ArticleDiagnostics (Basel, Switzerland)2021
Pretreatment MRI Radiomics Based Response Prediction Model in Locally Advanced Cervical Cancer.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
- Laparoscopic Radical Hysterectomy Combined with Neoadjuvant Chemotherapy for Cervical Cancer Patients Effectively Improves Immune Function.Disease markers · 2022Trial
- Radiomics and artificial intelligence in precision radiotherapy for cervical cancer: a narrative review.Frontiers in oncology · 2026Review
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- Associations between MRI radiomics analysis and tumor-micro milieu in uterine cervical cancer.Journal of cancer research and clinical oncology · 2025Article
- Pre-Treatment and Pre-Brachytherapy MRI first-order Radiomic Features by a Commercial software as survival predictors in radiotherapy for cervical cancer Objectives.Clinical and translational radiation oncology · 2025Article
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Cer-ConvN3Unet: an end-to-end multi-parametric MRI-based pipeline for automated detection and segmentation of cervical cancer.European radiology experimental · 2025Article
- Current Paradigm and Future Directions in the Management of Nodal Disease in Locally Advanced Cervical Cancer.Cancers · 2025Review
- Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study.Insights into imaging · 2024Article
- Automated Prediction of Neoadjuvant Chemoradiotherapy Response in Locally Advanced Cervical Cancer Using Hybrid Model-Based MRI Radiomics.Diagnostics (Basel, Switzerland) · 2023Article
- Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram.BMC medical imaging · 2023Article
- MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer.The British journal of radiology · 2022Article
- Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy.La Radiologia medica · 2022Review
- MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer.Insights into imaging · 2022Article
- Neoadjuvant Chemoradiotherapy With Simultaneous Integrated Boost in Locally Advanced Cervical Cancer: Long Term Results of a Single-Center Experience.Frontiers in oncology · 2022Article
- Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer.Frontiers in oncology · 2021Article
- A Multimodal Graph Neural Network Based on MRI Radi-omics for Prognostic Prediction in Locally Advanced Cervical Squamous Cell Carcinoma.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
18 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to create a radiomics model for Locally Advanced Cervical Cancer (LACC) patients to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NACRT) analysing T2-weighted 1.5 T magnetic resonance imaging (MRI) acquired before treatment start. Patients with LACC and an International Federation of Gynecology and Obstetrics stage from IB2 to IVA at diagnosis were retrospectively enrolled for this study. All patients underwent NACRT, followed by radical surgery; pCR-assessed on surgical specimen-was defined as absence of any residual tumour. Finally, 1889 features were extracted from MR images; features showing statistical significance in predicting pCR at the univariate analysis were selected following an iterative method, which was ad-hoc developed for this study. Based on this method, 15 different classifiers were trained considering the most significant features selected. Model selection was carried out using the area under the receiver operating characteristic curve (AUC) as target metrics. One hundred eighty-three patients from two institutions were analysed. The model, showing the highest performance with an AUC of 0.80, was the random forest method initialised with default parameters. Radiomics appeared to be a reliable tool in pCR prediction for LACC patients undergoing NACRT, supporting the identification of patient risk groups, which paves treatment pathways tailored according to the predicted outcome.
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