ArticleFrontiers in oncology2020
Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 51 citations in OpenAlex.
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Machine Learning based Radiomics from Multi-parametric Magnetic Resonance Imaging for Predicting Lymph Node Metastasis in Cervical Cancer.Current medical imaging · 2025Article
- Article
- Development and Validation of a Computed Tomography-based Radiomics Nomogram for Diagnosing Cervical Lymph Node Metastasis in Oropharyngeal Squamous Cell Carcinomas.Advances in radiation oncology · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Construction of the preoperative staging prediction model for cervical cancer based on deep learning and MRI: a retrospective study.Frontiers in oncology · 2025Article
- A Review on the Applications of Artificial Intelligence for Diagnosing and Treating Cervical Cancer.International journal of women's health · 2025Review
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.BMC women's health · 2024Article
- Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images.BMC medical imaging · 2023Article
- Multicentric development and evaluation ofEuropean journal of nuclear medicine and molecular imaging · 2023Article
- Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI.Insights into imaging · 2023Article
- A nomogram-based optimized Radscore for preoperative prediction of lymph node metastasis in patients with cervical cancer after neoadjuvant chemotherapy.Frontiers in oncology · 2023Article
- Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network.Frontiers in oncology · 2023Article
- RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study.BMC medical imaging · 2022Article
- Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?European radiology · 2022Article
- MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer.The British journal of radiology · 2022Article
- Accurate Prediction of Metachronous Liver Metastasis in Stage I-III Colorectal Cancer Patients Using Deep Learning With Digital Pathological Images.Frontiers in oncology · 2022Article
- Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images.Archives of gynecology and obstetrics · 2021Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
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.