ArticleThe British journal of radiology2019
Feasibility of an ADC-based radiomics model for predicting pelvic lymph node metastases in patients with stage IB-IIA cervical squamous cell carcinoma.
Article in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Nomogram Prediction Model and Prognostic Comparison of Cervical Clear Cell Carcinoma and Cervical Endometrioid Adenocarcinoma: A SEER Database Study.Cancer medicine · 2026Article
- A Hybrid Model-Based Clinicopathological Features and Radiomics Based on Conventional MRI for Predicting Lymph Node Metastasis and DFS in Cervical Cancer.Journal of imaging informatics in medicine · 2026Article
- Value of enhanced CT machine learning models combined with clinicoradiological characteristics in predicting lymphatic tissue metastasis in colon cancer.Radiologie (Heidelberg, Germany) · 2025Article
- Using Machine Learning to Predict the Prognosis of Cervical Cancer Patients with Lymph Node Metastasis: An Analysis Based on the SEER Database.Reproductive sciences (Thousand Oaks, Calif.) · 2025Article
- Current Paradigm and Future Directions in the Management of Nodal Disease in Locally Advanced Cervical Cancer.Cancers · 2025Review
- A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients.European radiology · 2024Article
- Hematological indicator-based machine learning models for preoperative prediction of lymph node metastasis in cervical cancer.Frontiers in oncology · 2024Article
- Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer.Frontiers in medicine · 2024Article
- Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer.Insights into imaging · 2023Article
- Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer.Diagnostics (Basel, Switzerland) · 2022Article
- MRI outcome evaluation in patients with IB2 and IIA2 squamous cervical cancer stages: preliminary results.Insights into imaging · 2022Article
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- Artificial Intelligence in Cervical Cancer Screening and Diagnosis.Frontiers in oncology · 2022Review
- Prediction Model of Residual Neural Network for Pathological Confirmed Lymph Node Metastasis of Ovarian Cancer.BioMed research international · 2022Article
- Combination of Estrogen Receptor Alpha and Histological Type Helps to Predict Lymph Node Metastasis in Patients with Stage IA2 to IIA2 Cervical Cancer.Cancer management and research · 2022Article
- Radiomics in radiation oncology for gynecological malignancies: a review of literature.The British journal of radiology · 2021Review
- Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma.European radiology · 2021Article
- Feasibility of TEuropean radiology · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the prediction value of a radiomics model based on apparent diffusion coefficient (ADC) maps for pelvic lymph node metastasis (PLNM) in patients with stage IB-IIA cervical squamous cell carcinoma (CSCC).
methodsA total of 153 stage IB-IIA CSCC patients who underwent preoperative MRI including DWI from January 2015 to October 2017 were retrospectively studied and divided into a training cohort (
resultsClinical stage, tumor diameter, and MR-reported lymph node (LN) status were significantly associated with LN status (
conclusionThe presented radiomics model, a non-invasive preoperative prediction tool, has the potential to have more predictive efficacy than clinical and radiological factors for differentiating between metastatic and non-metastatic lymph nodes. ADVANCES IN KNOWLEDGE: A radiomics model derived from the ADC maps of primary lesions demonstrated good performance for predicting PLNM in stage IB-IIA CSCC patients and may help to improve clinical decision-making.
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