ArticleInsights into imaging2022
MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06197126 (A Prognostic Model Based on MRI for Cervical Cancer Patients Treated With Radiotherapy), which is not on this map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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.
A Prognostic Model Based on MRI for Cervical Cancer Patients Treated With Radiotherapy
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Shaping surgical decisions: a practical MRI approach to cervical stromal invasion assessment in the post-SHAPE era.Abdominal radiology (New York) · 2026Review
- MRI- and report-based multimodal model with SHAP-based explanation for preoperative prediction of deep stromal invasion in early-stage cervical cancer.Insights into imaging · 2026Article
- Predicting the biological behavior of cervical squamous cell carcinoma: a machine learning approach using apparent transverse relaxation rate (R2* maps) radiomics nomogram.BMC medical imaging · 2026Article
- Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer.Scientific reports · 2025Article
- Tumor Invasion Distance Based on MRI Is a Novel Prognostic Indicator for I-IIIB Cervical Cancer Patients Treated with Radiotherapy.Current oncology (Toronto, Ont.) · 2025Article
- Development of a machine learning model for predicting the expression of proteins associated with targeted therapy in endometrial cancer.Frontiers in oncology · 2025Article
- AI tool for predicting MGMT methylation in glioblastoma for clinical decision support in resource limited settings.Scientific reports · 2024Article
- 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
- Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer.Journal of imaging informatics in medicine · 2024Article
- Automated Prediction of Neoadjuvant Chemoradiotherapy Response in Locally Advanced Cervical Cancer Using Hybrid Model-Based MRI Radiomics.Diagnostics (Basel, Switzerland) · 2023Article
- Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy.Scientific reports · 2023Article
- Staging of Cervical Cancer: A Practical Approach Using MRI and FDG PET.AJR. American journal of roentgenology · 2023Review
- MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.Cancer medicine · 2023Article
- Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation.Diagnostics (Basel, Switzerland) · 2023Article
- Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
15 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe depth of cervical stromal invasion is one of the important prognostic factors affecting decision-making for early stage cervical cancer (CC). This study aimed to develop and validate a T2-weighted imaging (T2WI)-based radiomics model and explore independent risk factors (factors with statistical significance in both univariate and multivariate analyses) of middle or deep stromal invasion in early stage CC.
methodsBetween March 2017 and March 2021, a total of 234 International Federation of Gynecology and Obstetrics IB1-IIA1 CC patients were enrolled and randomly divided into a training cohort (n = 188) and a validation cohort (n = 46). The radiomics features of each patient were extracted from preoperative sagittal T2WI, and key features were selected. After independent risk factors were identified, a combined model and nomogram incorporating radiomics signature and independent risk factors were developed. Diagnostic accuracy of radiologists was also evaluated.
resultsThe maximal tumor diameter (MTD) on magnetic resonance imaging was identified as an independent risk factor. In the validation cohort, the radiomics model, MTD, and combined model showed areas under the curve of 0.879, 0.844, and 0.886. The radiomics model and combined model showed the same sensitivity and specificity of 87.9% and 84.6%, which were better than radiologists (sensitivity, senior = 75.7%, junior = 63.6%; specificity, senior = 69.2%, junior = 53.8%) and MTD (sensitivity = 69.7%, specificity = 76.9%).
conclusionMRI-based radiomics analysis outperformed radiologists for the preoperative diagnosis of middle or deep stromal invasion in early stage CC, and the probability can be individually evaluated by a nomogram.
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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.