ArticleJournal of medical imaging (Bellingham, Wash.)2018
Predicting malignant nodules by fusing deep features with classical radiomics features.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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
43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 110 citations in OpenAlex.
- A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.BMC medical informatics and decision making · 2026Pooled it
- Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review.Journal of digital imaging · 2020Pooled it
- Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.Biomedical engineering online · 2024Trial
- Identification of ischemic stroke risk in patients with left ventricular non-compaction using echocardiography, deep learning, and radiomics.BMC medical imaging · 2026Article
- Screening opportunistic osteoporosis through multimodal techniques of hip joint CT images: exploring 2D and 3D deep learning, radiomics, clinical data, and their integration.Frontiers in endocrinology · 2026Article
- Preoperative CT-based Radiomics for Predicting Response to Neoadjuvant Chemoimmunotherapy in Esophageal Squamous Cell Carcinoma.Radiology. Imaging cancer · 2026Article
- Echocardiography Combined With Radiomics and Deep Transfer Learning to Diagnose Hypertrophic Cardiomyopathy and Other Etiologies of Left Ventricular Hypertrophy: A Multicenter Study Comparing the Performance With Echocardiologists.Reviews in cardiovascular medicine · 2025Article
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- Automated classification of chondroid tumor using 3D U-Net and radiomics with deep features.Scientific reports · 2025Article
- Development and validation of an LDCT-based deep learning radiomics nomogram for predicting postoperative recurrence of stage Ia lung adenocarcinoma.Frontiers in oncology · 2025Article
- Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
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- Article
- A graph neural network model for the diagnosis of lung adenocarcinoma based on multimodal features and an edge-generation network.Quantitative imaging in medicine and surgery · 2023Article
- Article
- Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques.Sensors (Basel, Switzerland) · 2023Article
- Multi-Layered Non-Local Bayes Model for Lung Cancer Early Diagnosis Prediction with the Internet of Medical Things.Bioengineering (Basel, Switzerland) · 2023Article
- A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer.PeerJ. Computer science · 2023Article
- Predictive performance of radiomic models based on features extracted from pretrained deep networks.Insights into imaging · 2022Article
- Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models.BMC cancer · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Lung cancer has a high incidence and mortality rate. Early detection and diagnosis of lung cancers is best achieved with low-dose computed tomography (CT). Classical radiomics features extracted from lung CT images have been shown as able to predict cancer incidence and prognosis. With the advancement of deep learning and convolutional neural networks (CNNs), deep features can be identified to analyze lung CTs for prognosis prediction and diagnosis. Due to a limited number of available images in the medical field, the transfer learning concept can be helpful. Using subsets of participants from the National Lung Screening Trial (NLST), we utilized a transfer learning approach to differentiate lung cancer nodules versus positive controls. We experimented with three different pretrained CNNs for extracting deep features and used five different classifiers. Experiments were also conducted with deep features from different color channels of a pretrained CNN. Selected deep features were combined with radiomics features. A CNN was designed and trained. Combinations of features from pretrained, CNNs trained on NLST data, and classical radiomics were used to build classifiers. The best accuracy (76.79%) was obtained using feature combinations. An area under the receiver operating characteristic curve of 0.87 was obtained using a CNN trained on an augmented NLST data cohort.
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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.