ArticleRadiation oncology (London, England)2020
Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis.
Article in Radiation oncology (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Clinical challenges in the adaptation of AI predictive models in radiation oncology for gynaecological cancer: a systematic review by the radiation oncology-AI MITO group.La Radiologia medica · 2026Pooled it
- Radiomics and artificial intelligence in precision radiotherapy for cervical cancer: a narrative review.Frontiers in oncology · 2026Review
- Machine Learning-Based Prognostic Modelling Using MRI Radiomic Data in Cervical Cancer Treated with Definitive Chemoradiotherapy and Brachytherapy.Current oncology (Toronto, Ont.) · 2025Article
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- The Utility of Contrast-Enhanced Magnetic Resonance Imaging in Uterine Cervical Cancer: A Systematic Review.Life (Basel, Switzerland) · 2023Review
- Applying multisequence MRI radiomics of the primary tumor and lymph node to predict HPV-related p16 status in patients with oropharyngeal squamous cell carcinoma.Quantitative imaging in medicine and surgery · 2023Article
- Risk factors for the recurrence of cervical cancer using MR-based T1 mapping: A pilot study.Frontiers in oncology · 2023Article
- The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer.Cancer medicine · 2023Article
- Machine learning in predictingFrontiers in immunology · 2023Article
- CT-based radiomics nomogram for overall survival prediction in patients with cervical cancer treated with concurrent chemoradiotherapy.Frontiers in oncology · 2023Article
- Development and validation of a prognostic prediction model including the minor lymphatic pathway for distant metastases in cervical cancer patients.Scientific reports · 2022Article
- Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis.Scientific reports · 2022Article
- The Value of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging Combined With Texture Analysis of Evaluating the Extramural Vascular Invasion in Rectal Adenocarcinoma.Frontiers in oncology · 2022Article
- Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer.Tomography (Ann Arbor, Mich.) · 2021Article
- Head-to-Head Comparison of NeckCancers · 2021Article
- Predictive Value of a Combined Model Based on Pre-Treatment and Mid-Treatment MRI-Radiomics for Disease Progression or Death in Locally Advanced Nasopharyngeal Carcinoma.Frontiers in oncology · 2021Article
- Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer.Frontiers in oncology · 2021Article
- Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients.Cancers · 2020Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundCurrent chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical cancer patients.
methodsWe included 93 node-positive cervical cancer patients treated with definitive chemoradiotherapy at our institution between 2006 and 2017. The median follow-up period was 38 months (range, 5-128). Primary tumor and involved lymph node were manually segmented on axial gadolinium-enhanced T1-weighted images as well as T2-weighted images and saved as 3-dimensional regions of interest (ROI). After the segmentation, imaging features related to histogram, shape, and texture were extracted from each ROI. Using these features, random survival forest (RSF) models were built to predict local control (LC), regional control (RC), distant metastasis-free survival (DMFS), and overall survival (OS) in the training dataset (n = 62). The generated models were then tested in the validation dataset (n = 31).
resultsFor predicting LC, models generated from primary tumor imaging features showed better predictive performance (C-index, 0.72) than those from lymph node features (C-index, 0.62). In contrast, models from lymph nodes showed superior performance for predicting RC, DMFS, and OS compared to models of the primary tumor. According to the 3-year time-dependent receiver operating characteristic analysis of LC, RC, DMFS, and OS prediction, the respective area under the curve values for the predicted risk of the models generated from the training dataset were 0.634, 0.796, 0.733, and 0.749 in the validation dataset.
conclusionsOur results suggest that tumor and lymph node imaging features may play complementary roles for predicting clinical outcomes in node-positive cervical cancer.
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