientific reports · 2024Article
- Detection and isolation of brain tumors in cancer patients using neural network techniques in MRI images.Scientific reports · 2024Article
- The BCPM method: decoding breast cancer with machine learning.BMC medical imaging · 2024Article
- Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing.Scientific reports · 2024Article
- Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.Sensors (Basel, Switzerland) · 2024Article
- Advancements in Circulating Tumor Cell Research: Bridging Biology and Clinical Applications.Cancers · 2024Review
- Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2024Article
- PulmoNet: a novel deep learning based pulmonary diseases detection model.BMC medical imaging · 2024Article
- Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Circulating tumor cells (CTCs) derived from primary tumors and/or metastatic tumors are markers for tumor prognosis, and can also be used to monitor therapeutic efficacy and tumor recurrence. Circulating tumor cells enrichment and screening can be automated, but the final counting of CTCs currently requires manual intervention. This not only requires the participation of experienced pathologists, but also easily causes artificial misjudgment. Medical image recognition based on machine learning can effectively reduce the workload and improve the level of automation. So, we use machine learning to identify CTCs. First, we collected the CTC test results of 600 patients. After immunofluorescence staining, each picture presented a positive CTC cell nucleus and several negative controls. The images of CTCs were then segmented by image denoising, image filtering, edge detection, image expansion and contraction techniques using python's openCV scheme. Subsequently, traditional image recognition methods and machine learning were used to identify CTCs. Machine learning algorithms are implemented using convolutional neural network deep learning networks for training. We took 2300 cells from 600 patients for training and testing. About 1300 cells were used for training and the others were used for testing. The sensitivity and specificity of recognition reached 90.3 and 91.3%, respectively. We will further revise our models, hoping to achieve a higher sensitivity and specificity.
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