ArticleTranslational oncology2019
A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors.
Article in Translational oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05782400 (Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma), which is not on this map. Cited by 63 papers.
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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.
Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma
Who cites it
63 citing papers in PubMed.
- Multimodal Prediction of Renal Tumor Malignancy From Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Development and validation of a deep learning radiomics model for predicting capsular invasion in small renal masses: a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- A two-stage deep learning framework for kidney disease detection using modified specular-free imaging and EfficientNetB2.Scientific reports · 2026Article
- Machine learning based classification of aggressive and malignant renal tumors from multimodal data.PLOS digital health · 2026Article
- Multimodal-Imaging-Based Interpretable Deep Learning Framework for Distinguishing Brucella from Tuberculosis Spondylitis: A Dual-Center Study.Diagnostics (Basel, Switzerland) · 2025Article
- Clinical-Oriented Hierarchical Machine Learning Framework for Early Kidney Tumor Detection and Malignant Subtype Classification.Tomography (Ann Arbor, Mich.) · 2025Article
- DeepGut: A collaborative multimodal large language model framework for digestive disease assisted diagnosis and treatment.World journal of gastroenterology · 2025Observational
- Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Intra- and Peritumoral CT-Based Radiomics for Assessing Pathologic T-Staging in Clear Cell Renal Cell Carcinoma: A Multicenter Study.Annals of surgical oncology · 2025Article
- An attention enhanced dilated bottleneck network for kidney disease classification.Scientific reports · 2025Article
- Using deep learning to differentiate among histology renal tumor types in computed tomography scans.BMC medical imaging · 2025Article
- CT-based deep learning for preoperative prediction of pathological grading of renal clear cell carcinoma.Frontiers in oncology · 2025Article
- MRI-based deep learning with clinical and imaging features to differentiate medulloblastoma and ependymoma in children.Frontiers in molecular biosciences · 2025Article
- Current and Future Applications of Artificial Intelligence to Diagnose and Treat Male Infertility.Advances in experimental medicine and biology · 2025Review
- Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.PloS one · 2025Article
- Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment.IEEE transactions on radiation and plasma medical sciences · 2024Article
- CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.Journal of magnetic resonance imaging : JMRI · 2024Article
- Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features.BMC medical imaging · 2024Article
- Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach.Heliyon · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the effect of transfer learning on computed tomography (CT) images for the benign and malignant classification on renal tumors and to attempt to improve the classification accuracy by building patient-level models.
methodsOne hundred ninety-two cases of renal tumors were collected and identified by pathologic diagnosis within 15 days after enhanced CT examination (66% male, 70% malignant renal tumors, average age of 62.27 ± 12.26 years). The InceptionV3 model pretrained by the ImageNet dataset was cross-trained to perform this classification. Five image-level models were established for each of the Slice, region of interest (ROI), and rectangular box region (RBR) datasets. Then, two patient-level models were built based on the optimal image-level models. The network's performance was evaluated through analysis of the receiver operating characteristic (ROC) and five-fold cross-validation.
resultsIn the image-level models, the test results of model trained on the Slice dataset [accuracy (ACC) = 0.69 and Matthews correlation coefficient (MCC) = 0.45] were the worst. The corresponding results on the ROI dataset (ACC = 0.97 and MCC = 0.93) were slightly better than those on the RBR dataset (ACC = 0.93 and MCC = 0.85) when freezing the weights before the mixed6 layer. Compared with the image-level models, both patient-level models could discriminate better (ACC increased by 2%-5%) on the RBR and Slice datasets.
conclusionsDeep learning can be used to classify benign and malignant renal tumors from CT images. Our patient-level models could benefit from 3D data to improve the accuracy.
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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.