ArticleInsights into imaging2024
Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Imaging based artificial intelligence for predicting lymph node metastasis in cervical cancer patients: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Preoperative non-invasive prediction of lymph node metastasis in cervical cancer using a multiparametric radiomics model based on transvaginal ultrasound.Abdominal radiology (New York) · 2026Article
- Lymph Node Metastases Prediction in Cervical Cancer Using Time-Dependent Diffusion MRI and Macromolecular Proton Fraction Imaging.Radiology. Imaging cancer · 2026Article
- Prediction of lymph node metastasis and recurrence risk in early-stage oral tongue squamous cell carcinoma with fully automated MRI deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Integrating domain-specific convolutional features with Swin Transformers for transparent cervical cancer diagnostics.Frontiers in oncology · 2026Article
- Prediction Model of Lymph Node Metastasis in Cervical Cancer Based on MRI Habitat Radiomics.Cancers · 2025Article
- Artificial intelligence-driven screening, early diagnosis, and treatment strategies for cervical cancer: an overview.Infectious agents and cancer · 2025Review
- Restricted spectrum imaging and diffusion kurtosis imaging for assessing lymph node metastasis in cervical cancer: a histogram analysis-based study.BMC cancer · 2025Article
- Cervical cancer prediction using deformable kernel darknet-53 and depth wise separable convolutional neural networks.Scientific reports · 2025Article
- Sentinel Lymph Node Dissection-Novelty, Trend, or a Paradigm Shift in Surgical Decision-Making for Early Cervical Cancer?Medicina (Kaunas, Lithuania) · 2025Review
- Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.BMC medical imaging · 2025Article
- 2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.BMC medical imaging · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Comprehensive analysis of mRNA and lncRNA expression for predicting lymph node metastasis in cervical cancer: a novel seven-gene signature approach.Frontiers in genetics · 2025Article
- Enhancing cervical cancer diagnosis with ensemble learning and shark optimization algorithm: comparative study of CT and MRI in cervical cancer diagnosis.Frontiers in oncology · 2025Article
- A quantitative analysis of artificial intelligence research in cervical cancer: a bibliometric approach utilizing CiteSpace and VOSviewer.Frontiers in oncology · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
objectivesTo develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer.
methodsA total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method.
resultsSCC-Ag, maximum lymph node short diameter (LN
conclusionThe proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. CRITICAL RELEVANCE STATEMENT: Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. KEY POINTS: • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer.
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