ArticleCancer medicine2023
MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- 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
- Multimodal fusion radiomic-immunologic scoring model: accurate identification of prostate cancer progression.BMC medical imaging · 2025Article
- Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.Scientific reports · 2025Article
- Development, validation, and visualization of a novel nomogram for predicting clinical outcomes of radiotherapy combined with chemotherapy in locally advanced cervical cancer.Frontiers in oncology · 2025Article
- Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer.World journal of surgical oncology · 2024Article
- MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.Cancer medicine · 2023Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). PURPOSE: To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC. STUDY TYPE: Retrospective. POPULATION: CC patients (n = 133) allocated into training FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T. ASSESSMENT: Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps). STATISTICAL TESTS: Radiomic signatures for prediction of DSS from T2WI (T2
resultsThe radiomic signatures T2 DATA
conclusionRadiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.
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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.