ArticleTomography (Ann Arbor, Mich.)2021
Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning.
Article in Tomography (Ann Arbor, Mich.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- The Next Frontier in Quantitative Co-Clinical Imaging to Advance Functional Precision Oncology.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Measurement of mouse head and neck tumors by automated analysis of CBCT images.Scientific reports · 2023Article
- The predictive value of chest computed tomography images, tumor markers, and metabolomics in the identification of benign and malignant pulmonary nodules.Journal of thoracic disease · 2023Article
- The National Cancer Institute's Co-Clinical Quantitative Imaging Research Resources for Precision Medicine in Preclinical and Clinical Settings.Tomography (Ann Arbor, Mich.) · 2023Review
- Neoadjuvant Radiation Therapy and Surgery Improves Metastasis-Free Survival over Surgery Alone in a Primary Mouse Model of Soft Tissue Sarcoma.Molecular cancer therapeutics · 2023Article
- Airway remodeling heterogeneity in asthma and its relationship to disease outcomes.Frontiers in physiology · 2023Review
- Article
- Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip.Frontiers in bioengineering and biotechnology · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76-0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice.
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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.